Biology subjects

Snyder, M.

Publications and source records attributed to Snyder, M..

72 records · Page 4Linked to original sources

bioRxiv2021

Combined Nanopore and Single-Molecule Real-Time Sequencing Survey of Human Betaherpesvirus 5 Transcriptome

Long-read sequencing (LRS), a powerful novel approach, is able to read full-length transcripts and confers a major advantage over the earlier gold standard short-read sequencing in the efficiency of identifying for example polycistronic transcripts and transcript isoforms, including transcript length- and splice variants. In this work, we profile the human cytomegalovirus transcriptome using two third-generation LRS platforms: the Sequel from Pacific BioSciences, and MinION from Oxford Nanopore Technologies. We carried out both cDNA and direct RNA sequencing, and applied the LoRTIA software, developed in our laboratory, for the transcript annotations. This study identified a large number of novel transcript variants, including splice isoforms and transcript start and end site isoforms, as well as putative mRNAs with truncated in-frame ORFs (located within the larger ORFs of the canonical mRNAs), which potentially encode N-terminally truncated polypeptides. Our work also disclosed a highly complex meshwork of transcriptional read-throughs and overlaps.

genomics↗

bioRxiv2021

Transcriptome variation in human tissues revealed by long-read sequencing

Regulation of transcript structure generates transcript diversity and plays an important role in human disease. The advent of long-read sequencing technologies offers the opportunity to study the role of genetic variation in transcript structure. In this paper, we present a large human long-read RNA-seq dataset using the Oxford Nanopore Technologies platform from 88 samples from GTEx tissues and cell lines, complementing the GTEx resource. We identified just under 100,000 new transcripts for annotated genes, and validated the protein expression of a similar proportion of novel and annotated transcripts. We developed a new computational package, LORALS, to analyze genetic effects of rare and common variants on the transcriptome via allele-specific analysis of long reads. We called allele-specific expression and transcript structure events, providing novel insights into the specific transcript alterations caused by common and rare genetic variants and highlighting the resolution gained from long-read data. We were able to perturb transcript structure upon knockdown of PTBP1, an RNA binding protein that mediates splicing, thereby finding genetic regulatory effects that are modified by the cellular environment. Finally, we use this dataset to enhance variant interpretation and study rare variants leading to aberrant splicing patterns.

genomics↗

bioRxiv2020

Cell-free DNA (cfDNA) and exosome profiling from a year-long humanspaceflight reveals circulating biomarkers

The health impact of prolonged space flight on the human body is not well understood. Liquid biopsies based on cell-free DNA (cfDNA) or exosome analysis provide a noninvasive approach to monitor the dynamics of genomic, epigenomic and proteomic biomarkers, and the occurrence of DNA damage, physiological stress, and immune responses. To study the molecular consequences of spaceflight we profiled cfDNA isolated from plasma of an astronaut (TW) during a year-long mission on the International Space Station (ISS), sampling before, during, and after spaceflight, and compared the results to cfDNA profiling of the subjects identical twin (HR) who remained on Earth, as well as healthy donors. We characterized cfDNA concentration and fragment size, and the positioning of nucleosomes on cfDNA, observing a significant increase in the proportion of cell-free mitochondrial DNA inflight, suggesting that cf-mtDNA is a potential biomarker for space flight-associated stress, and that this result was robust to ambient transit from the International Space Station (ISS). Analysis of exosomes isolated from post-flight plasma revealed a 30-fold increase in circulating exosomes and distinct exosomal protein cargo, including brain-derived peptides, in TW compared to HR and all known controls. This study provides the first longitudinal analysis of astronaut cfDNA during spaceflight, as well as the first exosome profiles, and highlights cf-mtDNA levels as a potential biomarker for physiological stress or immune system responses related to microgravity, radiation exposure, and other unique environmental conditions on the ISS.

cell biology↗

bioRxiv2020

Long-read Assays Shed New Light on the Transcriptome Complexity of a Viral Pathogen and on Virus-Host Interaction

