Pervasive mislocalization of pathogenic coding variants underlying human disorders.

Widespread sequencing has yielded thousands of missense variants predicted or confirmed as disease causing. This creates a new bottleneck: determining the functional impact of each variant-typically a painstaking, customized process undertaken one or a few genes and variants at a time. Here, we established a high-throughput imaging platform to assay the impact of coding variation on protein localization, evaluating 3,448 missense variants of over 1,000 genes and phenotypes. We discovered that mislocalization is a common consequence of coding variation, affecting about one-sixth of all pathogenic missense variants, all cellular compartments, and recessive and dominant disorders alike. Mislocalization is primarily driven by effects on protein stability and membrane insertion rather than disruptions of trafficking signals or specific interactions. Furthermore, mislocalization patterns help explain pleiotropy and disease severity and provide insights on variants of uncertain significance. Our publicly available resource extends our understanding of coding variation in human diseases.

An expanded universe of cancer targets.

The characterization of cancer genomes has provided insight into somatically altered genes across tumors, transformed our understanding of cancer biology, and enabled tailoring of therapeutic strategies. However, the function of most cancer alleles remains mysterious, and many cancer features transcend their genomes. Consequently, tumor genomic characterization does not influence therapy for most patients. Approaches to understand the function and circuitry of cancer genes provide complementary approaches to elucidate both oncogene and non-oncogene dependencies. Emerging work indicates that the diversity of therapeutic targets engendered by non-oncogene dependencies is much larger than the list of recurrently mutated genes. Here we describe a framework for this expanded list of cancer targets, providing novel opportunities for clinical translation.

HSP90 Shapes the Consequences of Human Genetic Variation.

HSP90 acts as a protein-folding buffer that shapes the manifestations of genetic variation in model organisms. Whether HSP90 influences the consequences of mutations in humans, potentially modifying the clinical course of genetic diseases, remains unknown. By mining data for >1,500 disease-causing mutants, we found a strong correlation between reduced phenotypic severity and a dominant (HSP90 ≥ HSP70) increase in mutant engagement by HSP90. Examining the cancer predisposition syndrome Fanconi anemia in depth revealed that mutant FANCA proteins engaged predominantly by HSP70 had severely compromised function. In contrast, the function of less severe mutants was preserved by a dominant increase in HSP90 binding. Reducing HSP90's buffering capacity with inhibitors or febrile temperatures destabilized HSP90-buffered mutants, exacerbating FA-related chemosensitivities. Strikingly, a compensatory FANCA somatic mutation from an "experiment of nature" in monozygotic twins both prevented anemia and reduced HSP90 binding. These findings provide one plausible mechanism for the variable expressivity and environmental sensitivity of genetic diseases.

Widespread Expansion of Protein Interaction Capabilities by Alternative Splicing.

While alternative splicing is known to diversify the functional characteristics of some genes, the extent to which protein isoforms globally contribute to functional complexity on a proteomic scale remains unknown. To address this systematically, we cloned full-length open reading frames of alternatively spliced transcripts for a large number of human genes and used protein-protein interaction profiling to functionally compare hundreds of protein isoform pairs. The majority of isoform pairs share less than 50% of their interactions. In the global context of interactome network maps, alternative isoforms tend to behave like distinct proteins rather than minor variants of each other. Interaction partners specific to alternative isoforms tend to be expressed in a highly tissue-specific manner and belong to distinct functional modules. Our strategy, applicable to other functional characteristics, reveals a widespread expansion of protein interaction capabilities through alternative splicing and suggests that many alternative "isoforms" are functionally divergent (i.e., "functional alloforms").

Human gene-centered transcription factor networks for enhancers and disease variants.

Gene regulatory networks (GRNs) comprising interactions between transcription factors (TFs) and regulatory loci control development and physiology. Numerous disease-associated mutations have been identified, the vast majority residing in non-coding regions of the genome. As current GRN mapping methods test one TF at a time and require the use of cells harboring the mutation(s) of interest, they are not suitable to identify TFs that bind to wild-type and mutant loci. Here, we use gene-centered yeast one-hybrid (eY1H) assays to interrogate binding of 1,086 human TFs to 246 enhancers, as well as to 109 non-coding disease mutations. We detect both loss and gain of TF interactions with mutant loci that are concordant with target gene expression changes. This work establishes eY1H assays as a powerful addition to the toolkit of mapping human GRNs and for the high-throughput characterization of genomic variants that are rapidly being identified by genome-wide association studies.

Widespread macromolecular interaction perturbations in human genetic disorders.

