Biology subjects

Snyder, M.

Publications and source records attributed to Snyder, M..

At least 37 records · Page 2Linked to original sources

bioRxiv2025

Spatial distribution of the proteome in human body and cancers

A comprehensive spatial distribution of the proteome in human body and cancers is fundamental for understanding human biology and diseases including cancers. Here, we present an anatomically resolved human proteome derived from 1781 benign and malignant samples from 58 major tissue types encompassing 251 specific tissues and 25 carcinomas. Based on a spectral library covering over 75% of the human protein-coding genes with 208 understudied and 82 missing proteins characterized, we quantified over 13,000 proteins in these samples using data-independent acquisition proteomics. This data resource presents the so far most comprehensive quantitative proteomic landscape of human tissues and common carcinomas. It allows systematic evaluation of tissue-specific drug responses, identification of drug candidates that may be repurposed as antineoplastics, and discovery of novel targets for anticancer therapy. This resource, available as an online knowledgebase, refines our knowledge of spatial distribution of the human proteome and tumor-specific protein modulation.

systems biology↗

bioRxiv2025

VISTA-induced tumor suppression by a four amino acid intracellular motif

VISTA is a key immune checkpoint receptor under investigation for cancer immunotherapy; however, its signaling mechanisms remain unclear. Here we identify a conserved four amino acid (NPGF) intracellular motif in VISTA that suppresses cell proliferation by constraining cell-intrinsic growth receptor signaling. The NPGF motif binds to the adapter protein NUMB and recruits Rab11 endosomal recycling machinery. We identify and characterize a class of triple-negative breast cancers with high VISTA expression and low proliferative index. In tumor cells with high VISTA levels, the NPGF motif sequesters NUMB at endosomes, which interferes with epidermal growth factor receptor (EGFR) trafficking and signaling to suppress tumor growth. These effects do not require canonical VISTA ligands, nor a functioning immune system. As a consequence of VISTA expression, EGFR receptor remains abnormally phosphorylated and cannot propagate ligand-induced signaling. Mutation of the VISTA NPGF domain reverts VISTA-induced growth suppression in multiple breast cancer mouse models. These results define a mechanism by which VISTA represses NUMB to control malignant epithelial cell growth and signaling. They also define distinct intracellular residues that are critical for VISTA-induced cell-intrinsic signaling that could be exploited to improve immunotherapy.

cancer biology↗

bioRxiv2024

An Expanded Registry of Candidate cis-Regulatory Elements for Studying Transcriptional Regulation

Mammalian genomes contain millions of regulatory elements that control the complex patterns of gene expression. Previously, The ENCODE consortium mapped biochemical signals across many cell types and tissues and integrated these data to develop a Registry of 0.9 million human and 300 thousand mouse candidate cis-Regulatory Elements (cCREs) annotated with potential functions1. We have expanded the Registry to include 2.35 million human and 927 thousand mouse cCREs, leveraging new ENCODE datasets and enhanced computational methods. This expanded Registry covers hundreds of unique cell and tissue types, providing a comprehensive understanding of gene regulation. Functional characterization data from assays like STARR-seq, MPRA, CRISPR perturbation, and transgenic mouse assays now cover over 90% of human cCREs, revealing complex regulatory functions. We identified thousands of novel silencer cCREs and demonstrated their dual enhancer/silencer roles in different cellular contexts. Integrating the Registry with other ENCODE annotations facilitates genetic variation interpretation and trait-associated gene identification, exemplified by discovering KLF1 as a novel causal gene for red blood cell traits. This expanded Registry is a valuable resource for studying the regulatory genome and its impact on health and disease.

genomics↗

bioRxiv2024

Improving design and normalization of multiplex proteomics study

Advances in multiplex mass spectrometry-based technologies have enabled high-throughput, quantitative proteome profiling of large cohort. However, certain experimental design configurations can amplify sample variability and introduce systematic biases. To address these challenges, we incorporated two novel features in a recent proteogenomic investigation: (1) the inclusion of two reference samples within each mass spectrometry run to serve as internal standards, and (2) the analysis of each specimen as technical replicates across two distinct mass spectrometry runs. Building on these enhancements, we present ProMix, a flexible analytical framework designed to fully leverage these supplementary experimental components. Using both simulated and real-world datasets, we demonstrate the improved performance of ProMix and highlight the advantages conferred by these refined experimental design strategies.

