Biology subjects

Gerstein, M.

Publications and source records attributed to Gerstein, M..

At least 55 records · Page 3Linked to original sources

bioRxiv2021

Minor intron splicing efficiency increases with the development of lethal prostate cancer

Here we explored the role of minor spliceosome (MiS) function and minor intron-containing gene (MIG) expression in prostate cancer (PCa). We show MIGs are enriched as direct interactors of cancer-causing genes and their expression discriminates PCa progression. Increased expression of MiS U6atac snRNA, including others, and 6x more efficient minor intron splicing was observed in castration-resistant PCa (CRPC) versus primary PCa. Notably, androgen receptor signalling influenced MiS activity. Inhibition of MiS through siU6atac in PCa caused minor intron mis-splicing and aberrant expression of MIG transcripts and encoded proteins, which enriched for MAPK activity, DNA repair and cell cycle. Single cell-RNAseq confirmed cell cycle defects and lineage dependency on the MiS from primary to CRPC and neuroendocrine PCa. siU6atac was [~]50% more efficient in lowering tumor burden of CRPC cells and organoids versus current state-of-the-art combination therapy. In all, MiS is a strong therapeutic target for lethal PCa and potentially other cancers. Graphical AbstractU6atac expression, MiS activity, and minor intron splicing correlate with PCa therapy resistance and PCa progression to CRPC-adeno and transdifferentiation to CRPC-NE. One major MiS regulator during that process is the AR-axis, which is re-activated during CRPC-adeno and blocked in CRPC-NE. Molecularly, an increase in MiS dependent splicing promotes changes of transcriptome and proteome. This results in cell cycle activation, increased MAPK signalling and increased DNA repair. U6atac mediated MiS inhibition renders MiS splicing error-prone through increased intron retention and alternative splicing events, which results in cell cycle block and decreased MAPK signalling and DNA repair. MiS inhibition blocks all stages of PCa. Figure created with BioRender.com. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=154 SRC="FIGDIR/small/471104v1_ufig1.gif" ALT="Figure 1"> View larger version (61K): org.highwire.dtl.DTLVardef@1c1c296org.highwire.dtl.DTLVardef@1ce1416org.highwire.dtl.DTLVardef@88d842org.highwire.dtl.DTLVardef@2ddf96_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2021

Constructing a multiple-layer interactome for SARS-CoV-2 in the context of lung disease: Linking the virus with human genes and co-infecting microbes

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has caused millions of deaths worldwide. Many efforts have focused on unraveling the mechanism of the viral infection to develop effective strategies for treatment and prevention. Previous studies have provided some clarity on the protein-protein interaction linkages occurring during the life cycle of viral infection; however, we lack a complete understanding of the full interactome, comprising human miRNAs and protein-coding genes and co-infecting microbes. To comprehensively determine this, we developed a statistical modeling method using latent Dirichlet allocation (called MLCrosstalk, for multiple-layer crosstalk) to fuse many types of data to construct the full interactome of SARS-CoV-2. Specifically, MLCrosstalk is able to integrate samples with multiple layers of information (e.g., miRNA and microbes), enforce a consistent topic distribution on all data types, and infer individual-level linkages (i.e., differing between patients). We also implement a secondary refinement with network propagation to allow our microbe-gene linkages to address larger network structures (e.g., pathways). Using MLCrosstalk, we generated a list of genes and microbes linked to SARS-CoV-2. Interestingly, we found that two of the identified microbes, Rothia mucilaginosa and Prevotella melaninogenica, show distinct patterns representing synergistic and antagonistic relationships with the virus, respectively. We also identified several SARS-COV-2-associated pathways, including the VEGFA-VEGFR2 and immune response pathways, which may provide potential targets for drug design.

bioinformatics↗

bioRxiv2021

Illuminating links between cis-regulators and trans-acting variants in the human prefrontal cortex

