Biology subjects

Snyder, M. P.

Publications and source records attributed to Snyder, M. P..

At least 55 records · Page 3Linked to original sources

bioRxiv2021

A cancer-associated RNA polymerase III identity drives robust transcription and expression of SNAR-A noncoding RNA

Dysregulation of the RNA polymerase III (Pol III) transcription program, which synthesizes tRNA and other classes of small noncoding RNA critical for cell growth and proliferation, is associated with cancer and human disease. Previous studies have identified two distinct Pol III isoforms defined by the incorporation of either subunit POLR3G (RPC7) during early development, or POLR3GL (RPC7{beta}) in specialized tissues. Though POLR3G is re-established in cancer and immortalized cell lines, the contributions of these isoforms to transcription potential and transcription dysregulation in cancer remain poorly defined. Using an integrated Pol III genomic profiling approach in combination with in vitro differentiation and subunit disruption experiments, we discover that loss of subunit POLR3G is accompanied by a restricted repertoire of genes transcribed by Pol III. In particular, we observe that a specific class of small noncoding RNA, SNAR-A, is exquisitely sensitive to the availability of subunit POLR3G in proliferating cells. Taken further, large-scale analysis of Pol III subunit expression and downstream chromatin features identifies concomitant loss of POLR3G and SNAR-A activity across a multitude of differentiated primary immune cell lineages, and conversely, coordinate re-establishment of POLR3G expression and SNAR-A features in a variety of human cancers. These results altogether argue against strict redundancy models for subunits POLR3G and POLR3GL, and instead support a model in which Pol III identity itself functions as an important transcriptional regulatory mechanism. Upregulation of POLR3G, which is driven by MYC, identifies a subgroup of patients with unfavorable survival outcomes in specific cancers, further implicating the POLR3G-enhanced transcription repertoire as a potential disease factor.

molecular biology↗

bioRxiv2021

Single-cell analyses reveal a continuum of cell state and composition changes in the malignant transformation of polyps to colorectal cancer

To chart cell composition and cell state changes that occur during the transformation of healthy colon to precancerous adenomas to colorectal cancer (CRC), we generated 451,886 single-cell chromatin accessibility profiles and 208,557 single-cell transcriptomes from 48 polyps, 27 normal tissues, and 6 CRCs collected from patients with and without germline APC mutations. A large fraction of polyp and CRC cells exhibit a stem-like phenotype, and we define a continuum of epigenetic and transcriptional changes occurring in these stem-like cells as they progress from normal to CRC. Advanced polyps contain increasing numbers of stem-like cells, regulatory T-cells, and a subtype of FOX-regulated pre-cancer associated fibroblasts. In the cancerous state, we observe T-cell exhaustion, RUNX1-regulated cancer associated fibroblasts, and increasing accessibility associated with HNF4A motifs in epithelia. Methylation changes in sporadic CRC are strongly anti-correlated with accessibility changes along this continuum, further identifying regulatory markers for molecular staging of polyps.

genomics↗

bioRxiv2021

Long-read sequencing of SARS-CoV-2 reveals novel transcripts and a diverse complex transcriptome landscape.

Severe Acute Respiratory Syndrome Coronavirus 2, SARS-CoV-2 (COVID-19), is a positive single-stranded RNA virus with a 30 kb genome that is responsible for the current pandemic. To date, the genomes of global COVID-19 variants have been primarily characterized via short-read sequencing methods. Here, we devised a long-read RNA (IsoSeq) sequencing approach to characterize the COVID-19 transcript landscape and expression of its [~]27 coding regions. Our analysis identified novel COVID-19 transcripts including a) a short [~]65-70 nt 5-UTR fused to various downstream ORFs encoding accessory proteins such as the envelope, ORF 8, and ORF 9 (nucleocapsid) proteins, that are relatively highly expressed, b) novel SNVs that are differentially expressed, whereby a subset are suggestive of partial RNA editing events, and c) SNVs at functional sites, whereby at least one is associated with a differentially expressed spike protein isoform. These previously uncharacterized COVID-19 isoforms, expressed genes, and gene variants were corroborated using ddPCR. Understanding this transcriptional complexity may help provide insight into the biology and pathogenicity of SARS-CoV-2 compared to other coronaviruses.

