Gene architecture directs splicing outcome in separate nuclear spatial regions.

How the splicing machinery defines exons or introns as the spliced unit has remained a puzzle for 30 years. Here, we demonstrate that peripheral and central regions of the nucleus harbor genes with two distinct exon-intron GC content architectures that differ in the splicing outcome. Genes with low GC content exons, flanked by long introns with lower GC content, are localized in the periphery, and the exons are defined as the spliced unit. Alternative splicing of these genes results in exon skipping. In contrast, the nuclear center contains genes with a high GC content in the exons and short flanking introns. Most splicing of these genes occurs via intron definition, and aberrant splicing leads to intron retention. We demonstrate that the nuclear periphery and center generate different environments for the regulation of alternative splicing and that two sets of splicing factors form discrete regulatory subnetworks for the two gene architectures. Our study connects 3D genome organization and splicing, thus demonstrating that exon and intron definition modes of splicing occur in different nuclear regions.

D'or: Deep orienter of protein-protein interaction networks.

MOTIVATION: Protein-protein interactions (PPIs) provide the skeleton for signal transduction in the cell. Current PPI measurement techniques do not provide information on their directionality which is critical for elucidating signaling pathways. To date, there are hundreds of thousands of known PPIs in public databases, yet only a small fraction of them have an assigned direction. This information gap calls for computational approaches for inferring the directionality of PPIs, aka network orientation. RESULTS: In this work we propose a novel deep learning approach for PPI network orientation. Our method first generates a set of proximity scores between a protein interaction and sets of cause and effect proteins using a network propagation procedure. Each of these score sets is fed, one at a time, to a deep set encoder whose outputs are used as features for predicting the interaction's orientation. On a comprehensive data set of oriented protein-protein interactions taken from five different sources, we achieve an area under the precision-recall curve of 0.89-0.92, outperforming previous methods. We further demonstrate the utility of the oriented network in prioritizing cancer driver genes and disease genes. AVAILABILITY: D'or is implemented in Python and is publicly available at https://github.com/pirakd/DeepOrienter.

Genome architecture and stability in the Saccharomyces cerevisiae knockout collection.

Despite major progress in defining the functional roles of genes, a complete understanding of their influences is far from being realized, even in relatively simple organisms. A major milestone in this direction arose via the completion of the yeast Saccharomyces cerevisiae gene-knockout collection (YKOC), which has enabled high-throughput reverse genetics, phenotypic screenings and analyses of synthetic-genetic interactions1-3. Ensuing experimental work has also highlighted some inconsistencies and mistakes in the YKOC, or genome instability events that rebalance the effects of specific knockouts4-6, but a complete overview of these is lacking. The identification and analysis of genes that are required for maintaining genomic stability have traditionally relied on reporter assays and on the study of deletions of individual genes, but whole-genome-sequencing technologies now enable-in principle-the direct observation of genome instability globally and at scale. To exploit this opportunity, we sequenced the whole genomes of nearly all of the 4,732 strains comprising the homozygous diploid YKOC. Here, by extracting information on copy-number variation of tandem and interspersed repetitive DNA elements, we describe-for almost every single non-essential gene-the genomic alterations that are induced by its loss. Analysis of this dataset reveals genes that affect the maintenance of various genomic elements, highlights cross-talks between nuclear and mitochondrial genome stability, and shows how strains have genetically adapted to life in the absence of individual non-essential genes.

A mutation-level covariate model for mutational signatures.

Mutational processes and their exposures in particular genomes are key to our understanding of how these genomes are shaped. However, current analyses assume that these processes are uniformly active across the genome without accounting for potential covariates such as strand or genomic region that could impact such activities. Here we suggest the first mutation-covariate models that explicitly model the effect of different covariates on the exposures of mutational processes. We apply these models to test the impact of replication strand on these processes and compare them to strand-oblivious models across a range of data sets. Our models capture replication strand specificity, point to signatures affected by it, and score better on held-out data compared to standard models that do not account for mutation-level covariate information.

Network propagation: a universal amplifier of genetic associations.

Biological networks are powerful resources for the discovery of genes and genetic modules that drive disease. Fundamental to network analysis is the concept that genes underlying the same phenotype tend to interact; this principle can be used to combine and to amplify signals from individual genes. Recently, numerous bioinformatic techniques have been proposed for genetic analysis using networks, based on random walks, information diffusion and electrical resistance. These approaches have been applied successfully to identify disease genes, genetic modules and drug targets. In fact, all these approaches are variations of a unifying mathematical machinery - network propagation - suggesting that it is a powerful data transformation method of broad utility in genetic research.

