Biology subjects

Rahman, G.

Publications and source records attributed to Rahman, G..

10 recordsLinked to original sources

bioRxiv2026

RNAquarium: an archive-scale atlas of zebrafish gene expression coupled with pan-taxonomic profiling reveals diverse viral drivers of transcriptomic states

Zebrafish RNA-seq studies span diverse developmental, physiological, and disease contexts, yet most analyses remain confined to individual experiments and disregard the non-zebrafish component of the data. We present RNAquarium, a scalable framework for joint transcriptomic and metatranscriptomic analysis of RNA-seq data and apply it to all publicly available zebrafish RNA-seq datasets in the Sequence Read Archive. This resource captures transcriptomic structure across development and tissues, reveals diverse microbial and viral associations, and identifies previously undescribed zebrafish viruses including a close relative of human influenza B virus linked to distinct host transcriptional states. We further demonstrate that archive-scale transcriptomes can support foundation-model training and prediction of infection-associated transcriptomic signatures. RNAquarium provides an open framework and interactive portal for exploring the breadth of zebrafish gene expression patterns and associated taxa profiled across a large re-search community and establishes a generalizable strategy for integrating transcriptomic and metatranscriptomic analyses across the diversity of life represented in public sequencing archives.

genomics↗

bioRxiv2026

A Multi-Omics Processing Pipeline (MOPP) for Extracting Taxonomic and Functional Insights from Metaribosome Profiling (metaRibo-Seq) data

Metaribosome profiling (metaRibo-Seq) enables genome-wide measurement of translation across complex microbial communities by sequencing ribosome-protected mRNA fragments, but the short length of these footprints creates substantial nonspecific mapping against large reference genome collections, leading to spurious taxonomic and functional assignments. Here we present MOPP (Multi-Omics Processing Pipeline), a modular reference-based workflow that denoises metaRibo-Seq data by leveraging matched metagenomic coverage breadth to identify genomes likely to be truly present in a sample before aligning metatranslatomic and optional metatranscriptomic reads. MOPP generates taxon-by-gene count tables across genomic, transcriptional and translational layers, enabling integrated downstream analyses of microbial function. We evaluated MOPP using a defined 79-member synthetic human gut community profiled by metagenomics and metaRibo-Seq. Coverage breadth filtering markedly improved detection accuracy relative to a standard baseline workflow, with performance remaining robust across a broad intermediate threshold range and peaking at 92-95% coverage breadth. At a 92% threshold, MOPP reduced the number of distinct detected operational genomic units by 99.4% while retaining 87.8% of aligned metaRibo-Seq reads on average, and increased the F1 score from 0.02 to 0.61. Residual false positives were predominantly attributable to genomes with extremely high nucleotide similarity to true community members, whereas false negatives were enriched among low-abundance taxa, indicating that remaining errors are driven primarily by biological similarity and detection limits rather than widespread nonspecific mapping. Together, these results establish MOPP as a high-throughput workflow for robust processing of metaRibo-Seq in the context of matched metagenomics and position it as a scalable framework for integrated taxonomic and functional analysis of microbial communities across genomic, transcriptional and translational layers.

bioinformatics↗

bioRxiv2026

Integrated multi-omics analysis identifies microbial and metabolic signatures and drivers of CNS autoimmunity