Psychiatric disorders exact immense human and economic tolls in societies globally. Underlying many of these disorders is a complex repertoire of genomic variants that influence the expression of genes involved in pathways and processes in the brain. Identifying such variants and their associated brain functions is thus essential for understanding the molecular underpinnings of psychiatric disorders. Genome-wide association studies (GWASes) have provided many variants associated with these disorders; however, our knowledge of the precise biological mechanisms by which these contribute to disease remains limited. In connection with this, expression quantitative trait loci (eQTLs) have provided useful information linking variants to genes and functions. However, most eQTL studies on human brain have focused exclusively on cis-eQTLs. A complete understanding of disease etiology should also include trans-regulatory mechanisms. Thus, we conduct one of the first genome-wide surveys of trans-eQTLs in the dorsolateral prefrontal cortex (DLPFC) by leveraging the large datasets from the PsychENCODE consortium. We identified [~]80,000 trans-eQTLs. We found that a significant number of these overlap with cis-eQTLs, thereby implicating cis-mediators as key players in trans-acting regulation. We show, furthermore, that trans-regulatory mechanisms provide novel insights into psychiatric disease. Particularly, colocalization analysis between trans-eQTLs and schizophrenia (SCZ) GWAS loci identified 90 novel SCZ risk genes and 23 GWAS loci previously uncharacterized by cis-eQTLs. Moreover, these 90 genes tend to be more central in transcriptome-wide co-expression networks and more susceptible to rare variants than SCZ-risk genes associated by cis-variation.

genomics↗

bioRxiv2021

Proteome-wide screening for mitogen-activated protein kinase docking motifs and interactors

Essential functions of mitogen-activated protein kinases (MAPKs) depend on their capacity to selectively phosphorylate a limited repertoire of substrates. MAPKs harbor a conserved groove located outside of the catalytic cleft that binds to short linear sequence motifs found in substrates and regulators. However, the weak and transient nature of these "docking" interactions poses a challenge to defining MAPK interactomes and associated sequence motifs. Here, we describe a yeast-based genetic screening pipeline to evaluate large collections of MAPK docking sequences in parallel. Using this platform we analyzed a combinatorial library based on the docking sequences from the MAPK kinases MKK6 and MKK7, defining features critical for binding to the stress-activated MAPKs JNK1 and p38. We subsequently screened a library consisting of ~12,000 sequences from the human proteome, revealing a large number of MAPK-selective interactors, including many not conforming to previously defined docking motifs. Analysis of p38/JNK1 exchange mutants identified specific docking groove residues mediating selective binding. Finally, we verified that docking sequences identified in the screen could function in substrate recruitment in vitro and in cultured cells. Collectively, these studies establish an approach for characterization of MAPK docking sequences and provide a resource for future investigation of signaling downstream of p38 and JNK MAP kinases.

cell biology↗

bioRxiv2021

PLIGHT: A tool to assess privacy risk by inferring identifying characteristics from sparse, noisy genotypes

Single nucleotide polymorphisms (SNPs) from omics data carry a high risk of reidentification for individuals and their relatives. While the ability of thousands of SNPs (especially rare ones) to identify individuals has been repeatedly demonstrated, the ready availability of small sets of noisy genotypes - such as from environmental DNA samples or functional genomics data - motivated us to quantify their informativeness. Here, we present a computational tool suite, PLIGHT ("Privacy Leakage by Inference across Genotypic HMM Trajectories"), that employs population-genetics-based Hidden Markov Models of recombination and mutation to find piecewise alignment of small, noisy query SNP sets to a reference haplotype database. We explore cases where query individuals are either known to be in a database, or not, and consider a variety of queries, including simulated genotype "mosaics" (composites from 2 source individuals) and genotypes from swabs of coffee cups from a known individual. Using PLIGHT on a database with ~5,000 haplotypes, we find for common, noise-free SNPs that only ten are sufficient to identify individuals, ~20 can identify both components in two-individual simulated mosaics, and 20-30 can identify first-order relatives (parents, children, and siblings). Using noisy coffee-cup-derived SNPs, PLIGHT identifies an individual (within the database) using ~30 SNPs. Moreover, even when the individual is not in the database, local genotype matches allow for some phenotypic information leakage based on coarse-grained GWAS SNP imputation and polygenic risk scores. Overall, PLIGHT maximizes the identifying information content of sparse SNP sets through exact or partial matches to databases. Finally, by quantifying such privacy attacks, PLIGHT helps determine the value of selectively sanitizing released SNPs without explicit assumptions about underlying population membership or allele frequencies. To make this practical, we provide a sanitization tool to remove the most identifying SNPs from a query set.