bioinformatics↗

bioRxiv2024

AQP1- A regulatory factor associated with brown adipose tissue silencing

The activation of non-shivering thermogenesis (NST) in brown adipose tissue (BAT) by environmental cold challenge yields strong metabolic benefit in the face of diet-induced obesity (DIO). Yet, a critical barrier to leveraging brown fat NST for therapeutic use against metabolic disease is that BAT is silenced and inactive at physiological ambient temperature conditions in humans. The mechanisms that govern this silencing process remain poorly understood. Here, we identified a putative BAT-silencing factor, aquaporin-1 (AQP1), in brown fat from wild-type (WT) mice via proteomics analysis. We generated the first BAT-specific AQP1 knockout mice (AQP1-KO) and revealed that AQP1-KO could activate NST under BAT silencing environmental conditions and that the AQP1-KO mice were significantly protected against DIO and metabolic dysfunction compared to Flox controls. We found that AQP1-KO mice on high fat diet (HFD) had reduced weight gain through reductions in fat mass, improved glucose tolerance, and increased whole body energy expenditure compared to Flox control mice. Mechanistically, we show that AQP1 ablation in mice had upregulated gene expression related to the electron transport chain (ETC) and mitochondrial translation contributing to the activation of NST under BAT environmental silenced conditions. Significance StatementNovel strategies to combat obesity-associated metabolic dysfunction are urgently needed to curb the growing obesity epidemic. Investigation of brown adipose tissue (BAT) silencing mechanisms may reveal novel therapeutic targets that when ablated, can activate BAT to increase energy expenditure and protect subjects against the metabolic dysfunction associated with obesity. We have identified Aquaporin 1 (AQP1) as a putative BAT silencer regulatory factor and show through the generation of the first BAT-specific aquaporin-1 knockout (AQP1-KO) mouse that BAT can be activated under environmental silencing conditions. We further show that these mice are protected against diet-induced obesity, with improved glucose tolerance, and increased energy expenditure. These findings highlight AQP1 as a promising therapeutic target in the emerging research field of BAT silencers.

molecular biology↗

bioRxiv2024

The proteomic landscape and temporal dynamics of mammalian gastruloid development

Gastrulation is the process by which the early embryo establishes a body plan and primes itself for organogenesis. As gastrulation is challenging to study in vivo, stem cell-derived "gastruloids" have emerged as powerful surrogates. Although transcriptomics and imaging have been applied extensively to such embryo models, the dynamics of their proteomes remains largely unknown. Here, we apply quantitative proteomics to human and mouse gastruloids at four key stages. We leverage these data to map the expression dynamics of protein complexes, and to nominate cooperative proteins. With matched transcriptome and phosphosite data, we investigate global and stage/pathway-specific discordance between the transcriptome and proteome and nominate kinase-substrate relationships based on phosphosite dynamics. Finally, we apply co-regulation network analysis to identify genes linked to the Commander complex whose perturbation leads to morphological defects in gastruloids. Altogether, our work showcases the potential of applying proteomics to embryo models to advance our understanding of mammalian development in ways challenging through transcriptomics alone.

systems biology↗

bioRxiv2024

The Aryl Hydrocarbon Receptor Controls IFNγ-Induced Immune Checkpoints PD-L1 and IDO via the JAK/STAT Pathway in Lung Adenocarcinoma

While immunotherapy has shown efficacy in lung adenocarcinoma (LUAD) patients, many respond only partially or not at all. One limitation in improving outcomes is the lack of a complete understanding of immune checkpoint regulation. Here, we investigated a possible link between an environmental chemical receptor implicated in lung cancer and immune regulation, (the aryl hydrocarbon receptor/AhR), a known but counterintuitive mediator of immunosuppression (IFN{gamma}), and regulation of two immune checkpoints (PD-L1 and IDO). AhR gene-edited LUAD cell lines, a syngeneic LUAD mouse model, bulk- and scRNA sequencing of LUADs and tumor-infiltrating leukocytes were used to map out a signaling pathway leading from IFN{gamma} through the AhR to JAK/STAT, PD-L1, IDO, and tumor-mediated immunosuppression. The data demonstrate that: 1) IFN{gamma} activation of the JAK/STAT pathway leading to PD-L1 and IDO1 upregulation is mediated by the AhR in murine and human LUAD cells, 2) AhR-driven IDO1 induction results in the production of Kynurenine (Kyn), an AhR ligand, which likely mediates an AhRIDO1KynAhR amplification loop, 3) transplantation of AhR-knockout LUAD cells results in long-term tumor immunity in most recipients. 4) The 23% of AhR-knockout tumors that do grow do so at a much slower pace than controls and exhibit higher densities of CD8+ T cells expressing markers of immunocompetence, increased activity, and increased cell-cell communication. The data definitively link the AhR to IFN{gamma}-induced JAK/STAT pathway and immune checkpoint-mediated immunosuppression and support the targeting of the AhR in the context of LUAD.

