Biology subjects

Malas, J.

Publications and source records attributed to Malas, J..

5 recordsLinked to original sources

bioRxiv2026

From Public Archive to Reusable Resource: Characterizing Gut Microbiome Metadata in the NCBI SRA

Public sequencing repositories contain large amounts of gut microbiome data that could support cross-study comparison, reproducibility analysis, and microbiome foundation model development. However, the extent to which these data are structured, harmonized, and reusable at archive scale remains unclear. Here, we characterized publicly available gut microbiome sequencing metadata from the NCBI Sequence Read Archive using Google BigQuery, focusing on human gut metagenome, mouse gut metagenome, and broadly annotated gut metagenome records. We evaluated temporal growth, sequencing depth, BioSample and BioProject structure, platform and instrument use, metadata completeness, host attribution, publication linkage, and research themes from linked literature. Public gut microbiome data increased substantially over time and were dominated by human-associated datasets and Illumina sequencing platforms. Core technical metadata fields were highly complete, but biological context needed for reuse, including host identity, phenotype, study design, and disease status, was often inconsistently encoded or required recovery from BioSample attributes and linked publications. In the generic "gut metagenome" cohort, host identity could be assigned for only 13.00% of BioSamples, highlighting the limitations of broad organism annotations for automated cohort construction. Publication linkage was also incomplete at the archive level, although usable text was recovered for most linked publications. Topic modeling of SRA-linked literature showed persistent emphasis on core gut microbiota composition and increasing representation of human cohort and infant microbiome studies. Overall, these findings show that public gut microbiome data are extensive and technically rich but not uniformly analysis ready. Improved metadata harmonization, publication linkage, and biological context recovery will be necessary to support reliable large-scale reuse and AI-ready microbiome data resources.

bioinformatics↗

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

Dietary Protein Sources Shape Gut Microbiome: Insights from Meta-analysis and Machine Learning

BackgroundDietary proteins shape gut microbial ecology, yet the taxonomic and functional consequences of plant- versus animal-based proteins remain poorly defined. Although digestibility and fermentation profiles differ by protein type, a systematic evaluation of how these differences influence microbial diversity, community structure, and metabolic capacity is lacking. This meta-analysis integrates taxonomic, machine learning, and functional inference approaches to identify microbial and metabolic signatures associated with dietary protein source in murine models. MethodsFollowing PRISMA guidelines, we analyzed 16S rRNA sequencing data from 10 murine studies (n = 187) comparing plant- and animal-protein diets. Alpha diversity was assessed using Shannon, Inverse Simpson, and Chao1 indices, and beta diversity with Aitchison distances. Differentially abundant taxa were identified using LEfSe and class-weighted Random Forest models. Functional potential was inferred with PICRUSt2, and taxon-pathway relationships were explored using correlation and network analyses. ResultsPlant-protein diets increased gut microbial diversity across all alpha diversity metrics and were associated with higher representation of saccharolytic and nitrogen-recycling genera such as Bacteroides, Muribaculaceae, and Allobaculum. Animal-protein diets favored proteolytic taxa, including Clostridium sensu stricto 1 and Colidextribacter. Microbial community structure differed significantly between diets (ANOSIM R = 0.663, p < 0.001). Random Forest models achieved >88% accuracy (AUC = 0.995) in predicting dietary groups, and LEfSe identified consistent discriminating taxa. Functional profiling showed that plant-based diets enriched pathways linked to short-chain fatty acid and aromatic amino acid metabolism, whereas animal-based diets favored sulfur- and branched-chain amino acid-associated pathways. Network analysis identified Muribaculaceae as a plant-associated hub and Lactobacillus as an animal-associated hub. ConclusionDietary protein source significantly influences gut microbiota composition and functional potential in mice. Plant- and animal-based proteins generate distinct metabolic signatures with implications for nitrogen cycling, sulfur metabolism, and microbial ecology. Future controlled dietary studies that harmonize protein source with other macronutrient variables are needed to isolate protein-specific effects. Statement of SignificanceThis study presents the first standardized meta-analysis of murine protein-intervention microbiome datasets, integrating taxonomic, machine learning, and predicted functional profiling to identify robust microbial and metabolic signatures that differentiate plant- from animal-based protein diets. By harmonizing evidence across diverse studies, it clarifies foundational ecological responses to protein source and provides mechanistic hypotheses that can inform future controlled nutrition and microbiome research

microbiology↗

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↗