DNA Barcoding of Tick-Borne Protists Using the 18S rRNA Gene: A Comprehensive Guide for Pathogen Discovery and Surveillance

Abstract

This article provides a comprehensive overview of DNA barcoding methodologies targeting the 18S rRNA gene for the identification and characterization of tick-borne protists. It explores the foundational principles of this approach, detailing its application in next-generation sequencing (NGS) workflows for uncovering protist diversity in tick vectors and animal hosts. The content addresses critical methodological considerations, common challenges in primer selection and bioinformatics, and strategies for result validation against conventional PCR. Aimed at researchers and drug development professionals, this resource synthesizes current literature to offer a practical framework for advancing surveillance of tick-borne diseases like babesiosis and theileriosis, and for informing the development of novel diagnostics and interventions.

The 18S rRNA Gene: A Cornerstone for Unraveling Tick-Borne Protist Diversity

Tick-borne protists constitute a significant threat to global human and animal health, causing diseases that impact livestock productivity, wildlife conservation, and public health systems. Among these parasitic protists, genera within the phylum Apicomplexa—particularly Theileria, Babesia, and Hepatozoon—stand out for their veterinary and medical importance. These intracellular parasites have evolved complex relationships with their tick vectors and vertebrate hosts, leading to sophisticated transmission dynamics and pathogenicity mechanisms.

Molecular characterization through DNA barcoding approaches, especially those targeting the 18S ribosomal RNA (rRNA) gene, has revolutionized our understanding of these pathogens' diversity, distribution, and evolutionary relationships. The 18S rRNA gene serves as an excellent molecular marker due to its highly conserved regions flanking variable domains, allowing for both broad phylogenetic analysis and precise species differentiation [1] [2]. This genetic locus has become the cornerstone for developing PCR-based detection systems, next-generation sequencing protocols, and molecular epidemiological surveys of tick-borne protists worldwide.

The epidemiological significance of these parasites is substantial. Babesia and Theileria species, classified under the order Piroplasmida, cause economically devastating diseases in livestock, including bovine babesiosis and theileriosis, with estimated global economic losses reaching billions of dollars annually [3] [4]. Meanwhile, Hepatozoon species, particularly H. canis, pose emerging threats to companion animal health, with documented cases across Europe, Asia, America, and Africa [5]. Recent studies have also highlighted the zoonotic potential of several species, with human infections reported for Babesia species and potential exposure to Theileria species documented among veterinary professionals [6].

Molecular Characterization Using 18S rRNA Gene

Primer Design and Target Regions

The effectiveness of 18S rRNA gene for protist identification stems from its molecular structure, containing both highly conserved and variable regions. Research indicates that different variable regions provide varying levels of taxonomic resolution. Studies on tick-borne protists have primarily focused on the V4 and V9 hypervariable regions for DNA barcoding applications [1] [2]. The V4 region typically provides greater sequence variation, enabling better discrimination between closely related species, while the V9 region offers robust amplification across diverse eukaryotic taxa.

Primer selection significantly influences detection sensitivity and specificity. For comprehensive screening, universal eukaryotic primers have been employed, such as:

• V4 region primers: Forward - 5'-CCA GCA GCC GCG GTA ATT CC-3', Reverse - 5'-ACT TTC GTT CTT GAT-3' [2]
• V9 region primers: Forward - 5'-CCC TGC CHT TTG TAC ACA C-3', Reverse - 5'-CCT TCY GCA GGT TCA CCT AC-3' [1] [2]

These primers are designed with Illumina adapter overhangs to facilitate next-generation sequencing library preparation. However, it is crucial to note that the performance of these primer sets varies, and the number and abundance of protists detected differ significantly depending on the primer sets used, necessitating careful optimization and validation [1] [7].

Comparative Genetic Features

Table 1: Comparative genetic features of tick-borne protists based on 18S rRNA gene analysis

Genus	Conserved Regions	Variable Regions	Phylogenetic Markers	Sequence Length (bp)
Babesia	V2, V5, V7	V4, V9	Specific signatures in V4 region	~1,600 [6]
Theileria	V2, V5, V7	V4, V9	Unique V4 polymorphisms	~1,600 [6]
Hepatozoon	V2, V5	V4, V9	Distinct V4 and V9 motifs	~1,400-1,600 [8]

The 18S rRNA gene sequences reveal distinct evolutionary relationships among these genera. Phylogenetic analyses consistently show Babesia and Theileria forming a monophyletic cluster within the Piroplasmida, while Hepatozoon occupies a more distant phylogenetic position [8] [5]. Within each genus, the 18S rRNA gene contains sufficient polymorphic sites to differentiate between species and even strains, providing valuable insights into their population genetics and evolutionary history.

Experimental Workflows for DNA Barcoding

Sample Collection and Processing

Field collection of ticks represents the critical first step in surveillance studies for tick-borne protists. Ticks should be collected using standardized methods such as flagging vegetation or direct removal from host animals [1] [2]. Proper taxonomic identification of ticks is essential, combining morphological characterization using standardized keys with molecular confirmation via mitochondrial gene markers (e.g., cox1 gene) [3].

For DNA extraction, ticks are typically processed in pools based on developmental stage: up to ten nymphs or fifty larvae per pool, with individual processing of adults by species and sex [1] [2]. Pooling strategies increase processing efficiency while maintaining detection sensitivity for surveillance purposes. DNA extraction employs commercial kits such as the DNeasy Blood & Tissue Kit (Qiagen), with subsequent quantification using fluorometric methods (e.g., Qubit dsDNA Assay Kits) to ensure accurate normalization for downstream applications [1] [2].

Library Preparation and Sequencing

For next-generation sequencing approaches, library preparation follows modified Illumina 16S Metagenomic Sequencing Library protocols adapted for 18S rRNA gene amplification [1] [2]. The process involves:

• Initial PCR amplification using gene-specific primers with adapter overhangs
• Indexing PCR with Nextera XT Indexed Primers
• Purification using AMPure beads
• Quality control via TapeStation D1000 ScreenTape
• Sequencing on Illumina platforms such as MiSeq

Critical cycling parameters include an initial denaturation at 95°C for 3 minutes, followed by 25 cycles of 95°C for 30 seconds, 55°C for 30 seconds, and 72°C for 30 seconds, with a final extension at 72°C for 5 minutes [2]. The number of amplification cycles requires careful optimization to minimize PCR bias while ensuring sufficient library yield.

Bioinformatics Analysis Pipeline

The bioinformatics processing of 18S rRNA sequencing data involves multiple critical steps to ensure accurate taxonomic assignment. Raw sequencing data first undergoes quality filtering and adapter trimming using tools like Cutadapt [1] [2]. Subsequent steps include:

• Denoising and merging of paired-end reads using DADA2 algorithm
• Chimera removal through the consensus method of removeBimeraDenovo function
• Amplicon Sequence Variant (ASV) generation for precise differentiation of sequence variants

Taxonomic classification employs BLAST alignment against comprehensive reference databases, preferably the NCBI NT database due to its extensive coverage of parasite sequences [1]. Phylogenetic analysis is then performed using software such as MEGA version 11.0, constructing neighbor-joining trees with p-distance models and bootstrap validation (1,000 replicates) [6].

Detection Methodologies and Protocols

DNA Barcoding and Metabarcoding

DNA barcoding using 18S rRNA gene fragments enables simultaneous detection of multiple tick-borne protists in a single assay. The metabarcoding approach is particularly valuable for surveillance studies, providing a comprehensive overview of pathogen diversity without prior knowledge of expected species [1] [2]. However, this method requires careful optimization, as demonstrated by Alkathiri et al. (2024), who found that detection efficiency varies significantly depending on the target region (V4 vs. V9) and primer sets used [1] [7].

Recent methodological advances have highlighted several technical considerations for optimizing 18S rRNA metabarcoding:

• Annealing temperature optimization: Testing various temperatures (40-70°C) during amplicon PCR to balance specificity and sensitivity [9]
• Template concentration normalization: Using fluorometric quantification (Qubit) rather than spectrophotometry for more accurate DNA normalization [2]
• Secondary structure considerations: DNA secondary structures in the V9 region may negatively impact output read distribution [9]

Conventional and Real-time PCR Assays

Despite advances in NGS technologies, conventional and real-time PCR remain workhorse methodologies for specific detection and quantification of tick-borne protists. SYBR Green real-time PCR assays have been developed for simultaneous detection and differentiation of Babesia and Theileria species based on melting temperature (Tm) analysis [3].

Table 2: Melting temperature profiles for differentiation of Babesia and Theileria species by SYBR Green real-time PCR

Species	Target Gene	Melting Temperature (°C)	Application
Babesia bigemina	Mitochondrial cytb	74.38 ± 0.04	Cattle blood and tick samples [3]
Babesia bovis	Mitochondrial cytb	75.7 ± 0.06	Cattle blood and tick samples [3]
Theileria orientalis	18S rRNA	74.61 ± 0.03	Cattle blood and tick samples [3]
Theileria sinensis	18S rRNA	75.84 ± 0.03	Cattle blood and tick samples [3]
Theileria annulata	18S rRNA	74.06 ± 0.03	Cattle blood and tick samples [3]

For conventional PCR, nested protocols targeting the 18S rRNA gene have demonstrated high sensitivity for detecting low-level infections. Primers such as Piro0F/Piro6R (outer) and Piro1F/Piro5.5R (inner) have been successfully employed in epidemiological studies, achieving detection of Theileria luwenshuni and novel Babesia species in human blood samples [6].

Supplementary Diagnostic Methods

While molecular methods dominate contemporary research on tick-borne protists, traditional techniques retain diagnostic value. Microscopic examination of Giemsa-stained blood smears remains useful for initial screening and morphological characterization, though it suffers from limited sensitivity and requires considerable expertise [6]. Serological assays including Western blot analysis using recombinant proteins (e.g., T. uilenbergi immunodominant protein) provide valuable evidence of exposure and active infection, particularly when combined with molecular methods [6].

Research Reagent Solutions

Table 3: Essential research reagents and materials for tick-borne protist studies

Reagent/Material	Specific Example	Application	Considerations
DNA Extraction Kits	DNeasy Blood & Tissue Kit (Qiagen)	Genomic DNA isolation from ticks and blood	Consistent yield for PCR-based applications [1]
PCR Master Mixes	KAPA HiFi HotStart ReadyMix	18S rRNA amplification for NGS	High fidelity for accurate sequence representation [9]
Quantification Assays	Qubit dsDNA HS Assay Kit	DNA quantification pre-library prep	Fluorometric method preferred over spectrophotometry [2]
Sequencing Platforms	Illumina MiSeq	18S rRNA amplicon sequencing	Optimal for targeted metabarcoding studies [1]
Cloning Kits	TOPcloner TA Kit	Plasmid controls for assay validation	Essential for generating positive controls [9]
Restriction Enzymes	NcoI (Thermo Scientific)	Plasmid linearization for NGS	Reduces steric hindrance in circular templates [9]
Staining Reagents	SYBR Green I nucleic acid stain	Real-time PCR detection	Enables melting curve analysis [3]
Cell Viability Assays	Cell Counting Kit-8 (CCK-8)	Cytotoxicity testing for drug screening	Assess compound toxicity to host cells [10]

Global Distribution and Host Associations

Epidemiological studies utilizing 18S rRNA gene sequencing have revealed complex patterns of tick-borne protist distribution across different geographical regions. These pathogens demonstrate remarkable adaptability to various ecological niches and host species.

Table 4: Global distribution and host associations of tick-borne protists based on molecular studies

Region	Tick Species	Protist Species Detected	Host Associations	Prevalence Data
East Asia (Japan)	Various hard ticks	Babesia spp., Theileria spp., Hepatozoon spp.	Feral raccoons, sika deer, Japanese martens	2.58% in tick samples (20/776) [8]
Korean Peninsula	Ixodes nipponensis	Hepatozoon canis, Theileria luwenshuni	Dogs, livestock	First report in I. nipponensis [1]
China (Yunnan)	Haemaphysalis longicornis	Theileria luwenshuni, novel Babesia spp.	Humans, goats, livestock	13 human cases of T. luwenshuni [6]
Southeast Asia (Thailand)	Rhipicephalus microplus	Babesia bigemina, Theileria orientalis	Cattle	6.1% B. bigemina in ticks [3]
Palestine	Rhipicephalus spp.	Theileria ovis, Hepatozoon canis	Sheep, goats, dogs	5.4% T. ovis in ticks [5]
Europe	Multiple species	Diverse Babesia and Theileria species	Wildlife, domestic animals, humans	Babesia canis most widespread protozoa [4]

Molecular epidemiological studies have identified several surprising host associations and transmission patterns. For instance, Hepatozoon canis and Toxoplasma gondii were recently detected in Ixodes nipponensis ticks in the Republic of Korea, suggesting previously unrecognized vector capacity and transmission routes [1] [7]. Similarly, human infections with Theileria luwenshuni in China challenge the traditional belief that Theileria species are not human pathogens [6].

The distribution patterns revealed through 18S rRNA sequencing highlight the importance of One Health approaches to understanding tick-borne protist transmission, as many pathogen species circulate among wildlife, domestic animals, and human populations [4]. This ecological complexity necessitates integrated surveillance systems that monitor pathogen prevalence across different host species and tick vectors.

Current Research and Therapeutic Approaches

Drug Discovery and Repurposing

Current therapeutic research for tick-borne protists explores both novel compounds and drug repurposing strategies. Etoposide (EP), a well-known anticancer drug that targets DNA topoisomerase II, has demonstrated promising anti-parasitic activity against Babesia and Theileria species [10]. Mechanistic studies indicate that etoposide inhibits parasite growth in a dose-dependent manner by stabilizing topoisomerase II-DNA cleavage complexes, leading to lethal DNA damage in rapidly dividing parasites [10].

In vitro drug sensitivity assays have established IC50 values for etoposide against various piroplasm species:

• Babesia bovis: 11.23 ± 2.82 μM
• Babesia caballi: 0.037 ± 0.039 μM
• Theileria equi: 0.68 ± 0.39 μM [10]

Notably, parasites treated with etoposide did not recover when returned to untreated culture conditions, suggesting potential long-lasting effects [10]. Morphological changes observed in treated parasites included distinct spots in B. bovis and B. caballi, along with abnormal structures in T. equi, indicating disrupted developmental cycles.

Diagnostic Challenges and Methodological Limitations

Despite significant advances in molecular detection methods, several challenges persist in the DNA barcoding of tick-borne protists. Primer bias remains a substantial limitation, as different primer sets can yield markedly different protist detection profiles from the same sample [1] [7]. This variability complicates comparative analyses across studies and may lead to underestimation of true pathogen diversity.

The analytical sensitivity of 18S rRNA metabarcoding is another consideration, particularly for detecting low-abundance infections. While conventional PCR can detect as few as 10 copy/μL of target DNA [3], metabarcoding approaches may fail to detect rare protist species in mixed infections due to sequencing depth limitations and amplification biases.

Future methodological improvements should focus on:

• Development of optimized primer sets with broader coverage of tick-borne protist diversity
• Standardization of bioinformatics pipelines to enable reproducible cross-study comparisons
• Integration of multi-locus sequencing to overcome limitations of single-gene barcoding
• Implementation of quality control standards including internal controls and reference materials

These advancements will enhance the reliability and comparability of DNA barcoding data, ultimately improving our understanding of tick-borne protist ecology, evolution, and transmission dynamics.

Why the 18S rRNA Gene? Principles of DNA Barcoding for Eukaryotic Pathogens

DNA barcoding has revolutionized species identification, and the 18S ribosomal RNA (rRNA) gene has emerged as a cornerstone marker for eukaryotic pathogens. This technical guide explores the fundamental principles behind selecting the 18S rRNA gene for barcoding, with specific application to tick-borne protists. We examine its genetic properties, variable region characteristics, and experimental methodologies while presenting current data from surveillance studies. The content provides researchers with comprehensive protocols, reagent solutions, and analytical frameworks for implementing 18S rRNA-based detection systems in vector-borne disease research.

DNA barcoding is a method of species identification using a short section of DNA from a specific gene or genes, functioning similarly to a supermarket scanner using barcodes to identify products [11]. The core premise is that by comparison with a reference library of DNA sequences, an individual sequence can uniquely identify an organism to species level. For eukaryotic pathogens, particularly protists, the 18S ribosomal RNA (rRNA) gene serves as the primary barcode region due to its optimal balance of conserved and variable regions [11] [12].

The 18S rRNA gene is a DNA sequence encoding the small subunit of eukaryotic ribosomes, featuring both conserved regions that allow for universal primer design and variable regions (V1-V9, excluding V6) that provide species discrimination capability [12]. This combination makes it particularly valuable for detecting and identifying parasitic protists of medical and veterinary importance, including those causing tick-borne diseases such as babesiosis, theileriosis, and hepatozoonosis [2] [13].

Genetic Properties of the 18S rRNA Gene

Structural Characteristics

The 18S rRNA gene possesses several intrinsic properties that make it ideal for DNA barcoding applications:

• High copy number: Present in multiple copies per cell, enhancing detection sensitivity
• Universal distribution: Found in all eukaryotic organisms, enabling broad pathogen screening
• Functional constraint: Highly conserved function maintains structural stability across species
• Mosaic evolution: Contains alternating conserved and hypervariable regions ideal for phylogenetic analysis

Comparative Analysis of Variable Regions

Different variable regions of the 18S rRNA gene offer varying levels of taxonomic resolution and amplification efficiency. The table below summarizes the key characteristics of commonly targeted regions:

Table 1: Comparison of 18S rRNA Variable Regions for DNA Barcoding

Region	Length (bp)	Taxonomic Resolution	Primer Design Efficiency	Common Applications
V1-V2	~300-400	Moderate	High	General eukaryotic diversity
V3	~200-300	Moderate	Moderate	Fungal and protist identification
V4	~400-500	High	High	Optimal for most eukaryotes [12]
V5-V7	~500-600	Moderate-High	Moderate	Specific protist groups
V8	~200-300	Moderate	Moderate	Rapid screening assays
V9	~150-200	Lower	High	High-throughput screening [9]

Recent research indicates that longer regions spanning multiple variable domains (e.g., V4-V9) provide enhanced species discrimination compared to shorter segments like V9 alone, particularly for closely related pathogens [13]. One study demonstrated that the V4-V9 region achieved more accurate species identification of Plasmodium species compared to the V9 region when using error-prone portable sequencers [13].

Experimental Design and Methodologies

Sample Collection and Preparation

For tick-borne pathogen surveillance, proper sample handling is critical:

• Tick collection: Collect questing ticks using flagging methods or remove from hosts with tweezers [2] [14]
• Preservation: Immediately preserve specimens in 70% ethanol at room temperature or freeze at -20°C/-80°C [2] [14]
• Morphological identification: Identify tick species and developmental stages using morphological keys before molecular analysis [2]
• Pooling strategies: Pool ticks by species, developmental stage, and collection location (e.g., up to 10 nymphs or 50 larvae per pool) to maximize cost efficiency [2]

DNA Extraction and Quality Control

• Extraction kits: Use commercial kits such as DNeasy Blood & Tissue Kit (Qiagen) or MagMAX DNA Multi-Sample Kit [2] [14]
• Inhibition removal: Implement additional cleaning steps if PCR inhibitors are present [11]
• Quality assessment: Quantify DNA using fluorometric methods (e.g., Qubit dsDNA Assay) rather than spectrophotometry for accurate normalization [2]
• Normalization: Normalize DNA concentrations across samples to minimize bias in subsequent amplification steps [2]

Primer Selection and PCR Amplification

Primer design is crucial for successful 18S rRNA barcoding. The following table presents commonly used primers and their characteristics:

Table 2: 18S rRNA Primer Sets for Eukaryotic Pathogen Detection

Primer Name	Target Region	Sequence (5'-3')	Application Context
1391F [9]	V9	GTACACACCGCCCGTC	General eukaryotic screening
EukBR [9]	V9	TGATCCTTCTGCAGGTTCACCTAC	General eukaryotic screening
F566 [13]	V4-V9	Custom design	Enhanced species resolution
1776R [13]	V4-V9	Custom design	Enhanced species resolution
V4 Forward [2]	V4	CCAGCAGCCGCGGTAATTCC	Tick-borne protist diversity
V4 Reverse [2]	V4	ACTTTCGTTCTTGAT	Tick-borne protist diversity
V9 Forward [2]	V9	CCCCTGCCHTTTGTACACAC	Tick-borne protist diversity
V9 Reverse [2]	V9	CCTTCYGCAGGTTCACCTAC	Tick-borne protist diversity

PCR Protocol for 18S rRNA Amplification [2]:

• Initial denaturation: 95°C for 3 minutes
• Amplification cycles (25-30 cycles):
  • Denaturation: 95°C for 30 seconds
  • Annealing: 55°C for 30 seconds (temperature may be optimized)
  • Extension: 72°C for 30 seconds
• Final extension: 72°C for 5 minutes

Critical considerations:

• Annealing temperature optimization (40-70°C range) significantly affects amplification efficiency and specificity [9]
• Cycle number should be minimized to reduce PCR artifacts while maintaining sensitivity
• Polymerase selection (e.g., KAPA HiFi HotStart ReadyMix) enhances fidelity for complex communities [9]

Blocking Primers for Host DNA Suppression

When analyzing tick samples or blood specimens, host DNA can overwhelm pathogen signals. Blocking primers specifically inhibit amplification of host 18S rRNA:

• C3 spacer-modified oligos: Compete with universal reverse primers but halt polymerase extension [13]
• Peptide nucleic acid (PNA) oligos: Bind strongly to target sequences and inhibit polymerase elongation [13]
• Combination approaches: Using multiple blocking mechanisms simultaneously maximizes host DNA suppression

Sequencing Platform Selection

Table 3: Comparison of Sequencing Platforms for 18S rRNA Barcoding

Platform	Read Length	Accuracy	Throughput	Best Applications
Illumina MiSeq/iSeq	2×250-300 bp	High	Moderate	V4/V9 region studies [2] [9]
Oxford Nanopore	>1 kb	Moderate	Variable	V4-V9 spanning regions [13]
PacBio	>1 kb	High	Low	Full-length gene analysis
Sanger	~500-1000 bp	Very High	Very Low	Validation and confirmation

Bioinformatics Analysis Workflow

Diagram 1: Bioinformatic analysis workflow for 18S rRNA data

Data Processing Steps

• Quality Control and Trimming

  • Tool: Cutadapt v3.2+ [1]
  • Remove adapter and primer sequences
  • Trim forward and reverse reads to 250bp and 200bp, respectively [1]
  • Quality filtering (Q-score > 20)

• Denoising and ASV Generation

  • Tool: DADA2 v1.18.0+ [1] [9]
  • Error rate learning from data
  • Paired-end read merging through overlapping
  • Amplicon Sequence Variant (ASV) generation

• Chimera Removal

  • Consensus method (removeBimeraDenovo in DADA2) [1]

• Taxonomic Assignment

  • Alignment using BLAST against reference databases [1]
  • Common databases: NCBI NT, SILVA [1] [15]
  • Taxonomic classifier: RDP naive Bayesian classifier [13]

Reference Databases

Comprehensive reference libraries are essential for accurate taxonomic identification:

• NCBI Nucleotide (NT): Broad coverage but requires careful curation [1]
• SILVA: High-quality aligned rRNA sequences [15]
• Specialized databases: Custom databases for specific pathogen groups
• Quality criteria: Reference sequences should include verified voucher specimens with complete collection metadata [11]

Applications in Tick-Borne Protist Research

Pathogen Detection and Diversity Studies

Recent surveillance studies demonstrate the utility of 18S rRNA barcoding for tick-borne protists:

Table 4: 18S rRNA Barcoding Applications in Tick-Borne Disease Surveillance

Study Location	Tick Species	Target Region	Protists Identified	Key Findings
Republic of Korea [2]	Multiple species	V4, V9	Hepatozoon canis, Theileria luwenshuni, Gregarine sp.	First identification of H. canis and T. gondii in Ixodes nipponensis
Kyrgyzstan [16] [14]	11 species from cattle, sheep	V9	Babesia spp. (13.3%), Theileria spp. (12.7%)	Highest Babesia prevalence in Osh region and nymphal ticks
Cattle Blood Validation [13]	-	V4-V9	Multiple Theileria species	Detection of co-infections in same host

Technical Considerations for Tick-Borne Protists

• Primer bias: Different primer sets detect varying protist communities even from identical samples [2]
• Validation requirement: DNA barcoding results should be confirmed with conventional or real-time PCR [2]
• Sensitivity optimization: Larger regions (V4-V9) improve species discrimination but require higher DNA quality [13]
• Database gaps: Incomplete reference libraries for some tick-borne protists limit identification accuracy [11]

Essential Research Reagents and Materials

Table 5: Essential Research Reagents for 18S rRNA Barcoding

Reagent Category	Specific Product Examples	Function/Application
DNA Extraction Kits	DNeasy Blood & Tissue Kit (Qiagen), MagMAX DNA Multi-Sample Kit	High-quality DNA extraction from tick tissues [2] [14]
Quantification Assays	Qubit dsDNA Quantification Assay Kits (Invitrogen)	Accurate DNA quantification for normalization [2]
PCR Enzymes	KAPA HiFi HotStart ReadyMix (Roche)	High-fidelity amplification with reduced error rates [9]
Library Prep Kits	Illumina 16S Metagenomic Sequencing Library	Adapted for 18S rRNA amplification [2]
Purification Systems	AMPure beads (Agencourt Bioscience)	PCR product purification before sequencing [2]
Quality Control	TapeStation D1000 ScreenTape (Agilent)	Library quality assessment before sequencing [2]
Blocking Primers	C3 spacer-modified oligos, PNA oligos	Host DNA suppression in complex samples [13]

Limitations and Future Directions

Despite its utility, 18S rRNA barcoding faces several challenges:

• Primer bias: Universal primers may not equally amplify all taxonomic groups [2] [15]
• Reference database gaps: Many protists lack reference sequences, complicating identification [11]
• Intraspecific variation: Some species show limited sequence variation in 18S rRNA [11]
• Multiple copies: Intra-genomic variation in multi-copy genes can complicate interpretation [13]

Future developments should focus on:

• Standardized primer sets validated across diverse protist groups
• Expanded reference databases with vouchered specimens
• Multi-marker approaches combining 18S rRNA with other genes
• Long-read technologies for complete gene sequencing
• Portable sequencing platforms for field applications [13]

The 18S rRNA gene remains a powerful tool for DNA barcoding of eukaryotic pathogens, particularly tick-borne protists. Its conserved regions enable broad primer design, while variable domains provide sufficient discrimination for species identification. Current methodologies leveraging next-generation sequencing platforms allow comprehensive pathogen surveillance, though technical considerations around primer selection, host DNA suppression, and bioinformatic analysis require careful attention. As reference databases expand and sequencing technologies advance, 18S rRNA barcoding will continue to enhance our understanding of tick-borne protist diversity, ecology, and transmission dynamics, ultimately supporting improved disease surveillance and control strategies.

Within the framework of DNA barcoding for tick-borne protists, the selection of an appropriate hypervariable region of the 18S rRNA gene is a critical methodological decision that directly influences the accuracy and depth of taxonomic identification. The V4 and V9 regions have emerged as the most commonly targeted markers in protist metabarcoding studies, each presenting distinct advantages and limitations [17] [18]. This technical guide provides a comprehensive comparative analysis of these two regions, specifically contextualized within 18S rRNA research aimed at identifying and characterizing tick-borne protists. As demonstrated in tick surveillance studies, the choice between V4 and V9 regions affects the detection and abundance of protist genera such as Hepatozoon, Theileria, and Gregarine [7] [1]. The optimization of these molecular tools is therefore essential for advancing our understanding of protist diversity, ecology, and evolutionary relationships, particularly in vector-borne disease systems.

