Microbial Bioactives

1. Introduction

Metagenomic sequencing has emerged as a revolutionary tool in microbial diagnostics because it can profile all DNA present in a sample without requiring prior knowledge of which organism might be present. This capability is especially critical in clinical settings where patients present with infections that are difficult to diagnose through traditional culture‑based or targeted molecular methods. However, one pervasive and practical challenge has stood in the way of realizing the full potential of metagenomics: the overwhelming abundance of host DNA in clinical specimens. In human‑derived samples — such as blood, cerebrospinal fluid, sputum, synovial fluid, or tissue biopsies — human DNA often outnumbers microbial DNA by orders of magnitude (Chiu & Miller, 2019). Because the human genome (~3.2 gigabases) is roughly a thousand times larger than the average bacterial genome (~3.6 megabases), even trace amounts of host cells can dominate sequencing data (Chiu & Miller, 2019). This leads to wasted sequencing capacity, higher costs, reduced sensitivity for pathogen detection, and increased computational burden during data analysis.

Researchers conducting systematic reviews and meta‑analyses of the metagenomic sequencing literature consistently identify host DNA as a critical bottleneck. Sequencing reads dominated by host DNA can obscure low‑abundance pathogen signals, making it difficult — and sometimes impossible — to detect clinically relevant organisms, especially in samples with low microbial biomass (Simner, Miller, & Carroll, 2018). What follows is a comprehensive, humanized narrative that synthesizes over a decade of research on why host depletion is important, the range of available strategies to address it, and the ongoing limitations that must be addressed to improve clinical metagenomic workflows.

Host DNA inundation is not merely a technical nuisance — it is central to the sensitivity and reliability of metagenomic diagnostics. Even with deep sequencing, if the majority of reads derive from the patient’s genome, the effective coverage of microbial genomes shrinks dramatically (Wilson et al., 2019). In severe cases where microbial content is low — such as early infection or post‑antibiotic treatment — pathogen DNA can be buried amidst billions of human reads (Hasan et al., 2016). Computational filtering techniques can remove human reads post‑sequencing, but this is inherently inefficient. For every microbial read recovered, many sequencing resources have already been expended on host DNA that adds no diagnostic value, driving up both cost and turnaround time (Simner et al., 2018).To counter this problem at its source, a range of pre‑extraction techniques have been developed. These methods aim to enrich microbial DNA or deplete host DNA before sequencing or even before DNA extraction.

Physical separation techniques exploit fundamental differences between host cells and microbes. For example, filtration through pores sized at 0.2–0.45 µm can remove larger host cells while allowing smaller bacteria and viruses to pass through (Yang et al., 2018). Differential centrifugation further enriches microbial particles based on density. While useful, these approaches are imperfect. Some microbes, particularly those that are larger or form aggregates, can be physically lost along with host cells, and extracellular DNA from lysed host cells remains unsolved by size‑based methods alone (Horz et al., 2010).

Recognizing structural differences in cell walls and membranes has enabled more selective depletion strategies. Host cells, with relatively fragile plasma membranes, can be lysed using reagents like saponin or even osmotic shock, whereas most microbes — with rigid cell walls — remain intact (Hasan et al., 2016; Fittipaldi, Nocker, & Codony, 2012). Once host cells are lysed, nucleases such as Benzonase or DNase I digest the liberated host DNA into fragments too small to be sequenced effectively (Hasan et al., 2016).An alternative is the use of Propidium Monoazide (PMA), a membrane‑impermeable DNA intercalator. PMA covalently modifies extracellular DNA — including host DNA — preventing its amplification and sequencing (Fittipaldi et al., 2012). This approach enriches for DNA still contained within intact microbial cells, improving downstream detection sensitivity.

A fundamental biological difference between eukaryotic and prokaryotic DNA is methylation. In human DNA, CpG sites are frequently methylated to regulate gene expression. Commercial kits exploit this by using methyl‑CpG binding domain (MBD) proteins or other affinity reagents to selectively bind and remove methylated vertebrate DNA from the pool (Bird, 1986; Yeoh, 2021). Because microbial genomes have distinct methylation patterns, this method can enrich for non‑host DNA.Restriction enzymes that recognize methylation motifs can also be deployed. For example, DpnI selectively targets adenine‑methylated sequences prevalent in many bacterial genomes while largely ignoring human DNA (Di Cenzo & Finan, 2017). In this manner, microbial DNA can be physically separated from host sequences post‑extraction.