genetics↗

bioRxiv2021

Endogenous Retroviral Elements Generate Pathologic Neutrophils and Elastase Rich Exosomes in Pulmonary Arterial Hypertension

Neutrophil elastase (NE) is implicated in pulmonary arterial hypertension (PAH) but the role of neutrophils in the pathogenesis of PAH is unclear. Here we show that neutrophils from PAH vs. control subjects produce and release increased NE associated with enhanced extracellular trap formation. PAH neutrophils are highly adherent and show decreased migration consistent with increased vinculin, identified on proteomic analysis and previously linked to an antiviral response. This was substantiated by a transcriptomic interferon signature in PAH neutrophils and an increase in human endogenous retrovirus (HERV-K) envelope protein. NE and interferon genes are induced by HERV-K envelope and vinculin is increased by HERV-K dUTPase that is elevated in PAH plasma. Neutrophil exosomes from PAH plasma contain increased NE and HERV-K envelope and induce pulmonary hypertension in mice, that is mitigated by the NE inhibitor and antiviral agent, elafin. Thus, elevated HERVs explain pathological neutrophils linked to PAH induction and progression.

immunology↗

bioRxiv2020

Genome-wide Identification of the Genetic Basis of Amyotrophic Lateral Sclerosis

Amyotrophic lateral sclerosis (ALS) is an archetypal complex disease centered on progressive death of motor neurons. Despite heritability estimates of 52%, GWAS studies have discovered only seven genome-wide significant hits, which are relevant to <10% of ALS patients. To increase the power of gene discovery, we integrated motor neuron functional genomics with ALS genetics in a hierarchical Bayesian model called RefMap. Comprehensive transcriptomic and epigenetic profiling of iPSC-derived motor neurons enabled RefMap to systematically fine-map genes and pathways associated with ALS. As a significant extension of the known genetic architecture of ALS, we identified a group of 690 candidate ALS genes, which is enriched with previously discovered risk genes. Extensive conservation, transcriptome and network analyses demonstrated the functional significance of these candidate genes in motor neurons and disease progression. In particular, we observed a genetic convergence on the distal axon, which supports the prevailing view of ALS as a distal axonopathy. Of the new ALS genes we discovered, we further characterized KANK1 that is enriched with coding and noncoding, common and rare ALS-associated genetic variation. Modelling patient mutations in human neurons reduced KANK1 expression and produced neurotoxicity with disruption of the distal axon. RefMap can be applied broadly to increase the discovery power in genetic association studies of human complex traits and diseases.

genomics↗

bioRxiv2020

The dynamic, combinatorial cis-regulatory lexicon of epidermal differentiation

Transcription factors (TFs) bind DNA sequence motif vocabularies in cis-regulatory elements (CREs) to modulate chromatin state and gene expression during cell state transitions. A quantitative understanding of how motif lexicons influence dynamic regulatory activity has been elusive due to the combinatorial nature of the cis-regulatory code. To address this, we undertook multi-omic data profiling of chromatin and expression dynamics across epidermal differentiation to identify 40,103 dynamic CREs associated with 3,609 dynamically expressed genes, then applied an interpretable deep learning framework to model the cis-regulatory logic of chromatin accessibility. This identified cooperative DNA sequence rules in dynamic CREs regulating synchronous gene modules with diverse roles in skin differentiation. Massively parallel reporter analysis validated temporal dynamics and cooperative cis-regulatory logic. Variants linked to human polygenic skin disease were enriched in these time-dependent combinatorial motif rules. This integrative approach reveals the combinatorial cis-regulatory lexicon of epidermal differentiation and represents a general framework for deciphering the organizational principles of the cis-regulatory code in dynamic gene regulation. HIGHLIGHTSO_LIAn integrative multi-omic resource profiling chromatin and expression dynamics across keratinocyte differentiation C_LIO_LIPredictive deep learning models of chromatin dynamics reveal a high-resolution cis-regulatory DNA motif lexicon of epidermal differentiation C_LIO_LIModel interpretation enables discovery of combinatorial cis-regulatory logic of homotypic and heterotypic motif combinations C_LIO_LIMassively parallel reporter experiments validate temporal dynamics and cis-regulatory logic of the combinatorial motif lexicon C_LI

genomics↗

bioRxiv2020

Convergent mutations in tissue-specific regulatory regions reveal novel cancer drivers