Technical Characteristics of V4 and V9 Regions

The V4 and V9 regions of the 18S rRNA gene differ significantly in their fundamental molecular properties, which in turn influences their application in protist identification. Understanding these basic characteristics is essential for selecting the appropriate marker for specific research objectives.

Table 1: Fundamental Characteristics of V4 and V9 Regions

Characteristic	V4 Region	V9 Region
Average Amplicon Length	300 bp (range: 146-564 bp) [17]	141 bp (range: 123-215 bp) [17]
Primary Strengths	Better phylogenetic resolution [17]	Enhanced detection of rare taxa and broader eukaryotic diversity [17]
Primary Limitations	May miss some rare taxa [17]	Lower phylogenetic resolution due to shorter length [17]
Common Primer Sets	F566: 5'-GYACACACCGCCCGTC-3' 1776R: 5'-TGATCCTTCTGCAGGTTCACCTAC-3' [13]	1391F: 5'-GTACACACCGCCCGTC-3' EukBR: 5'-TGATCCTTCTGCAGGTTCACCTAC-3' [9] [18]
Coverage of Eukaryotic Organisms	~60% of eukaryotic SSU entries with <3 mismatches [13]	Broad coverage of diverse eukaryotic lineages [17]

The V4 region's longer length provides more phylogenetic information, making it particularly valuable for differentiating between closely related protist species [17]. This characteristic is especially important in tick-borne pathogen research where precise identification of Theileria or Babesia species can have significant implications for understanding disease epidemiology. Conversely, the V9 region's shorter length allows for more efficient sequencing on certain platforms and potentially greater coverage of diverse protist groups, including rare members of the community that might be missed by the V4 region [17]. A study on tick-borne protists in the Republic of Korea found that the number and abundance of protists detected differed significantly depending on whether V4 or V9 primer sets were used [7] [1].

Performance Comparison in Protist Identification

Diversity Detection and Taxonomic Resolution

Empirical comparisons of the V4 and V9 regions across various ecosystems have revealed significant differences in their ability to detect and resolve protist diversity. These performance characteristics have direct implications for their application in tick-borne protist research.

Table 2: Performance Comparison of V4 and V9 Regions in Environmental Samples

Performance Metric	V4 Region	V9 Region
OTU Richness	915 OTUs from brackish water samples [17]	1,413 OTUs from the same brackish water samples [17]
Rare Taxa Detection	Lower detection of rare taxa (<1% of total reads) [17]	Superior detection of rare biosphere [17]
Classification Efficiency	Successfully assigned 99.95% of reads to supergroups [17]	Successfully assigned 99.99% of reads to supergroups [17]
Primer Bias	Failed to describe extant diversity for some major subdivisions [17]	Better representation of diverse eukaryotic lineages [17]
Intragenomic Variability	Less affected by intragenomic polymorphism [19]	Higher potential for intragenomic variation, though mostly due to sequencing errors [19]

A comparative study of eukaryotic communities in a brackish water pond found that the V9 region detected 54% more operational taxonomic units (OTUs) than the V4 region (1,413 versus 915 OTUs) [17]. This pattern of higher diversity detection with the V9 region has been consistently observed across various habitats and suggests that V9 may be more sensitive for capturing the full extent of protist diversity in complex samples like tick homogenates. The V9 region's superior detection of rare taxa is particularly relevant for tick-borne pathogen surveillance, where early detection of emerging pathogens or low-prevalence infections can inform public health responses.

Despite its shorter length, the V9 region has demonstrated practical utility in specific diagnostic contexts. For intestinal parasite identification, the V9 region successfully detected 11 different parasite species in a controlled experiment, though with considerable variation in read abundance across taxa [9]. This variation was influenced by factors such as DNA secondary structure and PCR annealing temperature, highlighting the importance of protocol optimization for specific target organisms [9].

Methodological Considerations for Tick-Borne Protist Research

In the specific context of tick-borne protist research, several methodological considerations emerge from comparative studies:

• Primer Selection Bias: Research on tick-borne protists in the Republic of Korea demonstrated that different primer sets targeting V4 and V9 regions yielded different protist compositions, detecting three genera of protozoa (Hepatozoon canis, Theileria luwenshuni, and Gregarine sp.) in varying abundances [7] [1].

• Complementary Approaches: The same study found that Toxoplasma gondii was not identified through DNA barcoding with either region but was detected by conventional PCR, suggesting that a combination of methods may be necessary for comprehensive pathogen detection [7] [1].

• Database Dependencies: Both regions require well-curated reference databases for accurate taxonomic assignment, with completeness of databases significantly impacting identification accuracy [1].

Figure 1: Experimental workflow for protist identification using V4 and V9 regions, showing the critical decision point at primer selection and the subsequent analytical pathways with their characteristic outcomes.

Enhanced and Emerging Methodologies

Advanced Primer Design and Blocking Strategies

To address the challenge of host DNA contamination in protist identification from complex samples like tick homogenates, advanced primer design and blocking strategies have been developed:

• Extended Amplicon Approaches: Research has demonstrated that targeting longer portions of the 18S rRNA gene, such as the V4-V9 region combination (~1,200 bp), can improve species-level identification, particularly when using third-generation sequencing platforms like Nanopore [13].

• Host DNA Blocking: The use of blocking primers, including C3 spacer-modified oligos and peptide nucleic acid (PNA) clamps, can selectively inhibit amplification of host 18S rDNA, thereby enriching for parasite sequences in host-dominated samples [13]. This approach has shown sensitivity in detecting blood parasites like Trypanosoma brucei rhodesiense, Plasmodium falciparum, and Babesia bovis in human blood samples with parasite densities as low as 1-4 parasites per microliter [13].

• Universal Primer Optimization: Carefully designed universal primers (e.g., F566 and 1776R) that target conserved regions flanking the V4-V9 super-region can provide broad coverage of eukaryotic pathogens while minimizing amplification of non-target organisms [13].

Bioinformatics and Sequencing Platform Considerations

The choice between V4 and V9 regions is also influenced by the available sequencing technologies and bioinformatic processing tools:

• Error Rate Management: For the error-prone Nanopore platform, longer amplicons (V4-V9) provide more sequence information for accurate species identification, compensating for the higher per-base error rate compared to Illumina sequencing [13].

• Denoising Algorithms: Tools like DADA2 have been shown to effectively manage intragenomic variability and sequencing errors in V9 data, providing more accurate OTU estimates compared to algorithms like SWARM, which may overestimate diversity, particularly for certain taxonomic groups like eupelagonemids [19].

• Multi-Region Approaches: Simultaneous sequencing of both V4 and V9 regions provides complementary data, balancing the need for comprehensive diversity assessment (V9) with robust phylogenetic placement (V4) [17].

Table 3: Research Reagent Solutions for 18S rRNA Protist Identification

Reagent Category	Specific Examples	Function and Application
Universal Primers	F566 (5'-GYACACACCGCCCGTC-3') [13] 1776R (5'-TGATCCTTCTGCAGGTTCACCTAC-3') [13] 1391F (5'-GTACACACCGCCCGTC-3') [9] EukBR (5'-TGATCCTTCTGCAGGTTCACCTAC-3') [9]	Amplification of V4-V9 and V9 regions respectively with broad eukaryotic coverage
Blocking Primers	C3 spacer-modified oligos [13] Peptide Nucleic Acid (PNA) clamps [13]	Selective inhibition of host DNA amplification to enrich parasite targets
DNA Extraction Kits	DNeasy Blood & Tissue Kit (Qiagen) [1] PowerSoil DNA Isolation Kit (MOBIO) [18] FastDNA SPIN Kit for Soil (MP Biomedicals) [9]	Efficient nucleic acid extraction from complex samples including ticks and sewage
PCR Reagents	KAPA HiFi HotStart ReadyMix (Roche) [9]	High-fidelity amplification for library preparation
Library Prep Kits	Nextera XT Indexed Primer [1] Illumina iSeq 100 i1 Reagent v2 [9]	Preparation of sequencing libraries for Illumina platforms

The comparative analysis of V4 and V9 hypervariable regions for protist identification reveals a complex landscape where each marker offers distinct advantages depending on research objectives. For comprehensive surveys of tick-borne protist diversity, particularly when seeking to identify rare or unexpected taxa, the V9 region provides superior sensitivity. Conversely, when phylogenetic resolution and precise taxonomic placement of known pathogens are prioritized, the V4 region offers better performance. Emerging methodologies that leverage longer amplicons spanning multiple variable regions, coupled with host DNA blocking strategies and advanced bioinformatic tools, represent the future of precise protist identification in complex sample matrices. For tick-borne pathogen research specifically, a dual-panel approach that utilizes both V4 and V9 regions may provide the most comprehensive understanding of protist communities, their ecology, and their potential impacts on human and animal health.

The accurate identification of tick-borne pathogens, particularly protists, is fundamental to both veterinary and human medicine. Traditional methods, primarily microscopy, have long served as the cornerstone of pathogen detection. However, the limitations of these approaches have driven the development of sophisticated molecular techniques that offer unprecedented precision and comprehensiveness. This evolution—from direct visualization to DNA-based analysis—represents a paradigm shift in diagnostic capabilities. The emergence of next-generation sequencing (NGS) technologies, especially methods leveraging the 18S rRNA gene for barcoding, has revolutionized our ability to screen for and identify tick-borne protists, uncovering a previously underestimated diversity [2] [1]. This technical guide examines the comparative advantages of these methodologies, framed within contemporary research on DNA barcoding of tick-borne protists using the 18S rRNA gene.

The Limitations of Traditional Microscopy

For over a century, microscopic examination has been the primary diagnostic method for identifying tick-borne pathogens. While this technique provides visual confirmation of pathogens, it suffers from several significant drawbacks that limit its efficacy in modern diagnostics and research.

• Low Sensitivity and Specificity: Microscopic examination often has low sensitivity, making it difficult to detect low-level or subclinical infections [2] [1]. Furthermore, species-level identification is frequently challenging due to morphological similarities between different pathogens, leading to potential misdiagnosis [20].
• Inability to Detect Co-infections: Ticks can harbor multiple pathogens simultaneously, which can influence disease progression and clinical outcomes. Microscopy is poorly suited to identifying these co-infections, especially when pathogens are present in different abundances [21].
• Expert Dependency and Low Throughput: The technique relies heavily on the skill and experience of the microscopist, and the process is labor-intensive and time-consuming, making it impractical for large-scale surveillance studies [2] [22].

Table 1: Key Limitations of Microscopy for Tick-Borne Protist Identification

Limitation	Impact on Diagnosis and Research
Low Sensitivity	Inability to detect low-level infections; high false-negative rates
Limited Taxonomic Resolution	Difficulty distinguishing between morphologically similar species
Poor Suitability for Co-infection Detection	Risk of missing polymicrobial infections that complicate treatment
Labor-Intensive Process	Low throughput; not scalable for large surveillance studies

The Rise of Molecular Methods and DNA Barcoding

The advent of the polymerase chain reaction (PCR) marked a significant advancement, offering greater sensitivity and specificity than microscopy. PCR allows for the targeted amplification of pathogen DNA, enabling the detection of specific agents. However, conventional PCR requires prior knowledge of the suspected pathogen and the use of specific primer sets, making it inefficient for discovering novel pathogens or comprehensively assessing microbial diversity in a single assay [2] [21].

This challenge is addressed by DNA barcoding, a method that uses a short, standardized genetic sequence to identify an organism. For protists, the 18S ribosomal RNA (rRNA) gene serves as a key barcode region. The method relies on the principle that this gene contains conserved regions, which allow for the design of universal primers, and variable regions (V1-V9), which provide the species-discriminatory power [11]. The resulting DNA barcode can then be compared to reference libraries for identification [11].

When applied to a sample containing DNA from multiple organisms (like a tick homogenate), the technique is known as DNA metabarcoding. This approach, powered by NGS, enables the parallel identification of entire microbial communities in a single, high-throughput experiment [2] [11].

Targeted Next-Generation Sequencing: A Paradigm Shift

Targeted NGS combines the scalability of high-throughput sequencing with the sensitivity of targeted PCR amplification. For tick-borne protists, this typically involves amplifying variable regions of the 18S rRNA gene (such as V4 and V9) with universal primers, followed by sequencing on a platform like Illumina MiSeq [2] [1]. This approach offers several transformative advantages over both traditional methods and broad metagenomic sequencing.

Comprehensive Advantages Over Traditional Methods

• Unbiased Diversity Screening: Targeted NGS does not require prior knowledge of the pathogens present. A 2024 study on ticks from the Republic of Korea used 18S rRNA metabarcoding to identify three genera of protozoa (Hepatozoon, Theileria, and Gregarine) simultaneously, demonstrating its power for uncovering pathogen diversity [2] [1].
• Superior Sensitivity: Targeted NGS is exceptionally sensitive. One study on vector-borne pathogens reported detection limits equivalent to real-time PCR cycle threshold (Ct) values of 35–36, allowing for the identification of pathogens in low abundance that would be missed by microscopy [21].
• Detection of Co-infections: The method excels at identifying co-infections. Research has shown that co-infections with multiple pathogens like Babesia spp., Hepatozoon spp., and others are common and can be efficiently detected with a single NGS assay, which is critical for appropriate treatment [21].
• High-Throughput and Scalability: Thousands of sequences can be generated from multiple samples in a single run, making it ideal for large-scale surveillance and ecological studies [23] [20].

Practical and Diagnostic Advantages

• Standardization and Reduced Bias: Unlike microscopy, DNA-based methods provide objective, sequence-based data that can be standardized across laboratories. However, the choice of primer sets and bioinformatics pipelines remains a source of variability that requires careful optimization [2] [22].
• Discovery of Novel and Unexpected Pathogens: The untargeted nature of metabarcoding allows for the detection of novel or unexpected pathogens. The Korean tick study reported the first identification of H. canis and T. gondii in Ixodes nipponensis, highlighting the discovery potential of this method [2] [7].

Table 2: Quantitative Comparison of Pathogen Detection Methods

Method	Sensitivity	Taxonomic Resolution	Co-infection Detection	Throughput	Primary Limitation
Microscopy	Low	Low (often genus-level)	Poor	Low	Low sensitivity and specificity
Conventional PCR	High	High (species-level)	Limited (targets specific pathogens)	Medium	Requires prior knowledge of pathogen
Metabarcoding (18S NGS)	Very High	High (species-level)	Excellent	Very High	Primer bias; requires bioinformatics

The following diagram illustrates the core workflow and logical progression of a targeted NGS study for tick-borne protists, from sample collection to biological insight:

Experimental Protocols for 18S rRNA Metabarcoding in Ticks

The following section details a standard experimental protocol, as cited in recent literature, for conducting DNA metabarcoding of tick-borne protists.

Sample Collection, Processing, and DNA Extraction

• Tick Collection: Ticks are collected from the environment using standardized methods such as flagging or dragging. In a recent study, 13,375 questing ticks were collected in the Republic of Korea and morphologically identified [2] [1].
• Sample Pooling: To manage costs and processing time, ticks are often pooled. A common approach is to pool up to ten nymphs or fifty larvae per sample. Adult ticks are typically processed individually by species and sex [2].
• Homogenization and DNA Extraction: Pooled ticks are homogenized, often using a bead-beating method to ensure thorough lysis. DNA is then extracted using commercial kits, such as the DNeasy Blood & Tissue Kit (Qiagen). DNA concentration and quality should be quantified using a spectrophotometer or fluorometer [2] [1].

Library Preparation and Sequencing

• Primer Selection: The choice of primer set is critical and influences the results. Primers targeting the V4 and V9 hypervariable regions of the 18S rRNA gene are commonly used. For example:
  • V4 Primers: Forward: 5′ TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGCCAGCAGCCGCGGTAATTCC; Reverse: GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGACTTTCGTTCTTGAT [1].
  • V9 Primers: Forward: TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGCCCTGCCHTTTGTACACAC; Reverse: GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGCCTTCYGCAGGTTCACCTAC [1].
• Amplification and Indexing: The initial PCR amplifies the target region with primers that have overhang adapter sequences. This is followed by a second, limited-cycle PCR to attach dual indices and sequencing adapters, a process that allows samples to be multiplexed in a single sequencing run [2] [20].
• Sequencing: The final purified library is sequenced on an NGS platform, such as the Illumina MiSeq, which is widely used for amplicon sequencing due to its read length and output suitability for these applications [2] [20].

Bioinformatics and Taxonomic Analysis

• Data Processing: Raw sequencing data is processed to remove adapters and primers. Tools like Cutadapt are used for this purpose [1].
• Sequence Inference: Denoising algorithms, such as those in the DADA2 software, are applied to correct sequencing errors, merge paired-end reads, and generate high-resolution Amplicon Sequence Variants (ASVs) [1].
• Taxonomic Assignment: Each ASV is classified by comparing it to a reference database (e.g., NCBI NT) using alignment tools like BLAST. The taxonomic identity is assigned based on the best match [2] [1].

The Scientist's Toolkit: Essential Research Reagents and Materials

Successful implementation of a targeted NGS workflow requires a suite of specific reagents and tools. The following table details key components used in the featured experiments.

Table 3: Research Reagent Solutions for 18S rRNA Tick Metabarcoding

Item	Function/Description	Example Product/Citation
DNA Extraction Kit	Purifies genomic DNA from tick homogenates; critical for removing inhibitors.	DNeasy Blood & Tissue Kit (Qiagen) [2]
18S rRNA Primers	PCR primers designed to amplify specific hypervariable regions of the 18S gene.	V4 and V9 primer sets with Illumina overhangs [1]
PCR Enzyme Master Mix	Enzyme mix for high-fidelity amplification of target regions.	Not specified, but standard high-fidelity mixes are used.
Library Prep Kit	Prepares amplified DNA for NGS by adding indices and adapters.	Illumina 16S Metagenomic Sequencing Library Prep Kit [2]
Sequencing Platform	Instrument for high-throughput DNA sequencing.	Illumina MiSeq System [2] [23]
Bioinformatics Tools	Software for processing and analyzing raw sequence data.	Cutadapt (trimming), DADA2 (denoising), BLAST (taxonomy) [1]

The journey from microscopy to targeted NGS represents a fundamental advancement in diagnostic and research capabilities for tick-borne protists. While microscopy provides a foundational visual tool, its limitations in sensitivity, specificity, and throughput are substantial. Targeted NGS, particularly 18S rRNA metabarcoding, overcomes these hurdles by offering a highly sensitive, comprehensive, and scalable approach. It enables the unbiased discovery of pathogen diversity, accurate detection of co-infections, and provides a robust framework for large-scale surveillance. Despite challenges such as primer bias and the need for bioinformatics expertise, the method provides a powerful and transformative toolkit. It is poised to drive future discoveries in the ecology and epidemiology of tick-borne diseases, ultimately informing better public and veterinary health outcomes.

The study of tick-borne diseases represents a critical frontier in public and veterinary health, particularly in the Republic of Korea (ROK) where changing ecological conditions have amplified disease transmission risks. This case study examines the initial discovery of two significant tick-borne protists, Hepatozoon canis and Theileria luwenshuni, within tick populations in the ROK, framed within a broader thesis on DNA barcoding of tick-borne protists using 18S rRNA gene fragments. The identification of these pathogens underscores the evolving complexity of tick-borne disease epidemiology and highlights the essential role of molecular diagnostic approaches in pathogen surveillance and discovery. Research conducted between 2021-2023 has fundamentally expanded our understanding of the sylvatic transmission cycles of these pathogens and their potential spillover into domestic animal and human populations [7] [24] [25].

The application of DNA barcoding techniques targeting the 18S rRNA gene has enabled researchers to overcome limitations of traditional morphological identification, providing unprecedented resolution in detecting and characterizing apicomplexan parasites in vector populations. This technical approach has revealed previously unrecognized pathogen diversity and distribution patterns, offering insights essential for developing targeted control strategies against tick-borne diseases affecting livestock, wildlife, and potentially human populations in the region [7] [2].

Background and Significance

Ecological Context in the Republic of Korea

The Korean Peninsula provides suitable ecological conditions for diverse tick species, with Haemaphysalis longicornis (the Asian long-horned tick) representing the most abundant species, followed by H. flava, Ixodes nipponensis, and Amblyomma testudinarium [25]. Recent studies have documented changing tick distribution patterns linked to climate change, land development, and increased human outdoor activity, all factors that have contributed to the emergence and recognition of novel tick-borne pathogens [26]. From 2021 to 2022, extensive tick surveillance efforts collected 13,375 ticks which were pooled into 1,003 samples for analysis, providing a robust dataset for understanding pathogen distribution [7] [2].

The Pathogens: Hepatozoon canis and Theileria luwenshuni

Hepatozoon canis is a tick-borne apicomplexan parasite with a complex life cycle involving asexual development in vertebrate hosts and sexual reproduction within tick vectors. Unlike many other tick-borne pathogens that are transmitted through salivary secretions during blood feeding, H. canis is primarily transmitted orally when definitive hosts ingest infected ticks [27]. The parasite primarily infects domestic and wild canids, causing a spectrum of clinical manifestations from asymptomatic infections to severe systemic disease in immunocompromised individuals [24] [28].

Theileria luwenshuni belongs to the transforming group of Theileria species that develop schizonts in leukocytes, potentially inducing fatal lymphoproliferation in small ruminants [29]. This pathogen is considered an economically significant disease that decreases productivity and causes high mortality rates in livestock, particularly sheep and goats [25] [29]. Prior to its discovery in Korean ticks, T. luwenshuni had been reported in other Eurasian countries, including China, Myanmar, Türkiye, northern India, and the Mediterranean region [29].

Materials and Methods

Tick Collection and Identification

Ticks were collected from March to October between 2021 and 2022 from four Korean provinces (Chungcheongbuk-do, Chungcheongnam-do, Jeollabuk-do, and Jeollanam-do) using the standard flagging method [7] [2]. Collected ticks were transported to the laboratory and preserved in 70% ethanol at room temperature until species and developmental stages could be identified based on morphological characteristics [2]. For DNA extraction, ticks were pooled with up to ten nymphs or fifty larvae per pool, while each adult was processed individually according to species and sex [2].

Table 1: Tick Collection and Pooling Strategy

Collection Period	Geographical Coverage	Total Ticks Collected	Number of Pools Created	Selected Pools for DNA Barcoding
2021-2022	4 Korean provinces	13,375	1,003	50 pools

DNA Extraction and Sample Preparation

Pooled ticks were combined with phosphate-buffered saline (PBS) and homogenized using the bead beating method. DNA was extracted using the DNeasy Blood & Tissue Kit (Qiagen, Hilden, Germany) following manufacturer instructions [2]. DNA concentration was quantified using a spectrophotometer (DeNovix, Wilmington, DE, USA), and samples were stored at -20°C for subsequent analysis. To mitigate potential bias associated with varying DNA concentrations among selected tick pools, DNA samples were normalized using Qubit dsDNA Quantification Assay Kits (Invitrogen, Waltham, MA, USA) [2].

DNA Barcoding Using 18S rRNA Gene Fragments

A total of 50 tick pools were selected for DNA barcoding targeting the V4 and V9 regions of the 18S rRNA gene using the Illumina MiSeq platform [7] [2]. The sequencing libraries were prepared following Illumina 16S Metagenomic Sequencing Library protocols with modifications to amplify the target regions:

• V4 Region Amplification: Forward primer: 5'-TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGCCAGCAGCCGCGGTAATTCC-3' Reverse primer: 5'-GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGACTTTCGTTCTTGAT-3'

• V9 Region Amplification: Forward primer: 5'-TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGCCCCTGCCHTTTGTACACAC-3' Reverse primer: 5'-GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGCCTTCTGCAGGTTCACCTAC-3'

The thermal cycling conditions for initial PCR were: 3 minutes at 95°C, followed by 25 cycles of 30 seconds at 95°C, 30 seconds at 55°C, and 30 seconds at 72°C, with a final extension of 5 minutes at 72°C [2]. A second PCR was performed to incorporate indexes using the Nextera XT Indexed Primer with the same conditions except the cycle number was reduced to 10. PCR products were purified using AMPure beads (Agencourt Bioscience, Beverly, MA, USA) after each amplification step [2].

Bioinformatic Analysis

Raw sequencing data underwent adapter removal and quality filtering before taxonomic analysis of amplicon sequence variants (ASVs). The bioinformatic pipeline involved:

• Demultiplexing of sequenced reads
• Quality control and filtering of low-quality sequences
• Clustering of sequences into ASVs
• Taxonomic assignment by comparison with reference databases
• Phylogenetic analysis to confirm pathogen identity [2]

Conventional PCR Validation

To validate DNA barcoding results, conventional PCR assays were performed using pathogen-specific primers targeting:

• Hepatozoon canis: 18S rRNA gene [24] [28]
• Theileria luwenshuni: 18S rRNA gene [25]
• Toxoplasma gondii: Specific genomic targets [7]

PCR products were sequenced using Sanger sequencing, and resulting sequences were compared with those in GenBank using BLAST analysis to confirm species identification [27].

Phylogenetic Analysis

Evolutionary relationships were reconstructed using the Neighbor-Joining method in MEGA software. Bootstrap analysis with 1,000 replicates was performed to determine the reliability of constructed phylogenetic trees. Evolutionary distances were calculated using the p-distance method, expressed as the number of nucleotide substitutions per site [27] [29].

Experimental Workflow

The following diagram illustrates the comprehensive experimental workflow from tick collection to pathogen identification and validation:

Results and Findings

Pathogen Detection by DNA Barcoding

DNA barcoding using 18S rRNA gene fragments identified three genera of protozoan parasites in the collected ticks:

• Hepatozoon canis
• Theileria luwenshuni
• Gregarine sp. [7] [2]

The detection efficiency varied significantly depending on the primer sets used, with different numbers and abundance of protists detected when comparing V4 versus V9 region targets [2]. Notably, Toxoplasma gondii was not identified through DNA barcoding despite being detected by conventional PCR, highlighting a limitation of the barcoding approach with the primers and conditions employed [7].

Table 2: Pathogens Identified through DNA Barcoding and Conventional PCR

Pathogen	DNA Barcoding Detection	Conventional PCR Detection	First Report in ROK	Key Tick Species
Hepatozoon canis	Detected (V4/V9 regions)	Confirmed	First identification in Ixodes nipponensis	Ixodes nipponensis, Haemaphysalis longicornis
Theileria luwenshuni	Detected (V4/V9 regions)	Confirmed	Previously documented	Haemaphysalis longicornis (especially nymphs)
Theileria sp.	Not separately specified	Detected	Previously documented	Haemaphysalis longicornis
Toxoplasma gondii	Not detected	Detected	First identification in Ixodes nipponensis	Ixodes nipponensis
Gregarine sp.	Detected (V4/V9 regions)	Not specified	Not specified	Not specified

Epidemiological Findings

The molecular epidemiology of Theileria species in Korean ticks revealed a significant prevalence in the collected samples. Of 6,914 ticks (541 pools) screened, 211 pools (39.0%) showed positivity for Theileria species, with a minimum infection rate (MIR) of 3.05% [25]. Two Theileria species were identified:

• T. luwenshuni (162/211, 76.78%; MIR: 2.34%)
• Theileria sp. (36/211, 17.06%; MIR: 0.52%)
• Co-infection of both species (13/211, 6.16%; MIR: 0.19%) [25]

Among tick species, H. longicornis, especially nymphs, showed the highest prevalence of Theileria infection. Seasonal variation was observed, with the highest prevalence noted in May [25].

For H. canis, a separate study investigating raccoon dogs (Nyctereutes procyonoides) in South Korea between 2021-2023 found a 21.5% prevalence (59/275) in blood samples, with the highest prevalence in the southern region (38.2%) and the lowest in the north (8.8%) [24] [28]. This infection rate was significantly higher than previously reported in Korean domestic dogs (0.2-0.9%) and ticks (0.09%), suggesting raccoon dogs may function as key sylvatic reservoirs for this pathogen [28].