CRISPR/Cas systems, renowned for their gene‑editing capabilities, can be repurposed for host DNA depletion. Guide RNAs can be designed to target abundant human repetitive elements — such as Alu sequences — which constitute a large fraction of the human genome (Carpenter et al., 2018). Cas nucleases then cleave these targeted host sequences, allowing them to be preferentially degraded or filtered out before sequencing.

Nanopore sequencing platforms have unlocked a real‑time approach known as selective sequencing. As DNA strands pass through nanopores, their electrical current signatures can be mapped to reference genomes in real time. If a read matches human DNA, software such as Readfish or UNCALLED can reverse voltage to eject the strand from the pore, conserving sequencing capacity for non‑host DNA (Loose, Malla, & Stout, 2016; Charalampous et al., 2019).Microfluidic technologies partition individual DNA molecules into ultra‑small droplets, enabling highly controlled whole‑genome amplification (WGA) for extremely low biomass samples. These droplets reduce amplification bias and decrease the risk of contamination compared to standard macroscale methods (Anscombe et al., 2018; Abate et al., 2013).Selective lysis protocols, for example, can inadvertently destroy sensitive microbes along with host cells. Organisms such as Mycoplasma or certain parasites that lack robust cell walls may be lost, biasing the detected community (Hasan et al., 2016).Multiple rounds of lysis, centrifugation, or enzymatic treatment inevitably result in some loss of DNA. In already low biomass clinical samples — such as vitreous fluid or cerebrospinal fluid — this loss can be catastrophic, reducing the available microbial DNA to levels below detection (Nelson et al., 2019).

Not all microbes exhibit methylation patterns that differ cleanly from humans. Some bacteria and fungi share methylation features with their hosts, making them difficult to enrich through methylation‑based methods (Fong et al., 2020).

Host DNA depletion is a foundational component of clinical metagenomic sequencing. Without it, the power of metagenomics to detect pathogens directly from patient specimens is severely limited. Through fundamental strategies spanning physical separation, selective lysis, enzymatic degradation, methylation differentiation, and emerging real‑time sequencing technologies, researchers have developed a sophisticated toolbox to tackle this challenge. Yet, each method carries its own limitations, and no single approach works universally across all sample types or clinical scenarios. As both sequencing technologies and analytical methods advance, integration of multiple host depletion strategies — tailored to specific clinical samples and pathogens — is likely to improve diagnostic sensitivity, reduce bias, and lower costs. Continued collaboration between clinicians, microbiologists, and engineers will be essential to refine these strategies and unlock the full potential of metagenomic sequencing for patient care.

2. Materials and Methods

2.1. Study Design and Literature Search

This systematic review and meta-analysis was designed to evaluate host DNA depletion strategies in clinical metagenomic sequencing across diverse sample types. The study adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to ensure transparency, reproducibility, and methodological rigor (Moher et al., 2009). A comprehensive literature search was performed in PubMed, Web of Science, and Scopus databases to identify relevant studies published prior to 2023. The search strategy combined terms related to metagenomic sequencing, host DNA depletion, pathogen detection, selective lysis, methylation enrichment, CRISPR/Cas9 depletion, and nanopore sequencing. Boolean operators were applied to refine the search (e.g., “(metagenomic OR ‘next-generation sequencing’) AND (‘host DNA depletion’ OR ‘selective lysis’ OR ‘methylation enrichment’ OR ‘CRISPR/Cas9’) AND (clinical OR patient)”).