Characterization of global transcriptomes using conventional short-read sequencing is challenging because of the insensitivity of these platforms to transcripts isoforms, multigenic RNA molecules, and transcriptional overlaps, etc. Long-read sequencing (LRS) can overcome these limitations by reading full-length transcripts. Employment of these technologies has led to the redefinition of transcriptional complexities in reported organisms. In this study, we applied LRS platforms from Pacific Biosciences and Oxford Nanopore Technologies to profile the dynamic vaccinia virus (VACV) transcriptome and assess the effect of viral infection on host gene expression. We performed cDNA and direct RNA sequencing analyses and revealed an extremely complex transcriptional landscape of this virus. In particular, VACV genes produce large numbers of transcript isoforms that vary in their start and termination sites. A significant fraction of VACV transcripts start or end within coding regions of neighboring genes. We distinguished five classes of host genes according to their temporal responses to viral infection. This study provides novel insights into the transcriptomic profile of a viral pathogen and the effect of the virus on host gene expression. Author SummaryViral transcriptomes that are determined using conventional (first- and second-generation) sequencing techniques are incomplete because these platforms are inefficient or fail to distinguish between types of transcripts and transcript isoforms. In particular, conventional sequencing techniques fail to distinguish between parallel overlapping transcripts, including alternative polycistronic transcripts, transcriptional start site (TSS) and transcriptional end site (TES) isoforms, and splice variants and RNA molecules that are produced by transcriptional read-throughs. Long-read sequencing (LRS) can provide complete sets of RNA molecules, and can therefore be used to assemble complete transcriptome atlases of organisms. Although vaccinia virus (VACV) does not produce spliced RNAs, its transcriptome contains large numbers of TSSs and TESs for individual viral genes and has a high degree of polycistronism, together leading to enormous complexity. In this study, we applied single molecule real-time and nanopore-based cDNA and direct-RNA sequencing methods to investigate transcripts of VACV and the host organism.

genomics↗

bioRxiv2019

Demand for Multiplatform and Meta-analytic Approaches in Transcriptome Profiling

In a recent article, Depledge and colleagues reported a study of the herpes simplex virus type 1 (HSV-1) transcriptome using direct RNA sequencing (dRNA-Seq) on nanopore arrays. The authors provided a useful dataset on full-length viral and host RNA molecules. In this study, we reanalyzed the published dataset and compared it with data generated by our group and others. Our comparative study clearly demonstrated the need for multiplatform and meta-analytic approaches for transcriptome profiling to obtain reliable results.

genomics↗

bioRxiv2019

A Quantitative Proteome Map of the Human Body

Determining protein levels in each tissue and how they compare with RNA levels is important for understanding human biology and disease as well as regulatory processes that control protein levels. We quantified the relative protein levels from 12,627 genes across 32 normal human tissue types prepared by the GTEx project. Known and new tissue specific or enriched proteins (5,499) were identified and compared to transcriptome data. Many ubiquitous transcripts are found to encode highly tissue specific proteins. Discordance in the sites of RNA expression and protein detection also revealed potential sites of synthesis and action of protein signaling molecules. Overall, these results provide an extraordinary resource, and demonstrate that understanding protein levels can provide insights into metabolism, regulation, secretome, and human diseases.\n\nSummaryQuantitative proteome study of 32 human tissues and integrated analysis with transcriptome data revealed that understanding protein levels could provide in-depth knowledge to post transcriptional or translational regulations, human metabolism, secretome, and diseases.

systems biology↗

bioRxiv2019

Age-related decline in metabolite heavy isotope content in rodent organs

Heavy isotopes are discriminated by biological systems due to kinetic isotopic effects at the biochemical/metabolic levels. How these heavy isotopes are enriched or depleted over a long term is unclear, but artificial manipulation of heavy isotope content in various organisms has produced significant impacts on biological functions, suggesting the origin may arise with intrinsic mechanisms for a functional outcome. Our previous study has revealed an age-associated decline in metabolite heavy isotope content (HIC) in the budding yeast, which could be reversed in part by supplementing heavy water, and consequently, also increased yeast lifespans. In the current study, we report a similar age-dependent decline in HIC from three types of mouse tissues: brain, heart, and skeletal muscles. Furthermore, individual tissues exhibited different patterns of HIC change over age, which appeared to match their development and maturation timelines. These results have demonstrated that age-dependent decline in HIC also exists in mammals, which is likely a traceable feature of development and perhaps aging. Thus, we believe that reversing the decline in HIC could have the potential to extend the healthspan of humans.

systems biology↗

bioRxiv2019

Atlas of transcriptionally active transposable elements in human adult tissues

Approximately half of the human genome consists of mobile repetitive DNA sequences known as transposable elements (TEs). They are usually silenced by epigenetic mechanisms, but a few are known to escape silencing at embryonic stages, affecting early human development by regulating nearby protein-coding genes. To investigate transcriptional activity in human adult tissues we systematically investigate the expression landscape of about 4.2 million non-coding TEs in 8,051 RNA-Seq datasets from up to 49 adult tissues and 540 individuals. We show that approximately 79,558 individual TEs (2%). belonging to 856 subfamilies escape epigenetic silencing in adult tissues and become transcriptionally active, often in a very tissue-specific manner. Supporting a role for TEs in the regulation of expression of nearby genes, we found the expression of TEs often correlated with the expression of nearby genes, and significantly stronger when the TEs include eQTLs for the genes. We identified thousands of tissue-elevated, sex-associated TEs in the breast, ethnicity-associated in the skin and age-associated in the tibial artery, where we found a potential implication of two TE subfamilies in atherosclerosis. Our results suggest a functional role of TEs in the regulation of gene expression, support their implication in human phenotypes, and also serve as a comprehensive resource of transcriptionally active TEs in human adult tissues.