Novel Findings and Host Associations

This research led to two significant first reports:

• First identification of H. canis in Ixodes nipponensis ticks in the ROK [7]
• First identification of T. gondii in Ixodes nipponensis ticks in the ROK [7]

Additionally, the study provided the first molecular detection of H. canis in raccoon dogs in South Korea, with sequencing of amplicons revealing high similarity to H. canis found in Ixodes nipponensis from the same region [24] [28]. This finding suggests a potential transmission cycle involving ticks and wild canids in Korean ecosystems.

The Scientist's Toolkit: Essential Research Reagents

Table 3: Essential Research Reagents for Tick-Borne Protist Studies

Reagent/Kit	Manufacturer	Primary Function in Research
DNeasy Blood & Tissue Kit	Qiagen	DNA extraction from tick samples
Qubit dsDNA Quantification Assay Kits	Invitrogen	Accurate DNA quantification for normalization
AMPure beads	Agencourt Bioscience	PCR product purification post-amplification
AccuPower PCR Premix Kit	Bioneer	Ready-to-use PCR master mix for pathogen detection
Nextera XT Indexed Primer	Illumina	Library indexing for multiplex sequencing
MiSeq Reagent Kits	Illumina	Sequencing reagents for NGS platform

Discussion

Technical Considerations for 18S rRNA DNA Barcoding

This case study demonstrates both the power and limitations of DNA barcoding using 18S rRNA gene fragments for identifying tick-borne protists. The approach proved highly effective for detecting H. canis and T. luwenshuni, but its performance varied depending on the primer sets and target regions (V4 vs. V9) utilized [2]. The failure to detect T. gondii via DNA barcoding despite successful conventional PCR confirmation highlights the critical importance of primer selection and the need for further optimization in library construction protocols specifically tailored for comprehensive tick-borne protist identification [7] [2].

The differential detection efficiency between primer sets underscores a fundamental challenge in molecular parasitology: no single universal primer pair can capture the full diversity of protistan parasites present in complex samples like tick homogenates. This limitation necessitates a multi-faceted approach combining DNA barcoding with targeted PCR assays for comprehensive pathogen surveillance [2]. Furthermore, the selection of reference databases for taxonomic assignment significantly influences results, emphasizing the need for curated, high-quality databases specific to tick-borne pathogens [2].

Ecological and Epidemiological Implications

The discovery of H. canis and T. luwenshuni in Korean ticks has significant implications for understanding the ecology of tick-borne diseases in the region. The finding that H. canis prevalence in raccoon dogs (21.5%) substantially exceeds that in domestic dogs (0.2-0.9%) suggests these wild canids may serve as key sylvatic reservoirs in transmission cycles [24] [28]. This reservoir competence, combined with the expanding raccoon dog population and increasing contact with domestic animals in shared habitats, creates conditions favorable for pathogen spillover at the wildlife-domestic animal interface [28].

For T. luwenshuni, the highest prevalence in H. longicornis nymphs indicates this life stage plays a particularly important role in transmission ecology [25]. The significant correlations observed among tick distribution, region, season, and Theileria prevalence provide valuable insights for targeted surveillance and control measures [25]. Given that T. luwenshuni can cause significant economic losses in small ruminants, its presence in Korean ticks represents a potential threat to livestock industries, especially for deer and goat production systems [25] [29].

Transmission Dynamics and One Health Implications

The detection of these pathogens in ticks reveals complex transmission networks operating within Korean ecosystems. For H. canis, the standard transmission route involves ingestion of infected ticks by definitive hosts [27]. However, the remarkably high prevalence in raccoon dogs suggests the possibility of alternative transmission pathways, including predation on infected intermediate hosts or vertical transmission [24] [28]. These findings align with the One Health framework, emphasizing the interconnectedness of human, animal, and environmental health in understanding disease dynamics [24].

The identification of T. luwenshuni in Korea connects the region to a broader geographical distribution of this pathogen across Asia, including recent reports from Myanmar, China, and Taiwan [29]. This expanded range may reflect climate change effects on tick distribution and activity, increased animal movement, or improved surveillance capabilities [26] [29]. The discovery of T. luwenshuni in Haemaphysalis mageshimaensis on Orchid Island, Taiwan, further demonstrates the ongoing expansion of detected vector associations for this pathogen [29].

This case study documents the initial discovery of Hepatozoon canis and Theileria luwenshuni in Republic of Korea ticks, representing significant advancements in understanding the diversity of tick-borne protists in the region. The application of DNA barcoding using 18S rRNA gene fragments has proven to be a powerful tool for screening tick-borne protist diversity, despite limitations requiring further optimization of primer selection and library construction methods.

These findings have substantially expanded the known pathogen inventory in Korean ticks and revealed new host-parasite relationships, particularly the role of raccoon dogs as sylvatic reservoirs for H. canis. The research underscores the importance of molecular tools in parasitology and the value of comprehensive surveillance strategies that integrate both DNA barcoding and conventional PCR approaches.

From a broader perspective, these discoveries highlight the dynamic nature of tick-borne disease systems and the ongoing need for vigilant surveillance within a One Health framework. The interconnectedness of wildlife, domestic animal, and human health necessitates continued research into host-vector dynamics, transmission pathways, and potential spillover risks at ecological interfaces. Future studies should focus on elucidating the complete transmission cycles of these pathogens, assessing their pathogenic potential for domestic animals and humans, and developing targeted intervention strategies to mitigate their impact on public and veterinary health in the Republic of Korea.

From Sample to Sequence: A Step-by-Step NGS Workflow for 18S rRNA Metabarcoding

Tick Collection, Morphological Identification, and Pooling Strategies

The accurate collection, identification, and processing of ticks are foundational steps in research aimed of detecting tick-borne protists using DNA barcoding of the 18S rRNA gene. These preliminary procedures significantly impact the reliability and interpretability of subsequent molecular analyses. This guide details standardized methodologies for field collection, morphological examination, and strategic pooling of tick specimens, specifically contextualized within the framework of 18S rRNA-based research. The goal is to provide researchers with a comprehensive technical protocol that ensures specimen integrity, minimizes contamination, and optimizes nucleic acid extraction for the detection of eukaryotic pathogens such as Babesia, Theileria, and Hepatozoon.

Tick Collection Methods

Proper collection is the first critical step in ensuring the quality of downstream genetic analyses. The following methods are routinely employed for gathering ticks from various sources.

Collection Method	Description	Common Use Cases	Key Considerations
Flagging/Dragging	A white flannel cloth (∼1m²) attached to a rod is dragged or waved over vegetation. Questing ticks attach to the cloth and are collected [2] [1].	Collecting host-seeking ticks from the environment [2] [1].	Effective for a variety of ixodid ticks; performance depends on vegetation type and humidity.
Hand-Picking/Patch Sampling	Ticks are manually removed from specific predilection sites on an animal host (e.g., ears, neck, perineum) [30].	Collecting ticks from domestic or wild animals (e.g., cattle, wombats) [31] [30].	Allows for sampling of specific tick species and life stages associated with the host.
Opportunistic Collection	Specimens are collected from deceased hosts (e.g., road-killed animals) or from the environment of wildlife rehabilitation centers [31].	Sourcing ticks from a variety of host species, often in conjunction with other studies.	Specimens may be more degraded; requires careful preservation.

Upon collection, specimens should be immediately preserved to prevent degradation of DNA and RNA. Preservation in 70% ethanol is the standard practice, as it effectively fixes tissues and stabilizes nucleic acids for long-term storage [31] [30]. Each sample must be accompanied by metadata, including the date of collection, geographic location (preferably with GPS coordinates), host species (if applicable), and habitat type [31].

Morphological Identification of Ticks

Morphological identification is an essential step that informs pooling strategies and provides ecological context. It is typically performed using a stereomicroscope and established taxonomic keys.

Key Morphological Features for Identification

The identification process involves examining specific morphological structures, which vary between hard (Ixodidae) and soft (Argasidae) ticks. For Ixodid ticks, key diagnostic features include [32] [33] [34]:

• Capitulum (mouthpart): Length and structure of the hypostome, palps, and basis capituli.
• Scutum (dorsal shield): Presence, size, ornamentation (color patterns), and surface punctations. In females, the scutum is small and located anteriorly, while it covers the entire dorsal surface in males.
• Body shape and festoons: The presence of rectangular grooves (festoons) along the posterior margin.
• Anal groove: Its position relative to the anus (e.g., anterior in Ixodes, posterior in other genera).
• Eyes: Presence or absence and their location on the scutum.

For damaged specimens or immature stages (larvae and nymphs), morphological identification can be challenging and may only be reliable to the genus level [31] [30]. In such cases, molecular identification becomes necessary.

Laboratory Protocol for Morphological Examination

Materials:

• Stereomicroscope (e.g., Zeiss Stemi 2000-C, Nikon SMZ445) with an external fiber optic light source [31] [33]
• Taxonomic keys specific to the region and tick species [31] [30] [33]
• Fine forceps, petri dishes
• 70% ethanol for storage
• Digital microscope camera (e.g., Olympus DP72, Axio-cam) for documentation [31] [33]

Procedure:

• Preparation: Remove the tick from 70% ethanol and place it on a petri dish. For detailed observation, specimens can be cleansed in water and clarified with a drop of lactophenol for microscopic slides [32].
• Examination: Under the stereomicroscope, systematically observe the key morphological features listed above.
• Identification: Use taxonomic keys to compare the observed characteristics and identify the specimen to species, developmental stage, and sex (for adults) [33].
• Documentation: Capture high-resolution images of the dorsal and ventral sides, focusing on key diagnostic features [30] [33].
• Storage: Return the identified tick to a labeled vial containing 70% ethanol for archival storage.

Tick Pooling Strategies

Strategic pooling of ticks before DNA extraction is a cost-effective approach for large-scale surveillance studies. The pooling strategy should be designed to minimize the dilution of pathogen DNA, which is critical for detecting low-prevalence protists.

Rationale and Design of Tick Pools

The primary goal of pooling is to balance cost-efficiency with diagnostic sensitivity. Pools are typically constructed based on shared biological and collection metadata to maintain meaningful results. Common grouping factors include:

• Tick species [33]
• Developmental stage (e.g., larvae, nymphs, adults) [2]
• Geographic location of collection [33]
• Host individual [33]
• Date of collection [33]

The following table summarizes quantitative pooling strategies derived from recent research:

Pooling Factor	Recommended Pool Size	Rationale & Context
Nymphs	Up to 10 individuals per pool [2] [1]	Balances DNA yield and minimizes excessive dilution of pathogen DNA.
Larvae	Up to 50 individuals per pool [2] [1]	The small size of larvae yields less DNA per individual, requiring larger numbers per pool.
Adults	Processed individually or in smaller pools (e.g., 1-8 individuals) [2] [33]	Adults are larger and provide more DNA; individual processing simplifies pathogen attribution.

Fully engorged ticks, which contain a large volume of host blood, are often excluded from pools to minimize the proportion of host DNA in the extraction, thereby increasing the relative abundance of tick and pathogen DNA [33].

Integration with DNA Barcoding of 18S rRNA for Protists

The methods of collection, identification, and pooling culminate in the molecular detection and identification of tick-borne protists via DNA barcoding of the 18S rRNA gene.

Workflow from Tick to Protist Identification

The following diagram illustrates the integrated workflow, from tick collection to the final identification of tick-borne protists:

Critical Considerations for 18S rRNA Barcoding

The 18S rRNA gene is a powerful marker for eukaryotic pathogens, but its use requires specific considerations:

• Primer Selection: The choice of primer set and the target hypervariable region (e.g., V4, V9) can significantly influence the diversity and abundance of protists detected. Different primer sets have varying amplification efficiencies for different protist groups, which can lead to biased results [2] [1]. In-silico validation of primers against known sequences of target protists is recommended [2] [1].
• Validation: Given the potential for primer bias, findings from NGS-based DNA barcoding should be confirmed using conventional or real-time PCR with specific primer sets [2] [1]. This step is crucial for verifying the presence of particular pathogens like Toxoplasma gondii, which may be missed by some 18S barcoding approaches [2] [1].

The Scientist's Toolkit: Essential Research Reagents and Materials

The following table details key reagents and materials essential for executing the workflows described in this guide.

Item	Function/Application	Example Products/Protocols
DNA Extraction Kit	Isolation of high-quality genomic DNA from single ticks or tick pools.	DNeasy Blood & Tissue Kit (Qiagen) [2] [1]; E.Z.N.A. Tissue DNA Kit (Omega Bio-Tek) [30]; DNeasy PowerSoil Pro Kit (Qiagen) [31].
PCR Reagents	Amplification of the 18S rRNA gene fragments for library preparation or validation.	Universal primers for 18S rRNA V4 and V9 regions [2] [1].
Next-Generation Sequencing Platform	High-throughput sequencing of amplified 18S rRNA libraries for protist diversity analysis.	Illumina MiSeq platform [31] [2] [1].
Taxonomic Keys	Morphological identification of tick specimens to species and developmental stage.	Region-specific keys, e.g., [30] for South African ticks; [citation:30-32 in citation:1].
Stereomicroscope with Camera	Visualization of morphological features and documentation of specimens.	Zeiss Stemi 508 [30]; Olympus models with DP72 camera [31] [32].
Preservative	Field and long-term storage of tick specimens to preserve morphological integrity and nucleic acids.	70% Ethanol [31] [30].

DNA Extraction Protocols from Tick and Host Blood Samples

The reliable detection and characterization of tick-borne protists, such as Babesia and Theileria, hinge on the efficient extraction of high-quality nucleic acids from both tick vectors and host blood samples. Within the context of DNA barcoding focused on the 18S rRNA gene, the initial extraction step is critical, as the yield and purity of the DNA directly influence the success of subsequent polymerase chain reaction (PCR) amplification and sequencing efforts [35]. The resilience of the tick exoskeleton and the often low abundance of protozoan pathogens in host blood present distinct technical challenges that require optimized and robust extraction methodologies [36] [37]. This guide provides a detailed overview of current protocols, comparing their efficacy and outlining standardized procedures to support advanced research and diagnostic applications in the field of tick-borne diseases.

Comparative Analysis of DNA Extraction Methods

The choice of DNA extraction method significantly impacts the yield, purity, and overall suitability of the nucleic acid for downstream 18S rRNA barcoding applications. The following table summarizes the performance characteristics of several common techniques as applied to tick and blood samples.

Table 1: Comparison of DNA Extraction Methods for Tick and Host Blood Samples

Method	Typical Yield	Key Advantages	Key Limitations	Best Suited For
Phenol-Chloroform Extraction	50-100 ng/µL [36]	High DNA yield [36]	Safety risks due to toxic organic solvents; time-consuming [36]	Applications requiring high DNA yield where cost is a primary concern and safety infrastructure exists.
Silica-Based/Column Methods	40-80 ng/µL [36]	Good balance of yield and purity; well-established protocols [36]	Reduced efficiency with samples having high microbial loads; can be costly [36]	Routine diagnostics and PCR-based detection from blood samples and individual ticks.
Magnetic Bead-Based Extraction	20-70 ng/µL [36]	Rapid processing; potential for automation [36]	Risk of bead carryover; requires specialized equipment [36]	Medium- to high-throughput laboratories processing many samples.
Modified Alkaline Lysis	Comparable to commercial kits [38]	Highly cost-effective; minimal equipment needs; ideal for field applications [38]	May require optimization for consistency across different tick life stages.	Resource-limited settings and field studies for population genetics [38].

Detailed Experimental Protocols

High Molecular Weight (HMW) DNA Extraction from Ticks for Genomics

This protocol is designed for extracting HMW DNA suitable for long-read sequencing platforms, such as Oxford Nanopore Technologies (ONT), which is ideal for de novo genome assembly of ticks or their symbionts [39].

• Step 1: Sample Preparation. Begin with approximately 1.25 grams of tick eggs or homogenized adult ticks. Mechanical homogenization is critical for disrupting the resilient chitinous exoskeleton.
• Step 2: Phenol-Based Extraction. Use a standard phenol-chloroform extraction protocol to isolate the HMW DNA. This involves digesting the homogenate with Proteinase K, followed by phase separation with phenol:chloroform and precipitation of the DNA with ethanol [39].
• Step 3: Quality Control (QC). Assess the integrity of the extracted DNA using agarose gel electrophoresis. The DNA should appear as a single, high-molecular-weight band (≥ 48 kbp) with minimal smearing, indicating minimal degradation [39].
• Step 4: DNA Shearing (Optional). For library preparation for ONT sequencing, gentle shearing of the HMW DNA may be necessary. This can be achieved by passing the DNA through a narrow-gauge needle (e.g., 29G) or by pipetting with a P1000 tip. The shearing method can be adjusted to balance read length and sequencing yield [39].

A Simple Modified Alkaline Lysis Protocol for Ethanol-Preserved Ticks

This cost-effective method is highly applicable for field collections and PCR-based pathogen surveillance or population genetics studies [38].

• Step 1: Tick Homogenization. Individually homogenize ethanol-preserved ticks (adults, nymphs, or larvae) using sterile micropestles in a microcentrifuge tube. Thorough homogenization is essential for efficient lysis.
• Step 2: Alkaline Lysis. Add an alkaline lysis buffer (e.g., 50-100 µL of a 25 mM NaOH, 0.2 mM EDTA solution) to the homogenate. Incubate the mixture at 95°C for 20-60 minutes to lyse the cells and inactivate nucleases.
• Step 3: Neutralization. Cool the samples and add an equal volume of neutralization buffer (e.g., 40 mM Tris-HCl, pH 5.0). Mix thoroughly by vortexing.
• Step 4: Clarification. Centrifuge the lysate at high speed (e.g., 10,000-15,000 × g) for 5 minutes to pellet debris. The resulting supernatant contains the extracted DNA and can be used directly in PCR assays targeting the 18S rRNA gene [38].

DNA Extraction from Host Blood for Protist Detection

This protocol outlines the procedure for detecting tick-borne protists like Babesia ovis and Theileria ovis in host blood, using molecular markers such as the 18S rRNA gene.

• Step 1: Sample Collection and Storage. Collect blood from small ruminants (e.g., sheep or goats) in EDTA-coated tubes to prevent coagulation. For molecular work, store samples at -20°C or -80°C long-term. Avoid repeated freeze-thaw cycles.
• Step 2: DNA Extraction. While commercial silica-based kits are commonly used and effective [35], the principles of lysis and purification remain consistent.
  • Lysis: Mix the blood sample with a lysis buffer containing Proteinase K to disrupt cells and degrade proteins.
  • Purification: Use a silica membrane column to bind the DNA. Wash the bound DNA with an ethanol-based buffer to remove contaminants like salts and hemoglobin.
  • Elution: Elute the pure DNA in a low-salt buffer or nuclease-free water.
• Step 3: Molecular Detection. Use the extracted DNA as a template in a PCR assay. For Babesia and Theileria species, target a region of the 18S rRNA gene. A typical reaction will yield a ~549 bp product for B. ovis and a ~509 bp product for T. ovis [35].

Table 2: Key Reagent Solutions for DNA Extraction and Analysis

Research Reagent	Function in Protocol	Specific Application Example
Proteinase K	Enzymatic digestion of proteins and nucleases	Critical for lysing tick tissues and digesting contaminating proteins in blood samples [39] [40].
Phenol-Chloroform	Liquid-phase separation of DNA from proteins and lipids	Used in HMW DNA extraction protocols for genomic studies of ticks [39].
Silica Membrane Columns	Selective binding and purification of DNA	Core component of many commercial kits used for purifying DNA from host blood [35].
Alkaline Lysis Buffer	Rapid chemical lysis of cells	Key component of the simple, cost-effective modified method for DNA extraction from preserved ticks [38].
18S rRNA Primers	PCR amplification of a specific genetic target	Used for molecular detection and barcoding of protist pathogens like Babesia and Theileria in host blood [35].

Workflow for 18S rRNA Barcoding of Tick-Borne Protists

The following diagram illustrates the integrated workflow from sample collection to pathogen identification, which is central to a thesis on DNA barcoding of tick-borne protists.

Diagram 1: 18S rRNA Barcoding Workflow

This workflow begins with Sample Collection, which involves gathering ticks from the environment or hosts, and collecting blood from potentially infected animals [35]. The next critical step is Nucleic Acid Extraction, where the choice of protocol (as detailed in Section 3) directly impacts downstream success. The purified DNA then undergoes PCR Amplification using primers specific to the 18S rRNA gene of protists, generating amplicons of defined length (e.g., 509-549 bp) for detection and analysis [35]. Following amplification, Sequencing & Analysis of the PCR products allows for the generation of DNA barcodes. Finally, Pathogen Identification is achieved by comparing these barcode sequences to curated databases to determine the species of Babesia, Theileria, or other protists present [35].

Advanced Methodologies and Future Directions

Beyond conventional PCR and sequencing, novel technologies are enhancing the detection and understanding of tick-borne protists.

• Hybrid Capture Next-Generation Sequencing (HCNGS): Panels like "TICKHUNTER" represent a significant advancement. This method uses a large set of oligonucleotide "baits" to enrich for target pathogen DNA prior to sequencing. For 18S rRNA barcoding, this allows for sensitive detection and subtyping of protists, even in cases of co-infections with multiple pathogens, and can be combined with host blood meal identification in the same assay [41].
• Low-Cost Genomic Sequencing: The use of a single, affordable long-read sequencing technology (e.g., Oxford Nanopore) combined with cloud computing resources demonstrates that producing draft genome assemblies for ticks and their symbionts is accessible to smaller laboratories. This approach facilitates capacity building and more widespread participation in genomic studies of tick-borne diseases [39].

In conclusion, the selection and optimization of DNA extraction protocols form the foundational step in a robust 18S rRNA barcoding pipeline for tick-borne protists. By matching the method to the research question and sample type, scientists can ensure the reliability of their data, thereby contributing to accurate diagnostics, effective surveillance, and a deeper understanding of pathogen ecology.

Universal Primers for the 18S rRNA V4 and V9 Regions

The accurate identification and diversity assessment of tick-borne protists are critical for public health, veterinary medicine, and ecological studies. DNA barcoding, which involves sequencing short, standardized genetic markers from organisms, has emerged as a powerful tool for species identification and discovery. Within this field, the 18S ribosomal RNA (rRNA) gene has become a cornerstone marker for eukaryotic pathogens, including tick-borne protists. Its utility stems from the presence of both highly conserved regions, which facilitate the design of universal primers, and hypervariable regions, which provide the phylogenetic resolution necessary for species discrimination. The V4 and V9 regions of the 18S rRNA gene are particularly favored in next-generation sequencing (NGS) applications due to their high phylogenetic informativeness and the availability of established primer sets [1] [2].

However, the design and application of universal primers are not without challenges. The results of DNA barcoding studies can be significantly influenced by the choice of primer set, PCR conditions, and the bioinformatic processing of sequence data [1] [9]. This technical guide provides a detailed overview of universal primer design and application for the 18S rRNA V4 and V9 regions, framed within the context of a broader thesis on DNA barcoding of tick-borne protists. It is intended to equip researchers with the methodologies and considerations necessary to conduct robust and reproducible metabarcoding studies.

Primer Sequences and Characteristics

The following table summarizes the core universal primer sequences for the 18S rRNA V4 and V9 regions, as validated in recent tick-borne pathogen research [1] [2]. These sequences are presented with Illumina adapter overhangs, which are essential for library preparation in NGS workflows.

Table 1: Universal Primer Sequences for 18S rRNA V4 and V9 Regions

Target Region	Primer Name	Sequence (5' to 3')	Core Target Sequence (5' to 3')	Amplicon Length (approx.)
V4	V4 Forward	TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGCCAGCAGCCGCGGTAATTCC	CCAGCAGCCGCGGTAATTCC	~380-420 bp
	V4 Reverse	GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGACTTTCGTTCTTGAT	ACTTTCGTTCTTGAT
V9	V9 Forward	TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGCCCTGCCHTTTGTACACAC	CCCCTGCCHTTTGTACACAC	~120-150 bp
	V9 Reverse	GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGCCCTTCYGCAGGTTCACCTAC	CCCTTCYGCAGGTTCACCTAC

The degeneracy codes in the V9 primer sequences are: H (A, C, or T) and Y (C or T). This degeneracy is incorporated to account for natural sequence variation across different eukaryotic lineages, thereby enhancing the breadth of amplification [1].

Experimental Workflow for 18S rRNA Metabarcoding

The process of conducting an 18S rRNA metabarcoding study, from sample collection to taxonomic identification, involves a series of critical steps. The diagram below outlines this comprehensive workflow.

Detailed Methodologies for Key Experiments

1. Tick Collection, Identification, and DNA Extraction

Ticks should be collected from the environment or host animals using standardized methods such as flagging or dragging [1]. Following collection, ticks are morphologically identified to species and developmental stage using stereomicroscopes and taxonomic keys [42]. For DNA extraction, ticks are typically pooled (e.g., up to ten nymphs per pool) and homogenized using a bead beater in phosphate-buffered saline (PBS). Genomic DNA is then extracted from the homogenate using commercial kits, such as the DNeasy Blood & Tissue Kit (Qiagen) [1] [2]. The concentration and quality of the extracted DNA should be quantified using a fluorometer or spectrophotometer before proceeding.

2. Library Preparation and Amplicon Sequencing

The initial PCR amplification is a critical step that can introduce bias. The protocol below is adapted from a study on tick-borne protists [1].

• Reaction Setup: Use a high-fidelity PCR master mix (e.g., KAPA HiFi HotStart ReadyMix). The 25 µL reaction should include the forward and reverse primers (from Table 1) and the normalized template DNA.
• Thermocycling Conditions:
  • Initial Denaturation: 95°C for 3 minutes.
  • Amplification (25 cycles):
    • Denaturation: 95°C for 30 seconds.
    • Annealing: 55°C for 30 seconds.
    • Extension: 72°C for 30 seconds.
  • Final Extension: 72°C for 5 minutes.
• Indexing PCR: A second, limited-cycle PCR (e.g., 8 cycles) is performed to attach dual indices and Illumina sequencing adapters using a kit such as the Nextera XT Index Kit.
• Purification and Pooling: The amplified products are purified using solid-phase reversible immobilization (SPRI) beads, such as AMPure XP beads. The final library pool is then quantified via qPCR and its quality is assessed using a TapeStation or Bioanalyzer before being sequenced on an Illumina MiSeq or iSeq platform [1] [9].

3. Bioinformatic Analysis

Raw sequencing data must be processed to generate meaningful taxonomic assignments. A standard pipeline involves the following steps, often implemented in QIIME 2 or similar environments [1] [9]:

• Demultiplexing and Trimming: Assign reads to samples and remove primer and adapter sequences using tools like Cutadapt.
• Quality Filtering and Denoising: Correct sequencing errors, merge paired-end reads, and remove chimeric sequences to generate high-resolution Amplicon Sequence Variants (ASVs) using the DADA2 algorithm.
• Taxonomic Assignment: ASVs are classified by alignment to a reference database (e.g., NCBI NT, SILVA) using BLASTn or a trained classifier. The confidence in the assignment is influenced by the quality and comprehensiveness of the reference database.

Critical Considerations for Primer and Protocol Optimization

Performance and Bias of V4 vs. V9 Primers

The choice between the V4 and V9 regions is not neutral and can significantly impact study outcomes. Research has demonstrated that the number and abundance of protists detected can differ substantially depending on the primer set used [1]. For instance, a study on tick-borne protists identified three genera of protozoa using these primers, but the results varied between the V4 and V9 assays [1]. The V9 region is often chosen for its ability to capture a broad range of eukaryotes, but its shorter length provides less phylogenetic information compared to the V4 region [9].