cancer biology↗

bioRxiv2024

predicTTE: An accessible and optimal tool for time-to-event prediction in neurological diseases

Time-to-event prediction is a key task for biological discovery, experimental medicine, and clinical care. This is particularly true for neurological diseases where development of reliable biomarkers is often limited by difficulty visualising and sampling relevant cell and molecular pathobiology. To date, much work has relied on Cox regression because of ease-of-use, despite evidence that this model includes incorrect assumptions. We have implemented a set of deep learning and spline models for time-to-event modelling within a fully customizable app and accompanying online portal, both of which can be used for any time-to-event analysis in any disease by a non-expert user. Our online portal includes capacity for end-users including patients, Neurology clinicians, and researchers, to access and perform predictions using a trained model, and to contribute new data for model improvement, all within a data-secure environment. We demonstrate a pipeline for use of our app with three use-cases including imputation of missing data, hyperparameter tuning, model training and independent validation. We show that predictions are optimal for use in downstream applications such as genetic discovery, biomarker interpretation, and personalised choice of medication. We demonstrate the efficiency of an ensemble configuration, including focused training of a deep learning model. We have optimised a pipeline for imputation of missing data in combination with time-to-event prediction models. Overall, we provide a powerful and accessible tool to develop, access and share time-to-event prediction models; all software and tutorials are available at www.predictte.org.

neuroscience↗

bioRxiv2024

The ENCODE mouse postnatal developmental time course identifies regulatory programs of cell types and cell states

Postnatal genomic regulation significantly influences tissue and organ maturation but is under-studied relative to existing genomic catalogs of adult tissues or prenatal development in mouse. The ENCODE4 consortium generated the first comprehensive single-nucleus resource of postnatal regulatory events across a diverse set of mouse tissues. The collection spans seven postnatal time points, mirroring human development from childhood to adulthood, and encompasses five core tissues. We identified 30 cell types, further subdivided into 69 subtypes and cell states across adrenal gland, left cerebral cortex, hippocampus, heart, and gastrocnemius muscle. Our annotations cover both known and novel cell differentiation dynamics ranging from early hippocampal neurogenesis to a new sex-specific adrenal gland population during puberty. We used an ensemble Latent Dirichlet Allocation strategy with a curated vocabulary of 2,701 regulatory genes to identify regulatory "topics," each of which is a gene vector, linked to cell type differentiation, subtype specialization, and transitions between cell states. We find recurrent regulatory topics in tissue-resident macrophages, neural cell types, endothelial cells across multiple tissues, and cycling cells of the adrenal gland and heart. Cell-type-specific topics are enriched in transcription factors and microRNA host genes, while chromatin regulators dominate mitosis topics. Corresponding chromatin accessibility data reveal dynamic and sex-specific regulatory elements, with enriched motifs matching transcription factors in regulatory topics. Together, these analyses identify both tissue-specific and common regulatory programs in postnatal development across multiple tissues through the lens of the factors regulating transcription.

genomics↗

bioRxiv2024

Integrative epigenetics and transcriptomics identify aging genes in human blood

Recent epigenome-wide studies have identified a large number of genomic regions that consistently exhibit changes in their methylation status with aging across diverse populations, but the functional consequences of these changes are largely unknown. On the other hand, transcriptomic changes are more easily interpreted than epigenetic alterations, but previously identified age-related gene expression changes have shown limited replicability across populations. Here, we develop an approach that leverages high-resolution multi-omic data for an integrative analysis of epigenetic and transcriptomic age-related changes and identify genomic regions associated with both epigenetic and transcriptomic age-dependent changes in blood. Our results show that these "multi-omic aging genes" in blood are enriched for adaptive immune functions, replicate more robustly across diverse populations and are more strongly associated with aging-related outcomes compared to the genes identified using epigenetic or transcriptomic data alone. These multi-omic aging genes may serve as targets for epigenetic editing to facilitate cellular rejuvenation.

systems biology↗

bioRxiv2024

Deconvolution of polygenic risk score in single cells unravels cellular and molecular heterogeneity of complex human diseases