Long reads capture simultaneous enhancer-promoter methylation status for cell-type deconvolution.

MOTIVATION: While promoter methylation is associated with reinforcing fundamental tissue identities, the methylation status of distant enhancers was shown by genome-wide association studies to be a powerful determinant of cell-state and cancer. With recent availability of long reads that report on the methylation status of enhancer-promoter pairs on the same molecule, we hypothesized that probing these pairs on the single-molecule level may serve the basis for detection of rare cancerous transformations in a given cell population. We explore various analysis approaches for deconvolving cell-type mixtures based on their genome-wide enhancer-promoter methylation profiles. RESULTS: To evaluate our hypothesis we examine long-read optical methylome data for the GM12878 cell line and myoblast cell lines from two donors. We identified over 100 000 enhancer-promoter pairs that co-exist on at least 30 individual DNA molecules. We developed a detailed methodology for mixture deconvolution and applied it to estimate the proportional cell compositions in synthetic mixtures. Analysis of promoter methylation, as well as enhancer-promoter pairwise methylation, resulted in very accurate estimates. In addition, we show that pairwise methylation analysis can be generalized from deconvolving different cell types to subtle scenarios where one wishes to resolve different cell populations of the same cell-type. AVAILABILITY AND IMPLEMENTATION: The code used in this work to analyze single-molecule Bionano Genomics optical maps is available via the GitHub repository https://github.com/ebensteinLab/Single_molecule_methylation_in_EP.

A data-driven approach for constructing mutation categories for mutational signature analysis.

Mutational processes shape the genomes of cancer patients and their understanding has important applications in diagnosis and treatment. Current modeling of mutational processes by identifying their characteristic signatures views each base substitution in a limited context of a single flanking base on each side. This context definition gives rise to 96 categories of mutations that have become the standard in the field, even though wider contexts have been shown to be informative in specific cases. Here we propose a data-driven approach for constructing a mutation categorization for mutational signature analysis. Our approach is based on the assumption that tumor cells that are exposed to similar mutational processes, show similar expression levels of DNA damage repair genes that are involved in these processes. We attempt to find a categorization that maximizes the agreement between mutation and gene expression data, and show that it outperforms the standard categorization over multiple quality measures. Moreover, we show that the categorization we identify generalizes to unseen data from different cancer types, suggesting that mutation context patterns extend beyond the immediate flanking bases.

Using deep learning to model the hierarchical structure and function of a cell.

Although artificial neural networks are powerful classifiers, their internal structures are hard to interpret. In the life sciences, extensive knowledge of cell biology provides an opportunity to design visible neural networks (VNNs) that couple the model's inner workings to those of real systems. Here we develop DCell, a VNN embedded in the hierarchical structure of 2,526 subsystems comprising a eukaryotic cell (http://d-cell.ucsd.edu/). Trained on several million genotypes, DCell simulates cellular growth nearly as accurately as laboratory observations. During simulation, genotypes induce patterns of subsystem activities, enabling in silico investigations of the molecular mechanisms underlying genotype-phenotype associations. These mechanisms can be validated, and many are unexpected; some are governed by Boolean logic. Cumulatively, 80% of the importance for growth prediction is captured by 484 subsystems (21%), reflecting the emergence of a complex phenotype. DCell provides a foundation for decoding the genetics of disease, drug resistance and synthetic life.

Alternative splicing regulates biogenesis of miRNAs located across exon-intron junctions.

The initial step in microRNA (miRNA) biogenesis requires processing of the precursor miRNA (pre-miRNA) from a longer primary transcript. Many pre-miRNAs originate from introns, and both a mature miRNA and a spliced RNA can be generated from the same transcription unit. We have identified a mechanism in which RNA splicing negatively regulates the processing of pre-miRNAs that overlap exon-intron junctions. Computational analysis identified dozens of such pre-miRNAs, and experimental validation demonstrated competitive interaction between the Microprocessor complex and the splicing machinery. Tissue-specific alternative splicing regulates maturation of one such miRNA, miR-412, resulting in effects on its targets that code a protein network involved in neuronal cell death processes. This mode of regulation specifically controls maturation of splice-site-overlapping pre-miRNAs but not pre-miRNAs located completely within introns or exons of the same transcript. Our data present a biological role of alternative splicing in regulation of miRNA biogenesis.

Comparing the utility of in vivo transposon mutagenesis approaches in yeast species to infer gene essentiality.