Technical Factors Influencing Results

Several technical factors must be optimized to ensure accurate representation of the protist community:

• Annealing Temperature: Variations in the amplicon PCR annealing temperature can alter the relative abundance of output reads for different parasite species, potentially due to differential primer binding efficiencies [9].
• DNA Secondary Structures: The secondary structure of the target 18S rDNA region can negatively impact amplification efficiency and the number of reads obtained for specific taxa, leading to quantification bias [9].
• Validation is Crucial: Given the potential for primer bias and the incomplete nature of reference databases, findings from DNA barcoding should be confirmed using conventional or real-time PCR with pathogen-specific primers [1] [2]. For example, Toxoplasma gondii was not detected via DNA barcoding in one study but was subsequently confirmed by conventional PCR [1].

The Scientist's Toolkit: Research Reagent Solutions

Table 2: Essential Reagents and Kits for 18S rRNA Metabarcoding

Item	Function/Description	Example Product(s)
DNA Extraction Kit	Purifies genomic DNA from complex tick samples.	DNeasy Blood & Tissue Kit (Qiagen) [1], TIANamp Genomic DNA Kit (Tiangen) [42]
High-Fidelity PCR Mix	Ensures accurate amplification during library PCR to minimize errors.	KAPA HiFi HotStart ReadyMix (Roche) [1] [9]
SPRI Magnetic Beads	Purifies PCR products by size selection and removes contaminants.	AMPure XP Beads (Beckman Coulter) [1]
Library Quantification Kit	Precisely quantifies the final DNA library pool for accurate sequencing loading.	KAPA Library Quantification Kit (Roche) [1]
Indexing Kit	Adds unique sample indices and full sequencing adapters.	Nextera XT Index Kit (Illumina) [1] [2]
Sequencing Platform	Performs high-throughput amplicon sequencing.	Illumina MiSeq, iSeq 100, NovaSeq 6000 [1] [9] [43]

The universal primer sets for the 18S rRNA V4 and V9 regions are powerful tools for uncovering the diversity of tick-borne protists. However, their application requires a meticulous and critical approach. Researchers must recognize the inherent biases introduced by primer choice and PCR conditions. A successful DNA barcoding study hinges on a holistic strategy that combines optimized wet-lab protocols, rigorous bioinformatic processing, and independent validation of results. By adhering to the detailed methodologies and considerations outlined in this guide, scientists can enhance the reliability and reproducibility of their research, ultimately contributing to a more accurate understanding of tick-borne protist communities and the associated disease risks.

Library Preparation and Sequencing on Illumina MiSeq and iSeq Platforms

Within the field of genomic research, next-generation sequencing (NGS) has become a cornerstone technology, enabling a wide range of applications from small-genome sequencing to targeted gene expression analysis. For researchers focusing on DNA barcoding of tick-borne protists, such as those identified through 18S rRNA gene fragments, the selection of an appropriate sequencing platform and optimized library preparation is critical for obtaining accurate and reliable results [2] [7]. This technical guide provides an in-depth comparison of two popular Illumina sequencing systems—the MiSeq and iSeq 100—detailing their specifications, experimental workflows, and application within the context of 18S rRNA-based pathogen identification. A recent study on tick-borne protists underscores the importance of these platforms, demonstrating their use in identifying diverse protozoan genera such as Hepatozoon canis and Theileria luwenshuni from complex tick samples [2] [7]. By framing this discussion within the practical requirements of 18S rRNA research, this guide aims to equip scientists with the knowledge to effectively leverage these platforms for their taxonomic and pathogen surveillance studies.

Platform Comparison: MiSeq vs. iSeq 100

Choosing between the MiSeq and iSeq 100 systems requires a clear understanding of their technical capabilities and how they align with project goals. The following section provides a detailed comparison of their specifications, performance, and ideal use cases, particularly for DNA barcoding applications.

Key Technical Specifications

Specification	Illumina iSeq 100	Illumina MiSeq
Maximum Output	1.2 Gb [44] [45]	15 Gb [46] [47]
Maximum Single Reads per Run	4 million [44] [45]	25 million [47]
Maximum Read Length	2 x 150 bp [44] [45]	2 x 300 bp [47]
Typical Run Time (2x150 bp)	~19 hours [44] [45]	~24 hours (v2 chemistry) [46]
Quality Scores (Q30) for 2x150 bp	>80% of bases [45]	>80% of bases (v2 chemistry) [46]
Instrument Dimensions (W x D x H)	30.5 cm x 33 cm x 42.5 cm [45]	68.6 cm x 56.5 cm x 52.3 cm [46]
Key Technology	CMOS & one-channel SBS [45]	SBS with paired-end sequencing [46] [47]

Performance and Operational Considerations

Both platforms utilize Illumina's proven Sequencing by Synthesis (SBS) chemistry, ensuring high base-calling accuracy [46] [45]. A comparative study on environmental DNA metabarcoding found that the iSeq 100 and MiSeq exhibited remarkably similar performance in species detectability and sequence quality, despite their different technological implementations [48]. The %Q30 scores (percentage of bases with a quality score of 30 or higher) were comparable, with iSeq reporting >96.8% for Read 1 and >95.3% for Read 2, and MiSeq reporting >97.3% and >96.48%, respectively [48]. A quality score of 30 (Q30) represents an error rate of 1 in 1000, equating to 99.9% base call accuracy [49].

The primary differentiators are throughput and flexibility. The iSeq 100, with its compact size and lower output, is designed for focused, small-scale projects and labs seeking an affordable entry into NGS. In contrast, the MiSeq offers a much wider output range and longer read lengths, making it suitable for more complex applications, such as larger metagenomic studies or sequencing through repetitive regions, which can be challenging with shorter reads [46] [47]. It is crucial to note that Illumina has announced the obsolescence of both the iSeq 100 and the original MiSeq System. They will be available for order until September 30, 2025, with full system support continuing through December 31, 2029. The recommended alternative is the MiSeq i100 Series [44] [47].

Experimental Protocol for 18S rRNA Barcoding

The following workflow and detailed methodology are adapted from a recent study that successfully identified tick-borne protists using 18S rRNA gene fragments on the MiSeq platform [2] [7].

The following diagram illustrates the comprehensive workflow from sample collection to data analysis for 18S rRNA DNA barcoding of tick-borne protists.

Detailed Methodologies

1. Sample Collection and DNA Extraction

• Tick Collection: Collect questing ticks from the field using the flagging method. Preserve samples in 70% ethanol at room temperature [2].
• Morphological Identification: Identify tick species and developmental stages under a microscope using standard morphological keys [2].
• Sample Pooling and DNA Extraction: Pool ticks (e.g., up to ten nymphs or fifty larvae per pool) and homogenize using a bead beater in PBS buffer. Extract genomic DNA using a commercial kit, such as the DNeasy Blood & Tissue Kit (Qiagen). Quantify DNA concentration using a spectrophotometer and store at -20°C [2].

2. Library Preparation for 18S rRNA Amplicon Sequencing This is a two-step PCR protocol to create sequence-ready libraries.

• Primer Selection: Choose primer sets that target hypervariable regions of the 18S rRNA gene, such as the V4 or V9 regions. The study on tick-borne protists highlighted that the number and abundance of protists detected can vary significantly depending on the primer set used, necessitating careful selection and potential optimization [2] [7].
  • Example V4 Primers:
    • Forward: 5′-TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGCCAGCAGCCGCGGTAATTCC-3′
    • Reverse: 5′-GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGACTTTCGTTCTTGAT-3′ [2]
  • The Illumina adapter overhang sequences (underlined above) are added to the gene-specific sequence.
• First-Stage PCR (Amplicon PCR): Amplify the target region using the primers above.
  • Cycle Conditions: 3 min at 95°C; 25 cycles of 30 s at 95°C, 30 s at 55°C, and 30 s at 72°C; final extension of 5 min at 72°C [2].
• Purification: Clean the initial PCR product using AMPure beads (Agencourt Bioscience) [2].
• Second-Stage PCR (Indexing PCR): Attach dual indices and Illumina sequencing adapters using the Nextera XT Index Kit.
  • Cycle Conditions: Use the same conditions as the first PCR but reduce the number of cycles to 8-10 [2].
• Final Purification and Quantification: Purify the final product with AMPure beads. Quantify using qPCR per the KAPA Library Quantification kit protocol and qualify the library using an electrophoretic system like the Agilent TapeStation [2].

3. Sequencing

• Library Loading and Run Setup: Denature and dilute the final library according to the Illumina protocol (e.g., as outlined in the "MiSeq System Denature and Dilute Libraries Guide") [50]. Load the library onto a MiSeq or iSeq flow cell.
• Sequencing Parameters: Select the appropriate run parameters. For 18S rRNA barcoding on the MiSeq, a 2x250 bp or 2x300 bp run is typical to ensure sufficient overlap of the paired-end reads for the targeted region [2] [46].

The Scientist's Toolkit

Successful execution of a DNA barcoding project relies on a suite of specific reagents and bioinformatics tools. The following table outlines essential solutions used in the featured tick-borne protist research and general requirements for the workflow.

Table: Research Reagent Solutions for 18S rRNA DNA Barcoding

Item	Function	Example Product/Kit
DNA Extraction Kit	Isolates high-quality genomic DNA from complex samples like tick pools.	DNeasy Blood & Tissue Kit (Qiagen) [2]
PCR Enzymes & Master Mix	Amplifies the target 18S rRNA gene regions during library preparation.	Not specified in search results, but standard high-fidelity PCR mixes are used.
Sequencing Adapter & Index Kit	Adds platform-specific adapters and sample-specific barcodes for multiplexing.	Nextera XT Index Kit (Illumina) [2]
Library Purification Beads	Purifies PCR products by removing primers, dimers, and other contaminants.	AMPure beads (Agencourt Bioscience) [2]
Library Quantification Kit	Accurately quantifies the final sequencing library prior to loading.	qPCR Quantification Kit (KAPA) [2]
Sequencing Reagent Cartridge	Contains enzymes, buffers, and nucleotides required for the sequencing reaction.	MiSeq Reagent Kit v3 (for 2x300 bp) [46]

Data Analysis and Bioinformatics Pipeline

The transformation of raw sequencing data into biologically meaningful results requires a robust bioinformatics pipeline. The following steps are critical for 18S rRNA amplicon analysis, as demonstrated in the tick-borne protist study [2].

1. Pre-processing of Raw Reads

• Adapter and Primer Trimming: Remove adapter and primer sequences from raw reads using tools like Cutadapt [2].
• Quality Filtering and Denoising: Process the trimmed reads with DADA2 (via QIIME2) or similar pipelines to perform quality control, error correction, merge paired-end reads, and generate a table of amplicon sequence variants (ASVs). ASVs offer a higher resolution than traditional OTUs by inferring exact biological sequences [2].

2. Taxonomic Assignment

• Assign taxonomy to the resulting ASVs by comparing them against a curated reference database of 18S rRNA sequences using a classifier. The choice of database is critical for accurate identification of protists [2].

3. Validation

• It is important to note that the featured study found discrepancies between NGS and conventional PCR; for instance, Toxoplasma gondii was not identified via DNA barcoding but was detected by conventional PCR [2] [7]. This underscores the importance of validating key findings with orthogonal methods, especially when working with novel primers or organisms.

The Illumina MiSeq and iSeq 100 platforms provide powerful and accessible solutions for DNA barcoding applications, including the identification of tick-borne protists via the 18S rRNA gene. The choice between them hinges on the specific scale and resolution required for the research project. The iSeq 100 offers a compact and cost-effective format suitable for focused, small-scale surveillance, while the MiSeq provides greater throughput and longer read lengths for more comprehensive biodiversity assessments. As the study on Korean ticks revealed, the success of such projects is not solely dependent on the sequencing platform but is also profoundly influenced by careful primer selection, optimized library construction, and a validated bioinformatics pipeline [2] [7]. By adhering to the detailed protocols and considerations outlined in this guide, researchers can effectively utilize these technologies to uncover the diversity and prevalence of pathogenic protists in complex environmental and clinical samples.

The application of 18S rRNA gene amplicon sequencing has revolutionized our ability to study tick-borne protists, organisms of significant importance to both human and animal health. This technical guide provides a comprehensive framework for implementing the DADA2 and QIIME2 pipeline specifically tailored for tick-borne protist research. The 18S ribosomal RNA gene serves as an excellent molecular marker for protist identification and classification due to its conserved regions, which allow for broad amplification, and variable regions, which provide sufficient resolution for distinguishing between species [51]. Unlike 16S rRNA gene sequencing commonly used for bacterial communities, 18S rRNA analysis requires specific considerations for primer selection, database choice, and processing parameters to accurately capture protist diversity.

The pipeline from raw sequencing reads to Amplicon Sequence Variants (ASVs) represents a significant advancement over traditional Operational Taxonomic Unit (OTU) methods. ASVs offer single-nucleotide resolution, providing greater accuracy in identifying and differentiating between closely related protist species [52]. This high resolution is particularly valuable in tick-borne pathogen research, where precise identification of protists such as Babesia, Theileria, and Hepatozoon species is crucial for understanding disease epidemiology and developing targeted interventions.

Workflow Schematic

Essential Research Reagents and Materials

Table 1: Key Research Reagents and Materials for 18S rRNA Amplicon Sequencing

Item	Function	Considerations for Tick-Borne Protists
18S rRNA Primers	Amplification of target gene region	Select primers with high coverage for protists (e.g., TAReuk454FWD1/TAReukREV3) [51]
DNA Extraction Kit	Isolation of high-quality genomic DNA	Optimize for efficient lysis of protist cells; include inhibition removal steps
PCR Components	Amplification of target regions	Use high-fidelity polymerase to minimize amplification errors
QIIME2 Classifier	Taxonomic assignment of ASVs	Train custom classifier on protist-specific 18S databases when necessary [53]
Reference Database	Taxonomic reference for classification	Use specialized databases (e.g., PR2, SILVA for eukaryotes) for improved protist identification [52]
Mock Community	Quality control and error rate estimation	Include protist-specific mock communities to validate pipeline performance [53]

Detailed Methodology

Primer Selection and Experimental Setup

For tick-borne protist research, careful primer selection is critical for comprehensive community capture. The 18S rRNA gene contains nine variable regions (V1-V9) with varying degrees of conservation. Research indicates that the V4 region often provides an optimal balance between taxonomic resolution and amplification efficiency for eukaryotic microbes [51]. Empirical evaluation of primer combinations using in silico tools should precede wet-lab experiments to verify coverage of target protist taxa.

The Earth Microbiome Project (EMP) recommended 18S primers provide a starting point, but researchers should verify their efficacy for specific tick-borne protists. Primer mismatches can significantly reduce detection sensitivity for certain taxa, potentially missing important pathogens. As noted in evaluations, "Changes in one single base may lead to changes in evaluation results or amplification products. The single degenerate base added to primers may cover more species, but it may also reduce the species specificity to some extent" [51]. This balance between coverage and specificity is particularly important when working with clinical or environmental samples that may contain low abundances of pathogenic protists alongside diverse background microbiota.

Data Import and Quality Control

The initial phase of the bioinformatics pipeline focuses on importing raw sequencing data into QIIME2 and performing essential quality control steps. Data must be properly formatted and imported into QIIME2 artifacts (.qza files) for subsequent analysis.

Manifest File Preparation: Create a manifest file in tab-separated format that maps sample identifiers to sequence file paths [54]:

Data Import Command:

Quality Assessment: Generate an interactive summary to evaluate raw sequence quality:

This quality report provides critical information on sequence length distribution, quality scores across sequencing cycles, and sample-wise sequence counts. For tick-borne protist samples, special attention should be paid to potential contamination from host (tick) DNA, which may dominate the sequencing library and reduce target protist sequence recovery.

DADA2 Denoising for ASV Generation

The DADA2 algorithm implements a sophisticated error model that distinguishes biological sequences from sequencing errors, producing Amplicon Sequence Variants (ASVs) with single-nucleotide resolution [55]. This approach provides significant advantages over OTU clustering for tick-borne protist research by enabling precise differentiation between closely related pathogen species.

Key DADA2 Parameters:

• --p-trunc-len-f and --p-trunc-len-r: Position to truncate forward and reverse reads based on quality profile
• --p-trim-left-f and --p-trim-left-r: Number of bases to remove from sequence starts
• --p-max-ee: Maximum expected errors allowed in a read
• --p-chimera-method: Method for chimera removal

DADA2 Execution:

Table 2: DADA2 Quality Control Steps and Their Functions [55]

Processing Step	Function	Impact on Results
Filtering	Removes sequences with excessive expected errors	Reduces erroneous sequences while retaining rare variants
Denoising	Corrects sequencing errors using error model	Increases true biological sequence accuracy
Merge Paired Reads	Combines forward and reverse reads	Creates full-length amplicon sequences
Chimera Removal	Identifies and removes artificial chimeras	Prevents false composite sequences

The denoising process is particularly important for tick-borne protist studies where pathogenic species may be present at low abundances. Proper parameter optimization ensures that true biological sequences are retained while technical artifacts are removed. The DADA2 algorithm "can effectively simulate and correct sequencing errors" through its quality control process [56].

Taxonomic Classification and Phylogenetic Analysis

Accurate taxonomic assignment of ASVs is essential for identifying tick-borne protists and understanding community composition. This requires specialized reference databases containing high-quality 18S rRNA sequences from known protists.

Classifier Training: For optimal results with protist samples, train a custom classifier on a relevant database:

Taxonomic Assignment:

Phylogenetic Tree Construction:

For tick-borne protist research, the phylogenetic tree provides essential evolutionary context, enabling researchers to determine relationships between detected ASVs and known pathogens. This phylogenetic framework is particularly valuable when encountering novel protist variants that may be related to known pathogenic species.

Diversity Analysis and Statistical Evaluation

Diversity metrics provide insights into the ecological complexity of tick-borne protist communities, enabling comparisons between sample groups and identification of factors influencing community structure.

Core Metrics Calculation:

This command generates both alpha diversity (within-sample diversity) and beta diversity (between-sample diversity) metrics. For protist communities, key alpha diversity indices include:

• Observed ASVs: Simple count of unique sequences
• Shannon's index: Accounts for both richness and evenness
• Faith's PD: Incorporates phylogenetic relationships

Beta diversity analysis includes:

• Weighted UniFrac: Phylogenetic distance accounting for abundance
• Unweighted UniFrac: Phylogenetic distance considering only presence/absence
• Bray-Curtis: Abundance-based dissimilarity

Rarefaction Analysis: To ensure adequate sequencing depth for diversity assessments:

For tick-borne protist studies, diversity analyses can reveal how factors such as tick species, geographic location, or season influence protist community composition. These insights are valuable for understanding disease transmission dynamics and ecological relationships within the tick microbiome.

Quality Control and Validation

Comprehensive QC Framework

Rigorous quality control is essential throughout the analytical pipeline to ensure reliable results. The q2-quality-control plugin provides specialized tools for evaluating data quality [56].

Sequence Quality Evaluation:

Compositional Accuracy Assessment: When using mock communities:

Contaminant Filtering

For tick samples, filtering of non-target sequences is often necessary:

Table 3: Quality Control Metrics and Interpretation

Metric	Target Range	Significance for Tick-Borne Protist Research
Sequence Depth	>10,000 reads/sample	Ensures sufficient coverage for rare pathogens
Alpha Rarefaction	Curve approaching plateau	Indicates adequate sampling depth for diversity estimates
Mock Community Recovery	>90% expected taxa	Validates pipeline accuracy for protist identification
Negative Controls	Minimal sequences	Confirms absence of contamination in reagents
Host DNA Contamination	Variable, ideally <50%	Maximizes sequencing effort on target protists

Advanced Applications for Tick-Borne Protist Research

Differential Abundance Analysis

Identifying protist taxa that significantly differ between sample groups (e.g., infected vs. uninfected ticks) provides crucial biological insights. The ANCOM (Analysis of Composition of Microbiomes) method is particularly suitable for this purpose:

This analysis identifies ASVs that are significantly enriched or depleted in specific sample groups, potentially revealing pathogenic protists associated with disease states or specific tick populations.

Data Integration and Visualization

Effective visualization techniques enhance interpretation of complex protist community data:

Taxonomic Composition Visualization:

Principal Coordinates Analysis:

For tick-borne protist research, these visualizations can reveal patterns in community structure related to ecological factors, temporal changes, or host associations, providing valuable insights for understanding disease transmission dynamics.

The integrated DADA2 and QIIME2 pipeline provides a robust, reproducible framework for analyzing tick-borne protist communities using 18S rRNA amplicon sequencing. The implementation of ASV-based analysis offers superior resolution compared to traditional OTU approaches, enabling precise identification of pathogenic protists and detection of rare variants that may have significant clinical implications.

Successful implementation for tick-borne protist research requires careful consideration of several factors: primer selection optimized for target taxa, appropriate reference databases for taxonomic classification, and validation using mock communities containing relevant protist species. Additionally, researchers should maintain consistency in laboratory protocols and bioinformatic parameters across studies to enable meaningful comparisons and meta-analyses.

As the field advances, integration of amplicon sequencing data with complementary approaches such as metagenomics and metatranscriptomics will provide more comprehensive insights into the functional potential and activity of tick-borne protists. The pipeline described here establishes a solid foundation for these advanced investigations, supporting continued progress in understanding and managing tick-borne protist infections.

Navigating Technical Challenges and Optimizing 18S rRNA Barcoding Assays

In the field of molecular ecology and pathogen surveillance, DNA metabarcoding of the 18S rRNA gene has become an indispensable tool for profiling eukaryotic communities and identifying protist pathogens. For researchers investigating tick-borne protists, the critical choice of which hypervariable region to target—V4 or V9—profoundly influences experimental outcomes, from observed diversity to detection sensitivity for specific pathogens. This technical guide examines the inherent biases introduced by primer selection and provides evidence-based protocols for optimizing detection of protists in complex samples, with direct implications for tick-borne disease research.

The fundamental challenge stems from the fact that no universal primer pair exists that equally captures all protistan lineages. As demonstrated across multiple studies, the V4 and V9 regions of the 18S rRNA gene differ significantly in length, variability, and taxonomic resolution, leading to markedly different community profiles from identical samples [57] [17]. This primer bias presents particular complications for tick-borne pathogen research, where detecting low-abundance pathogenic protists against a background of host and environmental DNA remains technically challenging.

Comparative Characteristics of V4 and V9 Regions

Table 1: Fundamental Characteristics of V4 and V9 18S rRNA Gene Regions

Feature	V4 Region	V9 Region
Amplicon Length	270-387 bp [17]	96-134 bp [17]
Primary Strengths	Better phylogenetic resolution [17] [58]; Broader amoeba lineage detection [58]	Enhanced richness estimation; Superior rare biosphere detection [17]
Taxonomic Limitations	Fails to detect some major subdivisions [17]; Misses Foraminifera [58]	Higher mismatches in taxonomy [58]; Poorer phylogenetic resolution [17]
Tick-Borne Protist Detection	Detected Hepatozoon canis, Theileria luwenshuni, Gregarine sp. [2]	Detected Hepatozoon canis, Theileria luwenshuni, Gregarine sp. [2]
Bioinformatic Considerations	Merging forward/reverse reads dramatically reduces sequences [57]	Less problematic for read merging due to shorter length

Experimental Evidence of Primer Bias in Protist Detection

Differential Detection in Environmental Samples

Comparative analyses of eukaryotic communities in brackish water samples revealed striking differences between V4 and V9 datasets. One study found 1,413 eukaryotic OTUs using the V9 primer set compared to only 915 OTUs with the V4 primer set from identical samples [17]. This pattern of V9 revealing greater richness has been consistently observed across environments, suggesting its particular advantage for comprehensive biodiversity surveys.

The V9 region's superiority in detecting rare taxa (those representing <1% of total reads) makes it particularly valuable for identifying low-abundance pathogens in complex tick samples [17]. However, this enhanced sensitivity comes with a trade-off: the shorter V9 region provides inferior phylogenetic resolution compared to the longer V4 region, potentially complicating precise taxonomic placement of novel organisms [17].

Practical Implications for Tick-Borne Protist Research

In applied research on tick-borne pathogens, these primer differences directly impact detection capabilities. A study screening ticks in the Republic of Korea identified three genera of protozoa (Hepatozoon canis, Theileria luwenshuni, and Gregarine sp.) using 18S rRNA metabarcoding, but noted that "the number and abundance of protists detected were different depending on the primer sets" [2] [7]. Notably, Toxoplasma gondii was not identified through DNA barcoding despite being detected by conventional PCR, highlighting how primer bias can lead to false negatives even with sophisticated NGS approaches [2].

The limited overlap between protist taxa detected by different primer regions is particularly concerning. One soil study found only 80 out of 549 protist taxa were common to both V4 and V9 datasets, demonstrating that each region captures largely distinct portions of the protist community [57]. This finding has profound implications for tick-borne disease studies, as reliance on a single primer region may miss clinically relevant pathogens.

Laboratory Protocols for Addressing Primer Bias

Optimized DNA Extraction and Amplification

The following protocol synthesizes methodologies from multiple studies investigating tick-borne protists [2] [59]:

Tick Sample Processing:

• Surface-sterilize ticks sequentially with 3% hydrogen peroxide (1 min vortex), 70% ethanol (two 30-second washes), and phosphate-buffered saline (2 min) [59]
• Homogenize ticks using sterile glass beads and pestles in lysis buffer
• Extract DNA using commercial kits (e.g., DNeasy Blood & Tissue Kit, Qiagen) with overnight proteinase K digestion at 56°C [2]
• Include extraction controls with nuclease-free water to monitor contamination

PCR Amplification Conditions:

• For V4 region: Use primers 616*F (5'-TTAAARVGYTCGTAGTYG-3') and 1132R (5'-CCGTCAATTHCTTYAART-3') [60]
• For V9 region: Use primers 1380F/1510R or 1391F/EukBr [57]
• Employ two-step PCR with universal tails for index addition [57]
• Cycling conditions: 98°C for 3 min; 35 cycles of 98°C for 30s, annealing (temperature optimized per primer), 72°C for 30s; final extension 72°C for 2 min [57]

Critical Optimization Steps:

• Test multiple annealing temperatures (e.g., 57°C, 61°C, 69°C for V9 primers) as temperature significantly influences sequencing depth and taxon richness [57]
• Use high-fidelity DNA polymerase to reduce amplification errors
• Normalize DNA concentrations across samples before pooling to mitigate quantitative bias

Bioinformatic Processing Considerations

Bioinformatic parameter choices substantially impact protist community analyses, particularly for the V4 region:

• For V4 data: Avoid merging forward and reverse reads as this dramatically reduces the number of sequences and taxon richness of protists [57]
• Employ denoising algorithms (DADA2) rather than clustering approaches (SWARM) which overestimate diversity, particularly for certain eukaryotic groups [19]
• Utilize specialized databases (PR2 - Protist Ribosomal Reference database) for taxonomic assignment of protistan sequences [57] [58]
• Apply consistent trimming parameters and filter chimeras specifically for eukaryotic sequences

Table 2: Essential Research Reagents and Computational Tools for 18S rRNA Metabarcoding

Category	Specific Tool/Reagent	Application Notes
Primer Pairs	616*F/1132R (V4) [60]	Amplicon size: ~509 bp; Detects broader amoeba lineages [58]
Primer Pairs	1380F/1510R (V9) [57]	Amplicon size: <200 bp; Superior for rare taxa detection [17]
DNA Extraction	DNeasy Blood & Tissue Kit (Qiagen) [2]	Include proteinase K digestion step for efficient tick tissue lysis
Polymerase	Phusion High-Fidelity DNA Polymerase [57]	Reduces amplification errors in complex community samples
Bioinformatic Tools	DADA2 [57] [19]	Denoising algorithm; more accurate than SWARM for eukaryotes [19]
Reference Database	PR2 (Protist Ribosomal Reference) [57] [58]	Specialized for protist taxonomy; essential for accurate assignment
Validation Methods	Conventional PCR [2] [7]	Essential confirmation for metabarcoding results

The selection between V4 and V9 regions for 18S rRNA metabarcoding involves significant trade-offs that directly impact detection capabilities for tick-borne protists. Based on current evidence, the following recommendations emerge:

• For Comprehensive Pathogen Discovery: Employ both V4 and V9 primer sets simultaneously to overcome the limited taxonomic overlap between regions [57] [17]. This approach is particularly valuable when screening for novel or unexpected tick-borne protists.