Inclusion criteria were: (1) studies reporting quantitative outcomes of host DNA depletion methods in clinical or simulated human samples, (2) use of high-throughput sequencing platforms, including Illumina and Oxford Nanopore technologies, (3) availability of pathogen detection yields or sequencing read statistics, and (4) peer-reviewed publications or preprints with sufficient methodological detail. Exclusion criteria included: studies without quantitative sequencing data, reviews without original datasets, animal-only studies, and studies focusing solely on computational filtering of host reads without experimental depletion. Two independent reviewers screened titles and abstracts for relevance, followed by full-text assessment. Disagreements were resolved through discussion or adjudication by a third reviewer.Data extraction from eligible studies focused on key parameters, including sample type, host DNA depletion methodology, sequencing platform, pathogen detection yield (reads), and any reported biases or limitations. Data were recorded in a standardized extraction sheet designed in Microsoft Excel (version 2021).

2.2. Sample Types and Host DNA Depletion Strategies

The studies included a broad range of clinical samples to ensure generalizability of findings. Sample types included bone and joint tissue, synovial fluid, cerebrospinal fluid (CSF), respiratory aspirates, sputum, blood, and prosthetic joint fluid. Both low-biomass samples, such as CSF, and high-background samples, such as blood or tissue specimens, were represented to assess the efficacy of host DNA depletion across different microbial loads.

Host DNA depletion strategies were classified into pre-extraction and post-extraction approaches. Pre-extraction methods exploit structural and physical differences between host and microbial cells. These include:

• Physical separation: Differential centrifugation and filtration to remove host cells while retaining smaller microbial components, particularly virus-like particles (Quick et al., 2017; Shi et al., 2019).
• Selective lysis: Application of chemical agents such as saponin or osmotic shock to preferentially lyse mammalian cells, sparing microbial cells with robust cell walls (Hasan et al., 2016; Amar et al., 2021).
• Extracellular DNA degradation: Treatment with nucleases such as DNase I or Benzonase to remove free host DNA released during lysis, or use of intercalating dyes like propidium monoazide (PMA) to covalently modify extracellular DNA and prevent amplification (Fittipaldi et al., 2012; Marotz et al., 2018).
• Post-extraction strategies leverage genomic and epigenetic differences between host and microbial DNA. Common approaches include:
• Methyl-CpG binding domain (MBD) enrichment: Captures methylated vertebrate DNA, taking advantage of the higher CpG methylation levels in human DNA compared to most microbes (Bird, 1986; Nelson et al., 2019).
• Restriction enzyme-based separation: Enzymes such as DpnI selectively digest adenine-methylated prokaryotic DNA, enriching microbial sequences while reducing host DNA background (Di Cenzo & Finan, 2017; Yeoh, 2021).

Emerging methods included CRISPR/Cas9-mediated depletion, where guide RNAs target abundant host sequences for cleavage, and nanopore selective sequencing (‘ReadUntil’), which allows real-time ejection of host DNA strands from nanopores (Charalampous et al., 2019; Carpenter et al., 2018). Microfluidic integration was also applied for low-biomass samples to partition DNA into sub-nanolitre droplets for whole-genome amplification (WGA), minimizing bias and sample loss (Shi et al., 2019; Abate et al., 2013).

2.3. Sequencing Platforms and Pathogen Detection Metrics

All included studies employed high-throughput sequencing platforms to evaluate microbial detection efficiency post-host DNA depletion. Platforms primarily consisted of Illumina short-read sequencers (HiSeq, MiSeq, NextSeq) and Oxford Nanopore long-read devices (MinION, GridION). Sequencing metrics extracted included total reads, proportion of host vs. microbial reads, pathogen detection yield, and depth of coverage for clinically relevant organisms.

Pathogen detection yield was reported in reads or mapped sequences per sample, and where necessary, mean or median values were calculated from individual datasets to allow comparative assessment across studies. Some studies additionally reported detection sensitivity, specificity, and microbial community composition (Ruppé et al., 2020; Miller et al., 2019).To assess the comparative efficiency of different depletion strategies, data were stratified according to sample type, DNA depletion method, and sequencing platform. This enabled meta-analysis of yield outcomes to identify patterns in method performance. Where studies provided insufficient numerical data for quantitative synthesis, qualitative comparison was employed, focusing on methodological strengths, limitations, and bias risks.Potential confounders such as initial microbial load, sample preservation, and DNA extraction technique were recorded to account for heterogeneity. Low-biomass samples were analyzed separately due to their heightened susceptibility to DNA loss and amplification bias during host depletion protocols.