Multiple sclerosis (MS) is an autoimmune disease of the central nervous system (CNS) driven by genetic and environmental determinants. The gut microbiome of people with MS (pwMS) is distinct and influences disease through immunomodulatory metabolite production. Circulating metabolites are altered in pwMS, but identifying microbial-metabolic drivers remains challenging. We previously showed that colonization by the gut commensal Limosilactobacillus reuteri (L. reuteri) exacerbates disease in the experimental autoimmune encephalomyelitis (EAE) model of MS, in a tryptophan-dependent manner. Here, we integrated microbiomic and metabolomic datasets from a longitudinal EAE study utilizing high and low tryptophan diets in mice colonized or not with L. reuteri. Gut microbiome dynamics under short- and long-term alterations in tryptophan bioavailability, were affected by diet, microbiome context, or disease. During short-term dietary intervention, L. reuteri colonization exerted a greater impact on microbiome composition than tryptophan bioavailability. With longer dietary exposure and EAE progression, high dietary tryptophan and L. reuteri colonization synergized to elicit profound microbiota changes, including alterations in Lachnospiraceae, Blautia, and Akkermansia. Integration of metabolomic and microbiomic datasets using joint Robust Aitchison PCA revealed clusters of associated metabolites and microbiota enriched for functional pathways, including bile acid and tryptophan metabolism. Metabolites outperformed microbiota in predicting EAE severity, identifying p-cresols and indoles as top disease-associated metabolites. Treatment with p-cresol or 3-indoleglyoxylic acid exacerbated EAE, enhanced proinflammatory T cell responses, and increased cerebellar pathology. These data demonstrate that dietary responses are shaped by gut microbiome composition and that integrated microbiomic-metabolomic analyses can identify drivers of disease worsening in MS. IMPORTANCEMS is a multifactorial disease influenced not only by genetics but also by environmental factors, potentially including diet and the composition of the gut microbiome. We show that interactions between diet and commensal gut microbiota profoundly impact levels of immunomodulatory systemic metabolites, including several that are associated with disease in pwMS. Importantly, we demonstrate that individual gut microbiota produced metabolites are sufficient to worsen disease in a mouse model of MS. Integration of gut microbiome and blood metabolite datasets combined with subsequent predictive modeling, may bolster biomarker identification and the capacity to predict disease severity in pwMS, as compared to performance of individual datasets alone. These findings highlight metabolites as key mediators linking diet and the gut microbiota to neuroinflammation. Importantly, this work suggests that targeting microbial metabolites or modifying diet-microbiome interactions may represent new strategies to reduce disease activity in MS and related autoimmune disorders.

bioinformatics↗

bioRxiv2024

Optimized LC-MS/MS method for Doxorubicin quantification: validating drug uptake and tumor reduction in zebrafish xenograft model of breast cancer

Doxorubicin, a potent chemotherapeutic drug, is widely used against various cancers, notably breast cancer. While its efficacy is well-documented, precise dosage determination in experimental models remains challenging. Zebrafish xenografts of various cancers confirm doxorubicins anti-cancerous effect; however, since doxorubicin treatment of zebrafish larva is done by adding doxorubicin to fish water, the precise chemotherapeutic dosage for zebrafish larva remains unknown. In this study, we provide a liquid chromatography tandem mass-spectrometry (LC-MS/MS) method for quantifying doxorubicin uptake in zebrafish larvae and thus provide a direct estimate of doses required for the therapeutic effect. Alongside quantification, we measured the therapeutic effect of doxorubicin in zebrafish larvae xenografted with triple negative breast cancer cell line, MDA-MB-231. LD50 value of doxorubicin was first determined by incubating 3-days post fertilization (dpf) larvae with different doses of doxorubicin for 72 h. Doxorubicin was quantified both from zebrafish larval homogenate and treatment solution. Analysis was performed by selected-reaction monitoring (SRM) scans in positive ionization mode. LD50 value for 72 h calculated to be 35.95 mg/L. As expected, doxorubicin-treated xenografts exhibited a significant reduction in tumor growth. The range of limit of detection (LOD) and limit of quantification (LOQ) for doxorubicin were 2 and 5 g/L respectively. Intra- and inter-day accuracy was within the range of 82-114%. Overall, in this study we describe a reliable method for quantifying doxorubicin in zebrafish larvae. Our study facilitates precise dosage estimation, enhancing the relevance of zebrafish xenograft model in cancer research and potentially improving translational applications of chemotherapeutic treatments.

cancer biology↗

bioRxiv2023

BIRDMAn: A Bayesian differential abundance framework that enables robust inference of host-microbe associations

Quantifying the differential abundance (DA) of specific taxa among experimental groups in microbiome studies is challenging due to data characteristics (e.g., compositionality, sparsity) and specific study designs (e.g., repeated measures, meta-analysis, cross-over). Here we present BIRDMAn (Bayesian Inferential Regression for Differential Microbiome Analysis), a flexible DA method that can account for microbiome data characteristics and diverse experimental designs. Simulations show that BIRDMAn models are robust to uneven sequencing depth and provide a >20-fold improvement in statistical power over existing methods. We then use BIRDMAn to identify antibiotic-mediated perturbations undetected by other DA methods due to subject-level heterogeneity. Finally, we demonstrate how BIRDMAn can construct state-of-the-art cancer-type classifiers using The Cancer Genome Atlas (TCGA) dataset, with substantial accuracy improvements over random forests and existing DA tools across multiple sequencing centers. Collectively, BIRDMAn extracts more informative biological signals while accounting for study-specific experimental conditions than existing approaches.

