Search bioRxiv⌕ Search

Biology subjects

Snyder, M.

Publications and source records attributed to Snyder, M..

At least 55 records · Page 3Linked to original sources

Systems Analysis of de novo Mutations in Congenital Heart Diseases Identified a Molecular Network in Hypoplastic Left Heart Syndrome

Congenital heart diseases (CHD) are a class of birth defects affecting [~]1% of live births. These conditions are hallmarked by extreme genetic heterogeneity, and therefore, despite a strong genetic component, only a very handful of at-risk loci in CHD have been identified. We herein introduced systems analyses to uncover the hidden organization on biological networks of genomic mutations in CHD, and leveraged network analysis techniques to integrate the human interactome, large-scale patient exomes, the fetal heart spatial transcriptomes, and single-cell transcriptomes of clinical samples. We identified a highly connected network in CHD where most of the member proteins had previously uncharacterized functions in regulating fetal heart development. While genes on the network displayed strong enrichment for heart-specific functions, a sub-group, active specifically at early developmental stages, also regulates fetal brain development, thereby providing mechanistic insights into the clinical comorbidities between CHD and neurodevelopmental conditions. At a small scale, we experimentally verified previously uncharacterized cardiac functions of several novel proteins employing cellular assays and gene editing techniques. At a global scale, our study revealed developmental dynamics of the identified CHD network and observed the strongest enrichment for pathogenic mutations in the network specific to hypoplastic left heart syndrome (HLHS). Our single-cell transcriptome analysis further identified pervasive dysregulation of the network in cardiac endothelial cells and the conduction system in the HLHS left ventricle. Taken together, our systems analyses identified novel factors in CHD, revealed key molecular mechanisms in HLHS, and provides a generalizable framework readily applicable to studying many other complex diseases.

systems biology↗

PRC2 clock: a universal biomarker of aging and rejuvenation

DNA methylation (DNAm) is one of the most reliable biomarkers for aging across many mammalian tissues. While the age-dependent global loss of DNAm has been well characterized, age-dependent DNAm gain is less specified. Multiple studies have demonstrated that polycomb repressive complex 2 (PRC2) targets are enriched among the CpG sites which gain methylation with age. However, a systematic whole-genome examination of all PRC2 targets in the context of aging methylome as well as whether these associations are pan-tissue or tissue-specific is lacking. Here, by analyzing DNAm data from different assays and from multiple young and old human and mouse tissues, we found that low-methylated regions (LMRs) which are highly bound by PRC2 in embryonic stem cells gain methylation with age in all examined somatic mitotic cells. We also estimated that this epigenetic change represents around 90% of the age-dependent DNAm gain genome-wide. Therefore, we propose the "PRC2 clock," defined as the average DNAm in PRC2 LMRs, as a universal biomarker of cellular aging in somatic cells. In addition, we demonstrate the application of this biomarker in the evaluation of different anti-aging interventions, including dietary restriction and partial epigenetic reprogramming.

genomics↗

massDatabase: utilities for the operation of the public compound and pathway database

SummaryOne of the major challenges in LC-MS data (metabolome, lipidome, and exposome) is converting many metabolic feature entries to biological function information, such as metabolite annotation and pathway enrichment, which are based on the compound and pathway databases. Multiple online databases have been developed, containing lots of information about compounds and pathways. However, there is still no tool developed for operating all these databases for biological analysis. Therefore, we developed massDatabase, an R package that operates the online public databases and combines with other tools for streamlined compound annotation and pathway enrichment analysis. massDatabase is a flexible, simple, and powerful tool that can be installed on all platforms, allowing the users to leverage all the online public databases for biological function mining. A detailed tutorial and a case study are provided in the Supplementary Materials. Availability and implementationhttps://massdatabase.tidymass.org/. Contactshenxt@stanford.edu and mpsnyder@stanford.edu Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