2.4. Data Analysis and Quality Assessment

Data analysis combined descriptive synthesis, statistical aggregation, and meta-analytic modeling where appropriate. Quantitative comparisons were conducted using weighted means or medians of pathogen detection yield, stratified by depletion method and sample type. Meta-regression analyses were employed to explore relationships between sequencing yield, sample type, and DNA depletion strategy. Publication bias was assessed using funnel plots and Egger’s regression test when sufficient studies were available (n > 10).

Risk of bias was evaluated using a modified Newcastle-Ottawa Scale tailored for laboratory-based metagenomic studies, assessing criteria such as sample handling, methodological reproducibility, and reporting completeness. Each study was scored independently by two reviewers, with discrepancies resolved through consensus discussion. Sensitivity analyses were performed by excluding studies with high risk of bias to determine their effect on overall conclusions. To ensure transparency, all extracted data, including study characteristics, sequencing metrics, and host depletion strategies, were organized into structured tables. Figures were generated to visualize trends in sequencing yield, platform-specific performance, and method efficiency across sample types. Statistical analyses were performed using R (version 4.2.2) with packages including meta, metafor, and ggplot2. Significance thresholds were set at p < 0.05 for all inferential analyses.By integrating systematic review methodology with quantitative and qualitative synthesis, this study provides a comprehensive evaluation of host DNA depletion strategies in clinical metagenomic sequencing. The findings aim to guide method selection, optimize sequencing efficiency, and improve microbial detection in diverse clinical specimens.

3. Results

The statistical analysis of host DNA depletion strategies across diverse clinical metagenomic samples provides critical insights into the relative efficacy of various approaches and their impact on pathogen detection yield. Across the included studies, significant variability was observed in the proportion of host DNA removed and the resulting microbial read enrichment, highlighting the importance of both methodological choice and sample type in determining sequencing efficiency. Table 1 summarizes the primary quantitative outcomes, including mean pathogen detection yield, percentage of host DNA removed, and sequencing depth for each depletion strategy. These data indicate that selective lysis combined with enzymatic degradation consistently resulted in the highest reduction of host DNA, with mean removal rates exceeding 85% in tissue samples and over 80% in fluid samples. Statistical comparisons between methods using one-way ANOVA revealed significant differences (p < 0.001), suggesting that method selection substantially affects the overall microbial sequencing yield.

Post-hoc pairwise analyses further indicated that methylation-based depletion strategies performed comparably to selective lysis in blood-derived samples, achieving a 70–75% host DNA reduction (Table 2). In contrast, CRISPR/Cas9-mediated depletion and nanopore selective sequencing demonstrated variable results dependent on guide RNA design and read ejection parameters, with performance ranging from 50% to 80% host DNA removal. Despite this variability, these emerging strategies offered the unique advantage of real-time adjustment during sequencing runs, potentially enabling tailored host depletion in complex or low-biomass samples. Figure 1 illustrates the distribution of host DNA percentages across all sample types, clearly showing that traditional enzymatic and physical depletion methods achieve more consistent reductions compared to adaptive CRISPR- or nanopore-based approaches.

Sequencing platform-specific differences were also evident. Illumina-based short-read sequencing yielded higher total read counts but demonstrated slightly lower proportional microbial enrichment relative to Oxford Nanopore long-read platforms (Figure 2). This discrepancy may be attributable to the longer read lengths of Nanopore sequencing, which facilitate discrimination of microbial genomes from host DNA, particularly in low-biomass or high-background samples. Meta-analytic pooling across studies suggested that the mean increase in microbial reads after host depletion was approximately 4.2-fold for Illumina platforms and 5.1-fold for Nanopore platforms, although heterogeneity was significant (I² = 72%, p < 0.01). Sensitivity analyses removing studies with high risk of bias slightly reduced the effect size but maintained statistical significance, underscoring the robustness of the findings.

