Biology subjects

Straub, T. J.

Publications and source records attributed to Straub, T. J..

5 recordsLinked to original sources

bioRxiv2026

High reproducibility demonstrated in a direct-to-consumer microbiome testing workflow under real-world conditions using fecal reference material

Recent studies have documented substantial variation in microbiome profiles recovered from identical biological samples across direct-to-consumer (DTC) gut microbiome testing providers and, in some cases, within individual providers. These reports underscore the need for transparent, published assessments of analytical performance from the companies offering these services. To assess analytical reproducibility in our own testing workflow, we processed 10 replicates of a homogenized National Institute of Standards and Technology (NIST) human fecal reference material (RM 8048) through our full sample processing pipeline, from sample collection to data generation. Replicates were shipped from five U.S. sites over a staggered 35-day window. They were processed amongst over 6,000 customer samples, batched for DNA extraction and sequencing based on their arrival in the lab in nine separate sequencing runs, and analyzed using our standard pipeline. The 10 reference material replicates yielded highly concordant community profiles regardless of shipping time or temperature. Technical variability was minimal (median Bray-Curtis dissimilarity = 0.04) compared with the variation observed in samples from the same Tiny Health customer collected the same day (median Bray-Curtis: 0.08), within one week (0.25), one to two weeks apart (0.28), and between unrelated individuals (0.88). When evaluated using NIST's Coefficient of Disagreement (COD) metric, our workflow yielded a maximum pairwise COD of 0.045, well below NIST's published upper bound of 0.073 for this reference material. All three spike-in control strains were successfully recovered from every sample. These results demonstrate the reproducibility of our DTC microbiome testing workflow under real-world conditions, with robust consistency across sample collection, shipping, laboratory processing, and bioinformatics analysis. They also highlight the value of RM 8048 and similar reference materials for evaluating and transparently reporting analytical performance in DTC microbiome testing.

microbiology↗

bioRxiv2026

Integrating stool chemistry measurements with metagenomics in adults reveals distinct gut health profiles

Stool chemistry metrics, including calprotectin, lactoferrin, and secretory IgA, are used to assess gut inflammation in clinical or research settings. However, these measures in isolation provide a limited view of gut physiology and lack microbial context. Shotgun metagenomics offers a comprehensive assessment of the gut microbiome's taxonomic composition and functional potential, yet its integration with stool chemistry metrics remains underexplored. We analyzed 877 adult stool samples with paired shotgun metagenomic sequencing and chemistry measurements to integrate the microbiome with 12 stool chemistry metrics. Calprotectin, lactoferrin, and secretory IgA were associated with shifts in microbiome composition (beta diversity), while deviations from typical levels of short-chain fatty acids (SCFAs), including butyrate, were associated with both changes in alpha diversity and microbiome composition. Network analysis revealed two distinct microbial neighborhoods: one associated with higher butyrate and lower calprotectin and lactoferrin and another associated with lower butyrate and higher calprotectin and lactoferrin. Machine learning models trained on taxonomic and functional data showed modest discrimination of stool chemistry states, performing best for SCFA deficiency and less well for host-derived inflammatory metrics. Together, these findings systematically characterize microbiome-stool chemistry relationships across a large paired dataset, providing a framework for interpreting stool chemistry metrics in the context of shotgun metagenomic profiles.

microbiology↗

bioRxiv2024

Distinct Escherichia coli transcriptional profiles in the guts of recurrent UTI sufferers revealed by pan-genome hybrid selection

Low-abundance members of microbial communities are difficult to study in their native habitats. This includes Escherichia coli, a minor, but common inhabitant of the gastrointestinal tract and opportunistic pathogen, including of the urinary tract, where it is the primary pathogen. While multi-omic analyses have detailed critical interactions between uropathogenic Escherichia coli (UPEC) and the bladder that mediate UTI outcome, comparatively little is known about UPEC in its pre-infection reservoir, partly due to its low abundance there (<1% relative abundance). To accurately and sensitively explore the genomes and transcriptomes of diverse E. coli in gastrointestinal communities, we developed E. coli PanSelect which uses a set of probes designed to specifically recognize and capture E. colis broad pangenome from sequencing libraries. We demonstrated the ability of E. coli PanSelect to enrich, by orders of magnitude, sequencing data from diverse E. coli using a mock community and a set of human stool samples collected as part of a cohort study investigating drivers of recurrent urinary tract infections (rUTI). Comparisons of genomes and transcriptomes between E. coli residing in the gastrointestinal tracts of women with and without a history of rUTI suggest that rUTI gut E. coli are responding to increased levels of oxygen and nitrate, suggestive of mucosal inflammation, which may have implications for recurrent disease. E. coli PanSelect is well suited for investigations of native in vivo biology of E. coli in other environments where it is at low relative abundance, and the framework described here has broad applicability to other highly diverse, low abundance organisms.

microbiology↗

bioRxiv2021

StrainGE: A toolkit to track and characterize low-abundance strains in complex microbial communities

Human-associated microbial communities comprise not only complex mixtures of bacterial species, but also mixtures of conspecific strains, the implications of which are mostly unknown since strain level dynamics are underexplored due to the difficulties of studying them. We introduce the Strain Genome Explorer (StrainGE) toolkit, which deconvolves strain mixtures and characterizes component strains at the nucleotide level from short-read metagenomic sequencing with higher sensitivity and resolution than other tools. StrainGE is able to identify nearest known references and find variants for multiple conspecific strains within a sample at relative abundances below 0.1% in typical metagenomic datasets.

bioinformatics↗

bioRxiv2019

QuantTB - A method to classify mixed Mycobacterium tuberculosis infections within whole genome sequencing data

BackgroundMixed infections of Mycobacterium tuberculosis, and antibiotic heteroresistance, continue to complicate tuberculosis (TB) diagnosis and treatment. Detection of mixed infections has been limited to molecular genotyping techniques, which lack the sensitivity and resolution to accurately estimate the multiplicity of TB infections. In contrast, whole genome sequencing offers sensitive views of the genetic differences between strains of M. tuberculosis within a sample. Although metagenomic tools exist to classify strains in a metagenomic sample, most tools have been developed for more divergent species, and therefore cannot provide the sensitivity required to disentangle strains within closely related bacterial species such as M. tuberculosis.\n\nHere we present QuantTB, a method to identify and quantify individual M. tuberculosis strains in whole genome sequencing data. QuantTB uses SNP markers to determine the combination of strains that best explain the allelic variation observed in a sample. QuantTB outputs a list of identified strains, their corresponding relative abundances, as well as a list of drugs for which resistance-conferring mutations (or heteroresistance) has been predicted within the sample.\n\nResultsWe show that QuantTB has a high degree of resolution, and is capable of differentiating communities differing by less than 25 SNPs and identifying strains down to 1x coverage. Using simulated data, we found QuantTB outperformed other metagenomic strain identification tools at detecting strains and quantifying strain multiplicity. In a real-world scenario, using a dataset of paired clinical isolates from a study of patients with either reinfections or relapses, we found that QuantTB could detect mixed infections and reinfections at rates concordant with a manually curated approach.\n\nConclusionQuantTB can determine infection multiplicity, identify hetero-resistance patterns, enable differentiation between relapse and re-infection, and clarify transmission events across seemingly unrelated patients - even in low-coverage (1x) samples. QuantTB outperforms existing tools and promises to serve as a valuable resource for both clinicians and researchers working with clinical TB samples.

genomics↗