Although much effort has been devoted to identifying coding mutations across cancer types, regulatory mutations remain poorly characterized. Here, we describe a framework to identify non-coding drivers by aggregating mutations in cell-type specific regulatory regions for each gene. Application of this approach to 2,634 patients across 11 human cancer types identified 60 pan-cancer, 22 pan-breast and 192 cancer specific candidate driver genes that were enriched for expression changes. Analysis of high-throughput CRISPR knockout screens revealed large, cancer specific growth effects for these genes, on par with coding mutations and exceeding that for promoter mutations. Amongst the five candidate drivers selected for further analysis, four (IPO9, MED8, PLEKHA6, and OXNAD1) were associated with survival across multiple cancer types. These studies demonstrate the power of our cell-type aware, convergent regulatory framework to define novel tissue specific cancer driver genes, considerably expanding evidence of functional non-coding mutations in cancer.

cancer biology↗

bioRxiv2020

Tyrosine kinase inhibitors induce mitochondrial dysfunction during cardiomyocyte differentiation through alteration of GATA4-mediated networks

Maternal drug exposure during pregnancy increases the risks of developmental cardiotoxicity, leading to congenital heart defects (CHDs). In this study, we used human stem cells as an in-vitro system to interrogate the mechanisms underlying drug-induced toxicity during cardiomyocyte differentiation, including anticancer tyrosine kinase inhibitor (TKI) drugs (imatinib, sunitinib, and vandetanib). H1-ESCs were treated with these drugs at sublethal levels during cardiomyocyte differentiation. We found that early exposure to TKIs during differentiation induced obvious toxic effects in differentiated cardiomyocytes, including disarranged sarcomere structure, interrupted Ca2+-handling, and impaired mitochondrial function. As sunitinib exposure showed the most significant developmental cardiotoxicity of all TKIs, we further examine its effect with in-vivo experiments. Maternal sunitinib exposure caused fetal death, bioaccumulation, and histopathologic changes in the neonatal mice. Integrative analysis of both transcriptomic and chromatin accessibility landscapes revealed that TKI-exposure altered GATA4-mediated regulatory network, which included key mitochondrial genes. Overexpression of GATA4 with CRISPR-activation restored morphologies, contraction, and mitochondria function in cardiomyocytes upon TKI exposure early during differentiation. Altogether, our study identified a novel crosstalk mechanism between GATA4 activity and mitochondrial function during cardiomyocyte differentiation, and revealed potential therapeutic approaches for reducing TKI-induced developmental cardiotoxicity for human health. HighlightsO_LIEarly-stage exposure to TKIs induced cardiotoxicity and mitochondrial dysfunction C_LIO_LIGATA4 transcriptional activity is inhibited by TKIs C_LIO_LINetwork analysis reveals interactions between GATA4 and mitochondrial genes C_LIO_LIGATA4-overexpression rescues cardiomyocytes and mitochondria from TKI exposure C_LI

systems biology↗

bioRxiv2020

Molecular Choreography of Acute Exercise

Exercise testing is routinely used in clinical practice to assess fitness - a strong predictor of survival - as well as causes of exercise limitations. While these studies often focus on cardiopulmonary response and selected molecular pathways, the dynamic system-wide molecular response to exercise has not been fully characterized. We performed a longitudinal multi-omic profiling of plasma and peripheral blood mononuclear cells including transcriptome, immunome, proteome, metabolome and lipidome in 36 well-characterized volunteers before and after a controlled bout of acute exercise (2, 15, 30 min and 1 hour in recovery). Integrative analysis revealed an orchestrated choreography of biological processes across key tissues. Most of these processes were dampened in insulin resistant participants. Finally, we discovered biological pathways involved in exercise capacity and developed prediction models revealing potential resting blood-based biomarkers of fitness.