• For Targeted Detection: Select primers based on the specific protists of interest, as differential amplification efficiency varies across taxonomic groups [58]. Preliminary in silico testing of primers against known target sequences is recommended.

• For Quantitative Comparisons: Maintain consistent laboratory and bioinformatic parameters within studies, as annealing temperature, read processing, and denoising algorithms significantly influence observed community structure [57] [19].

• For Clinical Applications: Always validate metabarcoding results with conventional PCR or other targeted methods, particularly for putative pathogens [2] [7]. No single primer pair currently provides comprehensive detection of all tick-borne protists.

The evolving understanding of primer bias highlights the need for continued method refinement in tick-borne protist research. As one study concluded, "further optimization is required for library construction to identify tick-borne protists in ticks" [2]. By implementing the rigorous protocols outlined in this guide and acknowledging the inherent limitations of current metabarcoding approaches, researchers can more reliably uncover the diversity and dynamics of tick-borne protists, ultimately advancing both ecological knowledge and clinical diagnostics.

In the field of tick-borne pathogen research, the accurate identification of protistan microbes via 18S rRNA gene barcoding is consistently challenged by a significant technical hurdle: the overwhelming abundance of host (tick) DNA within extracted samples. This contamination can obscure the target microbial signal, reducing detection sensitivity and potentially leading to false negatives, particularly for low-abundance pathogens. The following technical guide details established and emerging methodologies designed to mitigate host DNA contamination, thereby enriching for eukaryotic microbial signals in tick-derived samples. These techniques are essential for advancing the sensitivity and accuracy of DNA barcoding initiatives aimed at uncovering the diversity of tick-borne protists.

Core Challenges in Tick Microbiome Eukaryote Analysis

The fundamental challenge in detecting eukaryotic pathogens in tick samples stems from the massive disparity in DNA concentration between the tick and its associated microbes. Conventional polymerase chain reaction (PCR) with universal primers amplifies 18S rRNA genes from all eukaryotes present. Because tick DNA constitutes the majority of the sample, its DNA is preferentially amplified, which can cause rare but clinically significant protistan pathogens to be missed entirely [61]. This limitation impedes comprehensive understanding of the tick microbiome and its associated disease risks. Furthermore, the approach to DNA barcoding based on the 18S rRNA gene is not yet as standardized as 16S rRNA gene analysis for bacteria, with results known to vary significantly depending on the target region, primer set, and PCR conditions [1] [2].

Technical Approaches for Host DNA Suppression

Blocker Nucleic Acids

Principle: This method utilizes artificial nucleic acids, specifically Peptide Nucleic Acids (PNAs) or Locked Nucleic Acids (LNAs), which are designed to bind with high affinity to the tick 18S rRNA gene at a site between the universal primer binding locations [61]. During PCR, the blocker binds to the tick DNA and physically prevents the DNA polymerase from extending the primer, thereby selectively inhibiting the amplification of tick 18S rRNA while allowing the amplification of non-target eukaryotic microbes [61].

Experimental Protocol:

• Blocker Design:

  • Target Selection: Identify a conserved sequence within the V4 hypervariable region of the 18S rRNA gene that is specific to ticks (order Ixodida). This sequence should be flanked by the universal primer binding sites.
  • Specificity Check: Ensure the selected blocker sequence has nucleotide mismatches when aligned with common tick-borne protists (e.g., Babesia, Theileria) to prevent blocking the amplification of these targets.
  • Synthesis: The designed PNA or LNA blockers are commercially synthesized. For example, a study used the forward primer TAReuk454FWD1 and designed a corresponding blocker [61].

• PCR Setup with Blocker:

  • Prepare a standard PCR reaction mix containing the tick-derived DNA template, universal 18S rRNA primers (e.g., TAReuk454FWD1 and TAReukREV3), and PCR reagents.
  • Critical Step: Add the synthesized PNA or LNA blocker to the reaction. A typical concentration is 1 µM for PNA blockers [61].
  • Thermocycling Conditions: Use a standard thermocycling protocol with an annealing temperature of 60°C for 30 seconds. The blocker is most effective during the annealing/extension step [61].

Performance Characteristics: Studies have shown that the use of PNA or LNA blockers can dramatically increase the proportion of microeukaryotic reads in sequencing results and significantly boost alpha diversity metrics compared to conventional PCR. The PNA- and LNA-based methods are considered suitable for paneukaryotic analyses [61].

Non-Metazoan PCR Primers

Principle: An alternative to physical blocking is the use of primer sets specifically designed to exclude the amplification of metazoan (animal) DNA. The UNonMet-PCR method uses primers that are mismatched to the 18S rRNA gene of metazoans, including ticks, but are complementary to the 18S rRNA of non-metazoan eukaryotes such as protists, fungi, and algae [61].

Experimental Protocol:

• Primer Selection: Use published "non-metazoan" primer sets. These primers target the same 18S rRNA gene but are designed based on sequence alignments to avoid perfect binding to metazoan sequences.
• PCR Setup:
  • Prepare the PCR reaction using the same tick-derived DNA template as for the blocker method.
  • Use the UNonMet primer set instead of universal eukaryotic primers.
  • Perform thermocycling according to the recommended protocol for the selected primer set.

Performance Characteristics: Research indicates that the UNonMet-PCR method is particularly sensitive for the detection of fungi and other non-metazoan eukaryotes. It effectively suppresses tick DNA amplification, though its profile of detected eukaryotes may differ from that of the blocker-based methods, highlighting the value of a multi-method approach for comprehensive microbiome characterization [61].

Sample Processing and Wet-Lab Techniques

Principle: Careful sample handling and processing prior to DNA extraction can physically reduce the amount of host DNA in the sample.

Experimental Protocol:

• Tick Dissection: Rather than homogenizing the entire tick, specific tissues known to harbor pathogens can be dissected. For example, sequencing of salivary glands and midguts separately can enrich for pathogens present in these tissues and reduce the relative proportion of host genomic DNA from other body parts [62] [63].
• Surface Sterilization: To remove environmental contaminants and DNA from other organisms on the tick's surface, a rigorous washing protocol is essential before homogenization.
  • Protocol: Sequentially wash ticks with 3% hydrogen peroxide (vortex for 1 min), 70% ethanol (two 30-second washes), and phosphate-buffered saline (PBS, 2 min wash) [64] [61].
• DNA Normalization for Pooling: When preparing pooled tick samples for high-throughput sequencing, normalize the DNA concentration of each pool before combining them. This prevents bias in the final pooled sample towards ticks with higher DNA yields, which may not correlate with microbial load. Use fluorescence-based quantification assays (e.g., Qubit dsDNA Quantification Assay Kits) for greater accuracy compared to spectrophotometry [1] [65].

Comparative Analysis of Techniques

The table below summarizes the key characteristics of the primary host-DNA suppression techniques.

Table 1: Comparison of Host-DNA Suppression Techniques

Technique	Principle	Key Advantages	Key Limitations / Considerations
Blocker Nucleic Acids (PNA/LNA)	Physical blocking of polymerase extension on tick DNA [61].	Highly effective; applicable to paneukaryotic analysis; does not require specialized primers.	Requires design and synthesis of tick-specific blockers; optimal concentration needs empirical determination.
Non-Metazoan Primers (UNonMet-PCR)	Selective amplification via primer mismatch to metazoan DNA [61].	No custom reagents beyond primers; highly effective for fungi and protists.	May miss certain eukaryotic groups; community profile may differ from blocker-based methods.
Tick Dissection	Physical separation of pathogen-rich tissues [62].	Directly enriches for pathogens of interest; reduces host DNA at source.	Technically demanding and time-consuming; not suitable for large-scale studies.
Surface Sterilization	Removal of external contaminants [64] [61].	Reduces background noise from environmental DNA; standard good practice.	Does not reduce internal tick genomic DNA.

The Scientist's Toolkit: Essential Reagents and Materials

Table 2: Key Research Reagent Solutions

Item	Function in Host DNA Suppression	Example Use Case
Peptide Nucleic Acid (PNA) Blocker	Artificial nucleic acid that binds to tick 18S rRNA to block PCR amplification [61].	Added to PCR mix at ~1 µM to selectively inhibit tick DNA amplification in a paneukaryotic survey.
Locked Nucleic Acid (LNA) Blocker	High-affinity alternative to PNA for blocking tick DNA amplification [61].	Used similarly to PNA; binding efficiency may vary with salt conditions.
UNonMet Primer Sets	PCR primers designed to avoid amplification of metazoan 18S rRNA [61].	Used in place of universal 18S primers to directly target protists and fungi in tick DNA extracts.
DNeasy Blood & Tissue Kit	Silica-membrane-based extraction of total nucleic acids from tick homogenates [1] [65] [63].	Standardized DNA extraction ensuring high-quality template for downstream blocker or UNonMet-PCR.
Qubit dsDNA HS Assay	Fluorescence-based accurate quantification of DNA concentration for normalization [1].	Used to normalize DNA concentrations across tick pools prior to NGS library construction to mitigate bias.

The mitigation of host DNA contamination is not a one-size-fits-all endeavor but rather a strategic process. The most robust studies often integrate multiple techniques. For instance, one might begin with rigorous surface sterilization of ticks, followed by DNA extraction and then application of a PNA-blocker enhanced PCR protocol prior to 18S rRNA amplicon sequencing on an Illumina MiSeq platform [1] [61]. As the field of protist genomics advances, the development of these group-specific methodologies is paramount for moving beyond plant- and animal-centric genomic standards and fully uncovering the hidden diversity of tick-borne eukaryotes [66]. The techniques detailed herein provide a foundational toolkit for researchers to enhance the sensitivity and reliability of their DNA barcoding efforts in the complex milieu of the tick microbiome.

The precision of DNA barcoding, particularly in the surveillance of tick-borne protists using the 18S rRNA marker, is critically dependent on polymerase chain reaction (PCR) conditions. Annealing temperature is a pivotal factor that directly influences the specificity and efficiency of amplification, thereby introducing significant bias into the observed abundance of sequencing reads. This technical guide explores the mechanistic basis of this bias, presents experimental data quantifying its impact, and provides detailed protocols for researchers to optimize this parameter, ensuring accurate representation of protist communities in tick vectors for drug development and diagnostic applications.

DNA barcoding utilizing the 18S ribosomal RNA (rRNA) gene has emerged as a powerful tool for profiling complex eukaryotic communities, including the diverse protist pathogens transmitted by ticks [1]. Unlike targeted PCR assays, barcoding aims to simultaneously amplify template DNA from all present species for subsequent high-throughput sequencing. The fundamental assumption is that the abundance of sequenced reads proportionally reflects the original abundance of each species in the sample. However, this assumption is frequently violated by biases introduced during PCR amplification, with annealing temperature being a major contributing factor [67].

The annealing temperature of a PCR reaction determines the stringency with which primers bind to their target sequences. An suboptimal temperature can lead to either non-specific amplification (if too low) or inefficient primer binding (if too high), both of which distort the true biological profile [68]. For tick-borne protist research, where co-infections are common and pathogen load has clinical relevance, understanding and controlling for this bias is not merely a technical detail but a prerequisite for generating reliable data that can inform drug discovery and public health interventions [69].

The Core Mechanism: How Annealing Temperature Drives Bias

The central challenge in 18S rRNA barcoding stems from the genetic diversity within the primer target sites across different organisms. Even universal primers are not perfectly matched to all target sequences.

• Sequence Mismatches and Primer Binding Efficiency: Variations in the 18S rRNA gene sequence, especially in the hypervariable regions (V4, V9) used for barcoding, mean that a single, fixed annealing temperature will not be optimal for all templates. Templates with perfect or near-perfect matches to the primer will bind more efficiently and be amplified more readily than those with several mismatches [70].
• Impact on Read Abundance: This differential amplification efficiency is compounded over PCR cycles. A template that amplifies with even slightly higher efficiency in the early cycles will be over-represented in the final sequencing library, creating a skewed profile that may not reflect the true relative abundances of organisms in the original sample [67] [71]. This effect is particularly critical when comparing relative abundances between different protist species in a tick sample.

A recent study on intestinal parasite detection via 18S rRNA metabarcoding directly demonstrated that "variations in the amplicon PCR annealing temperature affected the relative abundance of output reads for each parasite" [71]. This finding confirms that annealing temperature is a powerful driver of quantitative bias in community profiles.

Experimental Evidence: Quantifying the Impact on Read Counts

Empirical data from metabarcoding studies provide clear evidence of how annealing temperature can alter observed community composition.

Differential Amplification of Parasite Species

A systematic investigation cloned the 18S rDNA V9 region of 11 intestinal parasite species into plasmids, creating a controlled mock community. When amplified at a standard annealing temperature (55°C), significant variation was observed in the number of output reads for each species, despite all plasmids being present in equal concentrations [71]. The read count ratio showed a more than 18-fold difference between the highest (Clonorchis sinensis, 17.2%) and lowest (Enterobius vermicularis, 0.9%) represented species. The study identified that secondary structures in the target DNA contributed to this bias, a factor directly influenced by annealing temperature [71].

Table 1: Impact of Annealing Temperature on Read Abundance in a Mock Parasite Community [71]

Parasite Species	Read Count at 55°C Annealing (%)	Read Count at Lower Annealing (40°C)	Read Count at Higher Annealing (70°C)
Clonorchis sinensis	17.2%	Increased	Decreased
Entamoeba histolytica	16.7%	Increased	Decreased
Dibothriocephalus latus	14.4%	Increased	Decreased
Trichuris trichiura	10.8%	Increased	Decreased
Enterobius vermicularis	0.9%	Increased	Decreased
Overall Effect	Skewed abundance	Reduced specificity	Reduced efficiency

Enhanced Coverage of AT-Rich Genomic Regions

While not specific to 18S rRNA, a relevant study on sequencing library amplification for the AT-rich genome of Plasmodium falciparum demonstrated that reducing the PCR extension temperature from 70°C to 60°C dramatically increased sequencing coverage in the most AT-rich regions [72]. This principle is analogous to annealing temperature effects, as both involve the stability of primer-template binding. It underscores that templates with challenging sequence compositions (e.g., extreme AT or GC content) are particularly susceptible to amplification bias, which is a relevant consideration for the diverse genomes of tick-borne protists.

Optimizing Annealing Temperature: Detailed Experimental Protocols

To mitigate bias and ensure accurate results in 18S rRNA barcoding of tick-borne protists, annealing temperature must be empirically optimized. Below are two detailed methodological approaches.

Protocol 1: Temperature Gradient Using a Mock Community

This protocol is the gold standard for identifying the optimal annealing temperature for a given primer set and sample type.

Research Reagent Solutions [71]

• Primers: Universal 18S rRNA primers targeting the V4 or V9 region (e.g., 1391F/EukBR).
• Mock Community: Comprised of cloned 18S rDNA V9 regions from protist pathogens of interest (e.g., Babesia spp., Theileria spp., Hepatozoon canis) in equal concentrations.
• DNA Polymerase: A high-fidelity polymerase (e.g., KAPA HiFi HotStart ReadyMix).
• Thermal Cycler: Capable of running a temperature gradient.

Methodology:

• Prepare Reaction Mix: Set up identical PCR reactions containing the mock community DNA, primers, and master mix.
• Set Temperature Gradient: Program the thermal cycler to run an annealing temperature gradient across a suitable range (e.g., 50°C to 65°C) for a single plate.
• Amplify and Purify: Run the PCR, then purify the amplicons from each reaction.
• Sequence and Analyze: Perform next-generation sequencing on the amplicons. The optimal temperature is identified as the one that produces the most uniform read distribution across all species in the mock community, minimizing the over- and under-representation seen in Table 1.

Protocol 2: Empirical Determination of Primer-Template Melting Temperature (Tm)

For highly specific detection, such as in a quantitative PCR (qPCR) assay, a more precise method can be used to fine-tune primer design for broad detection.

Methodology [70]:

• Design Primers: Design primers against a conserved region of the 18S rRNA gene, noting any mismatches to common tick-borne protists.
• Synthesize Oligonucleotides: Synthesize the primers and complementary oligonucleotides representing both perfectly matched and mismatched (across different species) target sequences.
• Empirical Tm Determination: In a real-time PCR system, combine primers with each oligonucleotide target and run a dissociation curve analysis without amplification. The peak of the first derivative of the melting curve is the empirical Tm.
• Refine Primer Sequence: Use the Tm data to adjust the primer sequence (e.g., changing the 3' end to better match divergent species) or to select the optimal annealing temperature that accommodates the Tm of all critical targets.

Diagram 1: Workflow for empirical Tm determination to optimize annealing temperature or primer design.

The Scientist's Toolkit: Essential Reagents for Optimization

Table 2: Key Research Reagents for PCR Bias Mitigation in Barcoding

Reagent / Solution	Function in Bias Reduction	Technical Notes
High-Fidelity DNA Polymerase (e.g., Kapa HiFi)	Reduces misincorporation errors and preferential amplification biases due to GC-content.	More accurate than standard Taq polymerase [67].
Mock Community	Provides a controlled standard to quantify and correct for amplification bias.	Should include cloned 18S rDNA from relevant tick-borne protists in known ratios [71].
Uniform PCR Primers	Universal primers designed for broad amplification across eukaryotic taxa.	Target 18S rRNA V4 or V9 regions; inosine can be used to reduce mismatch impact [1] [70].
TMAC or Betaine	PCR additives that help neutralize the effects of extreme GC or AT content.	Improves amplification efficiency of difficult templates [67].
Temperature Gradient Thermal Cycler	Essential for empirically testing a range of annealing temperatures simultaneously.	Allows for direct comparison of amplification efficiency and specificity across a single plate.

The evidence is clear: annealing temperature is not a mere technical setting but a fundamental variable that directly shapes the apparent abundance of species in DNA barcoding studies. For researchers investigating tick-borne protists using 18S rRNA metabarcoding, the following is recommended:

• Empirical Optimization is Critical: Never rely solely on calculated melting temperatures. Use a mock community in a temperature gradient experiment to determine the annealing temperature that provides the most accurate and representative profile [71].
• Validate with Controls: Include a mock community in every sequencing run as a control to monitor for batch-to-batch variation in amplification bias.
• Report Conditions Transparently: Fully document all PCR conditions, including annealing temperatures and polymerase used, in publications to ensure reproducibility and enable meaningful cross-study comparisons.

By adopting these rigorous optimization and reporting practices, the field of molecular parasitology can generate more reliable data on tick-borne protist diversity and abundance, thereby strengthening the foundation for subsequent drug development and diagnostic efforts.

Optimizing Sequencing Depth and Coverage for Adequate Alpha Diversity Assessment

In DNA barcoding studies of tick-borne protists targeting the 18S rRNA gene, optimizing sequencing depth and coverage is fundamental to achieving accurate alpha diversity assessments. Alpha diversity, which quantifies the within-sample diversity of parasitic protists, is highly sensitive to sequencing effort. Inadequate depth can lead to incomplete species representation, failing to detect rare pathogens and resulting in underestimated diversity metrics [7] [2]. The complex nature of tick samples, which may contain multiple protist species at varying abundances, necessitates careful experimental design to ensure sufficient sequencing depth captures the true taxonomic breadth present [2].

Recent research on tick-borne protists in the Republic of Korea demonstrates these challenges vividly. When employing 18S rRNA gene fragments for identifying tick-borne protists, researchers found that the number and abundance of detected protists varied significantly depending on the primer sets and sequencing approach used [7] [2]. This variability directly impacts alpha diversity measurements and underscores the importance of optimized sequencing protocols for reliable ecological conclusions and public health recommendations in parasitology research.

Key Principles of Sequencing Depth and Coverage

Defining Core Concepts in Diversity Assessment

Sequencing depth (also called sequencing effort) refers to the number of sequences obtained per sample, which directly influences the detection sensitivity for low-abundance taxa. Coverage represents the proportion of total species diversity captured by the sequencing effort, with higher coverage indicating a more complete assessment of the community [2]. Alpha diversity specifically quantifies the within-sample diversity through metrics such as species richness, Shannon index, and Simpson index, all of which are heavily influenced by both sequencing depth and coverage.

The relationship between these elements follows the law of diminishing returns—initially, each additional sequence reveals new taxa, but eventually, the rarefaction curve plateaus as fewer novel taxa remain undetected. The optimal sequencing depth occurs just before this plateau, where additional sequencing provides minimal gains in diversity detection [2] [73]. In practical terms, studies of tick-borne protists have demonstrated that different primer sets and target regions yield different rarefaction curves, necessitating pilot studies to determine appropriate sequencing depth for specific research questions [2].

Impact on Tick-Borne Protist Research

Inadequate sequencing depth has direct consequences for tick-borne pathogen research. A study analyzing 13,375 ticks pooled into 1,003 samples found that different primer sets targeting the V4 versus V9 regions of the 18S rRNA gene revealed different protist communities, with varying sensitivities for detecting pathogens like Hepatozoon canis, Theileria luwenshuni, and Gregarine sp. [7] [2]. This technical variability can lead to false negatives for important pathogens and incomplete understanding of transmission dynamics.

Furthermore, research on gastrointestinal parasites in Tibetan ruminants utilizing 18S rDNA sequencing demonstrated that sufficient sequencing depth enabled identification of 192 operational taxonomic units (OTUs), including 10 phyla and 27 genera of parasites [73]. The achievement of 99.09% coverage with 20,000 sequences per sample after rarefaction illustrates the level of sequencing effort required for comprehensive diversity assessment in complex eukaryotic communities [73].

Methodological Framework for Optimization

Experimental Design Considerations

Determining appropriate sequencing depth begins with strategic experimental design. The following workflow outlines key decision points in designing an optimal 18S rRNA sequencing experiment for tick-borne protists:

Before full-scale sequencing, conduct pilot studies with subset of samples sequenced at different depths. This approach allows for constructing rarefaction curves to determine the point of diminishing returns for sequencing effort [2]. Research on intestinal parasite detection using 18S rRNA metabarcoding demonstrated that annealing temperature during amplification significantly affects relative abundance readings, suggesting that both sequencing depth and PCR conditions require optimization [9].

Sample pooling strategy represents another critical consideration. The tick-borne protist study pooled 13,375 ticks into 1,003 samples before selecting 50 tick pools for DNA barcoding [7] [2]. Such pooling strategies affect individual pathogen detection sensitivity and must be accounted for when determining overall sequencing depth requirements.

Laboratory Protocols for 18S rRNA Metabarcoding

DNA Extraction and Quality Control

• Sample Preparation: For tick samples, homogenize pools using bead beating method in PBS [2].
• DNA Extraction: Use commercial kits such as DNeasy Blood & Tissue Kit (Qiagen) following manufacturer's instructions [2].
• Quality Assessment: Measure DNA concentration using spectrophotometry (e.g., DeNovix) or fluorometry (e.g., Qubit dsDNA Quantification Assay Kits) [2].
• Normalization: Normalize DNA concentrations across samples to minimize bias in amplification efficiency [2].

Library Preparation and Sequencing

• Target Region Selection: Choose 18S rRNA variable regions based on taxonomic resolution needs—V4 and V9 regions are commonly used for protist diversity studies [2] [74].
• Primer Design: Utilize published universal primer sets with Illumina adapter overhangs:
  • V4 region: 18S V4F (5′-CCAGCAGCCGCGGTAATTCC-3′) and 18S V4R (5′-ACTTTCGTTCTTGATTAA-3′) [74]
  • V9 region: 1380F (5′-CCCTGCCHTTTGTACACAC-3′) and 1510R (5′-CCTTCYGCAGGTTCACCTAC-3′) [74]
• PCR Amplification: Perform initial PCR with conditions: 95°C for 3 min; 25-35 cycles of 95°C for 30 s, 55°C for 30 s, 72°C for 30 s; final extension at 72°C for 5 min [2] [74].
• Indexing and Cleanup: Add dual indices in second PCR (8-10 cycles) followed by purification with AMPure beads [2].
• Sequencing: Use Illumina platforms (MiSeq, iSeq) with paired-end sequencing appropriate for target region length [2] [9].

Technical Optimization Strategies

Determining Optimal Sequencing Depth

The relationship between sequencing depth and alpha diversity assessment can be quantified through rarefaction analysis and coverage metrics. The following table summarizes key findings from recent parasite metabarcoding studies:

Table 1: Sequencing Depth and Outcomes in Parasite Metabarcoding Studies

Study Focus	Sequencing Platform	Average Reads/Sample	Achieved Coverage	Diversity Outcomes
Tick-borne protists [2]	Illumina MiSeq	V4: ~2,062 reads; V9: ~2,475 reads	Variable by primer set	Differential detection of Hepatozoon, Theileria, Gregarine
Gastrointestinal parasites in ruminants [73]	Illumina PE300	20,000 reads (after rarefaction)	99.09%	Identified 192 OTUs from 10 phyla and 27 genera
Human intestinal parasites [60]	Illumina platforms	100,000-140,000 reads per amplicon	Limited by primer bias	Detected 4 eukaryotic parasites despite high sequencing depth
Avian gastrointestinal parasites [74]	Illumina MiSeq	V4: ~10,310 reads; V9: ~12,377 reads	Sufficient for parasite identification	Different parasite taxa detected with V4 vs V9 regions

These data demonstrate that optimal sequencing depth varies significantly based on sample type, target region, and specific research questions. While 20,000 reads per sample provided 99% coverage in ruminant fecal samples [73], similar coverage for complex tick samples may require different sequencing depths.

Addressing Technical Biases and Challenges

Primer Selection and Bias Mitigation Primer selection creates substantial bias in protist detection and diversity assessment. Research comparing 18S rRNA V4 and V9 regions in great cormorants found completely different parasite taxa identified by each region [74]. The V4 region detected Baruscapillaria spiculata, Contracaecum sp., and Isospora lugensae, while the V9 region identified Tetratrichomonas sp., Histomonas meleagridis, and Fasciola gigantica [74]. This has direct implications for alpha diversity measurements, as different primer sets will yield different diversity estimates from the same sample.

To address primer bias:

• In Silico Validation: Compare primer sets against 18S rRNA sequences from target tick-borne protists before experimental work [2].
• Multi-Region Amplification: Target multiple variable regions (V4-V5, V9) for more comprehensive diversity assessment [60].
• Blocking Primers: Employ host DNA blocking primers (C3 spacer-modified oligos or PNA oligos) to enrich for parasite DNA in host-rich samples [75].

Bioinformatic Processing and Quality Control

• Sequence Processing: Use DADA2 or QIIME2 pipelines for quality filtering, denoising, and chimera removal [73] [60].
• Taxonomic Assignment: Employ curated databases (PR2, SILVA, NCBI) with appropriate similarity thresholds (e.g., >97% for species-level assignment) [76].
• Rarefaction: Normalize sequence counts across samples to enable valid alpha diversity comparisons [73].