In vivo transposon mutagenesis, coupled with deep sequencing, enables large-scale genome-wide mutant screens for genes essential in different growth conditions. We analyzed six large-scale studies performed on haploid strains of three yeast species (Saccharomyces cerevisiae, Schizosaccaromyces pombe, and Candida albicans), each mutagenized with two of three different heterologous transposons (AcDs, Hermes, and PiggyBac). Using a machine-learning approach, we evaluated the ability of the data to predict gene essentiality. Important data features included sufficient numbers and distribution of independent insertion events. All transposons showed some bias in insertion site preference because of jackpot events, and preferences for specific insertion sequences and short-distance vs long-distance insertions. For PiggyBac, a stringent target sequence limited the ability to predict essentiality in genes with few or no target sequences. The machine learning approach also robustly predicted gene function in less well-studied species by leveraging cross-species orthologs. Finally, comparisons of isogenic diploid versus haploid S. cerevisiae isolates identified several genes that are haplo-insufficient, while most essential genes, as expected, were recessive. We provide recommendations for the choice of transposons and the inference of gene essentiality in genome-wide studies of eukaryotic haploid microbes such as yeasts, including species that have been less amenable to classical genetic studies.

Modeling clinical and molecular covariates of mutational process activity in cancer.

MOTIVATION: Somatic mutations result from processes related to DNA replication or environmental/lifestyle exposures. Knowing the activity of mutational processes in a tumor can inform personalized therapies, early detection, and understanding of tumorigenesis. Computational methods have revealed 30 validated signatures of mutational processes active in human cancers, where each signature is a pattern of single base substitutions. However, half of these signatures have no known etiology, and some similar signatures have distinct etiologies, making patterns of mutation signature activity hard to interpret. Existing mutation signature detection methods do not consider tumor-level clinical/demographic (e.g. smoking history) or molecular features (e.g. inactivations to DNA damage repair genes). RESULTS: To begin to address these challenges, we present the Tumor Covariate Signature Model (TCSM), the first method to directly model the effect of observed tumor-level covariates on mutation signatures. To this end, our model uses methods from Bayesian topic modeling to change the prior distribution on signature exposure conditioned on a tumor's observed covariates. We also introduce methods for imputing covariates in held-out data and for evaluating the statistical significance of signature-covariate associations. On simulated and real data, we find that TCSM outperforms both non-negative matrix factorization and topic modeling-based approaches, particularly in recovering the ground truth exposure to similar signatures. We then use TCSM to discover five mutation signatures in breast cancer and predict homologous recombination repair deficiency in held-out tumors. We also discover four signatures in a combined melanoma and lung cancer cohort-using cancer type as a covariate-and provide statistical evidence to support earlier claims that three lung cancers from The Cancer Genome Atlas are misdiagnosed metastatic melanomas. AVAILABILITY AND IMPLEMENTATION: TCSM is implemented in Python 3 and available at https://github.com/lrgr/tcsm, along with a data workflow for reproducing the experiments in the paper. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A network-based analysis of colon cancer splicing changes reveals a tumorigenesis-favoring regulatory pathway emanating from ELK1.

Splicing aberrations are prominent drivers of cancer, yet the regulatory pathways controlling them are mostly unknown. Here we develop a method that integrates physical interaction, gene expression, and alternative splicing data to construct the largest map of transcriptomic and proteomic interactions leading to cancerous splicing aberrations defined to date, and identify driver pathways therein. We apply our method to colon adenocarcinoma and non-small-cell lung carcinoma. By focusing on colon cancer, we reveal a novel tumor-favoring regulatory pathway involving the induction of the transcription factor MYC by the transcription factor ELK1, as well as the subsequent induction of the alternative splicing factor PTBP1 by both. We show that PTBP1 promotes specific RAC1,NUMB, and PKM splicing isoforms that are major triggers of colon tumorigenesis. By testing the pathway's activity in patient tumor samples, we find ELK1,MYC, and PTBP1 to be overexpressed in conjunction with oncogenic KRAS mutations, and show that these mutations increase ELK1 levels via the RAS-MAPK pathway. We thus illuminate, for the first time, a full regulatory pathway connecting prevalent cancerous mutations to functional tumor-inducing splicing aberrations. Our results demonstrate our method is applicable to different cancers to reveal regulatory pathways promoting splicing aberrations.

An optimization framework for network annotation.

