Optimization of Metagenomic Next-Generation Sequencing Workflow with a Novel Host Depletion Method for Enhanced Pathogen Detection
Bibliographic record
Abstract
INTRODUCTION: Sepsis is a critical condition requiring timely and accurate pathogen identification. Traditional blood cultures are slow and often yield low sensitivity. Metagenomic next-generation sequencing (mNGS) offers broad and rapid pathogen detection but is hindered by excessive human DNA background in blood samples. This study evaluated a novel Zwitterionic Interface Ultra-Self-assemble Coating (ZISC)-based filtration device designed to deplete host cells and enhance microbial DNA recovery for improved mNGS diagnostics. METHODS: We assessed the novel filter's performance in depleting white blood cells (WBCs) while preserving microbial integrity using spiked blood samples. Comparisons were made with other host depletion techniques, including differential lysis and CpG-methylated DNA removal. Analytical sensitivity was tested using spiked microbial communities at varying genome equivalents (GEs). Clinical validation involved eight blood culture-positive sepsis patient samples, processed with and without filtration, for both genomic DNA (gDNA) and cell-free DNA (cfDNA)-based mNGS. All libraries were sequenced on a NovaSeq600 with at least 10 million reads per sample. RESULTS: The novel filter achieved > 99% WBC removal across various blood volumes and allowed unimpeded passage of bacteria and viruses. Compared to other depletion methods, the novel filtration was more efficient, less labor-intensive, and preserved microbial reads. mNGS with filtered gDNA detected all expected pathogens in 100% (8/8) of clinical samples, with an average microbial read count of 9351 reads per million (RPM), over tenfold higher than unfiltered samples (925 RPM). In contrast, cfDNA-based mNGS showed inconsistent sensitivity and was not significantly enhanced by filtration (1251-1488 RPM). Finally, the novel filtration did not alter the microbial composition, making it suitable for accurate pathogen profiling. CONCLUSION: The workflow with the novel host depletion method significantly enhanced the analytical sensitivity of gDNA-based mNGS by reducing the host DNA background and enriching microbial content. This approach improved diagnostic yield in sepsis and may be a valuable tool for further clinical infectious disease diagnostics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".