Essential Research Reagents and Tools

Table 2: Essential Research Reagent Solutions for 18S rRNA Metabarcoding

Reagent/Tool	Specific Example	Function in Protocol
DNA Extraction Kit	DNeasy Blood & Tissue Kit (Qiagen) [2]	High-quality DNA extraction from tick samples
DNA Quantification	Qubit dsDNA Assay Kit (Invitrogen) [2]	Accurate DNA concentration measurement
PCR Enzymes	KAPA HiFi HotStart ReadyMix (Roche) [9]	High-fidelity amplification of target regions
Library Prep Kit	Nextera XT Index Kit (Illumina) [2]	Addition of dual indices for sample multiplexing
Size Selection	AMPure XP Beads (Beckman Coulter) [2]	PCR product purification and size selection
Quality Control	TapeStation D1000 ScreenTape (Agilent) [2]	Library quality assessment before sequencing
Bioinformatics Tool	QIIME2 [60]	End-to-end analysis of metabarcoding data
Reference Database	PR2 Database [76]	Curated taxonomy for protist classification

Optimizing sequencing depth and coverage for alpha diversity assessment in 18S rRNA studies of tick-borne protists requires an integrated approach addressing both wet-lab and computational components. The strategic combination of appropriate experimental design, careful primer selection, sequencing depth determination through pilot studies, and robust bioinformatic processing enables researchers to obtain accurate alpha diversity measurements that reflect true biological variation rather than technical artifacts.

Future methodological improvements should focus on developing standardized protocols for tick-borne protist studies, establishing consensus regarding optimal sequencing depths for different sample types, and creating curated reference databases specific for tick-borne pathogens. Such advances will enhance the reproducibility and comparability of alpha diversity assessments across studies, ultimately improving our understanding of tick-borne protist communities and their impacts on human and animal health.

Overcoming Limitations in Species-Level Resolution for Closely Related Protists

DNA barcoding using the 18S ribosomal RNA (rRNA) gene has become a fundamental tool for identifying and classifying protists, including those of medical and veterinary importance such as tick-borne pathogens. However, this approach faces significant limitations when attempting to distinguish between closely related protist species. The conserved nature of the 18S rRNA gene, while excellent for broad phylogenetic studies and identifying deep evolutionary relationships, often lacks the sequence variation necessary for fine-scale species discrimination. This problem is particularly acute in clinical and environmental samples where precise identification of pathogens is crucial for diagnosis, treatment, and understanding transmission dynamics. The challenge is evident in studies of tick-borne protists, where 18S rRNA barcoding may fail to differentiate between closely related Theileria or Babesia species with different pathogenic potentials, leading to incomplete epidemiological understanding and potential misdiagnosis.

The limitations of 18S rRNA become especially problematic when dealing with cryptic species complexes—morphologically identical but genetically distinct organisms that may differ in host specificity, virulence, or drug susceptibility. Research on tick-borne protists in the Republic of Korea demonstrated that DNA barcoding using 18S rRNA gene fragments identified only three genera of protozoa (Hepatozoon canis, Theileria luwenshuni, and Gregarine sp.), while conventional PCR later confirmed additional species including Toxoplasma gondii [1] [2]. This discrepancy highlights how reliance on a single marker with insufficient resolution can underestimate true protist diversity and miss clinically relevant species.

Limitations of 18S rRNA Gene in Species Resolution

Technical and Biological Constraints

The 18S rRNA gene's limitations for species-level discrimination stem from both biological and technical factors. Biologically, the gene evolves slowly due to its crucial role in ribosome assembly and protein synthesis, resulting in minimal sequence divergence between recently separated species. Technically, the variable regions within the 18S rRNA that do contain discriminatory information present amplification and sequencing challenges that affect detection reliability.

A systematic evaluation of DNA barcoding practices reveals that errors in barcode data are not rare, with most attributable to human errors such as specimen misidentification, sample confusion, and contamination [77]. These issues are compounded when working with the 18S rRNA gene, particularly for protists:

• Primer Bias: Different primer sets targeting various hypervariable regions (e.g., V4, V9) of the 18S rRNA gene yield different taxonomic profiles from the same samples [1]. In silico comparison of primer sets with 18S rRNA gene sequences from tick-borne protozoa shows significant variation in amplification efficiency across taxa [1] [2].
• Secondary Structure Interference: The secondary structure of the 18S rDNA V9 region affects amplification efficiency during PCR, creating quantitative biases in next-generation sequencing output that do not reflect true biological abundance [9].
• Intragenomic Variation: Some protist species contain multiple slightly different copies of the 18S rRNA gene within their genomes, potentially leading to overestimation of diversity or misclassification.

Table 1: Comparison of 18S rRNA Variable Regions for Protist Barcoding

Variable Region	Length (approx.)	Resolution Potential	Limitations	Example Applications
V1-V2	~350 bp	Moderate for some protist groups	High variability makes alignment difficult; primer design challenges	Broad eukaryotic diversity surveys
V4	~400 bp	High for many protist lineages	Variable performance across taxa; requires optimized primers	Tick-borne protist identification [1]
V9	~120 bp	Lower for closely related species	Short length limits phylogenetic information; amplification bias	Microbial eukaryote diversity studies [9]

Alternative Genetic Markers for Enhanced Resolution

Comparative Analysis of Marker Performance

To overcome the resolution limitations of 18S rRNA, researchers have evaluated numerous alternative genetic markers. A comprehensive study comparing eight DNA regions (18S rRNA, 28S rRNA, ITS, ITS1, ITS2, and COI) for piroplasm identification found that the Internal Transcribed Spacer 2 (ITS2) region demonstrated superior performance for species-level discrimination [78].

Table 2: Performance Comparison of Genetic Markers for Protist Species Resolution

Genetic Marker	PCR Amplification Efficiency	Species Identification Efficiency	Advantages	Disadvantages
18S rRNA	100%	64% at species level	Highly conserved; universal primers; extensive reference databases	Limited species discrimination; intragenomic variation
ITS2	100%	92% at species level	High variation between species; conserved flanking regions for priming	Length variation; potential for indels; smaller reference databases
28S rRNA	100%	78% at species level	Moderate variation rate; good for closely related species	Intermediate resolution; less studied than 18S
COI	84%	Variable across protist groups	Standard for animal barcoding; good resolution	Poor amplification in some protists; primer design challenges

The ITS2 region emerged as the most promising DNA barcode for piroplasms, exhibiting 100% PCR amplification efficiency and 92% identification efficiency at the species level [78]. This region demonstrates the largest gap between intra- and inter-specific divergence, facilitating clearer species boundaries. The superior performance of ITS2 stems from its faster evolutionary rate compared to ribosomal RNA genes, while maintaining sufficient conservation in the flanking regions for reliable primer binding.

Mitochondrial Markers and Multi-Locus Approaches

For some protist groups, mitochondrial genes offer enhanced resolution. The cytochrome c oxidase I (COI) gene, while successful for metazoan barcoding, shows variable performance across protist lineages with generally lower amplification efficiency (84%) compared to ribosomal markers [78]. However, mitochondrial rRNA genes have demonstrated promise for helminth DNA metabarcoding, suggesting potential applications for certain protist groups [79].

Increasingly, multi-locus approaches that combine information from several genetic markers provide the most robust species identification. A typical workflow might include:

• Initial broad screening with 18S rRNA to assign organisms to major groups
• Follow-up species-level discrimination using ITS or mitochondrial markers
• Validation with specific PCR assays for pathogens of interest

This approach was effectively demonstrated in a study of tick-borne protists, where initial 18S rRNA barcoding provided a diversity overview, while conventional PCR targeting specific pathogens yielded additional detections [1].

Methodological Optimization Strategies

Wet-Lab Protocol Enhancements

Several technical adjustments can significantly improve species-level resolution in protist barcoding studies:

• Primer Selection and Validation: Carefully evaluate primer specificity in silico before wet-lab application. Improved 18S and 28S rDNA primer sets have been developed specifically for parasite detection to reduce amplification of non-target organisms [79]. For tick-borne protists, primer sets should be compared against 18S rRNA sequences from known tick-borne protozoa to ensure coverage of target taxa [1].

• PCR Condition Optimization: Annealing temperature significantly influences amplification efficiency and specificity. Testing a range of annealing temperatures (e.g., 40-70°C) during library preparation can optimize the relative abundance of target organisms in metabarcoding output [9].

• Template Preparation: For plasmid-based controls, linearization using restriction enzymes can reduce steric hindrance and improve amplification efficiency, particularly for circular templates [9].

• Blocking Primers: When targeting rare eukaryotes in bacteria-rich samples (e.g., fecal material, tick homogenates), incorporate blocking primers to prevent amplification of abundant non-target DNA [79].

Diagram 1: Enhanced DNA barcoding workflow for improved species-level resolution of protists, highlighting critical optimization steps.

Bioinformatic and Analytical Improvements

Bioinformatic processing choices significantly impact the resolution and accuracy of protist barcoding:

• Reference Database Curation: Use comprehensive, curated databases rather than limited custom databases. One study utilized the complete NCBI nucleotide database to enhance taxonomic assignment accuracy for parasites [9].

• Chimera Removal: Employ sophisticated chimera detection algorithms like those in DADA2 to eliminate artificial sequences formed during amplification [1] [9].

• Sequence Quality Filtering: Implement stringent quality control including adapter removal, read trimming, and error correction. One effective pipeline processes raw sequencing data through Cutadapt for adapter removal and trimming, followed by DADA2 for error correction, merging, denoising, and chimera removal [1].

• Secondary Structure Consideration: Account for DNA secondary structures in target regions, as these can create amplification biases. The secondary structure of the 18S rDNA V9 region shows a negative association with output read counts [9].

Case Study: Application to Tick-Borne Protists

The challenges and solutions for species-level resolution are clearly demonstrated in studies of tick-borne protists. A 2024 study in the Republic of Korea collected 13,375 ticks pooled into 1,003 samples, with 50 selected for DNA barcoding targeting the V4 and V9 regions of the 18S rRNA gene [1] [7] [2]. The findings illustrate both the promises and limitations of this approach:

• Primer-Dependent Results: The number and abundance of protists detected varied significantly depending on the primer sets used, with different regions (V4 vs. V9) revealing different subsets of the protist community [1].

• Complementary Validation Required: While DNA barcoding identified three genera of protozoa (Hepatozoon canis, Theileria luwenshuni, and Gregarine sp.), conventional PCR confirmed additional species including Toxoplasma gondii that were missed in the barcoding approach [1] [2].

• Novel Pathogen Discoveries: The combined molecular approaches enabled the first identification of H. canis and T. gondii in Ixodes nipponensis ticks, demonstrating the value of optimized barcoding for expanding knowledge of pathogen distribution [1].

This case study underscores the importance of using DNA barcoding as a screening tool rather than a definitive identification method, particularly when working with complex samples containing multiple closely related protist species.

The Scientist's Toolkit: Essential Research Reagents

Table 3: Key Research Reagents for Advanced Protist DNA Barcoding

Reagent/Kit	Specific Example	Function in Workflow	Considerations for Protist Research
DNA Extraction Kit	DNeasy Blood & Tissue Kit (Qiagen)	High-quality DNA extraction from complex samples	Effective for tick homogenates; removes PCR inhibitors
PCR Enzyme	KAPA HiFi HotStart ReadyMix (Roche)	High-fidelity amplification for barcoding	Reduces amplification errors in sequence data
Cloning Kit	TOPcloner TA Kit (Enzynomics)	Plasmid cloning for sequence validation	Creates positive controls for primer validation
Library Prep Kit	Illumina 16S Metagenomic Sequencing Library	Preparation of amplicon sequencing libraries	Adaptable to 18S rRNA gene targets with modified primers
Quantification Assay	Qubit dsDNA Quantification Assay Kits (Invitrogen)	Accurate DNA concentration measurement	Essential for normalization before multiplexing
Restriction Enzyme	NcoI (Thermo Scientific)	Plasmid linearization	Reduces steric hindrance in circular templates [9]

Overcoming species-level resolution limitations for closely related protists requires a multifaceted approach that addresses both technical and analytical challenges. No single genetic marker provides perfect discrimination across all protist lineages, necessitating marker selection based on the specific taxonomic groups being investigated. The 18S rRNA gene remains valuable for initial screening and placement within broad phylogenetic frameworks, but should be supplemented with more variable markers like ITS2 for definitive species identification.

Future developments in protist DNA barcoding will likely focus on three key areas:

• Expansion of curated reference databases, particularly for understudied lineages and alternative markers beyond 18S rRNA
• Standardization of multi-locus barcoding approaches that combine the strengths of ribosomal and mitochondrial markers
• Integration of long-read sequencing technologies to capture multiple variable regions in single reads, overcoming current limitations of short amplicon sequencing

For researchers studying tick-borne protists and other medically relevant microorganisms, adopting these optimized approaches will enhance detection accuracy, reveal hidden diversity, and ultimately improve our understanding of pathogen ecology and transmission dynamics.

Ensuring Accuracy: Validating NGS Findings with Conventional Molecular Tools

The Essential Role of Conventional PCR in Confirming NGS Results

Next-generation sequencing (NGS) has revolutionized the detection and diversity assessment of tick-borne protists, yet its findings remain incomplete without orthogonal confirmation by conventional PCR (cPCR). Within 18S rRNA gene-based DNA barcoding research, the synergy between these methods is paramount. This whitepaper elucidates the critical function of cPCR in validating NGS outputs, drawing on recent metabarcoding studies. We detail how cPCR confirms detected pathogens, identifies false negatives, and provides a framework for verifying primer-specific biases. Furthermore, we present standardized experimental protocols and a curated toolkit of research reagents to empower scientists in implementing a robust confirmation workflow, thereby enhancing the reliability of data used in downstream drug and diagnostic development.

The application of 18S rRNA gene metabarcoding has become a powerful tool for uncovering the diversity of tick-borne protists, from well-known pathogens like Babesia and Theileria to rarely documented organisms [1] [80] [2]. This high-throughput approach allows for the untargeted screening of complex tick-derived DNA samples, generating vast datasets on eukaryotic parasite communities. However, the diagnostic and research value of these findings is contingent upon their verification.

Despite its power, NGS is susceptible to methodological artifacts. The results of DNA barcoding can vary significantly depending on the primer sets and 18S rRNA target regions (e.g., V4 vs. V9) used for library construction [1] [7] [2]. Furthermore, the sensitivity of NGS can be compromised by low microbial burden, leading to false negatives that may escape notice without a targeted confirmatory test [81] [82]. Consequently, the research community has established that "the results obtained by DNA barcoding must be validated by conventional or real-time PCR" [1] [2]. This document outlines the framework for this essential validation process.

Quantitative Comparison of NGS and Conventional PCR Performance

The following table synthesizes findings from recent studies to illustrate the complementary performance of NGS and cPCR in detecting protist pathogens.

Table 1: Comparative Performance of NGS and Conventional PCR in Pathogen Detection

Study Context (Pathogen Group)	NGS Detection Rate	Conventional PCR Detection Rate	Key Findings
Tick-borne protists (e.g., Hepatozoon canis, Theileria luwenshuni) [1] [2]	Identified 3 genera of protozoa	Confirmed NGS findings & additionally identified Toxoplasma gondii	cPCR confirmed NGS results and uncovered false negatives from the metabarcoding approach.
Canine haemoparasites (Babesia vogeli, Hepatozoon canis) [80]	47% of dogs infected (n=100)	cPCR identified 13 B. vogeli and 17 H. canis infections	NGS was more sensitive than endpoint cPCR, but a *H. canis-specific cPCR identified infections not targeted by the NGS primer design.
Helicobacter pylori in pediatric biopsies [81]	35.0% (14/40 samples)	40.0% (16/40 samples)	Both real-time PCR variants were slightly more sensitive, identifying H. pylori in two additional samples.

This comparative data underscores that neither method is infallible. A combined approach leverages the broad screening power of NGS with the targeted precision of cPCR to produce a more accurate and comprehensive diagnostic outcome.

Experimental Protocols for Confirmation

Protocol 1: DNA Barcoding of Tick-Borne Protists using 18S rRNA Gene

This protocol is adapted from studies investigating tick-borne protist diversity in the Republic of Korea [1] [2].

• Step 1: Sample Collection and DNA Extraction

  • Collect questing ticks using the flagging method.
  • Identify ticks morphologically and pool them (e.g., up to 10 nymphs or 50 larvae per pool).
  • Homogenize pooled ticks using a bead beater in PBS.
  • Extract genomic DNA using a commercial kit (e.g., DNeasy Blood & Tissue Kit, Qiagen). Quantify DNA using a fluorometer (e.g., Qubit).

• Step 2: Library Preparation and NGS

  • Primer Sets: Amplify the V4 and V9 hypervariable regions of the 18S rRNA gene using universal primers with Illumina adapter overhangs.
    • V4 Forward: 5′-CCAGCAGCCGCGGTAATTCC-3′
    • V4 Reverse: 5′-ACTTTCGTTCTTGATTAA-3′
  • PCR Conditions: Initial denaturation at 95°C for 3 min; 25 cycles of 95°C for 30 s, 55°C for 30 s, 72°C for 30 s; final extension at 72°C for 5 min.
  • Purify PCR products using AMPure beads. Perform a second, limited-cycle (e.g., 10 cycles) PCR to attach dual indices.
  • Sequence the final library on an Illumina MiSeq platform.

• Step 3: Bioinformatic Analysis

  • Process raw reads (remove adapters, trim, correct errors) using tools like Cutadapt and DADA2 to generate Amplicon Sequence Variants (ASVs).
  • Taxonomically classify ASVs by aligning to a reference database (e.g., NCBI NT) using BLAST.

Protocol 2: Conventional PCR Validation of NGS Results

This protocol details the confirmatory steps following NGS analysis [1] [2] [74].

• Step 1: Primer Selection for cPCR

  • Design or select species-specific primer sets targeting the pathogen of interest based on the NGS results.
  • For protists, common targets include the 18S rRNA gene, but other genus- or species-specific genetic markers (e.g., ITS regions) can be used [74].

• Step 2: PCR Amplification and Sequencing

  • Use a standardized PCR premix (e.g., AccuPower HotStart PCR Premix Kit).
  • PCR Conditions: Must be optimized for each primer set. A typical reaction includes an initial denaturation (95°C for 5 min), 35-40 cycles of denaturation (95°C for 30 s), annealing (temp specific to primer for 30 s), and extension (72°C for 1 min/kb), followed by a final extension (72°C for 5-7 min).
  • Visualize PCR products on an agarose gel.
  • Purify positive amplicons and submit them for Sanger sequencing.

• Step 3: Phylogenetic Analysis

  • Compare the obtained sequences with those in GenBank using BLAST.
  • Construct a phylogenetic tree (e.g., using Maximum Likelihood method in MEGA software with 500 bootstrap replications) to confirm the molecular identity and relationship of the detected pathogen [74].

Visualizing the Confirmatory Workflow

The following diagram illustrates the integrated workflow of NGS followed by mandatory cPCR confirmation.

The Scientist's Toolkit: Essential Research Reagents

Successful implementation of the NGS-cPCR workflow requires a set of core reagents and tools. The following table itemizes these essential components.

Table 2: Research Reagent Solutions for NGS and cPCR Workflows

Research Reagent	Specific Examples	Function in Workflow
DNA Extraction Kit	DNeasy Blood & Tissue Kit (Qiagen), QIAamp Fast DNA Stool Mini Kit (Qiagen)	High-quality genomic DNA isolation from complex biological samples like tick pools or feces [1] [74].
NGS Library Prep Kit	Illumina 16S Metagenomic Sequencing Library protocols (adapted for 18S), IDSeq Micro DNA Kit (Vision Medicals)	Preparation of sequencing-ready libraries from amplified 18S rRNA gene fragments [1] [82].
18S rRNA Primer Panels	V4 region primers (CCAGCAGCCGCGGTAATTCC / ACTTTCGTTCTTGATTAA), Phylum-specific primers for Apicomplexa/Kinetoplastida	Amplification of target barcode regions for NGS metabarcoding [1] [80].
PCR Master Mix	AccuPower HotStart PCR Premix Kit (Bioneer), AmpliSens Helicobacter pylori-FRT PCR Kit	Robust and specific amplification of target sequences in confirmatory cPCR and RT-PCR [81] [74].
Bioinformatic Tools	DADA2 (v1.18.0), Cutadapt (v3.2), BLAST+ (v2.9.0), QIIME (v1.9.0)	Processing raw NGS data: quality filtering, denoising, chimera removal, and taxonomic classification [1] [83] [74].
Phylogenetic Software	MEGA 11	Molecular evolutionary genetics analysis; construction of phylogenetic trees to confirm pathogen identity [74].

In the rigorous field of tick-borne protist research, the path from a tick sample to a confirmed, actionable result is a two-stage process. 18S rRNA metabarcoding serves as a powerful hypothesis-generating engine, mapping the potential diversity of parasites within a sample. However, without the targeted, specific confirmation provided by conventional PCR, these hypotheses remain unproven. The experimental protocols and research toolkit detailed herein provide a roadmap for scientists to implement this essential confirmatory workflow, ensuring that the data driving scientific conclusions, diagnostic assays, and drug development efforts are both robust and reliable.

The accurate identification of pathogens is a cornerstone of public health and veterinary science, particularly for vector-borne diseases where timely and precise diagnosis informs control strategies. Within the context of a broader thesis on DNA barcoding of tick-borne protists using the 18S rRNA gene, this review provides an in-depth comparative analysis of two pivotal molecular diagnostic techniques: next-generation sequencing (NGS)-based metabarcoding and species-specific polymerase chain reaction (PCR). Each method presents a unique balance of scalability, sensitivity, and specificity. This article synthesizes current research to elucidate the concordance and discrepancies between these methodologies, offering a technical guide for researchers and drug development professionals tasked with selecting and optimizing diagnostic approaches for complex biological samples.

Fundamental Principles and Technical Workflows

Species-Specific PCR and qPCR

Species-specific PCR and quantitative PCR (qPCR) are targeted molecular assays designed to detect known pathogens. These methods rely on primers that are meticulously engineered to bind to unique genetic sequences of a pre-defined target organism, such as a specific tick-borne protist [84]. The subsequent amplification of this target sequence allows for its detection and, in the case of qPCR, quantification. The fundamental strength of this approach lies in its high specificity and sensitivity for the intended pathogen, making it an excellent confirmatory tool [80]. However, its major limitation is its inherent requirement for a priori knowledge of the pathogen, rendering it incapable of discovering novel or unexpected organisms and making the parallel screening of multiple pathogens a labor-intensive process [1] [80].

Metabarcoding

Metabarcoding is a hypothesis-free, high-throughput approach that enables the simultaneous identification of a broad spectrum of organisms within a single sample. This technique utilizes universal primers that target conserved regions of a barcode gene, such as the 18S rRNA for eukaryotes, flanking hypervariable regions that provide taxonomic resolution [1] [85]. The amplified fragments are then sequenced en masse using NGS platforms, and the resulting sequences are classified against reference databases to reconstruct the sample's taxonomic composition [80]. The principal advantage of metabarcoding is its ability to comprehensively profile a microbial community, uncovering rare, novel, or co-infecting pathogens without prior suspicion. Its challenges include the influence of primer choice, bioinformatic complexities, and the quality of reference databases, which can affect quantitative accuracy and taxonomic resolution [9] [85].

The following diagram illustrates the core decision-making workflow when choosing between these two diagnostic approaches.

Comparative Analysis of Diagnostic Performance

Concordance in Detection

When applied to well-characterized pathogens, metabarcoding and species-specific PCR often show strong concordance in detection. A study on tick-borne bacteria found that 16S rRNA metabarcoding successfully identified the presence of Rickettsia, Wolbachia, and Ehrlichia, while correctly indicating the absence of Bartonella—a result that was later confirmed by species-specific PCR assays [86]. This demonstrates that metabarcoding can serve as a reliable tool for large-scale initial screening, accurately reflecting the presence or absence of major pathogenic groups.

Key Discrepancies and Methodological Gaps

Despite areas of agreement, significant discrepancies frequently arise, primarily driven by differences in methodological sensitivity and primer bias.

Sensitivity and Detection Rates: Species-specific methods often demonstrate higher analytical sensitivity. A comparative study of ocean fish highlighted that while qPCR and MiFish metabarcoding showed a positive correlation, the detection rate for qPCR was consistently higher across all target species [84]. This suggests that for low-abundance targets, the focused amplification in qPCR provides a superior detection capability compared to the competitive amplification environment of metabarcoding PCRs.

Primer Bias and Taxonomic Resolution: The choice of primer and the target region of the 18S rRNA gene profoundly impact the results. Research on tick-borne protists revealed that the number and abundance of protists detected varied considerably depending on the primer sets (V4 vs. V9 regions) used for metabarcoding [1] [2]. Furthermore, Toxoplasma gondii, which was confirmed present by specific PCR, was not identified in the metabarcoding analysis, underscoring how primer mismatches or database limitations can lead to false negatives [1] [2]. The move towards full-length 18S rRNA sequencing is driven by this need for improved resolution. One study found that full-length 18S sequences identified 84% of genera in field samples, outperforming the V4 (76%) and V8-V9 (71%) regions [85].

Table 1: Summary of Key Comparative Studies Highlighting Concordance and Discrepancies

Study Context	Metabarcoding Findings	Species-Specific PCR Findings	Key Discrepancy/Concordance
Tick-borne protists (18S rRNA) [1] [2]	Detected Hepatozoon canis, Theileria luwenshuni, Gregarine sp. Results varied with primer set (V4 vs. V9).	Confirmed H. canis, T. luwenshuni, Theileria sp., and Toxoplasma gondii.	Discrepancy: T. gondii was missed by metabarcoding, highlighting primer/database limitations.
Canine haemoparasites (18S rRNA) [80]	Identified Babesia vogeli and H. canis, including co-infections.	Specific PCRs detected fewer positive samples for H. canis compared to NGS.	Discrepancy: Metabarcoding showed higher sensitivity for detecting co-infections and the overall haemoparasite microbiome.
Oceanic fish (12S rRNA) [84]	Spatial distribution patterns were consistent with qPCR.	Detection rates were higher for each target species.	Concordance/Discordance: Spatial results were congruent, but qPCR had a higher detection rate.
Tick-borne bacteria (16S rRNA) [86]	Presence of Rickettsia, Ehrlichia, Wolbachia; absence of Bartonella.	PCR confirmed the presence of Rickettsia, Ehrlichia, Wolbachia and absence of Bartonella.	Concordance: Metabarcoding results were fully validated by specific PCR, supporting its use for screening.

Optimizing Diagnostic Protocols

Experimental Factors Influencing Metabarcoding Output

The output of metabarcoding is not a direct reflection of biological reality but is modulated by several technical factors. Research on intestinal parasite detection using 18S rRNA metabarcoding demonstrated that the DNA secondary structure of the target amplicon can negatively associate with the number of output reads, potentially biasing abundance estimates [9]. Furthermore, variations in the amplicon PCR annealing temperature were shown to significantly alter the relative abundance of reads for each parasite, indicating that stringent optimization of PCR conditions is critical for reproducible and semi-quantitative results [9].

A Complementary Workflow for Comprehensive Analysis

Given the complementary strengths and weaknesses of each method, an integrated workflow is often the most powerful approach. This typically involves using metabarcoding for initial, unbiased community profiling to identify a wide range of potential pathogens, including novel or unexpected ones. The findings are then validated and supplemented with species-specific PCR or qPCR assays to confirm the identity of key pathogens, especially those present in low abundance, and to achieve precise quantification [1] [84]. This two-tiered strategy leverages the breadth of metabarcoding with the depth and precision of targeted PCR.