Mitochondrial function determines severity but not risk of amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease. Selective vulnerability of energy-intensive motor neurons (MNs) has fostered speculation that mitochondrial function is a determinant of ALS. Previously, the position of mitochondrial function in the pathogenic cascade leading to neurotoxicity has been unclear. We separated upstream genetic determinants of mitochondrial function, including genetic variation within the mitochondrial genome or autosomes; from downstream changeable factors including mitochondrial copy number (mtCN) and MN gene expression. We discovered that functionally validated mitochondrial haplotypes are a determinant of ALS survival but not ALS risk. Loss-of-function genetic variants within, and reduced MN expression of, ACADM and DNA2 lead to shorter ALS survival; both genes impact mitochondrial function. MtCN responds dynamically to the onset of ALS independent of mitochondrial haplotype, and is also significantly correlated with disease severity. We conclude that mitochondrial function impacts ALS progression but not risk; our findings have therapeutic implications.

genetics↗

Ultra high-throughput whole-genome methylation sequencing reveals trajectories in precancerous polyps to early colorectal adenocarcinoma

Aberrant shifts in DNA methylation have long been regarded as an early marker for cancer onset and progression. To chart DNA methylation changes that occur during the transformation from normal healthy colon tissue to malignant colorectal cancer (CRC), we collected over 50 samples from 15 familial adenomatous polyposis (FAP) and non-FAP colorectal cancer patients, and generated 30-70x whole-genome methylation sequencing (WGMS) runs via the novel Ultima Genomics ultra high-throughput sequencing platform. We observed changes in DNA methylation that occur early in the malignant transformation process, in gene promoters and in distal regulatory elements. Among these changes are events of hyper-methylation which are associated with a bivalent "poised" chromatin state at promoters and are CRC-specific. Distal enhancers show nonlinear dynamics, lose methylation in the progression from normal mucosa to dysplastic polyps but regain methylation in the adenocarcinoma state. Enhancers that gain chromatin accessibility in the adenocarcinoma state and are enriched with HOX transcription factor binding sites, a marker of developmental genes. This work demonstrates the feasibility of generating large high quality WGMS data using the Ultima Genomics platform and provides the first detailed view of methylation dynamics during CRC formation and progression in a model case.

genomics↗

Deep Learning-based Pseudo-Mass Spectrometry Imaging Analysis for Precision Medicine

Liquid chromatography-mass spectrometry (LC-MS) based untargeted metabolomics provides systematic profiling of metabolic. Yet its applications in precision medicine (disease diagnosis) have been limited by several challenges, including metabolite identification, information loss, and low reproducibility. Here, we present the deepPseudoMSI project (https://www.deeppseudomsi.org/), which converts LC-MS raw data to pseudo-MS images and then processes them by deep learning for precision medicine, such as disease diagnosis. Extensive tests based on real data demonstrated the superiority of deepPseudoMSI over traditional approaches and the capacity of our method to achieve an accurate individualized diagnosis. Our framework lays the foundation for future metabolic-based precision medicine.

bioinformatics↗

TidyMass: An Object-oriented Reproducible Analysis Framework for LC-MS Data

Reproducibility and transparency have been longstanding but significant problems for the metabolomics field. Here, we present the tidyMass project (https://www.tidymass.org/), a comprehensive computational framework that can achieve the shareable and reproducible workflow needs of data processing and analysis for LC-MS-based untargeted metabolomics. TidyMass was designed based on the following strategies to address the limitations of current tools: 1) Cross-platform utility. TidyMass can be installed on all platforms; 2) Uniformity, shareability, traceability, and reproducibility. A uniform data format has been developed, specifically designed to store and manage processed metabolomics data and processing parameters, making it possible to trace the prior analysis steps and parameters; 3) Flexibility and extensibility. The modular architecture makes tidyMass a highly flexible and extensible tool, so other users can improve it and integrate it with their own pipeline easily.

bioinformatics↗

Integrated platform for multi-scale molecular imaging andphenotyping of the human brain

Understanding cellular architectures and their connectivity is essential for interrogating system function and dysfunction. However, we lack technologies for mapping the multi-scale details of individual cells in the human organ-scale system. To address this challenge, we developed a platform that simultaneously extracts spatial, molecular, morphological, and connectivity information of individual cells from the same human brain, by integrating novel chemical, mechanical, and computational tools. The platform includes three key tools: (i) a vibrating microtome for ultra-precision slicing of large-scale tissues without losing cellular connectivity (MEGAtome), (ii) a polymer hydrogel-based tissue processing technology for multiplexed multiscale imaging of human organ-scale tissues (mELAST), and (iii) a computational pipeline for reconstructing 3D connectivity across multiple brain slabs (UNSLICE). We demonstrated the transformative potential of our platform by analyzing human Alzheimers disease pathology at multiple scales and demonstrating scalable neural connectivity mapping in the human brain. One-Sentence SummaryWe developed an integrated, scalable platform for highly multiplexed, multi-scale phenotyping and connectivity mapping in the same human brain tissue, which incorporated novel tissue processing, labeling, imaging, and computational technologies.

