S2903 Detection of Human Genomic DNA in Stool Samples From Seven Patients
Bibliographic record
Abstract
Introduction: Human stool samples are mainly composed of bacteria, virus, and fungi. With advances in therapeutics of microbes, one must also understand the complete composition of the microbiome. However, the gut microbiome only consists of 0.01% human genomic DNA making it extremely difficult to isolate and characterise. After numerous failed attempts to extract human genomic DNA from human fecal samples we tested 2 different methods of the OMGA Bio-tek EZNA Stool DNA Kit. Case Description/Methods: Human fecal samples from 7 individuals were collected in the Zymo DNA/RNA Sheild Fecal Collection tubes and extracted with the OMGA Bio-tek EZNA Stool DNA Kit. Method 1 extracted 200 µL following the E.Z.N.A.® Stool DNA Kit Human DNA Detection Protocol exactly as outlined in the instruction manual. Method 2 extracted 600 µL following the E.Z.N.A.® Stool DNA Kit Human DNA Detection Protocol starting from the inhibitor removal step due to previous reports of sample collection and extraction kit incompatibility. The extracts were quantified via Qubit fluorometry and examined by polymerase chain reaction for both human RNase P and LINE1. Discussion: As measured by Qubit fluorometry both methods extracted DNA (Method 1: 3.61±0.98 ng/µL vs method 2: 5.86±2.60 ng/µL), but Method 2 produce the highest human DNA when measured by polymerase chain reaction. Method 1 yielded 6/7 positives for human RNase P (DCt 33.88±1.82; 9.84x10-8 ng/µL), but 2/7 positives for LINE1 (DCt 35.67±1.27). Method 2 yielded 7/7 positives for human RNase P (DCt 32.21±2.18) with loads almost 100X higher than Method 1 (1.33x10-7 ng/µL), but 4/7 positives for LINE1 (DCt 36.7±1.23). While both methods extracted human DNA from stools, Method 2 produced a much higher load of human genomic DNA. This study demonstrates that human DNA can be detected in stools, which can be used for downstream genomic testing (Table 1). Table 1. - Two methods to extract human DNA from stools of 7 individuals are presented. Sample 1.LH 2.JL 3.GD 4.BK 5.AM 6.SK 7.SH Extraction Method 200 µL Total DNA (ng/µL) 4.32 4.16 4.18 2.66 1.82 3.74 4.38 Human RNase P (Cq) 32.26 31.54 35.36 35.27 35.81 33.05 NEG Human RNase P (ng/µL) 6.75E-08 4.38E-07 7.19E-09 7.62E-09 5.21E-09 3.83E-08 NEG Human LINE1 (Cq) NEG 36.57 NEG NEG NEG NEG 34.77 Extraction Method 600 µL Total DNA (ng/µL) 5.26 5.34 5.20 4.16 4.00 5.44 11.60 Human RNase P (Cq) 30.12 30.06 34.07 31.59 36.12 31.68 31.82 Human RNase P (ng/µL) 4.17E-07 1.13E-07 2.03E-08 1.36E-07 4.24E-09 1.27E-07 1.15E-07 Human LINE1 (Cq) NEG 36.90 NEG NEG 37.53 35.20 38.02
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".