Biology subjects

Mathe, E. A.

Publications and source records attributed to Mathe, E. A..

7 recordsLinked to original sources

bioRxiv2026

RD-OMICS: An Integrative Multi-Omics Data Inventory in Rare Diseases

Rare diseases (RD) impact over 30 million individuals in the United States, yet fewer than 5% of the identified conditions have FDA-approved treatments. Progress in RD research is hindered by small patient cohorts, biological heterogeneity, and the fragmented, inconsistently annotated publicly available omics data, which limits integrative analysis and translational discovery. Here, we present RD-OMICS, a data inventory with integrated and structured RD omics data from Gene Expression Omnibus (GEO), in the form of a knowledge graph. We developed a metadata harmonization pipeline that combines rule-based mapping and large language model (LLM)-assisted semantic categorization. The graph-based data model was defined to integrate different types of data including disease conditions, experiments, samples, platforms, projects, and publications into a centralized inventory graph. In this preliminary study, 11,049 GEO series for 126 rare diseases were processed and integrated into RD-OMICS, which includes 375,930 individual biospecimen samples, 1,578 sequencing and array platforms, 10,938 biological projects. Case studies demonstrate the use of RD-OMICS in supporting rare disease research, omics cohort construction, and transcriptome-based drug repurposing for amyotrophic lateral sclerosis (ALS). RD-OMICS provides a scalable foundation for transforming fragmented omics data into a structured, harmonized and interoperable resource, facilitating therapeutic development and other translational discoveries in rare diseases.

bioinformatics↗

bioRxiv2026

Metabolite profiles distinguish exposure to Dengue and Zika flaviviruses in human induced pluripotent stem cells (hiPSCs)

Flaviviruses such as Dengue virus (DENV), Zika virus (ZIKV) and West Nile Virus (WNV) pose growing public health threats, exacerbated by asymptomatic infections and non-vector transmission. Current diagnostics, including nucleic acid tests and serology, are limited by transient viremia and antibody cross-reactivity, underscoring the need for improved detection methods. We utilized untargeted metabolomics to differentiate DENV and ZIKV infections in human induced pluripotent stem cells (hiPSCs), a model relevant to study virus-host interactions, drug screening, and therapeutic safety. LC-MSbased profiling revealed virus-specific metabolic reprogramming during both acute and long-term infections. DENV3 induced early and sustained activation of metabolic features, while ZIKV-MR766 triggered transient suppression. Long term infection with DENV2, DENV3, or ZIKV-PRV resulted in distinct metabolic signatures in hiPSCs, with ZIKV-PRV showing the greatest divergence. Shared metabolite changes across conditions included amino acids (e.g., tryptophan, glutamate), lipids, and nucleosides. Functional studies demonstrated that tryptophan metabolism regulates infection dynamics: inhibiting serotonin biosynthesis reduced viral load, whereas blocking kynurenine synthesis enhanced viral replication. These findings position metabolomics as a viable approach for flavivirus detection and highlight metabolic pathways as potential therapeutic targets. ImportanceAccurate diagnosis of flavivirus infections remains challenging due to short periods of viremia, high rates of asymptomatic infection, and extensive serologic cross-reactivity among related viruses such as DENV and ZIKV. These limitations complicate clinical diagnosis, surveillance, and screening of human donor tissues. In this study, we demonstrate that untargeted metabolomics can distinguish ZIKV and DENV infections by identifying virus-specific host metabolic signatures during both acute and long-term infection. Using human induced pluripotent stem cells as a clinically relevant model, we show that metabolic reprogramming persists even in the absence of overt cytopathic effects and can reveal functional pathways that regulate viral replication. Our findings highlight host metabolite profiling as a complementary diagnostic strategy that may improve detection of flavivirus exposure, particularly in asymptomatic individuals or settings where conventional molecular and serologic tests are insufficient.

microbiology↗

bioRxiv2023

OMICmAge: An integrative multi-omics approach to quantify biological age with electronic medical records

Biological aging is a multifactorial process involving complex interactions of cellular and biochemical processes that is reflected in omic profiles. Using common clinical laboratory measures in ~30,000 individuals from the MGB-Biobank, we developed a robust, predictive biological aging phenotype, EMRAge, that balances clinical biomarkers with overall mortality risk and can be broadly recapitulated across EMRs. We then applied elastic-net regression to model EMRAge with DNA-methylation (DNAm) and multiple omics, generating DNAmEMRAge and OMICmAge, respectively. Both biomarkers demonstrated strong associations with chronic diseases and mortality that outperform current biomarkers across our discovery (MGB-ABC, n=3,451) and validation (TruDiagnostic, n=12,666) cohorts. Through the use of epigenetic biomarker proxies, OMICmAge has the unique advantage of expanding the predictive search space to include epigenomic, proteomic, metabolomic, and clinical data while distilling this in a measure with DNAm alone, providing opportunities to identify clinically-relevant interconnections central to the aging process.

bioinformatics↗

bioRxiv2022

RaMP-DB 2.0: a renovated knowledgebase for deriving biological and chemical insight from genes, proteins, and metabolites

