Biology subjects

Commichaux, S.

Publications and source records attributed to Commichaux, S..

6 recordsLinked to original sources

bioRxiv2026

Detecting Random Mutations in 16S rRNA Sequences

Motivation: High-throughput sequencing technologies have driven rapid growth of biological sequence databases. Public repositories must therefore rely on automated computational heuristics to screen submitted sequences for errors and low quality. For example, SILVA SSU Ref, which exploits the conserved nature of 16S and 18S rRNA sequences, applies strict algorithmic quality controls yet still accepts sequences with up to 30% of their nucleotides deviating from any previously accepted sequence. This permissiveness creates opportunities for the admission of modified sequences, such as biologically plausible sequences generated by DNA foundation models. The vulnerability of public sequence databases to becoming polluted or poisoned with modified sequences necessitates the development of methods to detect such sequences. Results: We present the first investigation, to our knowledge, of the detectability of modified sequences. We consider simple computationally-modified 16S rRNA sequences that pass the quality control inclusion criteria of the SILVA database, which we generate via random substitutions. We present classifiers that can distinguish such modified sequences from natural 16S rRNA using conserved motifs. Our best classifier achieves over 90% sensitivity and specificity on our testing set when using a 5% artificial mutation rate. One feature used in our classifiers, gapped k-mers constructed from universally conserved nucleotides, was conserved across all three domains of life despite relying on exact matches to patterns found in E. coli, advancing our understanding of conserved grammatical structure in small subunit rRNA sequences. Availability and Implementation: Our source code is available at https://github.com/rainhaworth/16S-Mutation-Classifiers.

bioinformatics↗

bioRxiv2025

DNA data (genome skims and metabarcodes) paired with chemical data demonstrate utility for retrospective analysis of forage linked to fatal poisoning of cattle

Prepared and stored feeds, fodder, silage, and hay may be contaminated by toxic plants resulting in the loss of livestock. Several poisonous plants have played significant roles in livestock deaths from forage consumption in recent years in the Western United States including Salvia reflexa. Metagenomic data, genome skims and metabarcodes, have been used for identification and characterization of plants in complex matrices including diet composition of animals, mixed forages, and herbal products. Here, chemistry, genome skims, and metabarcoding were used to retrospectively describe the composition of contaminated alfalfa hay from a case of Salvia reflexa poisoning that killed 165 cattle. Genome skims and metabarcoding provided similar estimates of the relative abundance of the Salvia in the hay samples when compared to chemical methods. Additionally, genome skims and metabarcoding provided similar estimates of species composition in the contaminated hay and rumen contents of poisoned animals. The data demonstrate that genome skims and DNA metabarcoding may provide useful tools for plant poisoning investigations.

genomics↗

bioRxiv2024

Paired metagenomic and chemical evaluation of an aflatoxin contaminated dog kibble

Identification of chemical toxins from complex or highly processed foods can present needle in the haystack challenges for chemists. Metagenomic data can guide chemical toxicity evaluations with DNA-based description of the wholistic composition (bacterial, eukaryotic, protozoal, viral, and antimicrobial resistance) of any food suspected to harbor toxins, allergens, or pathogens. This approach can focus chemistry-based diagnostics, improve risk assessment, and address data gaps. There is increasing recognition that simultaneously co-occurring mycotoxins, either from single or multiple species, can impact dietary toxicity. Here we evaluate an aflatoxin contaminated kibble with known levels of specific mycotoxins and demonstrate that the abundance of DNA from putative aflatoxigenic Aspergillus spp. correlated with levels of aflatoxin quantified by Liquid Chromatography Mass Spectrometry (LCMS). Metagenomic data also identified an expansive range of co-occurring fungal taxa which may produce additional mycotoxins. Metagenomic data paired with chemical data provides a novel modality to address current data gaps pertaining to mycotoxin toxicity exposures, toxigenic fungal taxonomy, and mycotoxins of emerging concern.

microbiology↗

bioRxiv2023

Database size positively correlates with the loss of species-level taxonomic resolution for the 16S rRNA and other prokaryotic marker genes