systems biology↗

bioRxiv2019

Longitudinal linked read sequencing reveals ecological and evolutionary responses of a human gut microbiome during antibiotic treatment

Gut microbial communities can respond to antibiotic perturbations by rapidly altering their taxonomic and functional composition. However, little is known about the strain-level processes that drive this collective response. Here we characterize the gut microbiome of a single individual at high temporal and genetic resolution through a period of health, disease, antibiotic treatment, and recovery. We used deep, linked-read metagenomic sequencing to track the longitudinal trajectories of thousands of single nucleotide variants within 36 species, which allowed us to contrast these genetic dynamics with the ecological fluctuations at the species level. We found that antibiotics can drive rapid shifts in the genetic composition of individual species, often involving incomplete genome-wide sweeps of pre-existing variants. These genetic changes were frequently observed in species without obvious changes in species abundance, emphasizing the importance of monitoring diversity below the species level. We also found that many sweeping variants quickly reverted to their baseline levels once antibiotic treatment had concluded, demonstrating that the ecological resilience of the microbiota can sometimes extend all the way down to the genetic level. Our results provide new insights into the population genetic forces that shape individual microbiomes on therapeutically relevant timescales, with potential implications for personalized health and disease.

microbiology↗

bioRxiv2019

AdaReg: Data Adaptive Robust Estimation in Linear Regression with Application in GTEx Gene Expressions

With the development of high-throughput RNA sequencing (RNA-seq) technology, the Genotype Tissue-Expression (GTEx) project (Consortium et al., 2015) generated a valuable resource of gene expression data from more than 11,000 samples. The large-scale data set is a powerful resource for understanding the human transcriptome. However, the technical variation, sequencing background noise and unknown factors make the statistical analysis challenging. To eliminate the possibility that outliers might affect the estimation of population distribution, we need a more robust estimation method, a method that will adapt to heterogeneous genes and further optimize the estimate for each gene. We followed the approach of the robust estimation based on{gamma} -density-power-weight (Fujisawa and Eguchi, 2008; Windham, 1995), where{gamma} is the exponent of density weight which controls the balance between bias and variance. As far as we know, our work is the first to propose a procedure to tune the parameter{gamma} to balance the bias-variance trade-off under the mixture distributions. We constructed a robust likelihood criterion based on weighted densities in the mixture model of Gaussian population distribution mixed with unknown outlier distribution, and developed a data-adaptive{gamma} -selection procedure embedded into the robust estimation. We provided a heuristic analysis on the selection criterion and found that our practical selection trend under various{gamma} s in average performance has similar capability to capture minimizer{gamma} as the inestimable Mean Squared Error (MSE) trend from our simulation studies under a series of settings. Our data-adaptive robustifying procedure in the linear regression problem (AdaReg) shows a significant advantage in both simulation studies and real data application of heart samples from the GTEx project compared to the fixed{gamma} procedure and other robust methods. This paper discusses some limitations of this method, and future work.

bioinformatics↗

bioRxiv2019

AdaTiSS: A Novel Data-Adaptive Robust Method forQuantifying Tissue Specificity Scores