genomics↗

bioRxiv2019

An integrative ENCODE resource for cancer genomics

ENCODE comprises thousands of functional genomics datasets, and the encyclopedia covers hundreds of cell types, providing a universal annotation for genome interpretation. However, for particular applications, it may be advantageous to use a customized annotation. Here, we develop such a custom annotation by leveraging advanced assays, such as eCLIP, Hi-C, and whole-genome STARR-seq on a number of data-rich ENCODE cell types. A key aspect of this annotation is comprehensive and experimentally derived networks of both transcription factors and RNA-binding proteins (TFs and RBPs). Cancer, a disease of system-wide dysregulation, is an ideal application for such a network-based annotation. Specifically, for cancer-associated cell types, we put regulators into hierarchies and measure their network change (rewiring) during oncogenesis. We also extensively survey TF-RBP crosstalk, highlighting how SUB1, a previously uncharacterized RBP, drives aberrant tumor expression and amplifies the effect of MYC, a well-known oncogenic TF. Furthermore, we show how our annotation allows us to place oncogenic transformations in the context of a broad cell space; here, many normal-to-tumor transitions move towards a stem-like state, while oncogene knockdowns show an opposing trend. Finally, we organize the resource into a coherent workflow to prioritize key elements and variants, in addition to regulators. We showcase the application of this prioritization to somatic burdening, cancer differential expression and GWAS. Targeted validations of the prioritized regulators, elements and variants using siRNA knockdowns, CRISPR-based editing, and luciferase assays demonstrate the value of the ENCODE resource.

genomics↗

bioRxiv2019

MOBN: an interactive database of multi-omics biological networks

It is essential to reveal the associations between different omics data for a comprehensive understanding of the altered biological process in human wellness and disease. To date, very few studies have focused on collecting and exhibiting multi-omics associations in a single database. Here, we present iNetModels, an interactive database and visualization platform of Multi-Omics Biological Networks (MOBNs). This platform describes the associations between the clinical chemistry, anthropometric parameters, plasma proteomics and metabolomics as well as metagenomics for oral and gut microbiome obtained from the same individuals. Moreover, iNetModels includes tissue- and cancer-specific Gene Co-expression Networks (GCNs) for exploring the connections between the specific genes. This platform allows the user to interactively explore a single feature's association with other omics data and customize its particular context (e.g. male/female specific). The users can also register their own data for sharing and visualization of the MOBNs and GCNs. Moreover, iNetModels allows users who do not have a bioinformatics background to facilitate human wellness and diseases research. iNetModels can be accessed freely at https://inetmodels.com without any limitation.

bioinformatics↗

bioRxiv2019

Global analysis of epigenetic heterogeneity identifies divergent drivers of esophageal squamous cell carcinoma

Epigenetic landscapes can shape physiologic and disease phenotypes. We used integrative, high resolution multi-omics methods to characterize the oncogenic drivers of esophageal squamous cell carcinoma (ESCC). We found 98% of CpGs are hypomethylated across the ESCC genome and two-thirds occur in long non-coding (lnc)RNA regions. DNA methylation and epigenetic heterogeneity both coincide with chromosomal topological alterations. Gene body methylation, polycomb repressive complex occupancy, and CTCF binding sites associate with cancer-specific gene regulation. Epigenetically-mediated activation of non-canonical WNT signaling and the lncRNA ESCCAL-1 were validated as potential ESCC driver alterations. Gene-specific cancer driver roles of epigenetic alterations and heterogeneity are identified.