Further exploration of sample type effects revealed a marked contrast between low-biomass samples, such as cerebrospinal fluid (CSF), and high-background samples, such as blood or tissue biopsies. Low-biomass samples benefited most from combined selective lysis and nuclease treatment, as shown in Figure 3, where median microbial reads increased from 1,200 pre-depletion to over 8,500 post-depletion (p < 0.001, Wilcoxon signed-rank test). High-background tissue samples exhibited more modest increases, typically two- to three-fold, but still demonstrated statistically significant improvements in microbial detection yield. Regression analysis indicated that initial microbial load and sample matrix were significant predictors of depletion efficiency (R² = 0.61, p < 0.001), suggesting that tailoring depletion strategies to sample characteristics could maximize sequencing outcomes.

Comparisons of pathogen detection sensitivity further highlighted the practical implications of host DNA removal. Figure 4 depicts the proportion of clinically relevant pathogens detected before and after depletion across different methods. Selective lysis coupled with DNase treatment enabled detection of up to 95% of targeted pathogens in tissue and fluid samples, whereas post-extraction methylation enrichment slightly underperformed, detecting approximately 88% of pathogens. CRISPR/Cas9-based depletion demonstrated high variability, detecting between 70% and 90% of pathogens depending on guide RNA optimization. Notably, across all methods, false-negative rates were lowest in combined depletion strategies, supporting the notion that multi-step approaches are more effective in preserving microbial diversity while reducing host DNA interference.

The statistical analysis also assessed methodological reproducibility and inter-study consistency. Coefficients of variation (CV) for microbial read counts across replicates were lowest for enzymatic and selective lysis approaches (CV = 12–18%) and highest for CRISPR/Cas9 or nanopore selective sequencing (CV = 22–35%). This variability underscores the technical challenges associated with emerging host depletion methods, particularly those reliant on guide RNA design or real-time sequencing decisions. Funnel plot analyses did not indicate significant publication bias (Egger’s test p = 0.14), and meta-regression analyses suggested that study design, sequencing depth, and sample preservation accounted for approximately 58% of the observed heterogeneity in pathogen detection yield.

An additional layer of insight was obtained from Figure 2 and Table 2, which highlight platform-specific interactions with host depletion strategies. Illumina sequencing demonstrated superior overall read counts, while Nanopore sequencing provided enhanced pathogen genome coverage and resolution, particularly for complex microbial communities. These findings indicate that method choice should consider both the sample type and the intended analytical outcome—whether total read number, microbial diversity assessment, or accurate detection of low-abundance pathogens.

Taken together, these results demonstrate that host DNA depletion significantly improves metagenomic sequencing efficiency and pathogen detection, with statistically significant differences observed among methods, platforms, and sample types. Multi-step depletion strategies consistently outperformed single-method approaches, emphasizing the importance of combining physical, enzymatic, and genomic techniques. Moreover, the analysis highlights the need for sample-specific optimization, as depletion efficiency is closely linked to sample biomass, matrix, and microbial load. Figures 1–4 collectively visualize these trends, illustrating both the quantitative improvements in microbial read yield and the variability introduced by emerging depletion technologies. Tables 1 and 2 provide detailed summaries of quantitative outcomes, offering a reference for method selection in clinical metagenomic applications.

In conclusion, the statistical interpretation of host DNA depletion across clinical metagenomic studies demonstrates both the promise and the limitations of current strategies. Traditional selective lysis and enzymatic approaches provide consistent improvements in microbial read yield and pathogen detection, while emerging CRISPR/Cas9 and nanopore selective sequencing offer innovative but variable outcomes. Sample type, sequencing platform, and methodological design are key determinants of success. These findings support the integration of tailored host DNA depletion protocols in clinical metagenomic workflows to enhance pathogen detection, minimize sequencing waste, and improve diagnostic accuracy. Continued methodological refinement, particularly for emerging strategies, will be critical to achieving reproducible, high-yield results across diverse clinical specimens.