Polygenic risk scores (PRSs) are commonly used for predicting an individuals genetic risk of complex diseases. Yet, their implication for disease pathogenesis remains largely limited. Here, we introduce scPRS, a geometric deep learning model that constructs single-cell-resolved PRS leveraging reference single-cell chromatin accessibility profiling data to enhance biological discovery as well as disease prediction. Real-world applications across multiple complex diseases, including type 2 diabetes (T2D), hypertrophic cardiomyopathy (HCM), and Alzheimers disease (AD), showcase the superior prediction power of scPRS compared to traditional PRS methods. Importantly, scPRS not only predicts disease risk but also uncovers disease-relevant cells, such as hormone-high alpha and beta cells for T2D, cardiomyocytes and pericytes for HCM, and astrocytes, microglia and oligodendrocyte progenitor cells for AD. Facilitated by a layered multi-omic analysis, scPRS further identifies cell-type-specific genetic underpinnings, linking disease-associated genetic variants to gene regulation within corresponding cell types. We substantiate the disease relevance of scPRS-prioritized HCM genes and demonstrate that the suppression of these genes in HCM cardiomyocytes is rescued by Mavacamten treatment. Additionally, we establish a novel microglia-specific regulatory relationship between the AD risk variant rs7922621 and its target genes ANXA11 and TSPAN14. We further illustrate the detrimental effects of suppressing these two genes on microglia phagocytosis. Our work provides a multi-tasking, interpretable framework for precise disease prediction and systematic investigation of the genetic, cellular, and molecular basis of complex diseases, laying the methodological foundation for single-cell genetics.

genetics↗

bioRxiv2024

Oxidative Stress is a shared characteristic of ME/CFS and Long COVID

More than 65 million individuals worldwide are estimated to have Long COVID (LC), a complex multisystemic condition, wherein patients of all ages report fatigue, post-exertional malaise, and other symptoms resembling myalgic encephalomyelitis / chronic fatigue syndrome (ME/CFS). With no current treatments or reliable diagnostic markers, there is an urgent need to define the molecular underpinnings of these conditions. By studying bioenergetic characteristics of peripheral blood lymphocytes in over 16 healthy controls, 15 ME/CFS, and 15 LC, we find both ME/CFS and LC donors exhibit signs of elevated oxidative stress, relative to healthy controls, especially in the memory subset. Using a combination of flow cytometry, bulk RNA-seq analysis, mass spectrometry, and systems chemistry analysis, we also observed aberrations in ROS clearance pathways including elevated glutathione levels, decreases in mitochondrial superoxide dismutase levels, and glutathione peroxidase 4 mediated lipid oxidative damage. Critically, these changes in redox pathways show striking sex-specific trends. While females diagnosed with ME/CFS exhibit higher total ROS and mitochondrial calcium levels, males with an ME/CFS diagnosis have normal ROS levels, but larger changes in lipid oxidative damage. Further analyses show that higher ROS levels correlates with hyperproliferation of T cells in females, consistent with the known role of elevated ROS levels in the initiation of proliferation. This hyperproliferation of T cells can be attenuated by metformin, suggesting this FDA-approved drug as a possible treatment, as also suggested by a recent clinical study of LC patients. Thus, we report that both ME/CFS and LC are mechanistically related and could be diagnosed with quantitative blood cell measurements. We also suggest that effective, patient tailored drugs might be discovered using standard lymphocyte stimulation assays.

immunology↗

bioRxiv2024

PRS-Net: Interpretable polygenic risk scores via geometric learning

Polygenic risk score (PRS) serves as a valuable tool for predicting the genetic risk of complex human diseases for individuals, playing a pivotal role in advancing precision medicine. Traditional PRS methods, predominantly following a linear structure, often fall short in capturing the intricate relationships between genotype and phenotype. We present PRS-Net, an interpretable deep learning-based framework designed to effectively model the nonlinearity of biological systems for enhanced disease prediction and biological discovery. PRS-Net begins by deconvoluting the genomewide PRS at the single-gene resolution, and then it encapsulates gene-gene interactions for genetic risk prediction leveraging a graph neural network, thereby enabling the characterization of biological nonlinearity underlying complex diseases. An attentive readout module is specifically introduced into the framework to facilitate model interpretation and biological discovery. Through extensive tests across multiple complex diseases, PRS-Net consistently outperforms baseline PRS methods, showcasing its superior performance on disease prediction. Moreover, the interpretability of PRS-Net has been demonstrated by the identification of genes and gene-gene interactions that significantly influence the risk of Alzheimers disease and multiple sclerosis. In summary, PRS-Net provides a potent tool for parallel genetic risk prediction and biological discovery for complex diseases.