For decades, the 16S rRNA gene has been used to taxonomically classify prokaryotic species and to taxonomically profile microbial communities. The 16S rRNA gene has been criticized for being too conserved to differentiate between distinct species. We argue that the inability to differentiate between species is not a unique feature of the 16S rRNA gene. Rather, we observe the gradual loss of species-level resolution for other marker genes as the number of gene sequences increases in reference databases. We demonstrate this effect through the analysis of three commonly used databases of nearly-universal prokaryotic marker genes: the SILVA 16S rRNA gene database, the Genome Taxonomy Database (GTDB), and a set of 40 taxonomically-informative single-copy genes. Our results reflect a more fundamental property of the taxonomies themselves and have broad implications for bioinformatic analyses beyond taxonomic classification. Effective solutions for fine-level taxonomic classification require a more precise, and operationally-relevant, definition of the taxonomic labels being sought, and the use of combinations of genomic markers in the classification process. ImportanceThe use of reference databases for assigning taxonomic labels to genomic and metagenomic sequences is a fundamental bioinformatic task in the characterization of microbial communities. The increasing accessibility of high throughput sequencing has led to a rapid increase in the size and number of sequences in databases. This has been beneficial for improving our understanding of the global microbial genetic diversity. However, there is evidence that as the microbial diversity is more densely sampled, increasingly longer genomic segments are needed to differentiate between distinct species. The scientific community needs to be aware of this issue and needs to develop methods that better account for it when assigning taxonomic labels to metagenomic sequences from microbial communities.

bioinformatics↗

bioRxiv2022

Advancing antimicrobial resistance monitoring in surface waters with metagenomic and quasimetagenomic methods

Surface waters present a unique challenge for the monitoring of critically important antimicrobial resistance. Metagenomic approaches provide unbiased descriptions of taxonomy and antimicrobial resistance genes in many environments, but for surface water, culture independent data is insufficient to describe critically important resistance. To address this challenge and expand resistome reporting capacity of antimicrobial resistance in surface waters, we apply metagenomic and quasimetagenomic (enriched microbiome) data to examine and contrast water from two sites, a creek near a hospital, and a reservoir used for recreation and municipal water. Approximately 30% of the National Antimicrobial Resistance Monitoring Systems critically important resistance gene targets were identified in enriched data contrasted to only 1% in culture independent data. Four different analytical approaches consistently reported substantially more antimicrobial resistance genes in quasimetagenomic data compared to culture independent data across most classes of antimicrobial resistance. Statistically significant differential fold changes (p<0.05) of resistance determinants were used to infer microbiological differences in the waters. Important pathogens associated with critical antimicrobial resistance were described for each water source. While the single time-point for only two sites represents a small pilot project, the successful reporting of critically important resistance determinants is proof of concept that the quasimetagenomic approach is robust and can be expanded to multiple sites and timepoints for national and global monitoring and surveillance of antimicrobial resistance in surface waters.

microbiology↗

bioRxiv2021

Paraoxonase and acylated homoserine lactones in urine from patients with urinary tract infections--relationship to microbial diversity by 16S rRNA gene sequencing

Paraoxonase (PON) comprises a trio of mammalian enzymes that have been reported to have a number of roles including the inhibition of bacterial virulence and biofilm formation by microorganisms that quorum sense with acylated homoserine lactones (AHLs). PON have previously been reported to inhibit P. aeruginosa biofilm formation in mammalian airways and skin. An innate immune role for PON in urinary tract infection has not previously been reported. We performed western blots for PON1 in urine from patients with urinary tract infection (UTI), and also tested UTI urine for the presence of AHLs using a cellular reporter system. Urine sample microbiota was assessed through sequencing of the 16S rRNA marker gene. We report here that PON1 was not found in the urine of control subjects, however, in patients with UTI, PON1 was associated with the presence of E. coli in urine. AHLs, but not PON, were found in the bulk urine of those with P. aeruginosa UTI. Microbial consortia of PON positive UTI urine was found to be distinct from PON negative UTI urine; differentially over-represented bacteria in PON positive samples included a number of environmental opportunists. We hypothesize that PON may inhibit the quorum sensing activity of AHLs in UTI, as has previously described in skin and airways.

microbiology↗