Motivation: A chief goal of systems biology is the reconstruction of large-scale executable models of cellular processes of interest. While accurate continuous models are still beyond reach, a powerful alternative is to learn a logical model of the processes under study, which predicts the logical state of any node of the model as a Boolean function of its incoming nodes. Key to learning such models is the functional annotation of the underlying physical interactions with activation/repression (sign) effects. Such annotations are pretty common for a few well-studied biological pathways. Results: Here we present a novel optimization framework for large-scale sign annotation that employs different plausible models of signaling and combines them in a rigorous manner. We apply our framework to two large-scale knockout datasets in yeast and evaluate its different components as well as the combined model to predict signs of different subsets of physical interactions. Overall, we obtain an accurate predictor that outperforms previous work by a considerable margin. Availability and implementation: The code is publicly available at https://github.com/spatkar94/NetworkAnnotation.git.

Quantifying deleterious effects of regulatory variants.

The majority of genome-wide association study (GWAS) risk variants reside in non-coding DNA sequences. Understanding how these sequence modifications lead to transcriptional alterations and cell-to-cell variability can help unraveling genotype-phenotype relationships. Here, we describe a computational method, dubbed CAPE, which calculates the likelihood of a genetic variant deactivating enhancers by disrupting the binding of transcription factors (TFs) in a given cellular context. CAPE learns sequence signatures associated with putative enhancers originating from large-scale sequencing experiments (such as ChIP-seq or DNase-seq) and models the change in enhancer signature upon a single nucleotide substitution. CAPE accurately identifies causative cis-regulatory variation including expression quantitative trait loci (eQTLs) and DNase I sensitivity quantitative trait loci (dsQTLs) in a tissue-specific manner with precision superior to several currently available methods. The presented method can be trained on any tissue-specific dataset of enhancers and known functional variants and applied to prioritize disease-associated variants in the corresponding tissue.

A network diffusion approach to inferring sample-specific function reveals functional changes associated with breast cancer.

Guilt-by-association codifies the empirical observation that a gene's function is informed by its neighborhood in a biological network. This would imply that when a gene's network context is altered, for instance in disease condition, so could be the gene's function. Although context-specific changes in biological networks have been explored, the potential changes they may induce on the functional roles of genes are yet to be characterized. Here we analyze, for the first time, the network-induced potential functional changes in breast cancer. Using transcriptomic samples for 1047 breast tumors and 110 healthy breast tissues from TCGA, we derive sample-specific protein interaction networks and assign sample-specific functions to genes via a diffusion strategy. Testing for significant changes in the inferred functions between normal and cancer samples, we find several functions to have significantly gained or lost genes in cancer, not due to differential expression of genes known to perform the function, but rather due to changes in the network topology. Our predicted functional changes are supported by mutational and copy number profiles in breast cancers. Our diffusion-based functional assignment provides a novel characterization of a tumor that is complementary to the standard approach based on functional annotation alone. Importantly, this characterization is effective in predicting patient survival, as well as in predicting several known histopathological subtypes of breast cancer.

BeWith: A Between-Within method to discover relationships between cancer modules via integrated analysis of mutual exclusivity, co-occurrence and functional interactions.

The analysis of the mutational landscape of cancer, including mutual exclusivity and co-occurrence of mutations, has been instrumental in studying the disease. We hypothesized that exploring the interplay between co-occurrence, mutual exclusivity, and functional interactions between genes will further improve our understanding of the disease and help to uncover new relations between cancer driving genes and pathways. To this end, we designed a general framework, BeWith, for identifying modules with different combinations of mutation and interaction patterns. We focused on three different settings of the BeWith schema: (i) BeME-WithFun, in which the relations between modules are enriched with mutual exclusivity, while genes within each module are functionally related; (ii) BeME-WithCo, which combines mutual exclusivity between modules with co-occurrence within modules; and (iii) BeCo-WithMEFun, which ensures co-occurrence between modules, while the within module relations combine mutual exclusivity and functional interactions. We formulated the BeWith framework using Integer Linear Programming (ILP), enabling us to find optimally scoring sets of modules. Our results demonstrate the utility of BeWith in providing novel information about mutational patterns, driver genes, and pathways. In particular, BeME-WithFun helped identify functionally coherent modules that might be relevant for cancer progression. In addition to finding previously well-known drivers, the identified modules pointed to other novel findings such as the interaction between NCOR2 and NCOA3 in breast cancer. Additionally, an application of the BeME-WithCo setting revealed that gene groups differ with respect to their vulnerability to different mutagenic processes, and helped us to uncover pairs of genes with potentially synergistic effects, including a potential synergy between mutations in TP53 and the metastasis related DCC gene. Overall, BeWith not only helped us uncover relations between potential driver genes and pathways, but also provided additional insights on patterns of the mutational landscape, going beyond cancer driving mutations. Implementation is available at https://www.ncbi.nlm.nih.gov/CBBresearch/Przytycka/software/bewith.html.