bioinformatics↗

bioRxiv2021

A community-driven roadmap to advance research on translated open reading frames detected by Ribo-seq

Ribosome profiling (Ribo-seq) has catalyzed a paradigm shift in our understanding of the translational vocabulary of the human genome, discovering thousands of translated open reading frames (ORFs) within long non-coding RNAs and presumed untranslated regions of protein-coding genes. However, reference gene annotation projects have been circumspect in their incorporation of these ORFs due to uncertainties about their experimental reproducibility and physiological roles. Yet, it is indisputable that certain Ribo-seq ORFs make stable proteins, others mediate gene regulation, and many have medical implications. Ultimately, the absence of standardized ORF annotation has created a circular problem: while Ribo-seq ORFs remain unannotated by reference biological databases, this lack of characterisation will thwart research efforts examining their roles. Here, we outline the initial stages of a community-led effort supported by GENCODE / Ensembl, HGNC and UniProt to produce a consolidated catalog of human Ribo-seq ORFs.

genomics↗

bioRxiv2021

A Network Propagation Approach to Prioritize Long Tail Genes in Cancer

IntroductionThe diversity of genomic alterations in cancer pose challenges to fully understanding the etiologies of the disease. Recent interest in infrequent mutations, in genes that reside in the "long tail" of the mutational distribution, uncovered new genes with significant implication in cancer development. The study of these genes often requires integrative approaches with multiple types of biological data. Network propagation methods have demonstrated high efficacy in uncovering genomic patterns underlying cancer using biological interaction networks. Yet, the majority of these analyses have focused their assessment on detecting known cancer genes or identifying altered subnetworks. In this paper, we introduce a network propagation approach that entirely focuses on long tail genes with potential functional impact on cancer development. ResultsWe identify sets of often overlooked, rarely to moderately mutated genes whose biological interactions significantly propel their mutation-frequency-based rank upwards during propagation in 17 cancer types. We call these sets "upward mobility genes" (UMGs, 28-83 genes per cancer type) and hypothesize that their significant rank improvement indicates functional importance. We report new cancer-pathway associations based on UMGs that were not previously identified using driver genes alone, validate UMGs role in cancer cell survival in vitro--alone and compared to other network methods--using extensive genome-wide RNAi and CRISPR data repositories, and further conduct in vitro functional screenings resulting the validation of 8 previously unreported genes. ConclusionOur analysis extends the spectrum of cancer relevant genes and identifies novel potential therapeutic targets.

bioinformatics↗

bioRxiv2021

Cancer relevance of human genes

BackgroundIt is unclear how many of genes contribute to the biology of cancer. We hypothesize that genes that interact with core cancer gene (CCG) in a protein-protein interaction network (PPI) may have functional importance. MethodsWe categorized genes into 1- (n=6791), 2- (n=7724), 3- (n=1587), and >3-steps (n=362) removed from the nearest CCG in the STRING PPI and demonstrate that the cancer-biology related functional contribution of the genes in these different neighborhood categories decreases as their distance from the CCGs increases. ResultsGenes closer to cancer genes manifest greater connectedness in the network, show greater importance in maintaining cell viability in a broad range of cancer cells in vitro, are also under greater negative germline selection pressure in the healthy populations, and have higher somatic mutation frequency and cancer effect. ConclusionsApproximately 70% of human genes are 1 or 2 steps removed from cancer genes in protein network and show functional importance in cancer-biology. These results suggest that the universe of cancer-relevant genes extends to thousands of genes that can contribute functional effects when dysregulated.

genomics↗

bioRxiv2021

DECODE: A Deep-learning Framework for Condensing Enhancers and Refining Boundaries with Large-scale Functional Assays