Table 2: Essential Research Reagent Solutions for 18S rRNA-Based Protist Research

Research Reagent / Tool	Function and Application in Protist Research
Universal 18S rRNA Primers (e.g., targeting V4, V9, or full-length)	Amplify a broad range of eukaryotic DNA for metabarcoding. Primer choice (e.g., 1391F/EukBR) is critical for taxonomic coverage and resolution [9] [85].
DNeasy Blood & Tissue Kit (Qiagen)	Standardized system for high-quality DNA extraction from complex samples like tick pools, essential for downstream molecular analysis [1] [86].
KAPA HiFi HotStart ReadyMix (Roche)	High-fidelity PCR enzyme mix crucial for accurate amplification during library preparation for metabarcoding, minimizing amplification biases and errors [9].
Illumina MiSeq Platform	Widely used NGS platform for metabarcoding, enabling sequencing of amplicon libraries (e.g., 2x300 bp for V4 region) to profile microbial communities [1] [84].
SILVA / PR2 Databases	Curated ribosomal RNA sequence databases used for taxonomic classification of metabarcoding sequences (ASVs), with PR2 being specialized for protists [85] [86].
DADA2 (QIIME 2 plugin)	A key bioinformatic package for processing raw sequencing data. It performs quality filtering, dereplication, chimera removal, and infers exact Amplicon Sequence Variants (ASVs) [1] [9].

The comparative analysis between metabarcoding and species-specific PCR reveals a landscape defined not by the superiority of one method over the other, but by their strategic complementarity. Species-specific PCR remains the gold standard for sensitive and quantitative detection of known pathogens. In contrast, metabarcoding offers an unparalleled capacity for holistic pathogen discovery and community profiling. The observed discrepancies in sensitivity and detection, often attributable to primer bias and methodological constraints, are not merely limitations but informative parameters that guide protocol refinement. For researchers navigating the complexities of tick-borne protist diagnostics and beyond, the most robust strategy involves leveraging the strengths of both techniques in a synergistic workflow. This integrated approach, framed within the rigorous demands of modern molecular ecology and diagnostic science, ensures both broad surveillance and precise confirmation, ultimately strengthening our ability to monitor and mitigate the threats posed by emerging and endemic pathogens.

The molecular detection of tick-borne pathogens is crucial for public health and veterinary medicine. Among these pathogens, protozoan parasites like Toxoplasma gondii present significant diagnostic challenges due to their complex life cycles and low abundance in carrier hosts. DNA barcoding using the 18S rRNA gene has emerged as a powerful tool for screening eukaryotic pathogen diversity, but its performance varies considerably depending on experimental conditions [1]. This case study examines a specific research scenario where DNA barcoding failed to detect T. gondii in tick samples, while conventional PCR methods succeeded, highlighting critical methodological considerations for researchers studying tick-borne protists.

The objective is framed within the broader context of DNA barcoding tick-borne protists using 18S rRNA research, where comprehensive detection of parasitic organisms is essential for accurate risk assessment and epidemiological understanding. As next-generation sequencing (NGS) platforms become more accessible, evaluating their limitations alongside their strengths becomes increasingly important for proper implementation in diagnostic and surveillance workflows.

Experimental Design and Methodological Approaches

Sample Collection and Processing

The foundational research collected 13,375 questing ticks from multiple Korean provinces between 2021-2022 using the flagging method [1]. These specimens were morphologically identified and pooled into 1,003 samples, with adults examined individually by species and sex, while nymphs and larvae were pooled (up to 10 nymphs and 50 larvae per pool) [1]. After homogenization via bead beating, DNA was extracted using the DNeasy Blood & Tissue Kit, with concentrations quantified via spectrophotometry [1].

For DNA barcoding analysis, 50 tick pools were selected based on collection year, region, tick species, and developmental stage [1]. To mitigate potential bias from varying DNA concentrations, the samples were normalized using Qubit dsDNA Quantification Assay Kits before being pooled into a single sample for sequencing [1].

DNA Barcoding Methodology

The DNA barcoding approach targeted the V4 and V9 regions of the 18S rRNA gene, following Illumina 16S Metagenomic Sequencing Library protocols with modifications [1]. The specific primer sequences used were:

• V4 region: Forward: 5′-TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGCCAGCAGCCGCGGTAATTCC-3′, Reverse: 5′-GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGACTTTCGTTCTTGAT-3′ [1]
• V9 region: Forward: 5′-TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGCCCCTGCCHTTTGTACACAC-3′, Reverse: 5′-GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGCCTTCYGCAGGTTCACCTAC-3′ [1]

Library preparation involved an initial PCR with 25 cycles, followed by index incorporation with 10 cycles using Nextera XT Indexed Primers [1]. After purification with AMPure beads, the final libraries were quantified via qPCR and qualified with TapeStation D1000 ScreenTape before sequencing on the MiSeq platform [1].

Conventional PCR Validation

Following DNA barcoding, conventional PCR assays specifically targeting T. gondii and other protozoan pathogens were performed to validate the NGS results [1]. While the specific primer sequences used for T. gondii detection weren't detailed in the core study, comparative research indicates that effective conventional PCR for T. gondii typically targets multi-copy genes such as:

• B1 gene: A 35-copy repetitive gene often detected via nested PCR with outer primers (OB1/F: 5′-GGAACTGCATCCGTTCATGAG-3′, OB1/R: 5′-TCTTTAAAGCGTTCGTGGTC-3′) producing a 200 bp product, and inner primers (IB1/F: 5′-TGCATAGGTTGCAGTCACTG-3′, IB1/R: 5′-GGCGACCAATCTGCGAATACACC-3′) producing a 100 bp product [87]
• 529 bp REP element: A highly repetitive fragment (200-300 copies) that provides superior sensitivity compared to other targets [88]

Bioinformatic Analysis

The bioinformatic pipeline for processing DNA barcoding data involved multiple critical steps [1]. Raw sequencing data first underwent adapter and primer removal using Cutadapt v3.2, with forward and reverse reads trimmed to 250 and 200 base pairs, respectively [1]. Error correction, read merging, denoising, and amplicon sequence variant (ASV) generation were performed using DADA2 v1.18.0 [1]. Chimeras were removed using the consensus method of the removeBimeraDenovo function [1]. Finally, ASVs were taxonomically classified by alignment to the NCBI_NT database using BLAST [1]. All analyses and visualizations were conducted in R (version 4.4.0) within the RStudio environment [1].

Results: Divergent Outcomes Between Molecular Methods

DNA Barcoding Failure

The DNA barcoding approach, despite generating extensive sequencing data from the 50 tick pools, failed to detect any T. gondii sequences in the collected ticks [1]. The taxonomic analysis of amplicon sequence variants identified only three genera of protozoa: Hepatozoon canis, Theileria luwenshuni, and Gregarine sp. [1]. The study authors noted that "the number and abundance of protists detected were different depending on the primer sets," indicating primer bias as a significant factor in the failed T. gondii detection [1].

Conventional PCR Success

In stark contrast to the DNA barcoding results, conventional PCR assays confirmed the presence of T. gondii in the collected ticks [1]. Additionally, the conventional PCR detected H. canis, T. luwenshuni, and Theileria sp. [1]. Notably, this study represented the first identification of H. canis and T. gondii in Ixodes nipponensis ticks [1], highlighting the discovery potential of targeted molecular approaches even when broader screening methods fail.

Performance Comparison of Molecular Detection Methods

Table 1: Comparative Performance of Molecular Detection Methods for T. gondii

Method Type	Specific Approach	Target Gene	Detection Sensitivity	T. gondii Detection	Key Limitations
DNA Barcoding	18S rRNA V4 region	18S rRNA	Varies with primer set	Failed	Primer bias, database limitations
DNA Barcoding	18S rRNA V9 region	18S rRNA	Varies with primer set	Failed	Primer bias, database limitations
Conventional PCR	B1 gene nested PCR	B1 (35 copies)	~1.41 GE/PCR [88]	Successful	Non-specific amplification risk [88]
Real-time PCR	529 bp REP element	529 RE (200-300 copies)	1.067-1.561 GE/PCR [88]	Successful (in other studies)	Requires specialized equipment
Real-time PCR	Bradyzoite genes	SAG-4, MAG-1	0.1 GE/PCR [89]	Successful (in other studies)	Lower sensitivity in blood samples [89]

Table 2: Sample Type Performance for T. gondii Detection by PCR

Sample Type	Sensitivity in Ocular Toxoplasmosis	Advantages	Limitations
Peripheral Blood Mononuclear Cells (PBMCs)	90% with B1 real-time PCR [89]	High sensitivity, good for chronic infection	More complex processing
Whole Blood	50% with nested PCR [89]	Simple collection	Lower sensitivity
Serum	0% with nested PCR [89]	Simple collection, minimal processing	Very low sensitivity for PCR
Aqueous Humor	53-57% with B1 PCR [90]	Direct sample from infection site	Invasive collection procedure

Technical Analysis: Explaining the Methodological Discrepancy

Primer Bias and Target Region Variability

The failure of DNA barcoding to detect T. gondii likely stems from fundamental issues with primer compatibility and target region selection. The universal 18S rRNA primers used in DNA barcoding (targeting V4 and V9 regions) were designed for broad eukaryotic detection but may have mismatches with T. gondii-specific 18S rRNA sequences, reducing amplification efficiency [1]. This primer bias is a well-documented challenge in metabarcoding approaches, where "the results of DNA barcoding using 18S rRNA gene fragments can vary depending on the primer sets" [1].

Before library construction, the research team compared the primer sets in silico with 18S rRNA gene sequences from tick-borne protozoa, suggesting they were aware of potential limitations [1]. However, the practical implementation still failed to detect T. gondii, indicating that in silico compatibility doesn't always translate to experimental efficacy.

Template Competition and Amplification Dynamics

In DNA barcoding approaches, template competition presents a significant challenge for detecting low-abundance pathogens like T. gondii [91]. When using universal primers, abundant host (tick) DNA and other eukaryotic DNA significantly outcompete rare pathogen DNA during amplification [91]. This results in insufficient sequencing coverage of the target pathogen, effectively masking its presence in the sample.

The competitive dynamics are further complicated by the fact that target sequence abundance doesn't directly correlate with organism abundance in the original sample due to variations in gene copy number, genome size, and amplification efficiency [91]. For T. gondii, which may be present in low numbers in tick tissues, this amplification bias can be particularly detrimental to detection sensitivity.

Sensitivity Threshold Considerations

The analytical sensitivity of detection methods plays a crucial role in their ability to identify pathogens present at low concentrations. DNA barcoding, while capable of detecting diverse microorganisms, has inherent sensitivity limitations compared to targeted PCR approaches [1] [91]. Conventional and real-time PCR methods targeting multi-copy genes like the 529 bp REP element can detect as little as 0.1 to 1.5 genome equivalents per reaction [88] [89], providing superior detection limits for low-level infections.

In the case of tick-borne T. gondii, the parasite load in individual ticks is likely minimal, potentially falling below the detection threshold of DNA barcoding approaches, especially given the additional dilution effect from pooling multiple ticks before DNA extraction [1]. This sensitivity limitation represents a critical constraint for surveillance applications where infection prevalence and intensity may be low.

Diagram 1: Comparative Workflow Showing Divergent Detection Outcomes for T. gondii. The visualization contrasts the DNA barcoding and conventional PCR methodologies, highlighting critical failure points in the barcoding approach that led to false-negative results.

Optimization Strategies for Protist Detection in Ticks

Primer Selection and Validation

The critical importance of primer selection for successful detection of tick-borne protists cannot be overstated. Research indicates that primer optimization is essential for improving DNA barcoding efficacy [1]. Rather than relying solely on universal eukaryotic primers, researchers should consider:

• Custom primer design: Developing primers specifically validated against target pathogens like T. gondii while maintaining broad detection capability for other protists
• Multi-target approach: Using several primer sets targeting different variable regions to overcome biases inherent in any single primer pair
• In silico validation: Comprehensive testing of primer specificity and sensitivity against available sequence databases before experimental implementation
• Empirical validation: Testing primer performance with known positive controls spiked into representative sample matrices

Recent advances in primer design for apicomplexan detection include the development of specialized primers such as ApiF18Sv1v5 and ApiR18Sv1v5, which target V1-V5 regions of the 18S rRNA gene and have shown efficacy in detecting diverse Apicomplexa in wildlife samples [92].

Technical Workflow Enhancements

Several technical modifications can improve detection sensitivity for low-abundance pathogens like T. gondii in complex tick samples:

• Target enrichment: Using capture probes or pre-amplification strategies to enrich pathogen DNA before library preparation
• Sample processing optimization: Implementing methods that reduce host DNA background while preserving pathogen DNA
• Sequencing depth adjustment: Increasing sequencing depth specifically for samples where low pathogen abundance is suspected
• Multi-locus sequencing: Combining 18S rRNA with additional molecular markers to improve detection confidence

Research on protozoan detection in complex matrices like shellfish has demonstrated that background amplification of host and other eukaryotic DNA significantly competes with target protozoan amplification, necessitating specialized approaches to improve target recovery [91].

Bioinformatics Pipeline Improvements

Enhanced bioinformatic strategies can potentially rescue signals that might otherwise be missed in standard analyses:

• Customized reference databases: Creating comprehensive databases specifically tailored to tick-borne pathogens and related organisms
• Sensitive variant calling: Implementing parameters that improve detection of low-abundance sequences while maintaining specificity
• Cross-validation: Using multiple classification algorithms and threshold settings to identify potential false negatives
• Metadata integration: Incorporating sample metadata (collection location, tick species, season) to inform detection priors

The development of specialized bioinformatic pipelines, such as the "Meat-Borne-Parasite" workflow for Apicomplexa detection using Nanopore sequencing data, demonstrates how tailored computational approaches can improve parasite detection and classification in complex samples [92].

The Researcher's Toolkit: Essential Reagents and Materials

Table 3: Essential Research Reagents and Materials for Tick-Borne Protist Detection

Category	Specific Product/Kit	Application Purpose	Performance Notes
DNA Extraction	DNeasy Blood & Tissue Kit (Qiagen)	Nucleic acid purification from tick samples	Standardized recovery, suitable for diverse sample types [1]
DNA Quantification	Qubit dsDNA Quantification Assay (Invitrogen)	Accurate DNA concentration measurement	Fluorometric method, more accurate than spectrophotometry for NGS [1]
PCR Amplification	Hot Fire Polymerase (Solis Biodyne)	Conventional PCR amplification	High fidelity, good performance with complex templates [92]
NGS Library Prep	Illumina 16S Metagenomic Sequencing Library	18S rRNA amplicon library construction	Adaptable for eukaryotic targets with modified primers [1]
Indexing	Nextera XT Indexed Primer	Sample multiplexing for NGS	Enables pooling of multiple samples in one sequencing run [1]
Library Cleanup	AMPure beads (Agencourt Bioscience)	PCR product purification	Size-selective purification, removes primers and dimers [1]
Library Qualification	TapeStation D1000 ScreenTape (Agilent)	Quality control of final libraries	Assesses fragment size distribution and library integrity [1]
Positive Controls	T. gondii RH strain genomic DNA	Assay validation and sensitivity determination	Essential for establishing detection limits [89]

This case study demonstrates that while DNA barcoding using 18S rRNA gene fragments shows promise for screening tick-borne protist diversity, significant limitations remain for detecting specific pathogens like T. gondii. The failure of DNA barcoding contrasted with successful conventional PCR detection underscores that methodological selection should be guided by specific research objectives rather than assuming comprehensive efficacy from any single approach.

For researchers studying tick-borne protists, a hybrid strategy combining broad-spectrum screening with targeted validation appears most prudent. DNA barcoding serves as an excellent discovery tool for identifying expected and unexpected protists in tick populations, but targeted PCR methods remain essential for confirming specific pathogens of interest, particularly those present at low abundance. Future methodological development should focus on improving primer inclusivity, reducing amplification bias, and enhancing bioinformatic sensitivity to bridge the current detection gap between these complementary approaches.

The findings further highlight that negative results from DNA barcoding approaches should be interpreted cautiously and validated with orthogonal methods when specific pathogens are of primary interest. As the authors of the foundational study noted, "further optimization is required for library construction to identify tick-borne protists in ticks" [1], emphasizing that metabarcoding methodologies for this application remain in development rather than representing fully matured solutions.

Phylogenetic Analysis for Definitive Species Identification and Discovery of Novel Genotypes

Phylogenetic analysis, grounded in molecular data such as the 18S rRNA gene, serves as an indispensable tool for the definitive identification of tick-borne protists and the discovery of novel genotypes. This methodology provides the resolution necessary to differentiate between closely related species and to uncover genetic diversity that is often inaccessible through morphological examination alone [1]. The 18S rRNA gene is particularly valuable for such analyses due to the presence of conserved regions, which facilitate amplification and alignment across diverse taxa, and hypervariable regions (such as V4 and V9), which provide the nucleotide polymorphism necessary for fine-scale discrimination between species and strains [1] [93]. Within the field of tick-borne disease research, applying phylogenetic analysis to 18S rRNA gene sequences has directly enabled scientists to identify new pathogenic species, characterize known pathogens with greater precision, and elucidate the complex ecological relationships between ticks, their protist parasites, and animal hosts [1] [42] [4].

The limitations of traditional diagnostic methods underscore the critical importance of robust phylogenetic frameworks. Microscopic examination, the historical standard, suffers from low sensitivity and offers limited capability for species-level identification, especially in cases of low parasitemia or co-infection [1] [93]. In contrast, phylogenetic analysis of sequence data offers a powerful, high-fidelity alternative. For instance, studies on Theileria annulata populations have revealed nucleotide heterogeneity of 0.1% to 8.6% in the 18S rRNA gene, leading to the identification of novel genotypes that cluster separately from known reference strains [93]. Similarly, DNA barcoding initiatives using the 18S rRNA gene have successfully identified and differentiated protist genera such as Hepatozoon canis, Theileria luwenshuni, and Gregarine sp. within tick populations, discoveries that are pivotal for assessing disease risk and understanding transmission dynamics [1].

Core Workflow for Phylogenetic Analysis

The process of conducting a phylogenetic analysis for species identification and genotype discovery is a multi-stage endeavor, integrating laboratory bench work with sophisticated computational biology. The workflow can be conceptualized in two primary phases: the Wet-Lab Phase, encompassing sample collection and molecular biology techniques to generate sequence data, and the Dry-Lab Phase, involving computational analysis and tree building.

Visual Overview of the Phylogenetic Analysis Workflow for Tick-Borne Protist Identification. This diagram outlines the two major phases: the Wet-Lab Phase (sample collection to sequencing) and the Dry-Lab Phase (sequence processing to tree interpretation), highlighting the sequential steps from biological sample to phylogenetic insight.

Wet-Lab Phase: From Sample to Sequence

The initial phase focuses on generating high-quality genetic data from biological samples.

• Sample Collection and Identification: Ticks are collected from the environment using methods like flagging or directly from host animals [1] [42]. Accurate morphological identification of tick species and developmental stage is a critical first step, as it provides essential context for the ecological interpretation of subsequent findings [1] [65].
• DNA Extraction and Target Amplification: Total genomic DNA is extracted from pooled or individual ticks using commercial kits [1] [93]. The target genetic locus—in this context, the 18S rRNA gene—is then amplified via Polymerase Chain Reaction (PCR). Researchers must carefully select primer sets that balance broad specificity with the ability to capture the hypervariable regions essential for discrimination [1]. For instance, one study on tick-borne protists utilized different primer sets targeting the V4 and V9 regions of the 18S rRNA gene, noting that the number and abundance of protists detected could vary significantly depending on the primers chosen [1].
• Sequencing: The amplified PCR products are sequenced. While Sanger sequencing remains a reliable and cost-effective method for individual clones or samples [93], Next-Generation Sequencing (NGS) platforms, such as Illumina's MiSeq, enable deep sequencing of amplicons from complex samples, allowing for the detection of rare pathogens and the characterization of population-level diversity through metabarcoding approaches [1] [65].

Dry-Lab Phase: From Sequence to Phylogeny

The second phase transforms raw sequence data into a phylogenetic tree that can be interpreted biologically.

• Sequence Processing and Alignment: Raw sequencing reads undergo a rigorous preprocessing pipeline. This includes removing adapter sequences, trimming based on quality scores, error correction, merging of paired-end reads, and the removal of chimeric sequences to derive exact Amplicon Sequence Variants (ASVs) or consensus sequences [1] [65]. The resulting high-quality sequences are then aligned using multiple sequence alignment algorithms (e.g., ClustalW2) to ensure nucleotide positions are homologous [93].
• Phylogenetic Model Selection and Tree Building: The aligned sequences are used to infer evolutionary relationships. A key step is selecting the best-fit nucleotide substitution model (e.g., Kimura 2-parameter, Tamura 3-parameter) [93] [65]. Phylogenetic trees are then constructed using algorithms such as:
  • Maximum Likelihood (ML): A method that finds the tree topology most likely to have produced the observed sequence data under a given substitution model [93].
  • Neighbor-Joining (NJ): A distance-based method that clusters sequences based on calculated evolutionary distances [42] [93].
• Tree Visualization and Interpretation: The final tree is visualized using specialized software. Researchers then interpret the tree by examining clades, bootstrap support values (which indicate the robustness of branches), and the clustering of unknown sequences with well-characterized reference strains to make identifications or flag potential novel lineages [93] [94].

Detailed Experimental Protocols

Protocol 1: 18S rRNA Gene Amplification and Cloning forTheileriaGenotyping

This protocol, adapted from Theileria research, is designed for high-fidelity amplification and sequencing of the nearly full-length 18S rRNA gene, enabling robust phylogenetic analysis and the detection of novel genotypes [93].

• Sample Preparation: Extract genomic DNA from host blood samples or tick homogenates using a commercial DNA extraction kit (e.g., QIAamp DNA Mini Kit, Qiagen). Assess DNA quality and concentration via spectrophotometry and gel electrophoresis [93].
• PCR Amplification:
  • Primary PCR: Perform the first round of PCR using primers Nbab-1F (5'-GAC AAG TCC TGC CCT TTT GTA C-3') and Nbab-1R (5'-GAC TCA AGA CGG AAG TCT TTG-3') to generate a ~1,600 bp amplicon of the 18S rRNA gene [93].
  • Reaction Setup: 100 ng genomic DNA, 1x PCR buffer, 200 µM dNTPs, 0.5 µM each primer, 1 U high-fidelity DNA polymerase (e.g., Speed Star HS, Takara).
  • Cycling Conditions: Initial denaturation at 95°C for 1 min; 35 cycles of 95°C for 10 s, 61°C for 20 s, 72°C for 10 s; final extension at 72°C for 1 min [93].
• Cloning and Sequencing:
  • Purify the PCR product and clone it into a suitable vector (e.g., TOPO TA cloning vector, Invitrogen).
  • Transform into competent E. coli cells and select positive colonies via ampicillin resistance.
  • Sequence multiple clones (e.g., five per sample) using Sanger sequencing with universal vector primers (e.g., M13 forward and reverse) to capture potential intra-individual genetic variation [93].

Protocol 2: DNA Barcoding of Tick-Borne Protists Using NGS

This protocol utilizes next-generation sequencing for a comprehensive, high-throughput survey of protist diversity within tick populations [1].

• Library Preparation for Metabarcoding:
  • DNA Normalization and Pooling: Normalize the DNA concentration from multiple tick pool extracts and combine them into a single sample for efficient sequencing [1] [65].
  • Amplification with Overhang Adapters: Perform the initial PCR to amplify the target 18S rRNA region (e.g., V4 or V9) using primers that contain Illumina adapter overhangs.
    • V4 Region Primers: Forward: 5'-TCG TCG GCA GCG TCA GAT GTG TAT AAG AGA CAG CCA GCA GCC GCG GTA ATT CC-3'; Reverse: 5'-GTC TCG TGG GCT CGG AGA TGT GTA TAA GAG ACA GAC TTT CGT TCT TGA T-3' [1].
  • Indexing and Library Purification: A second, limited-cycle PCR attaches unique dual indices and sequencing adapters to the amplicon. Purify the final library using solid-phase reversible immobilization (SPRI) beads [1].
• Bioinformatics Analysis:
  • Sequence Processing: Process raw paired-end reads using a pipeline such as DADA2 within the R environment to perform quality filtering, error correction, read merging, and chimera removal, resulting in a table of exact Amplicon Sequence Variants (ASVs) [1] [65].
  • Taxonomic Assignment: Assign taxonomy to each ASV by comparing it to a reference database (e.g., NCBI NT) using a BLAST search or a trained classifier. The outcome is a table detailing the identity and abundance of each protist taxon found in the sample [1].

Data Analysis: Building and Interpreting the Phylogenetic Tree

The construction of a reliable phylogenetic tree is a critical step for genotype identification and discovery.

• Sequence Alignment and Curation: Align your sequenced 18S rRNA gene fragments (e.g., the ~1728 bp from cloning or the ASVs from NGS) with a set of reference sequences obtained from public databases like GenBank using tools like ClustalX2 or MAFFT. Visually inspect and manually curate the alignment to ensure accuracy [93].
• Model Selection and Tree Inference:
  • Use software like MEGA7 or jModelTest to determine the nucleotide substitution model that best fits your alignment (e.g., Kimura 2-parameter model) [93].
  • Construct a phylogenetic tree using the Maximum Likelihood method with 1000 bootstrap replicates. Bootstrap values above 70% are generally considered to indicate good support for a given clade [93].
• Tree Interpretation for Novel Genotypes: A sequence is suggestive of a novel genotype if it forms a distinct, well-supported clade separate from known species, or if it shows significant nucleotide divergence (e.g., >1-2% in the 18S rRNA gene) from its closest known relative in the database [93].

Table 1: Key Bioinformatics Tools for Phylogenetic Analysis

Tool Name	Primary Function	Application in Protocol	Reference
DADA2 (R package)	Amplicon sequence variant (ASV) inference from NGS data	Processing of 16S/18S rRNA metabarcoding data; denoising and chimera removal	[1] [65]
MEGA (Software)	Molecular Evolutionary Genetics Analysis	Multiple sequence alignment, model selection, and tree building (ML, NJ)	[42] [93]
Clustal X2 / BioEdit	Multiple Sequence Alignment and Editing	Aligning 18S rRNA sequences from cloned fragments or Sanger sequencing	[42] [93]
ColorTree (Perl Script)	Batch customization of phylogenetic trees	Automated coloring of tree labels/branches based on metadata for visual inspection	[95]
Dendroscope	Interactive tree visualization	Viewing and editing large phylogenetic trees, including those customized by ColorTree	[95]

Visualization and Interpretation of Phylogenetic Data

Effective visualization is paramount for interpreting and presenting the results of a phylogenetic analysis. Different tree layouts can highlight various aspects of the data.

• Tree Layouts: Rectangular phylograms display branch lengths proportional to evolutionary change, while radial or circular layouts use space more efficiently, making them ideal for visualizing large numbers of taxa [94].
• Customization for Clarity: Strategic customization, such as coloring branches or tip labels based on metadata (e.g., host species, geographic origin), can reveal patterns that might otherwise remain hidden. Tools like ColorTree allow for batch customization of trees based on a configuration file, automating this process for large datasets [95]. For example, one can automatically color all sequences from a specific host in red and another in blue, making it immediately apparent if the phylogeny correlates with host species.

The following diagram illustrates a sample phylogenetic tree of Theileria 18S rRNA sequences, demonstrating how such a tree is structured and can be interpreted to identify known species and novel genotypes.

Interpreting a Phylogenetic Tree for Genotype Discovery. This diagram models a simplified phylogenetic output. Sequences clustering within well-defined clades containing reference strains (e.g., Clade I, III) can be reliably identified as known species. Sequences that form a distinct, well-supported clade with no close relationship to reference sequences (e.g., Clade II) represent potential novel genotypes.

The Scientist's Toolkit: Essential Reagents and Materials

Successful phylogenetic analysis relies on a suite of reliable research reagents and materials. The following table catalogs key solutions used in the featured experimental protocols.