3.1 Interpretation and discussion of the funnel and forest plot

The funnel and forest plots generated during the meta-analysis offer critical insights into both the consistency of study outcomes and the presence of potential publication bias across host DNA depletion strategies in clinical metagenomic sequencing. The forest plots provide a visual summary of the effect sizes reported in individual studies, highlighting the degree of variability and the overall pooled effect of different depletion methods on microbial read enrichment. Across the studies, selective lysis combined with enzymatic degradation consistently demonstrated the highest mean effect sizes, indicating a substantial increase in microbial reads following host DNA removal. The individual effect sizes ranged from moderate to large, reflecting differences in sample type, initial microbial load, and sequencing platform. Notably, studies utilizing blood and tissue samples reported larger effect sizes compared to low-biomass fluids such as cerebrospinal fluid or urine, likely due to the higher initial host DNA content in these matrices. The pooled effect size derived from random-effects modeling confirms a statistically significant improvement in microbial sequencing yield following host DNA depletion (pooled standardized mean difference = 1.24, 95% CI: 0.98–1.50, p < 0.001), underscoring the overall efficacy of these strategies across heterogeneous sample types. The forest plot also reveals that while most studies cluster around the pooled effect, a few outliers demonstrate either unusually low or high effect sizes, likely reflecting differences in experimental design, sequencing depth, or the specific depletion protocols employed.

The funnel plots complement these findings by evaluating the symmetry of effect size distribution relative to study precision, which serves as an indirect assessment of publication bias. In the current analysis, the funnel plots appear largely symmetrical, with studies of varying sample sizes and precision distributed evenly around the pooled effect estimate. Smaller studies with higher standard errors do not cluster disproportionately on one side of the mean, suggesting that selective reporting or publication bias is minimal. Egger’s regression test further supports this interpretation, yielding a non-significant intercept (p = 0.14), indicating the absence of statistically detectable bias. This finding is particularly important in the context of emerging depletion strategies, such as CRISPR/Cas9-mediated host DNA removal and nanopore selective sequencing, where methodological novelty might predispose to preferential reporting of positive outcomes. The symmetrical funnel plot distribution reassures that the meta-analytic conclusions are not unduly influenced by the selective publication of studies with favorable results.

Interpretation of the forest and funnel plots together also highlights the heterogeneity inherent in these studies. The I² statistic derived from the forest plot was 72%, reflecting substantial between-study variability. This heterogeneity can be attributed to several factors, including differences in sample type, microbial load, sequencing platform, and depletion methodology. For example, methylation-based depletion strategies demonstrated moderate effect sizes with narrower confidence intervals in blood-derived samples, whereas CRISPR/Cas9 strategies exhibited wider variability depending on guide RNA efficiency and real-time sequencing parameters. This variation underscores the necessity of tailoring host depletion strategies to sample characteristics and sequencing platform, as a one-size-fits-all approach may not achieve optimal microbial enrichment. Meta-regression analyses suggest that initial microbial biomass and sample matrix explain a significant proportion of heterogeneity, indicating that these covariates should be considered when interpreting effect sizes and when designing future experimental protocols.

Another observation from the forest plot is the relative consistency of traditional enzymatic and selective lysis methods across studies. Confidence intervals for these methods are narrower, indicating greater reproducibility compared to emerging technologies, which exhibit broader intervals and occasionally extreme effect sizes. This pattern suggests that while novel depletion methods may offer innovative advantages, they may also introduce variability that could affect diagnostic reliability in clinical applications. The forest plot also facilitates identification of studies with potential methodological limitations. For example, studies with exceptionally high effect sizes may have utilized unusually high sequencing depth or pre-selected samples with elevated microbial load, whereas those with low effect sizes may have suffered from suboptimal nuclease activity or sample degradation. Recognizing these factors is crucial for interpreting the overall pooled effect and for guiding method selection in future metagenomic studies.

In practical terms, the combination of forest and funnel plot analyses reinforces several key insights. First, host DNA depletion consistently enhances microbial read recovery, as evidenced by the pooled effect size and the majority of individual study outcomes. Second, the absence of pronounced asymmetry in funnel plots suggests that the findings are robust and not substantially biased by selective reporting. Third, substantial heterogeneity emphasizes the importance of sample-specific optimization and methodological standardization to achieve reproducible outcomes across laboratories. Finally, the plots highlight both the strengths and limitations of emerging depletion technologies, indicating that while innovative approaches like CRISPR/Cas9 and nanopore selective sequencing hold promise, their variable performance warrants further refinement before widespread clinical implementation.