Proteomic Analysis of Dynein-Interacting Proteins in Amyotrophic Lateral Sclerosis Synaptosomes Reveals Alterations in the RNA-Binding Protein Staufen1.

Synapse disruption takes place in many neurodegenerative diseases, including amyotrophic lateral sclerosis (ALS). However, the mechanistic understanding of this process is still limited. We set out to study a possible role for dynein in synapse integrity. Cytoplasmic dynein is a multisubunit intracellular molecule responsible for diverse cellular functions, including long-distance transport of vesicles, organelles, and signaling factors toward the cell center. A less well-characterized role dynein may play is the spatial clustering and anchoring of various factors including mRNAs in distinct cellular domains such as the neuronal synapse. Here, in order to gain insight into dynein functions in synapse integrity and disruption, we performed a screen for novel dynein interactors at the synapse. Dynein immunoprecipitation from synaptic fractions of the ALS model mSOD1(G93A) and wild-type controls, followed by mass spectrometry analysis on synaptic fractions of the ALS model mSOD1(G93A) and wild-type controls, was performed. Using advanced network analysis, we identified Staufen1, an RNA-binding protein required for the transport and localization of neuronal RNAs, as a major mediator of dynein interactions via its interaction with protein phosphatase 1-beta (PP1B). Both in vitro and in vivo validation assays demonstrate the interactions of Staufen1 and PP1B with dynein, and their colocalization with synaptic markers was altered as a result of two separate ALS-linked mutations: mSOD1(G93A) and TDP43(A315T). Taken together, we suggest a model in which dynein's interaction with Staufen1 regulates mRNA localization along the axon and the synapses, and alterations in this process may correlate with synapse disruption and ALS toxicity.

Systematic identification and correction of annotation errors in the genetic interaction map of Saccharomyces cerevisiae.

The yeast mutant collections are a fundamental tool in deciphering genomic organization and function. Over the last decade, they have been used for the systematic exploration of ∼6 000 000 double gene mutants, identifying and cataloging genetic interactions among them. Here we studied the extent to which these data are prone to neighboring gene effects (NGEs), a phenomenon by which the deletion of a gene affects the expression of adjacent genes along the genome. Analyzing ∼90,000 negative genetic interactions observed to date, we found that more than 10% of them are incorrectly annotated due to NGEs. We developed a novel algorithm, GINGER, to identify and correct erroneous interaction annotations. We validated the algorithm using a comparative analysis of interactions from Schizosaccharomyces pombe. We further showed that our predictions are significantly more concordant with diverse biological data compared to their mis-annotated counterparts. Our work uncovered about 9500 new genetic interactions in yeast.

ANAT 2.0: reconstructing functional protein subnetworks.

BACKGROUND: ANAT is a graphical, Cytoscape-based tool for the inference of protein networks that underlie a process of interest. The ANAT tool allows the user to perform network reconstruction under several scenarios in a number of organisms including yeast and human. RESULTS: Here we report on a new version of the tool, ANAT 2.0, which introduces substantial code and database updates as well as several new network reconstruction algorithms that greatly extend the applicability of the tool to biological data sets. CONCLUSIONS: ANAT 2.0 is an up-to-date network reconstruction tool that addresses several reconstruction challenges across multiple species.

Regulation of Sec16 levels and dynamics links proliferation and secretion.

We currently lack a broader mechanistic understanding of the integration of the early secretory pathway with other homeostatic processes such as cell growth. Here, we explore the possibility that Sec16A, a major constituent of endoplasmic reticulum exit sites (ERES), acts as an integrator of growth factor signaling. Surprisingly, we find that Sec16A is a short-lived protein that is regulated by growth factors in a manner dependent on Egr family transcription factors. We hypothesize that Sec16A acts as a central node in a coherent feed-forward loop that detects persistent growth factor stimuli to increase ERES number. Consistent with this notion, Sec16A is also regulated by short-term growth factor treatment that leads to increased turnover of Sec16A at ERES. Finally, we demonstrate that Sec16A depletion reduces proliferation, whereas its overexpression increases proliferation. Together with our finding that growth factors regulate Sec16A levels and its dynamics on ERES, we propose that this protein acts as an integrator linking growth factor signaling and secretion. This provides a mechanistic basis for the previously proposed link between secretion and proliferation.