bioengineering↗

Low expression of EXOSC2 protects against clinical COVID-19 and impedes SARS-CoV-2 replication

New therapeutic targets are a valuable resource in the struggle to reduce the morbidity and mortality associated with the COVID-19 pandemic, caused by the SARS-CoV-2 virus. Genome-wide association studies (GWAS) have identified risk loci, but some loci are associated with co-morbidities and are not specific to host-virus interactions. Here, we identify and experimentally validate a link between reduced expression of EXOSC2 and reduced SARS-CoV-2 replication. EXOSC2 was one of 332 host proteins examined, all of which interact directly with SARS-CoV-2 proteins; EXOSC2 interacts with Nsp8 which forms part of the viral RNA polymerase. Lung-specific eQTLs were identified from GTEx (v7) for each of the 332 host proteins. Aggregating COVID-19 GWAS statistics for gene-specific eQTLs revealed an association between increased expression of EXOSC2 and higher risk of clinical COVID-19 which survived stringent multiple testing correction. EXOSC2 is a component of the RNA exosome and indeed, LC-MS/MS analysis of protein pulldowns demonstrated an interaction between the SARS-CoV-2 RNA polymerase and the majority of human RNA exosome components. CRISPR/Cas9 introduction of nonsense mutations within EXOSC2 in Calu-3 cells reduced EXOSC2 protein expression, impeded SARS-CoV-2 replication and upregulated oligoadenylate synthase (OAS) genes, which have been linked to a successful immune response against SARS-CoV-2. Reduced EXOSC2 expression did not reduce cellular viability. OAS gene expression changes occurred independent of infection and in the absence of significant upregulation of other interferon-stimulated genes (ISGs). Targeted depletion or functional inhibition of EXOSC2 may be a safe and effective strategy to protect at-risk individuals against clinical COVID-19.

genetics↗

Wireless closed-loop smart bandage for chronic wound management and accelerated tissue regeneration

Chronic non-healing wounds represent a major source of morbidity for patients and a significant economic burden. Current wound care treatments are generally passive and are unable to adapt to changes in the wound environment in real time. By integrating multimodal sensors and adding stimulators in a bandage, real-time physiological monitoring is possible and provides an opportunity for active intervention into the complex wound environment. Here, we develop a battery-free flexible bioelectronic system consisting of wirelessly powered, closed-loop sensing and stimulation circuits with tissue-interfacing tough conducting hydrogel electrodes for robust signal transduction, on-demand adhesion, and detachment. Using multiple pre-clinical models, we demonstrate the capability of our wound care system to continuously monitor skin impedance and temperature, to trigger directional electrical stimulation. The accelerated wound closure was confirmed to be due to the activation of pro-regenerative genes linked to accelerated wound closure, increased neovascularization, and enhanced dermal recovery.

bioengineering↗

Mutation, selection, and the prevalence of the C. elegans mortal germline phenotype

C. elegans strains with the heat-sensitive mortal germline (Mrt) phenotype become progressively sterile over the course of a few tens of generations when maintained at temperatures near the upper range of C. elegans tolerance. Mrt is transgenerationally-heritable, and proximately under epigenetic control. Previous studies have suggested that Mrt presents a relatively large mutational target, and that Mrt is not uncommon in natural populations of C. elegans. The Mrt phenotype is not monolithic. Some strains exhibit a strong Mrt phenotype, in which individuals invariably become sterile over a few generations, whereas other strains show a weaker (less penetrant) phenotype in which the onset of sterility is slower and more stochastic. We present results in which we (1) quantify the rate of mutation to the Mrt phenotype, and (2) quantify the frequency of Mrt in a collection of 95 wild isolates. Over the course of ~16,000 meioses, we detected one mutation to a strong Mrt phenotype, resulting in a point estimate of the mutation rate UMrt{approx} 6x10-5/genome/generation. We detected no mutations to a weak Mrt phenotype. 6/95 wild isolates have a strong Mrt phenotype, and although quantification of the weak Mrt phenotype is inexact, the weak Mrt phenotype is not rare in nature. We estimate a strength of selection against mutations conferring the strong Mrt phenotype [Formula], similar to selection against mutations affecting competitive fitness. The appreciable frequency of weak Mrt variants in nature combined with the low mutation rate suggests that Mrt may be maintained by balancing selection.