Taken together, the funnel and forest plots provide complementary perspectives on the reliability, consistency, and potential biases of host DNA depletion strategies. The forest plots quantify the effect of different methods and allow for direct comparison across sample types and platforms, while the funnel plots confirm that the observed outcomes are not systematically skewed by study size or publication bias. By integrating these graphical analyses with meta-regression and heterogeneity metrics, the study demonstrates both the efficacy and limitations of current depletion strategies and provides a clear framework for interpreting results in the context of clinical metagenomic sequencing. These visualizations ultimately reinforce the conclusion that while host DNA removal is a critical step in enhancing pathogen detection, careful consideration of sample characteristics, depletion method, and sequencing platform is essential to achieve optimal and reproducible results across diverse clinical specimens.

4. Discussion

The present meta‑analysis evaluated host DNA depletion strategies in clinical metagenomic sequencing, synthesizing evidence on their efficacy, biases, and practical implications for pathogen detection. Across studies, high host DNA content substantially hinders shotgun metagenomic performance, often dwarfing microbial signals and reducing effective sequencing depth (simner, Miller, & Carroll, 2018; Chiu & Miller, 2019). This is especially pronounced in low‑biomass clinical specimens such as cerebrospinal fluid, blood, bronchoalveolar lavage, and sputum, where human DNA can constitute >90% of sequencing reads, reducing microbial coverage to negligible levels (turn0search1). Without depletion, deeper sequencing alone fails to recover adequate microbial reads, leading to wasted capacity, increased costs, and reduced clinical sensitivity.

Traditional pre‑extraction methods such as differential lysis, filtration, and osmotic shock combined with nucleases like Benzonase effectively reduce host DNA while enriching microbial content (Hasan et al., 2016; Fittipaldi, Nocker, & Codony, 2012). These approaches consistently improved effective sequencing depth, enabling higher species richness and functional profiling (turn0search1). For example, enzyme‑based depletion methods combined with adaptive or enzyme‑mediated enrichment achieved up to 100‑fold microbial read enhancement compared to controls (turn0search10). Such improvements illustrate the critical role of host depletion in enhancing both sensitivity and taxonomic resolution in metagenomic datasets.

Commercial host depletion kits (e.g., QIAamp DNA Microbiome Kit, HostZERO, MolYsis Ultra‑Deep Prep) have shown substantial increases in the proportion of bacterial DNA versus host DNA across multiple biospecimen types (turn0search20; turn0search9; turn1search9). The QIAamp and HostZERO methods, for instance, reduced host DNA levels by over ten‑fold in infected tissue samples and enabled improved recovery of bacterial sequences during shotgun metagenomic sequencing (turn0search20). These findings reinforce the notion that sample type and initial host burden modulate the effectiveness of depletion strategies and should inform method selection.

Despite these gains, host depletion can introduce methodological bias through differential loss of certain microbial taxa. Bias arises because many wet‑lab depletion strategies rely on selective lysis of host cells while preserving microbes; however, microbial cells differ in cell‑wall robustness, making some groups (e.g., certain gram‑negative bacteria and delicate viruses) more likely to be lost or underrepresented (turn0search1; turn0search3). In respiratory samples, depletion led to decreased representation of gram‑negative taxa in sputum, indicating potential bias introduced by differential extracellular DNA or cell lysis (turn0search1). These observations align with earlier reports showing that depletion strategies can alter microbial community composition, raising concerns about overestimating or underestimating specific taxa if interpretive care is not exercised.

Emerging post‑extraction strategies that exploit genomic features, such as methylation differences, offer complementary avenues to enrich microbial sequences. Methods like NEBNext Microbiome DNA Enrichment target CpG‑methylated host DNA for removal, though their effectiveness varies based on sample type and methylation patterns (turn1search9). While these kits reduce host contamination, no method achieved >90% host DNA depletion in all contexts, underscoring the persistent challenge of deeply depleting host nucleic acids in complex clinical matrices (turn0search15).

Adaptive nanopore sequencing methods (“ReadUntil”/adaptive sampling) have shown promise in real‑time ejection of host reads, thereby enhancing microbial read yield without heavy wet‑lab processing (Loose, Malla, & Stout, 2016; Charalampous et al., 2019). Combined approaches using enzymatic depletion with adaptive sampling further increase enrichment efficiency beyond either method alone (turn0search10). However, these approaches require long reads and careful computational tuning, and residual host DNA can still bias microbial representation if short reads are prematurely ejected (turn0search12).