SummaryMapping distal regulatory elements, such as enhancers, is the cornerstone for investigating genome evolution, understanding critical biological functions, and ultimately elucidating how genetic variations may influence diseases. Previous enhancer prediction methods have used either unsupervised approaches or supervised methods with limited training data. Moreover, past approaches have operationalized enhancer discovery as a binary classification problem without accurate enhancer boundary detection, producing low-resolution annotations with redundant regions and reducing the statistical power for downstream analyses (e.g., causal variant mapping and functional validations). Here, we addressed these challenges via a two-step model called DECODE. First, we employed direct enhancer activity readouts from novel functional characterization assays, such as STARR-seq, to train a deep neural network classifier for accurate cell-type-specific enhancer prediction. Second, to improve the annotation resolution ([~]500 bp), we implemented a weakly-supervised object detection framework for enhancer localization with precise boundary detection (at 10 bp resolution) using gradient-weighted class activation mapping. ResultsOur DECODE binary classifier outperformed the state-of-the-art enhancer prediction methods by 24% in transgenic mouse validation. Further, DECODE object detection can condense enhancer annotations to only 12.6% of the original size, while still reporting higher conservation scores and genome-wide association study variant enrichments. Overall, DECODE improves the efficiency of regulatory element mapping with graphic processing units for deep-learning applications and is a powerful tool for enhancer prediction and boundary localization. Contactpi@gersteinlab.org

bioinformatics↗

bioRxiv2020

Broad transcriptomic dysregulation across the cerebral cortex in ASD

Classically, psychiatric disorders have been considered to lack defining pathology, but recent work has demonstrated consistent disruption at the molecular level, characterized by transcriptomic and epigenetic alterations.1-3 In ASD, upregulation of microglial, astrocyte, and immune signaling genes, downregulation of specific synaptic genes, and attenuation of regional gene expression differences are observed.1,2,4-6 However, whether these changes are limited to the cortical association areas profiled is unknown. Here, we perform RNA-sequencing (RNA-seq) on 725 brain samples spanning 11 distinct cortical areas in 112 ASD cases and neurotypical controls. We identify substantially more genes and isoforms that differentiate ASD from controls than previously observed. These alterations are pervasive and cortex-wide, but vary in magnitude across regions, roughly showing an anterior to posterior gradient, with the strongest signal in visual cortex, followed by parietal cortex and the temporal lobe. We find a notable enrichment of ASD genetic risk variants among cortex-wide downregulated synaptic plasticity genes and upregulated protein folding gene isoforms. Finally, using snRNA-seq, we determine that regional variation in the magnitude of transcriptomic dysregulation reflects changes in cellular proportion and cell-type-specific gene expression, particularly impacting L3/4 excitatory neurons. These results highlight widespread, genetically-driven neuronal dysfunction as a major component of ASD pathology in the cerebral cortex, extending beyond association cortices to involve primary sensory regions.

neuroscience↗

bioRxiv2020

Non-adaptive factors determine which equally effective regulatory motif evolves to generate pulses

Transcriptional regulatory networks (TRNs) are enriched for certain "motifs". Motif usage is commonly interpreted in adaptationist terms, i.e. that the optimal motif evolves. But certain motifs can also evolve more easily than others. Here, we computationally evolved TRNs to produce a pulse of an effector protein. Two well-known motifs, type 1 incoherent feed-forward loops (I1FFLs) and negative feedback loops (NFBLs), evolved as the primary solutions. Which motif evolves more often depends on selection conditions, but under all conditions, either motif achieves similar performance. I1FFLs generally evolve more often than NFBLs, unless we select for a tall pulse. I1FFLs are more evolutionarily accessible early on, before the effector protein evolves high expression; when NFBLs subsequently evolve, they tend to do so from a conjugated I1FFL-NFBL genotype. In the empirical S. cerevisiae TRN, output genes of NFBLs had higher expression levels than those of I1FFLs. These results suggest that evolutionary accessibility, and not relative functionality, shapes which motifs evolve in TRNs, and does so as a function of the expression levels of particular genes.

evolutionary biology↗

bioRxiv2020

Discovering a less-is-more effect to select transcription factor binding sites informative for motif inference

