Biology subjects

Hampton-Marcell, J.

Publications and source records attributed to Hampton-Marcell, J..

5 recordsLinked to original sources

bioRxiv2026

Home is Where the Heterogeneity Is: Housing Facility-level Differences in the Gut Microbiome and Metabolic Phenotype Confound Arsenic Effects on Glucose Homeostasis in Male Mice

Inorganic arsenic (iAs) exposure is linked to impaired glucose homeostasis and type 2 diabetes, yet the magnitude and direction of reported effects vary substantially across studies and populations. The gut microbiome is both a target and a mediator of arsenic toxicity, suggesting that pre-exposure community composition may modulate the development of metabolic dysfunction. To test this, we conducted parallel 50 ppm iAs drinking-water exposures in male C57BL/6J mice at two animal facilities. Results were compared across facilities for metabolic phenotypes, hepatic arsenic levels, targeted and untargeted metabolomics, and shotgun metagenomics. Hepatic arsenic confirmed comparable exposure at both sites; however, the housing facility explained more variance than the iAs treatment group across every data layer. Baseline microbial communities and metabolic phenotypes at each institution differed, and this difference propagated into the iAs treatment effect. Critically, iAs exposure impaired glucose clearance at one site while trending toward improvement at the other. Facility explained 19 to 26% of variance in microbiome, bile acid, polar, and untargeted metabolite ordinations, while iAs treatment did not reach significance. A random forest classifier identified the facility with 96% cross-validated accuracy from 22 microbial species, whereas treatment classification did not exceed 67% accuracy. Functional metagenomic analyses revealed nearly 11,733 (63%) of genes were differentially abundant between facilities compared 139 with iAs treatment. Our results indicate that identical genetics and exposure may produce differential metabolic outcomes on different microbial backgrounds. Characterizing the baseline microbiome and metabolome is therefore critical both for identifying which individuals are most susceptible to the metabolic effects of arsenic exposure and for potentially reducing the risk of exposure through modulation of the gut microbiome.

microbiology↗

bioRxiv2026

Real-time measurement of short-chain fatty acids via microwave sensing: A pilot study

BackgroundShort-chain fatty acids (SCFAs) - acetate, propionate, and butyrate - are key microbial metabolites that influence host metabolism, barrier function, and immune tone. Yet, in vivo measurements of SCFAs remain poorly characterized because stool measurements only capture a small, spatially averaged fraction of luminal production. PurposeTo evaluate whether label-free microwave sensing using a vector network analyzer (VNA) can differentiate SCFA identity, concentration, and their ratios via unique dielectric signatures. MethodsSodium acetate, sodium propionate, and sodium butyrate solutions (10 uM - 1M) were prepared by measuring their electrochemical properties (pH and mV). Using a 2-port VNA interfaced with a high-frequency copper plate (1 - 3 GHz), scattering and impedance parameters were measured for SCFAs and their ratios. A per-frequency ANOVA was employed to identify discriminatory frequency bands, and supervised machine learning (random forest) was employed to identify additional features differentiating SCFAs. ResultsSCFAs displayed distinct (ANOVA, p < 0.05) electrochemical signatures for both pH and mV. The 21 transmission spectrum revealed discriminatory frequencies with prominent bands at 1.38 - 1.40 GHz and 1.79 - 1.85 GHz. For SCFA ratios, the random forest model achieved up to 80.6% accuracy (k = 0.72) with S11 (77.7%) and S22 (69.9%) unwrapped phases observed as the top two important features contributing to the models performance. ConclusionVNA-based microwave sensing resolves SCFA-specific, frequency-dependent dielectric signatures across biologically relevant concentrations and classifies SCFA-like mixtures with high accuracy. These findings support microwave sensing as a foundation for real-time, non-invasive monitoring of gut microbial metabolism. IMPORTANCEShort-chain fatty acids (SCFAs) are central to host-microbe interactions, yet conventional stool assays provide only snapshots of microbial metabolism and poorly reflect real-time intestinal production of these microbial metabolites. SCFAs mediate intestinal barrier integrity, immune regulation, energy availability, and energy metabolism. Alterations in SCFA production and composition are increasingly recognized as biomarkers of gut microbial dysbiosis, with strong connections to conditions such as inflammatory bowel disease, metabolic syndrome, obesity, and neurodegenerative disorders. As such, the dynamic and quantitative assessment of SCFA profiles in vivo may provide valuable insight into gut health, disease progression, and therapeutic response. Despite large technological advances in the past decade, there is a limited understanding of in vivo SCFA dynamics within the gut, largely due to the inaccessibility and limitation of developing non-invasive diagnostic tools. Furthermore, the spatiotemporal resolution of microbial-derived metabolites in the gut is also very limited, with most current research methods relying on utilizing stool samples, which only contain a fraction of produced SCFA concentrations. In this pilot study, SCFA solutions of known concentrations and identity were measured and analyzed via their capacitance to use as an emerging diagnostic tool in clinical and/or athletic research and to further practice assessing gut microbial dynamics. We demonstrate that microwave sensing - a label-free, electrical approach - can distinguish among SCFAs and their concentrations by exploiting their dielectric differences and frequency-resolved behavior. Using a Vector Network Analyzer and a low-loss dielectric sensor, we identify reproducible GHz-scale bands that separate acetate, propionate, and butyrate. Results indicated that these metabolites can be differentiated via their S parameter (frequency response) electrical signals and pH values. Additionally, we classify physiological SCFA mixtures with strong performance.The study will be repeated by measuring various fluid media, starting with acidic solutions and testing with fluid mixes such as mock carbohydrate solutions that best mimic gut microbial conditions in hopes of developing a diagnostic tool (i.e., swallowable sensor) that can be used to monitor real-time human gut environments. This establishes a practical path toward dynamic, non-invasive readouts of gut microbial activity with potential applications in clinical monitoring.