Table 2: Key Research Reagent Solutions for Phylogenetic Analysis of Tick-Borne Protists

Reagent / Kit	Function	Specific Example & Citation
DNA Extraction Kit	Isolation of high-quality genomic DNA from complex samples (ticks, blood).	DNeasy Blood & Tissue Kit (Qiagen) [1]; TIANamp Genomic DNA Kit (Tiangen) [42]; QIAamp DNA Mini Kit (Qiagen) [93].
High-Fidelity DNA Polymerase	Accurate amplification of long or GC-rich target genes (e.g., ~1.6 kb 18S rRNA).	Speed Star HS DNA Polymerase (Takara) [93].
PCR Cloning Kit	High-efficiency insertion of PCR products into a vector for Sanger sequencing of individual clones.	TOPO TA Cloning Kit (Invitrogen) [93].
NGS Library Prep Kit	Preparation of sequencing-ready libraries from amplicons for metabarcoding.	Nextera XT DNA Library Preparation Kit (Illumina) [1] [65].
Sequence Alignment & Phylogenetic Software	Multiple sequence alignment, evolutionary model testing, and phylogenetic tree construction.	MEGA Software [93]; Clustal X2 [42].
Tree Visualization Software	Interactive viewing, editing, and graphical customization of phylogenetic trees.	Dendroscope [95]; MEGA [93].

Phylogenetic analysis, particularly when applied to genetic markers like the 18S rRNA gene, provides an unparalleled framework for the definitive identification of tick-borne protists and the discovery of previously unknown genotypes. The integrated workflow—from meticulous sample collection and DNA extraction through advanced computational analysis—enables researchers to move beyond simple detection to a deeper understanding of pathogen diversity, evolution, and ecology. As sequencing technologies continue to advance and datasets grow, the application of these robust phylogenetic methods will remain fundamental to tracking emerging tick-borne diseases, informing control strategies, and safeguarding both public and animal health on a global scale.

Assaying Diagnostic Sensitivity and Specificity Against Gold Standard Methods

The accurate identification of tick-borne protists is crucial for both public health and veterinary medicine. DNA barcoding, particularly using the 18S rRNA gene, has emerged as a powerful tool for pathogen detection and diversity studies [7]. However, the diagnostic accuracy of any new molecular method must be rigorously validated against reference standards. This technical guide examines the framework for evaluating diagnostic sensitivity and specificity within the context of 18S rRNA-based research on tick-borne protists, providing researchers with methodologies to ensure their assays meet rigorous scientific standards.

The "gold standard" test represents the best available diagnostic method under current conditions, though it is rarely perfect in practice [96]. For tick-borne diseases, diagnostic testing approaches include serology, microscopy, and molecular methods, with the preferred approach varying by specific disease and clinical context [97]. As new diagnostic technologies emerge, proper validation against appropriate standards becomes essential for clinical and research applicability.

Fundamental Concepts of Diagnostic Accuracy

Gold Standard Tests

A gold standard test is the time-honored diagnostic method considered the definitive test for a particular disease [96]. In an ideal scenario, this test would have both 100% sensitivity (identifying all true positive cases) and 100% specificity (correctly identifying all true negative cases). In practice, however, such perfection is unattainable, and researchers must use tests that approach this ideal as closely as possible [96].

Gold standards may change over time as new diagnostic technologies emerge. For some diseases, the definitive gold standard may be highly invasive or only applicable post-mortem, such as brain biopsy for Alzheimer's disease [96]. In tick-borne pathogen research, gold standards might include cell culture, tissue histopathology, or other established molecular methods.

Sensitivity, Specificity, and Predictive Values

Sensitivity measures a test's ability to correctly identify individuals with a disease, calculated as the proportion of true positives out of all patients with the condition [98]. The formula for sensitivity is:

Sensitivity = True Positives / (True Positives + False Negatives)

Specificity measures a test's ability to correctly identify individuals without the disease, calculated as the proportion of true negatives out of all disease-free subjects [98]. The formula for specificity is:

Specificity = True Negatives / (True Negatives + False Positives)

Predictive values are influenced by disease prevalence in the population [98]:

• Positive Predictive Value (PPV) = True Positives / (True Positives + False Positives)
• Negative Predictive Value (NPV) = True Negatives / (True Negatives + False Negatives)

Table 1: Diagnostic Test Outcome Matrix

	Gold Standard Positive	Gold Standard Negative
New Test Positive	True Positive (TP)	False Positive (FP)
New Test Negative	False Negative (FN)	True Negative (TN)

Table 2: Calculation of Diagnostic Test Parameters

Parameter	Formula	Application Context
Sensitivity	TP / (TP + FN)	Screening tests where missing cases has serious consequences
Specificity	TN / (TN + FP)	Confirmatory tests where false positives are problematic
Positive Predictive Value	TP / (TP + FP)	Interpreting positive results in clinical practice
Negative Predictive Value	TN / (TN + FN)	Interpreting negative results in clinical practice

Likelihood Ratios

Likelihood ratios (LRs) provide another statistical tool for understanding diagnostic tests, with the advantage of being unaffected by disease prevalence [98]:

• Positive Likelihood Ratio (LR+) = Sensitivity / (1 - Specificity)
• Negative Likelihood Ratio (LR-) = (1 - Sensitivity) / Specificity

These ratios indicate how much a test result will alter the probability of disease, with LR+ values >10 and LR- values <0.1 representing large, often conclusive changes in probability [98].

Experimental Design for Validation Studies

Sample Size and Composition

Proper validation of a new diagnostic test requires careful consideration of sample composition. The sample population should include both confirmed positive and confirmed negative individuals, with sample sizes large enough to provide precise estimates of sensitivity and specificity. Statistical programs can calculate required sample sizes based on desired confidence intervals and expected test performance.

When studying tick-borne protists, sample collection should represent the genetic diversity of the target pathogens as well as related organisms that might cause cross-reactivity. For DNA barcoding studies using 18S rRNA gene fragments, this includes collecting ticks from different geographical regions and using validated morphological identification before molecular analysis [7] [2].

Blinding and Randomization

To minimize bias, validation studies should implement blinding so that those performing the reference and index tests are unaware of the other test's results. Sample testing order should be randomized to prevent systematic errors. This is particularly important in tick-borne pathogen studies where sample processing might involve multiple steps including DNA extraction, PCR amplification, and sequencing [7].

Handling of Indeterminate Results

All diagnostic tests produce indeterminate or equivocal results in some cases. A predefined protocol for handling these results is essential, including whether to exclude them from analysis, count them as positive, or count them as negative. This protocol should be established before beginning the validation study.

Workflow for Validating DNA Barcoding Assays

The following diagram illustrates the comprehensive workflow for validating DNA barcoding assays against gold standard methods:

Specific Considerations for 18S rRNA DNA Barcoding

Primer Selection and Design

The choice of primer pairs targeting variable regions of the 18S rRNA gene significantly impacts detection sensitivity and specificity. Different primer sets can yield substantially different results in identifying tick-borne protists [7] [2]. When designing primers:

• Target appropriate variable regions (V4, V9) based on the specific protists of interest
• Consider using fungi-specific primers that avoid amplification of host DNA [99]
• Account for genetic diversity within target organisms to ensure broad detection
• Test multiple primer sets to identify the most effective combination

Research has demonstrated that results of DNA barcoding using 18S rRNA gene fragments can vary considerably depending on the primer sets used, necessitating further optimization for library construction to identify tick-borne protists in ticks [7].

Bioinformatics and Taxonomic Classification

Accurate taxonomic classification presents special challenges in eukaryotic microorganisms due to database inconsistencies, synonyms, and misclassifications [100]. The BROCC (BLAST Read and Operational Taxonomic Unit Consensus Classifier) pipeline was developed specifically to address these challenges by:

• Using BLAST-based methods rather than kmer-based classifiers
• Implementing a voting system across taxonomic levels
• Applying configurable thresholds for species (99%), genus (96%), and higher-level (80%) attribution for 18S rRNA gene amplicons [100]
• Handling environmental sequences with minimal classification

Addressing Co-amplification

Co-amplification of non-target DNA, including from host organisms or food sources, can reduce test specificity [100]. Strategies to minimize this include:

• Using blocking oligonucleotides to prevent amplification of non-target sequences [99]
• Designing primers with mismatches to host sequences at the 3' end [100]
• Applying bioinformatic filters to remove non-target sequences during analysis

Statistical Analysis and Interpretation

Calculating Test Performance

Using the 2x2 table comparing new test results against the gold standard, calculate sensitivity, specificity, predictive values, and likelihood ratios. For example, in a hypothetical validation study:

Table 3: Example Validation Study Results

Parameter	Value	95% Confidence Interval
Sensitivity	96.1%	93.8% - 97.8%
Specificity	90.6%	88.1% - 92.7%
Positive Predictive Value	86.4%	82.8% - 89.4%
Negative Predictive Value	97.4%	95.8% - 98.4%
Positive Likelihood Ratio	10.22	8.4 - 12.5
Negative Likelihood Ratio	0.043	0.026 - 0.070

These results show excellent sensitivity (96.1%) and good specificity (90.6%), with a high positive likelihood ratio (10.22) indicating this test would be valuable for confirming disease presence [98].

Confidence Intervals

Always calculate confidence intervals for sensitivity, specificity, and predictive values to understand the precision of your estimates. The binomial exact method (Clopper-Pearson) is commonly used for this purpose. The width of confidence intervals depends on sample size, with larger samples providing more precise estimates.

Comparing Multiple Tests

When comparing the performance of multiple new tests against a gold standard, adjust for multiple comparisons to reduce the risk of Type I errors. The McNemar test is appropriate for comparing paired proportions (sensitivity and specificity) between two diagnostic tests.

Case Study: Validating 18S rRNA Barcoding for Tick-Borne Protists

A recent study on tick-borne protists in the Republic of Korea illustrates the validation process for DNA barcoding methods [7] [2]. Researchers collected 13,375 ticks, pooled them into 1,003 samples, and selected 50 pools for DNA barcoding targeting the V4 and V9 regions of the 18S rRNA gene. The study demonstrated that:

• Taxonomic analysis identified three genera of protozoa: Hepatozoon canis, Theileria luwenshuni, and Gregarine sp.
• The number and abundance of protists detected differed depending on the primer sets used
• Toxoplasma gondii was not identified in DNA barcoding but was detected by conventional PCR
• The study identified H. canis and T. gondii in Ixodes nipponensis for the first time

This research highlights both the potential of DNA barcoding using 18S rRNA gene fragments for screening tick-borne protist diversity and the importance of validating results with complementary methods like conventional PCR [7].

Advanced Method: Validation Without Perfect Gold Standard

When a true gold standard is unavailable, methods exist to estimate sensitivity and specificity using latent class models or by comparing against an established test with known characteristics [101]. The following formulas allow calculation of a new test's performance characteristics (Se₂ and Sp₂) when compared against an established test with known sensitivity (Se₁) and specificity (Sp₁), where Se₂,₁ and Sp₂,₁ represent the new test's sensitivity and specificity against the established test, and pr represents the apparent prevalence:

True Prevalence (π) = (pr + Sp₁ - 1) / (Se₁ + Sp₁ - 1)

Sensitivity of New Test (Se₂) = (Se₂,₁ × Se₁ × π + (1 - Sp₂,₁) × (1 - Sp₁) × (1 - π)) / (Se₁ × π + (1 - Sp₁) × (1 - π))

Specificity of New Test (Sp₂) = (Sp₂,₁ × Sp₁ × (1 - π) + (1 - Se₂,₁) × (1 - Se₁) × π) / (Sp₁ × (1 - π) + (1 - Se₁) × π)

This approach is particularly valuable in tick-borne disease research where perfect gold standards may be unavailable for novel or emerging pathogens [101].

Research Reagent Solutions

Table 4: Essential Research Reagents for 18S rRNA Barcoding Validation

Reagent/Category	Specific Examples	Function & Application Notes
DNA Extraction Kits	DNeasy Blood & Tissue Kit (Qiagen)	Efficient DNA extraction from tick samples; critical for yield and purity
18S rRNA Primers	V4 region: 18S0067adeg/NSR399V9 region primers [100] [2]	Target amplification of specific variable regions; primer selection significantly impacts detected diversity
PCR Master Mixes	High-fidelity polymerases	Accurate amplification with minimal errors for downstream sequencing
Quantification Kits	Qubit dsDNA Assay Kits (Invitrogen)	Precise DNA quantification prior to library preparation
Sequence Library Prep	Illumina 16S Metagenomic Sequencing Library	Preparation of sequencing libraries with minimal bias
Blocking Oligonucleotides	SAR group blockers, Telonema blockers [99]	Reduce co-amplification of non-target eukaryotic sequences
Bioinformatic Tools	BROCC classifier, QIIME pipeline [100]	Taxonomic classification of eukaryotic sequences with complex nomenclature

Validating the diagnostic sensitivity and specificity of DNA barcoding methods for tick-borne protists requires careful experimental design, appropriate statistical analysis, and understanding of methodological limitations. The 18S rRNA gene provides a valuable target for pathogen detection, but researchers must account for primer biases, bioinformatic challenges, and the imperfect nature of available gold standards. By applying the principles and methods outlined in this guide, researchers can ensure their diagnostic assays provide reliable results that advance our understanding of tick-borne disease ecology and improve detection capabilities for both clinical and surveillance purposes. As DNA barcoding technologies continue to evolve, rigorous validation against appropriate standards remains fundamental to their successful implementation in public health and research contexts.

Conclusion

DNA barcoding of the 18S rRNA gene represents a powerful, high-throughput tool for revealing the hidden diversity of tick-borne protists, fundamentally advancing our understanding of pathogen ecology. While the technique excels at comprehensive community profiling, its success is contingent on careful optimization of wet-lab and computational steps, and its findings must be rigorously validated with complementary molecular methods. Future directions should focus on standardizing protocols across laboratories, expanding reference databases for improved taxonomic resolution, and integrating 18S rRNA metabarcoding with other 'omics' technologies like metatranscriptomics to distinguish active infections from mere presence. For biomedical research, these refined approaches will be crucial for tracking emerging pathogens, understanding the dynamics of co-infections, and ultimately developing next-generation diagnostics and targeted therapeutics for tick-borne diseases.

References

• 1. DNA Barcoding Using 18S rRNA Gene Fragments for ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC11597262/]
• 2. DNA Barcoding Using 18S rRNA Gene Fragments for ... [https://www.mdpi.com/2076-0817/13/11/941]
• 3. Piroplasmida) in hard ticks and cattle blood from Thailand [https://pmc.ncbi.nlm.nih.gov/articles/PMC12380414/]
• 4. Exploring the diversity of tick-borne pathogens [https://www.sciencedirect.com/science/article/pii/S0378113523002468]
• 5. Molecular detection of Theileria, Babesia, and Hepatozoon ... [https://www.sciencedirect.com/science/article/abs/pii/S1877959X16300401]
• 6. Theileria luwenshuni and Novel Babesia spp. Infections in ... [https://wwwnc.cdc.gov/eid/article/31/9/24-1919_article]
• 7. DNA Barcoding Using 18S rRNA Gene Fragments for ... [https://pubmed.ncbi.nlm.nih.gov/39599494/]
• 8. Detection and molecular characterization of Babesia ... [https://pubmed.ncbi.nlm.nih.gov/28501503/]
• 9. Optimization of 18 S rRNA metabarcoding for the ... [https://www.nature.com/articles/s41598-024-76304-1]
• 10. Leveraging topoisomerase II-mediated DNA damage [https://www.frontiersin.org/journals/veterinary-science/articles/10.3389/fvets.2025.1689833/full]
• 11. DNA barcoding [https://en.wikipedia.org/wiki/DNA_barcoding]
• 12. A Beginner's Guide to 16S/18S/ITS Amplicon Sequencing [https://www.cd-genomics.com/resource-16s-18s-its-amplicon-sequencing.html]
• 13. Enhanced blood parasite species identification using V4– ... [https://www.nature.com/articles/s41598-025-25108-y]
• 14. Nationwide investigation of eukaryotic pathogens in ticks from ... [https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0327953]
• 15. Optimal Eukaryotic 18S and Universal 16S/18S Ribosomal ... [https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0090053]
• 16. Nationwide investigation of eukaryotic pathogens in ticks ... [https://pubmed.ncbi.nlm.nih.gov/40763172/]
• 17. Comparative analyses of the V4 and V9 regions of 18S ... [https://www.nature.com/articles/s41598-020-63561-z]
• 18. An 18S rRNA Workflow for Characterizing Protists in ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC5653731/]
• 19. Intragenomic diversity of the V9 hypervariable domain in ... [https://www.sciencedirect.com/science/article/pii/S2589004223013688]
• 20. 16S and ITS rRNA Sequencing | Identify bacteria & fungi with NGS [https://www.illumina.com/areas-of-interest/microbiology/microbial-sequencing-methods/16s-rrna-sequencing.html]
• 21. Targeted Next-Generation Sequencing for Comprehensive ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC9503002/]
• 22. Comparing microscopy and DNA metabarcoding ... [https://www.sciencedirect.com/science/article/pii/S1568988322000166]
• 23. Recent insights into the tick microbiome gained through next ... [https://parasitesandvectors.biomedcentral.com/articles/10.1186/s13071-017-2550-5]
• 24. First description of Hepatozoon canis in raccoon dog ... [https://www.sciencedirect.com/science/article/pii/S2213224425000975]
• 25. Molecular epidemiology of Theileria species in ticks and its ... [https://www.sciencedirect.com/science/article/abs/pii/S0001706X22004715]
• 26. Detection of tick-borne pathogens in blood-fed ticks from ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC11878029/]
• 27. Molecular identification and associated risk factors of ... [https://bmcvetres.biomedcentral.com/articles/10.1186/s12917-025-05118-8]
• 28. First description of Hepatozoon canis in raccoon dog ( ... [https://pubmed.ncbi.nlm.nih.gov/40978524/]
• 29. The First Molecular Detection of Theileria luwenshuni from ... [https://www.mdpi.com/2076-0817/14/3/241]
• 30. Morphological and Molecular Characterization of Tick Species ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC11680315/]
• 31. Morphological identification of ticks and molecular detection of ... [https://parasitesandvectors.biomedcentral.com/articles/10.1186/s13071-020-04565-6]
• 32. Morphological identification of ticks [https://bio-protocol.org/exchange/minidetail?id=10103541&type=30]
• 33. 2.4. Morphological Identification of Ticks [https://bio-protocol.org/exchange/minidetail?id=10283627&type=30]
• 34. DPDx - Ticks [https://www.cdc.gov/dpdx/ticks/index.html]
• 35. Molecular prevalence and associated infection risk factors of tick -borne... [https://bmcvetres.biomedcentral.com/articles/10.1186/s12917-023-03702-4]
• 36. Methods for Nucleic Acid Extraction from Ticks [https://pubmed.ncbi.nlm.nih.gov/40238186/]
• 37. Update on tick-borne pathogens detection methods within ... [https://www.sciencedirect.com/science/article/pii/S2667114X2400030X]
• 38. A simple improved method for extracting DNA from ethanol ... [https://www.sciencedirect.com/science/article/pii/S1567134824001618]
• 39. Tick genomics through a Nanopore: a low-cost approach ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12211944/]
• 40. Troubleshooting Guide for Genomic DNA Extraction & ... [https://www.neb.com/en-us/tools-and-resources/troubleshooting-guides/troubleshooting-guide-for-t3010?srsltid=AfmBOoq4CixmZQzYmWi9i-3VCEsV7rvciZWxw_M-vIMaRZSvZMvQoeXD]
• 41. TICKHUNTER: A Targeted Hybridization-Capture ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12157462/]
• 42. Molecular characterization of tick-borne bacterial and ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12139197/]
• 43. Analysis of tick-borne pathogens using next-generation ... [https://www.vetsci.org/DOIx.php?id=10.4142/jvs.24351]
• 44. iSeq 100 Sequencing System | Compact, affordable sequencing [https://www.illumina.com/systems/sequencing-platforms/iseq.html]
• 45. iSeq 100 Sequencing System specifications [https://www.illumina.com/systems/sequencing-platforms/iseq/specifications.html]
• 46. MiSeq Sequencing System specifications [https://www.illumina.com/systems/sequencing-platforms/miseq/specifications.html]
• 47. MiSeq System | Rapid and cost-effective sequencing [https://sapac.illumina.com/systems/sequencing-platforms/miseq.html]
• 48. Illumina iSeq 100 and MiSeq exhibit similar performance in ... [https://www.nature.com/articles/s41598-021-95360-5]
• 49. Sequencing Quality Scores [https://www.illumina.com/science/technology/next-generation-sequencing/plan-experiments/quality-scores.html]
• 50. MiSeq System Denature and Dilute Libraries Guide [https://support.illumina.com/downloads/prepare_libraries_for_sequencing_miseq_15039740.html]
• 51. 16S/18S/ITS扩增子高通量测序引物的生物信息学评估和改进 [http://journals.im.ac.cn/html/wswxtbcn/2020/9/tb20092897.htm]
• 52. 使用QIIME 2 2025.4分析微生物组16S rDNA基因和ITS扩增 ... [https://cn.bio-protocol.org/mv2/e1011032]
• 53. QIIME 2教程. 14数据评估和质控q2-quality-control(2020.11) ... [https://blog.csdn.net/woodcorpse/article/details/102760635]
• 54. 随手“一片”SCI，Qiime2扩增子处理流程确定不了解一下？ 原创 [https://blog.csdn.net/qq_42491125/article/details/104753667]
• 55. NGS QIIME2: DADA2 序列品質管制– 留下好序列，篩掉壞序列-8 [https://blog.petsci.tw/bioinfomatic/ngs-qiime2-dada2/]
• 56. q2-quality-control：QIIME 2 质量控制插件实战指南原创 [https://blog.csdn.net/weixin_42514750/article/details/149452298]
• 57. Measurements of soil protist richness and ... - PubMed Central [https://pmc.ncbi.nlm.nih.gov/articles/PMC11267882/]
• 58. Evaluation of different primers of the 18S rRNA gene to ... [https://www.sciencedirect.com/science/article/pii/S2772735122000774]
• 59. Unbiased Characterization of the Microbiome and Virome of ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC8153229/]
• 60. 18S/28S rDNA metabarcoding identifies Cryptosporidium ... [https://parasitesandvectors.biomedcentral.com/articles/10.1186/s13071-025-07043-z]
• 61. Applications of Blocker Nucleic Acids and Non-Metazoan ... [https://www.mdpi.com/2076-2607/9/5/1051]
• 62. Recent insights into the tick microbiome gained through ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC5755153/]
• 63. Identification of tick-borne pathogens by metagenomic next ... [https://parasitesandvectors.biomedcentral.com/articles/10.1186/s13071-021-04740-3]
• 64. Unbiased Characterization of the Microbiome and Virome ... [https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2021.627327/full]
• 65. 16S rRNA metabarcoding for the identification of tick-borne ... [https://www.nature.com/articles/s41598-024-70815-7]
• 66. Protist genomics: key to understanding eukaryotic evolution [https://www.sciencedirect.com/science/article/pii/S0168952525001118]
• 67. Bias in RNA-seq Library Preparation: Current Challenges ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC8079181/]
• 68. Tm and Optimizing | Thermo Fisher Scientific - US Annealing [https://www.thermofisher.com/us/en/home/brands/thermo-scientific/molecular-biology/molecular-biology-learning-center/molecular-biology-resource-library/spotlight-articles/optimizing-tm-and-annealing.html]
• 69. Microfluidic PCR and network analysis reveals complex tick ... [https://parasitesandvectors.biomedcentral.com/articles/10.1186/s13071-023-06098-0]
• 70. Development of a Real-Time PCR Method for the Detection ... [https://www.mdpi.com/2036-7481/14/4/106]
• 71. of Optimization metabarcoding for the simultaneous... 18 S rRNA [https://www.nature.com/articles/s41598-024-76304-1?error=cookies_not_supported&code=89dd1f04-a669-4fb8-bd4a-b9ef8bee8421]
• 72. Effect of PCR extension temperature on high-throughput ... [https://www.sciencedirect.com/science/article/abs/pii/S0166685110002902]
• 73. 18S rDNA next-generation sequencing uncovers the ... [https://bmcvetres.biomedcentral.com/articles/10.1186/s12917-025-04887-6]
• 74. 18S rRNA gene metabarcoding for investigation of ... [https://www.nature.com/articles/s41598-025-01774-w]
• 75. Enhanced blood parasite species identification using V4–V9 ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12639079/]
• 76. Papers citing PR2 [https://pr2-database.org/papers/papers-citing-pr2/]
• 77. The devil is in the details: Problems in DNA barcoding ... [https://www.frontiersin.org/journals/ecology-and-evolution/articles/10.3389/fevo.2023.1149839/full]
• 78. A DNA barcode for Piroplasmea [https://www.sciencedirect.com/science/article/abs/pii/S0001706X12002513]
• 79. Improved 18S and 28S rDNA primer sets for NGS-based ... [https://www.semanticscholar.org/paper/Improved-18S-and-28S-rDNA-primer-sets-for-NGS-based-Kounosu-Murase/dec1242912941b5029f34b787e28c48be34a7bc2]
• 80. a proof-of-concept study using canines from the tropics [https://www.nature.com/articles/s41598-019-49118-9]
• 81. Comparison of Next-Generation Sequencing, Real-Time ... [https://www.mdpi.com/2076-2607/13/10/2344]
• 82. Comparison and evaluation of metagenomic next- ... [https://www.frontiersin.org/journals/cellular-and-infection-microbiology/articles/10.3389/fcimb.2025.1694179/full]
• 83. 18S rDNA next-generation sequencing uncovers the ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12225251/]
• 84. Comparison of species-specific qPCR and metabarcoding ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC9451083/]
• 85. A full‐length 18S ribosomal DNA metabarcoding approach ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC11007259/]
• 86. 16S rRNA metabarcoding for the identification of tick-borne ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC11344767/]
• 87. Detection of Toxoplasma gondii DNA by PCR in blood ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC6080984/]
• 88. Performance of three molecular methods for detection ... [https://www.sciencedirect.com/science/article/pii/S2405676618300416]
• 89. Comparison of real-time PCR and nested PCR for ... [https://bmcinfectdis.biomedcentral.com/articles/10.1186/s12879-021-06873-3]
• 90. Evaluation of a Real-time PCR-based assay using the ... [https://www.sciencedirect.com/science/article/abs/pii/S0020751904002942]
• 91. Application of next generation sequencing for detection ... [https://www.sciencedirect.com/science/article/pii/S2405676620300251]
• 92. Meat-Borne-Parasite: A Nanopore-Based Meta-Barcoding ... [https://www.mdpi.com/1467-3045/46/5/3810]
• 93. Molecular and Phylogenetic revealed new genotypes of... analysis [https://parasitesandvectors.biomedcentral.com/articles/10.1186/s13071-015-1075-z]
• 94. A reference guide for tree analysis and visualization - PMC [https://pmc.ncbi.nlm.nih.gov/articles/PMC2844399/]
• 95. ColorTree: a batch customization tool for phylogenic trees [https://bmcresnotes.biomedcentral.com/articles/10.1186/1756-0500-2-155]
• 96. Gold Standard Test - Sensitivity and specificity [https://influentialpoints.com/Training/gold_standard_test.htm]
• 97. Update on North American tick-borne diseases and how to ... [https://pubmed.ncbi.nlm.nih.gov/40643260/]
• 98. Diagnostic Testing Accuracy: Sensitivity, Specificity, Predictive Values and Likelihood Ratios - StatPearls - NCBI Bookshelf [https://www.ncbi.nlm.nih.gov/books/NBK557491/]
• 99. A comprehensive fungi-specific 18S rRNA gene sequence ... [https://bmcmicrobiol.biomedcentral.com/articles/10.1186/s12866-018-1331-4]
• 100. A tool kit for quantifying eukaryotic rRNA gene sequences ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC4053730/]
• 101. On determining the sensitivity and specificity of a new diagnostic test through comparing its results against a non-gold-standard test - PMC [https://pmc.ncbi.nlm.nih.gov/articles/PMC9807233/]