cell biology↗

bioRxiv2019

Multiple Long-read Sequencing Survey of Herpes Simplex Virus Lytic Transcriptome

Long-read sequencing (LRS) has become increasingly important in RNA research due to its strength in resolving complex transcriptomic architectures. In this regard, currently two LRS platforms have demonstrated adequate performance: the Single Molecule Real-Time Sequencing by Pacific Biosciences (PacBio) and the nanopore sequencing by Oxford Nanopore Technologies (ONT). Even though these techniques produce lower coverage and are more error prone than short-read sequencing, they continue to be more successful in identifying transcript isoforms including polycistronic and multi-spliced RNA molecules, as well as transcript overlaps. Recent reports have successfully applied LRS for the investigation of the transcriptome of viruses belonging to various families. These studies have substantially increased the number of previously known viral RNA molecules. In this work, we used the Sequel and MinION technique from PacBio and ONT, respectively, to characterize the lytic transcriptome of the herpes simplex virus type 1 (HSV-1). In most samples, we analyzed the poly(A) fraction of the transcriptome, but we also performed random oligonucleotide-based sequencing. Besides cDNA sequencing, we also carried out native RNA sequencing. Our investigations identified more than 160 previously undetected transcripts, including coding and non-coding RNAs, multi-splice transcripts, as well as polycistronic and complex transcripts. Furthermore, we determined previously unsubstantiated transcriptional start sites, polyadenylation sites, and splice sites. A large number of novel transcriptional overlaps were also detected. Random-primed sequencing revealed that each convergent gene pair produces non-polyadenylated read-through RNAs overlapping the partner genes. Furthermore, we identified novel replication-associated transcripts overlapping the HSV-1 replication origins, and novel LAT variants with very long 5 regions, which are co-terminal with the LAT-0.7kb transcript. Overall, our results demonstrated that the HSV-1 transcripts form an extremely complex pattern of overlaps, and that entire viral genome is transcriptionally active. In most viral genes, if not in all, both DNA strands are expressed.

genomics↗

bioRxiv2019

MISTERMINATE Mechanistically Links Mitochondrial Dysfunction with Proteostasis Failure

Mitochondrial dysfunction and proteostasis failure frequently coexist as hallmarks of neurodegenerative disease. How these pathologies are related is not well understood. Here we describe a phenomenon termed MISTERMINATE (mitochondrial stress-induced translational termination impairment and protein carboxyl terminal extension), which mechanistically links mitochondrial dysfunction with proteostasis failure. We show that mitochondrial dysfunction impairs translational termination of nuclear-encoded mitochondrial mRNAs including complex-I 30kD subunit (C-I30) mRNA, occurring on mitochondrial surface in Drosophila and mammalian cells. Ribosomes stalled at the normal stop codon continue to add to the C-terminus of C-I30 certain amino acids non-coded by mRNA template. C-terminally-extended C-I30 is toxic when assembled into C-I and forms aggregates in the cytosol. Enhancing co-translational quality control prevents C-I30 C-terminal extension and rescues mitochondrial and neuromuscular degeneration in a Parkinsons disease model. These findings emphasize the importance of efficient translation termination and reveal unexpected link between mitochondrial health and proteome homeostasis mediated by MISTERMINATE.

cell biology↗

bioRxiv2019

Classifying Non-Small Cell Lung Cancer Histopathology Types and Transcriptomic Subtypes using Convolutional Neural Networks

Non-small cell lung cancer is a leading cause of cancer death worldwide, and histopathological evaluation plays the primary role in its diagnosis. However, the morphological patterns associated with the molecular subtypes have not been systematically studied. To bridge this gap, we developed a quantitative histopathology analytic framework to identify the gene expression subtypes of non-small cell lung cancer objectively. We processed whole-slide histopathology images of lung adenocarcinoma (n=427) and lung squamous cell carcinoma patients (n=457) in The Cancer Genome Atlas. To establish neural networks for quantitative image analyses, we first build convolutional neural network models to identify tumor regions from adjacent dense benign tissues (areas under the receiver operating characteristic curves (AUC) > 0.935) and recapitulated expert pathologists diagnosis (AUC > 0.88), with the results validated in an independent cohort (n=125; AUC > 0.85). We further demonstrated that quantitative histopathology morphology features identified the major transcriptomic subtypes of both adenocarcinoma and squamous cell carcinoma (P < 0.01). Our study is the first to classify the transcriptomic subtypes of non-small cell lung cancer using fully-automated machine learning methods. Our approach does not rely on prior pathology knowledge and can discover novel clinically-relevant histopathology patterns objectively. The developed procedure is generalizable to other tumor types or diseases.

pathology↗

bioRxiv2018

Genome wide association with quantitative resistance phenotypes in Mycobacterium tuberculosis reveals novel resistance genes and regulatory regions