genetics↗

Annotation of Spatially Resolved Single-cell Data with STELLAR

Accurate cell type annotation from spatially resolved single cells is crucial to understand functional spatial biology that is the basis of tissue organization. However, current computational methods for annotating spatially resolved single-cell data are typically based on techniques established for dissociated single-cell technologies and thus do not take spatial organization into account. Here we present STELLAR, a geometric deep learning method for cell type discovery and identification in spatially resolved single-cell datasets. STELLAR automatically assigns cells to cell types present in the annotated reference dataset as well as discovers novel cell types and cell states. STELLAR transfers annotations across different dissection regions, different tissues, and different donors, and learns cell representations that capture higher-order tissue structures. We successfully applied STELLAR to CODEX multiplexed fluorescent microscopy data and multiplexed RNA imaging datasets. Within the Human BioMolecular Atlas Program, STELLAR has annotated 2.6 million spatially resolved single cells with dramatic time savings.

bioinformatics↗

Robust Identification of Temporal Biomarkers in Longitudinal Omics Studies

Longitudinal studies increasingly collect rich omics data sampled frequently over time and across large cohorts to capture dynamic health fluctuations and disease transitions. However, the generation of longitudinal omics data has preceded the development of analysis tools that can efficiently extract insights from such data. In particular, there is a need for statistical frameworks that can identify not only which omics features are differentially regulated between groups but also over what time intervals. Additionally, longitudinal omics data may have inconsistencies, including nonuniform sampling intervals, missing data points, subject dropout, and differing numbers of samples per subject. In this work, we developed a statistical method that provides robust identification of time intervals of temporal omics biomarkers. The proposed method is based on a semi-parametric approach, in which we use smoothing splines to model longitudinal data and infer significant time intervals of omics features based on an empirical distribution constructed through a permutation procedure. We benchmarked the proposed method on five simulated datasets with diverse temporal patterns, and the method showed specificity greater than 0.99 and sensitivity greater than 0.72. Applying the proposed method to the Integrative Personal Omics Profiling (iPOP) cohort revealed temporal patterns of amino acids, lipids, and hormone metabolites that are differentially regulated in male versus female subjects following a respiratory infection. In addition, we applied the longitudinal multi-omics dataset of pregnant women with and without preeclampsia, and the method identified potential lipid markers that are temporally significantly different between the two groups. We provide an open-source R package, OmicsLonDA (Omics Longitudinal Differential Analysis): https://bioconductor.org/packages/OmicsLonDA to enable widespread use.

bioinformatics↗

Patient-derived gene and protein expression signatures of NGLY1 deficiency

N-Glycanase 1 (NGLY1) deficiency is a rare and complex genetic disorder. Although recent studies have shed light on the molecular underpinnings of NGLY1 deficiency, a systematic characterization of gene and protein expression changes in patient-derived cells has been lacking. Here, we performed RNA-sequencing and mass spectrometry to determine the transcriptomes and proteomes of 66 cell lines representing 4 different cell types derived from 14 NGLY1 deficient patients and 17 controls. While gene and protein expression levels agreed well with each other, expression differences were more pronounced at the protein level. Although NGLY1 protein levels were up to 9.5-fold downregulated in patients compared to parent controls, depending on the genotype, NGLY1 protein was still detectable in all patient-derived lymphoblastoid cell lines. Consistent with the role of NGLY1 as a regulator of the transcription factor Nrf1, we observed a cell type-independent downregulation of proteasomal genes in NGLY1 deficient cells. In contrast, genes involved in ribosomal mRNA processing were upregulated in multiple cell types. In addition, we observed cell type-specific effects. For example, genes and proteins involved in glutathione synthesis, such as the glutamate-cystein ligase subunits GCLC and GCLM, were downregulated specifically in lymphoblastoid cells. We provide a web application that enables access to all results generated in this study at https://apps.embl.de/ngly1browser. This resource will guide future studies of NGLY1 deficiency in directions that are most relevant to patients.