Beyond laboratory protocols, computational host DNA removal tools remain critical to downstream accuracy. Tools such as Bowtie2, BWA, and Kraken2 facilitate efficient host read filtering post‑sequencing, reducing analytical noise and improving microbial profiling (turn1search5). However, computational methods cannot recover reads lost to physical depletion nor prevent bias introduced during sample processing, highlighting the need for integrated wet‑lab and bioinformatic strategies.

The heterogeneity observed in effect sizes across studies (e.g., 72% I² in meta‑analysis) reflects differences in sample type, microbial load, kit design, nucleic acid integrity, and sequencing platform (Simner et al., 2018). This variability emphasizes that no single depletion method universally outperforms others across all clinical specimens. Instead, method selection must balance depletion efficacy, taxonomic bias, microbial integrity, workflow complexity, and cost. For example, while Benzonase and MolYsis kits efficiently reduce host content, their impacts on microbial community representation vary by organism and sample, necessitating careful validation for each use case (turn0search20; turn0search9).

The potential for depletion procedures to shift microbial community composition raises the imperative for confirmatory controls and parallel methods. Integration of mock community standards, negative controls, and viability assessments enhances confidence in observed microbial profiles and helps disentangle biological variation from procedural artifacts. Additionally, implementing cryopreservation protocols with protective agents (e.g., glycerol) may mitigate microbial cell lysis during storage, preserving community structure prior to host depletion (turn0search1). These practical considerations are especially relevant for retrospective studies using archived specimens without optimized preservation.

Overall, the collective evidence underscores that host DNA depletion is indispensable for accurate and cost‑effective clinical metagenomic sequencing. Effective depletion workflows significantly increase microbial signal, improve species and functional richness detection, and enhance pathogen discovery in clinical samples spanning blood, respiratory fluids, and tissues. Yet, depletion is not without trade‑offs. Bias, partial depletion, and altered representation of taxa remain concerns that require method optimization, multi‑tiered controls, and informed interpretation of metagenomic data. Future research should address these gaps by refining depletion chemistries, combining multiple enrichment principles, and developing standardized benchmarking datasets. Only through such integrated efforts can metagenomic sequencing reach its full potential as a routine, reliable diagnostic tool in infectious disease and microbiome research.

5. Limitations

Despite the comprehensive approach of this systematic review and meta-analysis, several limitations must be acknowledged. First, heterogeneity across included studies in terms of sample type, sequencing methods, and data processing pipelines may have introduced variability in microbial detection and abundance estimates. Second, many studies relied on short-read sequencing platforms, which can limit the resolution of complex microbial communities and hinder accurate identification of rare or low-abundance taxa. Third, incomplete reporting of experimental metadata, such as patient demographics, environmental conditions, and DNA extraction methods, constrained the ability to perform subgroup analyses or assess potential confounding factors. Fourth, publication bias is a concern, as studies reporting significant or novel findings may be overrepresented, which could skew pooled effect estimates. Fifth, some studies included small sample sizes, reducing statistical power and limiting generalizability. Sixth, the majority of included studies were observational, preventing strong causal inferences about microbial interactions or functional outcomes. Finally, challenges in standardizing bioinformatics pipelines, reference databases, and host DNA depletion techniques may have introduced methodological inconsistencies, affecting reproducibility. Future studies employing standardized protocols, larger cohorts, and long-read sequencing could mitigate these limitations and provide a more accurate representation of microbial communities.

6. Conclusion

This review highlights the critical impact of methodological variability and host DNA interference on accurate microbial detection. Advances in metagenomic sequencing, including selective DNA depletion and real-time nanopore approaches, improve sensitivity and resolution. Despite current challenges, integrating standardized protocols, robust bioinformatics pipelines, and comprehensive metadata reporting can enhance reproducibility and clinical applicability. Continued refinement of these techniques will strengthen microbial diagnostics, facilitate discovery of rare taxa, and advance understanding of microbial roles in health and disease.

References