bioinformatics↗

bioRxiv2022

Scalable power analysis and effect size exploration of microbiome community differences with Evident

Differentiating microbial communities among samples is a major objective in biomedicine. Quantifying the effect size of these differences allows researchers to understand the factors most associated with communities and to optimize the design and clinical resources required to address particular research questions. Here, we present Evident, a package for effect size calculations and power analysis on microbiome data and show that Evident scales to large datasets with numerous metadata covariates.

bioinformatics↗

bioRxiv2022

Multi-omic analysis along the gut-brain axis points to a functional architecture of autism

Autism is a highly heritable neurodevelopmental disorder characterized by heterogeneous cognitive, behavioral and communication impairments. Disruption of the gut-brain axis (GBA) has been implicated in autism, with dozens of cross-sectional microbiome and other omic studies revealing autism-specific profiles along the GBA albeit with little agreement in composition or magnitude. To explore the functional architecture of autism, we developed an age and sex-matched Bayesian differential ranking algorithm that identified autism-specific profiles across 10 cross-sectional microbiome datasets and 15 other omic datasets, including dietary patterns, metabolomics, cytokine profiles, and human brain expression profiles. The analysis uncovered a highly significant, functional architecture along the GBA that encapsulated the overall heterogeneity of autism phenotypes. This architecture was determined by autism-specific amino acid, carbohydrate and lipid metabolism profiles predominantly encoded by microbial species in the genera Prevotella, Enterococcus, Bifidobacterium, and Desulfovibrio, and was mirrored in brain-associated gene expression profiles and restrictive dietary patterns in individuals with autism. Pro-inflammatory cytokine profiling and virome association analysis further supported the existence of an autism-specific architecture associated with particular microbial genera. Re-analysis of a longitudinal intervention study in autism recapitulated the cross-sectional profiles, and showed a strong association between temporal changes in microbiome composition and autism symptoms. Further elucidation of the functional architecture of autism, including of the role the microbiome plays in it, will require deep, multi-omic longitudinal intervention studies on well-defined stratified cohorts to support causal and mechanistic inference.

microbiology↗

bioRxiv2022

Bacterial metatranscriptomes in wastewater can differentiate virally infected human populations

Monitoring wastewater samples at building-level resolution screens large populations for SARS-CoV-2, prioritizing testing and isolation efforts. Here we perform untargeted metatranscriptomics on virally-enriched wastewater samples from 10 locations on the UC San Diego campus, demonstrating that resulting bacterial taxonomic and functional profiles discriminate SARS-CoV-2 status even without direct detection of viral transcripts. Our proof-of-principle reveals emergent threats through changes in the human microbiome, suggesting new approaches for untargeted wastewater-based epidemiology.

microbiology↗

bioRxiv2020

EMPress enables tree-guided, interactive, and exploratory analyses of multi-omic datasets

Standard workflows for analyzing microbiomes often include the creation and curation of phylogenetic trees. Here we present EMPress, an interactive tool for visualizing trees in the context of microbiome, metabolome, etc. community data scalable beyond modern large datasets like the Earth Microbiome Project. EMPress provides novel functionality--including ordination integration and animations--alongside many standard tree visualization features, and thus simplifies exploratory analyses of many forms of omic data.

bioinformatics↗

bioRxiv2019

Visualizing 'omic feature rankings and log-ratios using Qurro

Many tools for dealing with compositional "omics" data produce feature-wise values that can be ranked in order to describe features associations with some sort of variation. These values include differentials (which describe features associations with specified covariates) and feature loadings (which describe features associations with variation along a given axis in a biplot). Although prior work has discussed the use of these "rankings" as a starting point for exploring the log-ratios of particularly high-or low-ranked features, such exploratory analyses have previously been done using custom code to visualize feature rankings and the log-ratios of interest. This approach is laborious, prone to errors, and raises questions about reproducibility. To address these problems we introduce Qurro, a tool that interactively visualizes a plot of feature rankings (a "rank plot") alongside a plot of selected features log-ratios within samples (a "sample plot"). Qurros interface includes various controls that allow users to select features from along the rank plot to compute a log-ratio; this action updates both the rank plot (through highlighting selected features) and the sample plot (through displaying the current log-ratios of samples). Here we demonstrate how this unique interface helps users explore feature rankings and log-ratios simply and effectively.

bioinformatics↗