Many statistical methods have been developed to infer the binding motifs of a transcription factor (TF) from a subset of its numerous binding regions in the genome. We refer to such regions, e.g. detected by ChIP-seq, as binding sites. The sites with strong binding signals are selected for motif inference. However, binding signals do not necessarily indicate the existence of target motifs. Moreover, even strong binding signals can be spurious due to experimental artifacts. Here, we observe that such uninformative sites without target motifs tend to be "crowded" -- i.e. have many other TF binding sites present nearby. In addition, we find that even if a crowded site contains recognizable target motifs, it can still be uninformative for motif inference due to the presence of interfering motifs from other TFs. We propose using less crowded and shorter binding sites in motif interference and develop specific recommendations for carrying this out. We find our recommendations substantially improve the resulting motifs in various contexts by 30%-70%, implying a "less-is-more" effect.

bioinformatics↗

bioRxiv2020

Recovering genomes and phenotypes using allele-specific gene expression

With the recent increase in RNA sequencing efforts using large cohorts of individuals, studying allele-specific gene expression is becoming increasingly important. Here, we report that, despite not containing explicit variant information, a list of allele-specific gene names of an individual is enough to recover key variants and link the individual back to their genome or phenotype. This creates a privacy conundrum.

bioinformatics↗

bioRxiv2020

Compression-based Network Interpretability Schemes

Deep learning methods have achieved state-of-the-art performance in many domains of artificial intelligence, but are typically hard to interpret. Network interpretation is important for multiple reasons, including knowledge discovery, hypothesis generation, fairness and establishing trust. Model transformations provide a general approach to interpreting a trained network post-hoc: the network is approximated by a model, which is typically compressed, whose structure can be more easily interpreted in some way (we call such approaches interpretability schemes). However, the relationship between compression and interpretation has not been fully explored: How much should a network be compressed for optimal extraction of interpretable information? Should compression be combined with other criteria when selecting model transformations? We investigate these issues using two different compression-based schemes, which aim to extract orthogonal kinds of information, pertaining to feature and data instance-based groupings respectively. The first (rank projection trees) uses a structured sparsification method such that nested groups of features can be extracted having potential joint interactions. The second (cascaded network decomposition) splits a network into a cascade of simpler networks, allowing groups of training instances with similar characteristics to be extracted at each stage of the cascade. We use predictive tasks in cancer and psychiatric genomics to assess the ability of these approaches to extract informative feature and data-point groupings from trained networks. We show that the generalization error of a network provides an indicator of the quality of the information extracted; further we derive PAC-Bayes generalization bounds for both schemes, which we show can be used as proxy indicators, and can thus provide a criterion for selecting the optimal compression. Finally, we show that the PAC-Bayes framework can be naturally modified to incorporate additional criteria alongside compression, such as prior knowledge based on previous models, which can enhance interpretable model selection.

bioinformatics↗

bioRxiv2020

Latent Evolutionary Signatures: A General Framework for Analyzing Music and Cultural Evolution

Cultural processes of change bear many resemblances to biological evolution. The underlying units of non-biological evolution have, however, remained elusive, especially in the domain of music. Here, we introduce a general framework to jointly identify underlying units and their associated evolutionary processes. We model musical styles and principles of organization in dimensions such as harmony and form as following an evolutionary process. Furthermore, we propose that such processes can be identified by extracting latent evolutionary signatures from musical corpora, analogous to identifying mutational signatures in genomics. These signatures provide a latent embedding for each song or musical piece. We develop a deep generative architecture for our model, which can be viewed as a type of Variational Autoencoder with an evolutionary prior constraining the latent space; specifically, the embeddings for each song are tied together via an energy-based prior, which encourages songs close in evolutionary space to share similar representations. As illustration, we analyze songs from the McGill Billboard dataset. We find frequent chord transitions and formal repetition schemes and identify latent evolutionary signatures related to these features. Finally, we show that the latent evolutionary representations learned by our model outperform non-evolutionary representations in such tasks as period and genre prediction.

evolutionary biology↗

bioRxiv2020

NIMBus: a Negative Binomial Regression based Integrative Method for Mutation Burden Analysis