bioengineering↗

bioRxiv2025

Going green: Recycling transcriptomes to infer evolutionary relationships, gene duplication, gene tree conflict, and patterns of molecular evolution in the Apocynaceae

Background and AimsThe flowering plant family Apocynaceae exhibits diverse adaptations with biological and pharmaceutical significance, many of which have been studied with RNA-seq. However, despite the available transcriptomic data, no focused phylotranscriptomic study has been conducted to characterize the patterns of molecular evolution in this group. In this study, we leverage a dataset composed predominately of publicly available transcriptomes to infer relationships within Apocynaceae and explore the molecular processes that have shaped their divergences. MethodsWe extracted nuclear, chloroplast, and mitochondrial genes from 47 publicly available and one newly sequenced transcriptome to assemble and infer species relationships across Apocynaceae. Leveraging the gene-rich nuclear phylotranscriptomic data, we inferred molecular dates and investigated the complex history of gene tree conflict, molecular rate shifts, and gene duplications. To investigate the genomic basis of adaptations, we analyzed the inferred 14,838 gene duplications at the base of Apocynaceae for shared functional enrichment of genes related to evolutionary innovations. Key ResultsThe Apocynaceae topology inferred from our phylotranscriptomic analysis is highly concordant with the current consensus. Notably, a genome-wide acceleration in molecular rate subtends the Ceropegieae tribe. We observed that a decreased time between divergences is associated with a higher rate of gene tree conflict, a pattern especially prevalent across the Apocynaceaes historically recalcitrant backbone relationships. Furthermore, gene duplications may underlie evolutionary innovations, such as immunity-related gene expansion in the genus Asclepias and duplications associated with trichome modifications in the epiphytic Hoya. Finally, we discuss the contentious history of whole-genome duplication (WGD) within the Apocynaceae and emphasize the need for further investigation into the placement of WGDs. ConclusionsRepurposing transcriptomes is a powerful means of accumulating data for novel insights in plant evolution, especially during uncertain funding and cases where budget restrictions exist. Leveraging this gene-dense dataset, we obtained novel insights into the molecular evolution of Apocynaceae and identified areas for future investigations.

evolutionary biology↗

bioRxiv2025

Utility of a quantitative approach to microbial dysbiosis using machine learning in an African American cohort with self-reported hair loss