MotivationAccurately detecting tissue specificity (TS) in genes helps researchers understand tissue functions at the molecular level, and further identify disease mechanisms and discover tissue-specific therapeutic targets. The Genotype-Tissue Expression (GTEx) project (Consortium, 2015), and the Human Protein Atlas (HPA) project (Uhlen, et al., 2015) are two publicly available data resources, providing large-scale gene expressions across multiple tissue types. Multiple tissue comparisons, technical background noise and unknown variation factors make it challenging to accurately identify tissue specific gene expressions. Several methods worked on measuring the overall TS in gene expressions and classifying genes into tissue-enrichment categories. There still lacks a robust method to provide quantitative TS scores for each tissue. MethodsWe recognized that the key to quantify tissue specific gene expressions is to properly define a concept of expression population. We considered that inside the population, the sample expressions from various tissues are more or less balanced, and the outlier expressions outside the population may indicate tissue specificity. We then formulated the question to robustly estimate the population distribution. In a linear regression problem, we developed a novel data-adaptive robust estimation based on density-power-weight under unknown outlier distribution and non-vanishing outlier proportion (Wang, et al., 2019). In the question of quantifying TS, we focused on the Gaussian-population mixture model. We took into account gene heterogeneities and applied the robust data-adaptive procedure to estimate the population. With the robustly estimated population parameters, we constructed the AdaTiSS algorithm to obtain data-adaptive quantitative TS scores. ResultsOur TS scores from the AdaTiSS algorithm achieve the goal that the TS scores are comparable across tissues and also across genes, which standardize gene expressions in terms of TS. Compared to the categorical TS method such as the HPA criterion, our method provides more information on the population fitting, and shows advantages in quantitatively analyzing tissue specific functions, making the biology functional analysis more precise. We also discuss some limitations and possible future work. Contactmpsnyder@stanford.edu

bioinformatics↗

bioRxiv2019

A limited set of transcriptional programs define major histological types and provide the molecular basis for a cellular taxonomy of the human body

We have produced RNA sequencing data for a number of primary cells from different locations in the human body. The clustering of these primary cells reveals that most cells in the human body share a few broad transcriptional programs, which define five major cell types: epithelial, endothelial, mesenchymal, neural and blood cells. These act as basic components of many tissues and organs. Based on gene expression, these cell types redefine the basic histological types by which tissues have been traditionally classified. We identified genes whose expression is specific to these cell types, and from these genes, we estimated the contribution of the major cell types to the composition of human tissues. We found this cellular composition to be a characteristic signature of tissues, and to reflect tissue morphological heterogeneity and histology. We identified changes in cellular composition in different tissues associated with age and sex and found that departures from the normal cellular composition correlate with histological phenotypes associated to disease. One Sentence SummaryA few broad transcriptional programs define the major cell types underlying the histology of human tissues and organs.

genomics↗

bioRxiv2019

A genome-wide almanac of co-essential modules assigns function to uncharacterized genes

A central remaining question in the post-genomic era is how genes interact to form biological pathways. Measurements of gene dependency across hundreds of cell lines have been used to cluster genes into co-essential pathways, but this approach has been limited by ubiquitous false positives. Here, we develop a statistical method that enables robust identification of gene co-essentiality and yields a genome-wide set of functional modules. This almanac recapitulates diverse pathways and protein complexes and predicts the functions of 102 uncharacterized genes. Validating top predictions, we show that TMEM189 encodes plasmanylethanolamine desaturase, the long-sought key enzyme for plasmalogen synthesis. We also show that C15orf57 binds the AP2 complex, localizes to clathrin-coated pits, and enables efficient transferrin uptake. Finally, we provide an interactive web tool for the community to explore the results (coessentiality.net). Our results establish co-essentiality profiling as a powerful resource for biological pathway identification and discovery of novel gene functions.

genomics↗

bioRxiv2019

RobNorm: Model-Based Robust Normalization for High-Throughput Proteomics from Mass Spectrometry Platform

MotivationData normalization is an important step in processing proteomics data generated in mass spectrometry (MS) experiments, which aims to reduce sample-level variation and facilitate comparisons of samples. Previously published methods for normalization primarily depend on the assumption that the distribution of protein expression is similar across all samples. However, this assumption fails when the protein expression data is generated from heterogenous samples, such as from various tissue types. This led us to develop a novel data-driven method for improved normalization to correct the systematic bias meanwhile maintaining underlying biological heterogeneity. MethodsTo robustly correct the systematic bias, we used the density-power-weight method to down-weigh outliers and extended the one-dimensional robust fitting method described in the previous work of (Windham, 1995, Fujisawa and Eguchi, 2008) to our structured data. We then constructed a robustness criterion and developed a new normalization algorithm, called RobNorm. ResultsIn simulation studies and analysis of real data from the genotype-tissue expression (GTEx) project, we compared and evaluated the performance of RobNorm against other normalization methods. We found that the RobNorm approach exhibits the greatest reduction in systematic bias while maintaining across-tissue variation, especially for datasets from highly heterogeneous samples. Availabilityhttps://github.com/mwgrassgreen/RobNorm Contacthuatang@stanford.edu and mpsnyder@stanford.edu