BackgroundIdentifying frequently mutated regions is a key approach to discover DNA elements influencing cancer progression. However, it is challenging to identify these burdened regions due to mutation rate heterogeneity across the genome and across different individuals. Moreover, it is known that this heterogeneity partially stems from genomic confounding factors, such as replication timing and chromatin organization. The increasing availability of cancer whole genome sequences and functional genomics data from the Encyclopedia of DNA Elements (ENCODE) may help address these issues. ResultsWe developed a Negative binomial regression-based Integrative Method for mutation Burden analysiS (NIMBus). Our approach addresses the over-dispersion of mutation count statistics by (1) using a Gamma-Poisson mixture model to capture the mutation-rate heterogeneity across different individuals and (2) estimating regional background mutation rates by regressing the varying local mutation counts against genomic features extracted from ENCODE. We applied NIMBus to whole-genome cancer sequences from the PanCancer Analysis of Whole Genomes project (PCAWG) and other cohorts. It successfully identified well-known coding and noncoding drivers, such as TP53 and the TERT promoter. To further characterize the burdening of non-coding regions, we used NIMBus to screen transcription factor binding sites in promoter regions that intersect DNase I hypersensitive sites (DHSs). This analysis identified mutational hotspots that potentially disrupt gene regulatory networks in cancer. We also compare this method to other mutation burden analysis methods. ConclusionNIMBus is a powerful tool to identify mutational hotspots. The NIMBus software and results are available as an online resource at github.gersteinlab.org/nimbus.

bioinformatics↗

bioRxiv2020

SCAN-ATAC Sim: a scalable and efficient method to simulate single-cell ATAC-seq from bulk-tissue experiments

SummaryscATAC-seq is a powerful approach for characterizing cell-type-specific regulatory landscapes. However, it is difficult to benchmark the performance of various scATAC-seq analysis techniques (such as clustering and deconvolution) without having a priori a known set of gold-standard cell types. To simulate scATAC-seq experiments with known cell-type labels, we introduce an efficient and scalable scATAC-seq simulation method (SCAN-ATAC-Sim) that down-samples bulk ATAC-seq data (e.g., from representative cell lines or tissues). Our protocol uses a consistent but tunable signal-to-noise ratio across cell types in a scATAC-seq simulation for integrating bulk experiments with different levels of background noise, and it independently samples twice without replacement to account for the diploid genome. Because it uses an efficient weighted reservoir sampling algorithm and is highly parallelizable with OpenMP, our implementation in C++ allows millions of cells to be simulated in less than an hour on a laptop computer. AvailabilitySCAN-ATAC-Sim is available at scan-atac-sim.gersteinlab.org. Contactpi@gersteinlab.org Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2020

DiNeR: a Differential Graphical Model for analysis of co-regulation Network Rewiring

BackgroundDuring transcription, numerous transcription factors (TFs) bind to targets in a highly coordinated manner to control the gene expression. Alterations in groups of TF-binding profiles (i.e. "co-binding changes") can affect the co-regulating associations between TFs (i.e. "rewiring the co-regulator network"). This, in turn, can potentially drive downstream expression changes, phenotypic variation, and even disease. However, quantification of co-regulatory network rewiring has not been comprehensively studied. MethodsTo address this, we propose DiNeR, a computational method to directly construct a differential TF co-regulation network from paired disease-to-normal ChIP-seq data. Specifically, DiNeR uses a graphical model to capture the gained and lost edges in the co-regulation network. Then, it adopts a stability-based, sparsity-tuning criterion -- by sub-sampling the complete binding profiles to remove spurious edges -- to report only significant co-regulation alterations. Finally, DiNeR highlights hubs in the resultant differential network as key TFs associated with disease. ResultsWe assembled genome-wide binding profiles of 104 TFs in the K562 and GM12878 cell lines, which loosely model the transition between normal and cancerous states in chronic myeloid leukemia (CML). In total, we identified 351 significantly altered TF co-regulation pairs. In particular, we found that the co-binding of the tumor suppressor BRCA1 and RNA polymerase II, a well-known transcriptional pair in healthy cells, was disrupted in tumors. Thus, DiNeR successfully extracted hub regulators and discovered well-known risk genes. ConclusionsOur method DiNeR makes it possible to quantify changes in co-regulatory networks and identify alterations to TF co-binding patterns, highlighting key disease regulators. Our method DiNeR makes it possible to quantify changes in co-regulatory networks and identify alterations to TF co-binding patterns, highlighting key disease regulators.

bioinformatics↗