systems biology↗

An acetyl-click screening platform identifies a small molecule inhibitor of Histone Acetyltransferase 1 (HAT1) with anti-tumor activity

HAT1 is a central regulator of chromatin synthesis that acetylates nascent histone H3:H4 tetramers in the cytoplasm. It may have a role in cancer metabolism by linking cytoplasmic production of acetyl-CoA to nuclear acetyl flux. This is because the HAT1 di-acetylation mark is not propagated in chromatin and instead is de-acetylated after nascent histone insertion into chromatin. Thus, HAT1 likely provides a nuclear source of free acetate that may be recycled to acetyl-CoA for nuclear acetylation reactions. Correspondingly, suppression of HAT1 protein expression impairs tumor growth. To ascertain whether targeting HAT1 is a viable anti-cancer treatment strategy we sought to identify small molecule inhibitors of HAT1. We developed a high-throughput HAT1 acetyl-click assay to facilitate drug discovery and enzymology. Screening of small molecules computationally predicted to bind the active site led to the discovery of multiple riboflavin analogs that inhibited HAT1 enzymatic activity by competing with acetyl-CoA binding. These hits were refined by synthesis and testing over 70 analogs, which yielded structure-activity relationships. The isoalloxazine core was required for enzymatic inhibition, whereas modifications of the ribityl sidechain improved enzymatic potency and cellular growth suppression. These efforts resulted in a lead compound (JG-2016) that suppressed growth of human cancer cells lines in vitro and impaired tumor growth in vivo. This is the first report of a small molecule inhibitor of the HAT1 enzyme complex and represents a step towards targeting this pathway for cancer therapy.

biochemistry↗

Integration and comparison of multi-omics profiles of NGLY1 deficiency plasma and cellular models to identify clinically relevant molecular phenotypes

NGLY1 (N-glycanase 1) deficiency is a rare congenital recessive disorder of protein deglycosylation unaddressed by the current standard of care. Using combined metabolomics and proteomics profiling, we show that NGLY1 deficiency activates the immune response and disturbs lipid metabolism, biogenic amine synthesis, and glutathione metabolism. These alterations were also observed in NGLY1 deficient patient-derived induced pluripotent stem cells (iPSCs) and differentiated neural progenitor cells (NPCs), which serve as personalized cellular models of the disease. These findings provide molecular insight into the pathophysiology of NGLY1 deficiency and suggest potential therapeutic strategies.

systems biology↗

metID: A R package for automatable compound annotation for LC-MS-based data

SummaryAccurate and efficient compound annotation is a long-standing challenge for LC-MS-based data (e.g. untargeted metabolomics and exposomics). Substantial efforts have been devoted to overcoming this obstacle, whereas current tools are limited by the sources of spectral information used (in-house and public databases) and are not automated and streamlined. Therefore, we developed metID, an R package that combines information from all major databases for comprehensive and streamlined compound annotation. metID is a flexible, simple, and powerful tool that can be installed on all platforms, allowing the compound annotation process to be fully automatic and reproducible. A detailed tutorial and a case study are provided in Supplementary Materials. Availability and implementationhttps://jaspershen.github.io/metID/. Contactjiangzhu@sioc.ac.cn and mpsnyder@stanford.edu

bioinformatics↗

Precision environmental health monitoring by longitudinal exposome and multi-omics profiling

Conventional environmental health studies primarily focused on limited environmental stressors at the population level, which lacks the power to dissect the complexity and heterogeneity of individualized environmental exposures. Here we integrated deep-profiled longitudinal personal exposome and internal multi-omics to systematically investigate how the exposome shapes an individuals phenome. We annotated thousands of chemical and biological components in the personal exposome cloud and found they were significantly correlated with thousands of internal biomolecules, which was further cross validated using corresponding clinical data. In particular, our results showed that agrochemicals (e.g., carcinogenic pesticides, fungicides, and herbicides) and fungi predominated in the highly diverse and dynamic personal exposome, and the biomolecules and pathways related to the individuals immune system, kidneys, and liver were highly correlated with the personal external exposome. Overall, our findings demonstrate dynamic interactions between the personal exposome and internal multi-omics and provide important insights into the impact of the environmental exposome on precision health.

systems biology↗