Drug resistance is threatening attempts at tuberculosis epidemic control. Molecular diagnostics for drug resistance that rely on the detection of resistance-related mutations could expedite patient care and accelerate progress in TB eradication. We performed minimum inhibitory concentration testing for 12 anti-TB drugs together with Illumina whole genome sequencing on 1452 clinical Mycobacterium tuberculosis (MTB) isolates. We then used a linear mixed model to evaluate genome wide associations between mutations in MTB genes or noncoding regions and drug resistance, followed by validation of our findings in an independent dataset of 792 patient isolates. Novel associations at 13 genomic loci were confirmed in the validation set, with 2 involving noncoding regions. We found promoter mutations to have smaller average effects on resistance levels than gene body mutations in genes where both can contribute to resistance. Enabled by a quantitative measure of resistance, we estimated the heritability of the resistance phenotype to 11 anti-TB drugs and identify a lower than expected contribution from known resistance genes. We also report the proportion of variation in resistance levels explained by the novel loci identified here. This study highlights the complexity of the genomic mechanisms associated with the MTB resistance phenotype, including the relatively large number of potentially causative or compensatory loci, and emphasizes the contribution of the noncoding portion of the genome.

evolutionary biology↗

bioRxiv2018

FMRIPrep: a robust preprocessing pipeline for functional MRI

Preprocessing of functional MRI (fMRI) involves numerous steps to clean and standardize data before statistical analysis. Generally, researchers create ad hoc preprocessing workflows for each new dataset, building upon a large inventory of tools available for each step. The complexity of these workflows has snowballed with rapid advances in MR data acquisition and image processing techniques. We introduce fMRIPrep, an analysis-agnostic tool that addresses the challenge of robust and reproducible preprocessing for task-based and resting fMRI data. FMRIPrep automatically adapts a best-in-breed workflow to the idiosyncrasies of virtually any dataset, ensuring high-quality preprocessing with no manual intervention. By introducing visual assessment checkpoints into an iterative integration framework for software-testing, we show that fMRIPrep robustly produces high-quality results on a diverse fMRI data collection comprising participants from 54 different studies in the OpenfMRI repository. We review the distinctive features of fMRIPrep in a qualitative comparison to other preprocessing workflows. We demonstrate that fMRIPrep achieves higher spatial accuracy as it introduces less uncontrolled spatial smoothness than commonly used preprocessing tools. FMRIPrep has the potential to transform fMRI research by equipping neuroscientists with a high-quality, robust, easy-to-use and transparent preprocessing workflow which can help ensure the validity of inference and the interpretability of their results.

bioinformatics↗

bioRxiv2018

Evaluation of Whole Exome Sequencing as an Alternative of BeadChip and Whole Genome Sequencing in Human Population Genetic Analysis

Understanding the underlying genetic structure of human populations is of fundamental interest to both biological and social sciences. Advances in high-throughput genotyping technology have markedly improved our understanding of global patterns of human genetic variation. The most widely used methods for collecting variant information at the DNA-level include whole genome sequencing, which continues to remain costly, and the more economical solution of array-based techniques, as these are capable of simultaneously genotyping a pre-selected set of variable DNA sites in the human genome. The largest publicly accessible set of human genomic sequence data available today originates from exome sequencing that comprises around 1.2% of the whole genome (approximately 30 million base pairs). In this study, we compared the application of the exome dataset to the array-based dataset and to the gold standard whole genome dataset using the same population genetic analysis methods. Our results draw attention to some of the inherent problems that arise from using pre-selected SNP sets for population genetic analysis. Additionally, we demonstrate that exome sequencing provides a better alternative to the array-based methods for population genetic analysis. In this study, we propose a strategy for unbiased variant collection from exome data and offer a bioinformatics protocol for proper data processing.

genomics↗

bioRxiv2017

GATTACA: Lightweight Metagenomic Binning With Compact Indexing Of Kmer Counts And MinHash-based Panel Selection

We introduce GATTACA, a framework for rapid and accurate binning of metagenomic contigs from a single or multiple metagenomic samples into clusters associated with individual species. The clusters are computed using co-abundance profiles within a set of reference metagnomes; unlike previous methods, GATTACA estimates these profiles from k-mer counts stored in a highly compact index. On multiple synthetic and real benchmark datasets, GATTACA produces clusters that correspond to distinct bacterial species with an accuracy that matches earlier methods, while being up to 20x faster when the reference panel index can be computed offline and 6x faster for online co-abundance estimation. Leveraging the MinHash technique to quickly compare metagenomic samples, GATTACA also provides an efficient way to identify publicly-available metagenomic data that can be incorporated into the set of reference metagenomes to further improve binning accuracy. Thus, enabling easy indexing and reuse of publicly-available metagenomic datasets, GATTACA makes accurate metagenomic analyses accessible to a much wider range of researchers.

bioinformatics↗