genetics↗

bioRxiv2023

Population genetic analyses of longitudinal vaginal microbiome reveal racioethnic evolutionary dynamics and prevailing positive selection of Lactobacillus adhesins

The vaginal microbiome is important for reproductive health and should constantly evolve in response to dynamic host-microbe interactions. The composition of the vaginal microbiome is associated with ethnicity; however, the evolutionary landscape of the vaginal microbiome, especially in the multi-ethnic context, remains under-studied. In this study, we performed a longitudinal evolutionary vaginal microbiome analysis of 351 samples from 35 pregnant women with diverse ethnic backgrounds and validated the main findings in two cohorts totaling 462 samples from 90 multi-ethnic women. Microbiome alpha diversity and community states showed strong ethnic signatures. Lactobacillaceae species had a significantly higher nonsynonymous/synonymous mutation ratio (pN/pS) than non-Lactobacillaceae species in all ethnicities. In addition, non-Lactobacillaceae anaerobic bacteria were enriched in Black and Latino women, with significantly elevated nucleotide diversity and lower pN/pS in Black women. Intriguingly, the Lactobacillaceae species had a large repertoire of positively selected genes, including the human mucin-binding and bacterial cell wall anchor genes, which showed independent, recurrent signatures of positive selection across multiple strains, indicating that the host-microbiome interactions directly drive microbial evolution at the molecular interface. Finally, we propose that the evolutionary metrics reflect the environmental niches of adapting microbes. Our study revealed the extensive ethnic signatures in vaginal microbial diversity, composition, community state, and evolutionary dynamics at species and gene levels, highlighting the importance of studying the host-microbiome ecosystem from an evolutionary perspective. HighlightsO_LIExtensive ethnic signatures in the vaginal microbial diversity, composition, community state, and evolutionary dynamics were demonstrated. C_LIO_LIHealthy Lactobacillaceae species showed lower nucleotide diversity but much more relaxed or even positive selection of genes than non-Lactobacillaceae species. C_LIO_LIIn Black women, non-Lactobacillaceae species displayed higher nucleotide diversity and more stringent negative selection. C_LIO_LILactobacillus mucin-binding and cell wall anchor genes showed convergent signatures of positive selection across vaginal microbiomes. C_LI

microbiology↗

bioRxiv2023

microbiomedataset: A tidyverse-style framework for organizing and processing microbiome data

Microbial communities exert a substantial influence on human health and have been unequivocally associated with a spectrum of human maladies, encompassing conditions such as anxiety1, depression2, hypertension3, cardiovascular diseases4, obesity4,5, diabetes6, inflammatory bowel disease7, and cancer8,9. This intricate interplay between microbiota community structures and host pathophysiology has kindled substantial interest and spurred active research endeavors across various scientific domains. Despite significant strides in sequencing technologies, which have unveiled the vast diversity of microbial populations across diverse ecosystems, the analysis of microbiome data remains a formidable challenge. The complexity inherent in such data, compounded by the absence of standardized data processing and analysis workflows, continues to pose substantial hurdles. The tidyverse paradigm, comprised of a suite of R packages meticulously crafted to facilitate efficient data manipulation and visualization, has garnered considerable acclaim within the data science community10. Its appeal stems from its innate simplicity and efficacy in organizing and processing data10. In recent times, a plethora of tools have been devised to address distinct omics data processing and analysis needs, including notable initiatives such as the tidymass project11, tidyomics project12, tidymicro13, and MicrobiotaProcess13,14. However, a conspicuous gap persists in the form of a standardized, tidyverse-based package for seamless and rigorous microbiome data processing and analysis. To address this burgeoning demand for standardized and reproducible microbiome data analysis, we introduce microbiomedataset, an R package that embraces the tidyverse ethos to furnish a structured framework for the organization and processing of microbiome data. Microbiomedataset offers a comprehensive, customizable solution for the management, structuring, and processing of microbiome data. Importantly, this package seamlessly integrates with established bioinformatics tools, facilitating its incorporation into existing analytical pipelines11,13,14,15. Within this manuscript, we proffer an in-depth overview of the microbiomedataset package, elucidating its multifarious functionalities. Moreover, we substantiate its utility through illustrative case studies employing a publicly available microbiome dataset. It is imperative to underscore that microbiomedataset constitutes an integral component of the larger tidymicrobiome project, accessible via www.tidymicrobiome.org. Tidymicrobiome epitomizes an ecosystem of R packages that share a coherent design philosophy, grammar, and data structure, collectively engendering a robust, reproducible, and object-oriented computational framework. This project's development has been guided by several key tenets: (1) Cross-platform compatibility, (2) Uniformity, shareability, traceability, and reproducibility, and (3) Flexibility and extensibility. We further expound upon the advantages inherent in adopting a tidyverse-style framework for microbiome data analysis, underscoring the pronounced benefits in terms of standardization and reproducibility that microbiomedataset offers. In sum, microbiomedataset furnishes an accessible and efficient avenue for microbiome data analysis, catering to both neophyte and seasoned R users alike.