ObjectiveHair loss is a common issue that affects a large proportion of the population, leading to lower self-confidence and quality of life. Microbial dysbiosis of the scalp has been shown to be associated with several different disorders leading to hair loss. Though several "microbiome friendly" cosmetic treatments are currently on the market, there is no agreement on the best technique for assessing dysbiosis leading to a lack of scientific rigor for quantifying the effective treatments. To help address this, the association between self-perceived hair loss and the scalp microbiome in an African-American cohort (n=36) was investigated. MethodsUsing a self-controlled design, swabs were collected from both "sparse" and "normal" scalp sites. The scalp microbiome was characterized via 16S rRNA gene sequencing and a dysbiosis score was calculated based on the proportion of all taxa within the samples. Further, we identified the taxa that contributed most to abnormal or dysbiotic hair sites using a machine learning random forest classifier and a negative binomial mixed effects model. ResultsThe dysbiosis index is sensitive to participants self-assessment of hair loss and interindividual variation. We found a core set of operational taxonomic units (OTUs) assigned to 7 genera that significantly contributed to increased scalp dysbiosis. ConclusionThis study demonstrates that self-perceived hair loss is associated with significant and measurable alterations in the scalp microbiome using, making the reported dysbiosis index a practical tool that may be used to assess microbiome changes following cosmetic or medical interventions for hair loss and other microbiome-associated disorders.

microbiology↗

bioRxiv2018

Crowdsourced study of children with autism and their typically developing siblings identifies differences in taxonomic and predicted function for stool-associated microbes using exact sequence variant analysis.

BackgroundThe existence of a link between the gut microbiome and autism spectrum disorder (ASD) is well established in mice, but in human populations efforts to identify microbial biomarkers have been limited due to problems stratifying participants within the broad phenotype of ASD and a lack of appropriately matched controls. To overcome these limitations and investigate the relationship between ASD and the gut microbiome, we ran a crowdsourced study of families 2-7 year old sibling pairs, where one child of the pair had a diagnosis of ASD and the other child did not.\n\nMethodsParents of age-matched sibling pairs electronically consented and completed study procedures via a secure web portal (microbiome.stanford.edu). Parents collected stool samples from each child, responded to behavioral questionnaires about the ASD childs typical behavior, and whenever possible provided a home video of their ASD childs natural social behavior. We performed DNA extraction and 16S rRNA amplicon sequencing on 117 stool samples (60 ASD and 57 NT) that met all study design eligibility criteria. Using DADA2, Exact Sequence Variants (ESVs) were identified as taxonomic units, and three statistical tests were performed on ESV abundance counts: (1) permutation test to determine differences between sibling pairs, (2) differential abundance test using a zero-inflated gaussian mixture model to account for the sparse abundance matrix, and (3) differential abundance test after modeling under a negative binomial distribution. The potential functional gene abundance for each sample was also inferred from the 16S rRNA data, providing KEGG Ortholog (KO), which were analyzed for differential abundance.\n\nResultsIn total, 21 ESVs had significantly differentially proportions in stool of children with ASD and their neurotypical siblings. Of these 21 ESVs, 11 were enriched in neurotypical children and ten were enriched in children with ASD. ESVs enriched in the ASD cohort were predominantly associated with Ruminococcaceae and Bacteroidaceae; while those enriched in controls were more diverse including taxa associated with Bifidobacterium, Porphyromonas, Slackia, Desulfovibrio, Acinetobacter johnsonii, and Lachnospiraceae. Exact Variant Analysis suggested that Lachnospiraceae was specific to the control cohort, while Ruminococcaceae, Tissierellaceae and Bacteroidaceae were significantly enriched in children with ASD. Metabolic gene predictions determined that while both cohorts harbor the butyrogenic pathway, the ASD cohort was more likely to use the 4-aminobutanoate (4Ab) pathway, while the control cohort was more likely to use the pyruvate pathway. The 4Ab pathway releases harmful by-products like ammonia and can shunt glutamate, affecting its availability as an excitatory neurotransmitter. Finally, we observed differences in the carbohydrate uptake capabilities of various ESVs identified between the two cohorts.

microbiology↗