bioinformatics↗

bioRxiv2019

HAT1 drives a gene-metabolite circuit that links nutrient metabolism to histone production

The energetic costs of duplicating chromatin along with DNA replication are large and therefore likely depend on nutrient sensing checkpoints and metabolic inputs. By studying chromatin modifiers regulated by epithelial growth factor, we identify histone acetyltransferase 1 (HAT1) as an induced gene that enhances cell proliferation by coordinating histone production with glucose metabolism. In addition to its canonical role as a cytoplasmic free histone H4 acetyltransferase, a HAT1-containing complex binds specifically at promoters of H4 genes. HAT1 stimulated acetate delivery and consumption at H4 promoters to drive S-phase H4 transcription. This required the presence of a histone H4-specific promoter element in the region of HAT1 chromatin binding. These data describe a feed-forward circuit whereby HAT1-dependent capture of acetyl-groups drives further H4 production to support growth-factor dependent proliferation. These findings also extend to human disease and animal models, as high HAT1 levels associate with poor outcomes across multiple cancer types.

molecular biology↗

bioRxiv2019

Chromosome-level de novo assembly of the pig-tailed macaque genome using linked-read sequencing and HiC proximity scaffolding

Old world monkey species share over 93% genome homology with humans and develop many disease phenotypes similar to those of humans, making them highly valuable animal models for the study of numerous human diseases. However, the quality of genome assembly and annotation for old world monkeys including macaque species lags behind the human genome effort. To close this gap and enhance functional genomics approaches, we employed a combination of de novo linked-read assembly and scaffolding using proximity ligation assay (HiC) to assemble the pig-tailed macaque (Macaca nemestrina) genome. This combinatorial method yielded large scaffolds at chromosome-level with a scaffold N50 of 127.5 Mb; the 23 largest scaffolds covered 90% of the entire genome. This assembly revealed large-scale rearrangements between pig-tailed macaque chromosomes 7,12, and13 and human chromosomes 2, 14, and 15.

genomics↗

bioRxiv2019

Cross-species blastocyst chimerism between nonhuman primates using iPSCs

Through the production of chimeric animals, induced pluripotent stem cells (iPSCs) can generate personalized organs with diverse applications for both basic research and translational medicine. This concept was first validated in rodents by forming a rat pancreas in mice and vice versa. However, the potential use of human iPSCs to generate xenogenic organs in other species is technically and ethically difficult. Recognizing these concerns, we explored the generation of chimeric nonhuman primates (NHP) embryos, by injecting either chimpanzee or pig-tailed macaque iPSCs into rhesus macaque embryos. We first derived iPSCs from chimpanzees and pig-tailed macaques. We found that the chimpanzee iPSCs mixed well with human iPSCs during in vitro co-culture and differentiation. The differentiation of mixed human and chimpanzee iPSCs formed functioning cardiomyocyte layers in vitro, whereas human or chimpanzee iPSC mixed with pig-tailed macaque or mouse cells do not; these results indicate that chimpanzee and human cells are closely related in function. Considering the ethical aspects of injecting human iPSCs into nonhuman primate blastocysts, we tested whether chimpanzee iPSCs injected into 99 macaque 5-day-old embryos formed cross-species chimeras two days after injection. Strikingly, the chimpanzee iPSCs survived, proliferated and integrated near the inner cell mass (ICM) of rhesus macaque embryos. These findings highlight the broad potential of primate iPSCs in forming cross-species chimeras beyond rodents and provides a foundational basis for organ generation using human iPSCs.

developmental biology↗