bioinformatics↗

bioRxiv2023

Integration of spatial and single-cell data across modalities with weak linkage

single-cell sequencing methods have enabled the profiling of multiple types of molecular readouts at cellular resolution, and recent developments in spatial barcoding, in situ hybridization, and in situ sequencing allow such molecular readouts to retain their spatial context. Since no technology can provide complete characterization across all layers of biological modalities within the same cell, there is pervasive need for computational cross-modal integration (also called diagonal integration) of single-cell and spatial omics data. For current methods, the feasibility of cross-modal integration relies on the existence of highly correlated, a priori "linked" features. When such linked features are few or uninformative, a scenario that we call "weak linkage", existing methods fail. We developed MaxFuse, a cross-modal data integration method that, through iterative co-embedding, data smoothing, and cell matching, leverages all information in each modality to obtain high-quality integration. MaxFuse is modality-agnostic and, through comprehensive benchmarks on single-cell and spatial ground-truth multiome datasets, demonstrates high robustness and accuracy in the weak linkage scenario. A prototypical example of weak linkage is the integration of spatial proteomic data with single-cell sequencing data. On two example analyses of this type, we demonstrate how MaxFuse enables the spatial consolidation of proteomic, transcriptomic and epigenomic information at single-cell resolution on the same tissue section.

bioinformatics↗

bioRxiv2023

Multiomic identification of key transcriptional regulatory programs during endurance exercise training

Transcription factors (TFs) play a key role in regulating gene expression. We conducted an integrated analysis of chromatin accessibility, DNA methylation, mRNA expression, protein abundance and phosphorylation across eight tissues in fifty rats of equally represented sexes following endurance exercise training (EET) to identify coordinated epigenomic and transcriptional changes and determine key TFs involved. We uncovered tissue-specific EET associated changes and TF motif enrichment across differentially expressed genes (DEGs), accessible regions (DARs), and methylated regions (DMRs). We discovered distinct routes of EET-induced regulation through either epigenomic alterations providing better access for TFs to affect target genes, or via changes in TF expression or activity enabling target gene responses. We identified TF motifs enriched among correlated epigenomic and transcriptomic alterations, DEGs correlated with exercise-related phenotypic and cell type composition changes, and EET-induced activity changes of TFs whose target genes are enriched for DEGs. This analysis elucidates the unique gene regulatory mechanisms mediating diverse transcriptional responses to EET across tissues.

genomics↗

bioRxiv2022

Longitudinal Urine Metabolic Profiling and Gestational Age Prediction in Pregnancy

Pregnancy is a critical time that has long-term impacts on both maternal and fetal health. During pregnancy, the maternal metabolome undergoes dramatic systemic changes, although correlating longitudinal changes in maternal urine remain largely unexplored. We applied an LCMS-based untargeted metabolomics profiling approach to analyze 346 longitudinal maternal urine samples collected throughout pregnancy for 36 women from diverse ethnic backgrounds with differing clinical characteristics. We detected 20,314 metabolic peaks and annotated 875 metabolites. Altered metabolites include a broad panel of glucocorticoids, lipids, and amino acid derivatives, which revealed systematic pathway alterations during pregnancy. We also developed a machine-learning model to precisely predict gestational age (GA) at time of sampling using urine metabolites that provides a non-invasive method for pregnancy dating. This longitudinal maternal urine study demonstrates the clinical utility of using untargeted metabolomics in obstetric settings. One Sentence SummaryMachine-learning based gestational age and due date using longitudinal urine samples of pregnancy.

systems biology↗