RaMP-DB 2.0 is a web interface, API, relational database and R package designed for straightforward and comprehensive functional interpretation of metabolomic and multi-omic data. Since its first release in 2018, RaMP-DB 2.0 has been upgraded with an expanded breadth and depth of functional and chemical annotation. Content from the source databases (Reactome, HMDB, and Wikipathways) has been updated, and new data types related to metabolite annotations have been incorporated. Structural information incorporated in RaMP-DB 2.0 includes SMILES strings, InChIs, InChIKeys. Chemical classes have been sourced from ClassyFire and LIPID MAPS. Accordingly, the RaMP-DB 2.0 R package has been updated and supports queries on pathways, common reactions, ontologies, chemical classes, and chemical structures. Additionally, RaMP-DB 2.0 now supports enrichment analyses on pathways and chemical classes. Our process for integrating annotations across resources has also been upgraded to lessen the burden of harmonization, thereby supporting more frequent updates. The code used to build all components of RaMP-DB 2.0 is freely available on GitHub at https://github.com/ncats/ramp-db and https://github.com/ncats/RaMP-Backend.

bioinformatics↗

bioRxiv2020

A Workflow of Integrated Resources to Catalyze Network Pharmacology Driven COVID-19 Research

MotivationIn the event of an outbreak due to an emerging pathogen, time is of the essence to contain or to mitigate the spread of the disease. Drug repositioning is one of the strategies that has the potential to deliver therapeutics relatively quickly. The SARS-CoV-2 pandemic has shown that integrating critical data resources to drive drug-repositioning studies, involving host-host, hostpathogen and drug-target interactions, remains a time-consuming effort that translates to a delay in the development and delivery of a life-saving therapy. ResultsHere, we describe a workflow we designed for a semi-automated integration of rapidly emerging datasets that can be generally adopted in a broad network pharmacology research setting. The workflow was used to construct a COVID-19 focused multimodal network that integrates 487 host-pathogen, 74,805 host-host protein and 1,265 drug-target interactions. The resultant Neo4j graph database named "Neo4COVID19" is accessible via a web interface and via API calls based on the Bolt protocol. We believe that our Neo4COVID19 database will be a valuable asset to the research community and will catalyze the discovery of therapeutics to fight COVID-19. Availabilityhttps://neo4covid19.ncats.io

systems biology↗

bioRxiv2020

An OpenData portal to share COVID-19 drug repurposing data in real time

The National Center for Advancing Translational Sciences (NCATS) has developed an online open science platform - named the NCATS OpenData Portal (ODP) - for quickly and freely sharing complete NCATS translational datasets via an open-access, user-friendly interface. This paper describes the establishment of the ODP, initially deployed during the COVID-19 crisis, and provides a detailed analysis of COVID-19 drug repurposing screening datasets that served as the first large-scale use case for the platform. Over 10,000 compounds were tested across 17 quantitative high-throughput assays, covering a wide spectrum of the SARS-CoV-2 life cycle. In total, over 87,000 concentration-response curves and 426,000 data points were made publicly available on ODP in near real-time, enabling immediate access to complete datasets. The resource is flexible in accommodating various types and structures of data, and it has already expanded since its launch to host additional datasets for COVID-19, other viruses of pandemic potential, and beyond. The OpenData Portal has been designed as a scalable platform for real-time data sharing across drug discovery campaigns, regardless of disease area, with the overarching goal of accelerating discovery at NCATS, the NIH, and the greater scientific community.

microbiology↗

bioRxiv2020

Genome-wide analyses of transcription factors and co-regulators across seven cohorts identified reduced PPARGC1A expression as a driver of prostate cancer progression

Defining altered transcription factor (TF) and coregulators that are oncogenic drivers remains a challenge, in part because of the multitude of TFs and coregulators. We addressed this challenge by using bootstrap approaches to test how expression, copy number alterations or mutation of TFs (n = 2662), coactivators (COA; n= 766); corepressor (COR; n = 599); mixed function coregulators (MIXED; n = 511) varied across seven prostate cancer (PCa) cohorts (three of localized and four advanced disease). COAS, CORS, MIXED and TFs all displayed significant down-regulated expression (q.value < 0.1) and correlated with protein expression ({rho} 0.4 to 0.55). Stringent expression filtering identified commonly altered TFs and coregulators including well-established (e.g. ERG) and underexplored (e.g. PPARGC1A, encodes PGC1) in localized PCa. Reduced PPARGC1A expression significantly associated with worse disease-free survival in two cohorts of localized PCa. Stable PGC1 knockdown in LNCaP cells increased growth rates and invasiveness and RNA-Seq revealed a profound basal impact on gene expression (~2300 genes; FDR < 0.05, logFC > 1.5), but only modestly impacted PPAR{gamma} responses. GSEA analyses of the PGC1 transcriptome revealed that it significantly altered the AR-dependent transcriptome, and was enriched for epigenetic modifiers. PGC1-dependent genes were overlapped with PGC1-ChIP-Seq genes and significantly associated in TCGA with higher grade tumors and worse disease-free survival. Together these data demonstrate an approach to identify cancer-driver coregulators in cancer and that PGC1 expression is clinically significant yet underexplored coregulator in aggressive early stage PCa.

cancer biology↗