How disease-associated mutations impair protein activities in the context of biological networks remains mostly undetermined. Although a few renowned alleles are well characterized, functional information is missing for over 100,000 disease-associated variants. Here we functionally profile several thousand missense mutations across a spectrum of Mendelian disorders using various interaction assays. The majority of disease-associated alleles exhibit wild-type chaperone binding profiles, suggesting they preserve protein folding or stability. While common variants from healthy individuals rarely affect interactions, two-thirds of disease-associated alleles perturb protein-protein interactions, with half corresponding to "edgetic" alleles affecting only a subset of interactions while leaving most other interactions unperturbed. With transcription factors, many alleles that leave protein-protein interactions intact affect DNA binding. Different mutations in the same gene leading to different interaction profiles often result in distinct disease phenotypes. Thus disease-associated alleles that perturb distinct protein activities rather than grossly affecting folding and stability are relatively widespread.

A proteome-scale map of the human interactome network.

Just as reference genome sequences revolutionized human genetics, reference maps of interactome networks will be critical to fully understand genotype-phenotype relationships. Here, we describe a systematic map of ?14,000 high-quality human binary protein-protein interactions. At equal quality, this map is ?30% larger than what is available from small-scale studies published in the literature in the last few decades. While currently available information is highly biased and only covers a relatively small portion of the proteome, our systematic map appears strikingly more homogeneous, revealing a "broader" human interactome network than currently appreciated. The map also uncovers significant interconnectivity between known and candidate cancer gene products, providing unbiased evidence for an expanded functional cancer landscape, while demonstrating how high-quality interactome models will help "connect the dots" of the genomic revolution.

AD-Syn-Net: systematic identification of Alzheimer's disease-associated mutation and co-mutation vulnerabilities via deep learning.

Alzheimer's disease (AD) is one of the most challenging neurodegenerative diseases because of its complicated and progressive mechanisms, and multiple risk factors. Increasing research evidence demonstrates that genetics may be a key factor responsible for the occurrence of the disease. Although previous reports identified quite a few AD-associated genes, they were mostly limited owing to patient sample size and selection bias. There is a lack of comprehensive research aimed to identify AD-associated risk mutations systematically. To address this challenge, we hereby construct a large-scale AD mutation and co-mutation framework ('AD-Syn-Net'), and propose deep learning models named Deep-SMCI and Deep-CMCI configured with fully connected layers that are capable of predicting cognitive impairment of subjects effectively based on genetic mutation and co-mutation profiles. Next, we apply the customized frameworks to data sets to evaluate the importance scores of the mutations and identified mutation effectors and co-mutation combination vulnerabilities contributing to cognitive impairment. Furthermore, we evaluate the influence of mutation pairs on the network architecture to dissect the genetic organization of AD and identify novel co-mutations that could be responsible for dementia, laying a solid foundation for proposing future targeted therapy for AD precision medicine. Our deep learning model codes are available open access here: https://github.com/Pan-Bio/AD-mutation-effectors.

mi-IsoNet: systems-scale microRNA landscape reveals rampant isoform-mediated gain of target interaction diversity and signaling specificity.

MicroRNA (miRNA) is not a single sequence, but a series of multiple variants (also termed isomiRs) with sequence and expression heterogeneity. Whether and how these isoforms contribute to functional variation and complexity at the systems and network levels remain largely unknown. To explore this question systematically, we comprehensively analyzed the expression of small RNAs and their target sites to interrogate functional variations between novel isomiRs and their canonical miRNA sequences. Our analyses of the pan-cancer landscape of miRNA expression indicate that multiple isomiRs generated from the same miRNA locus often exhibit remarkable variation in their sequence, expression and function. We interrogated abundant and differentially expressed 5' isomiRs with novel seed sequences via seed shifting and identified many potential novel targets of these 5' isomiRs that would expand interaction capabilities between small RNAs and mRNAs, rewiring regulatory networks and increasing signaling circuit complexity. Further analyses revealed that some miRNA loci might generate diverse dominant isomiRs that often involved isomiRs with varied seeds and arm-switching, suggesting a selective advantage of multiple isomiRs in regulating gene expression. Finally, experimental validation indicated that isomiRs with shifted seed sequences could regulate novel target mRNAs and therefore contribute to regulatory network rewiring. Our analysis uncovers a widespread expansion of isomiR and mRNA interaction networks compared with those seen in canonical small RNA analysis; this expansion suggests global gene regulation network perturbations by alternative small RNA variants or isoforms. Taken together, the variations in isomiRs that occur during miRNA processing and maturation are likely to play a far more complex and plastic role in gene regulation than previously anticipated.

Functional variomics and network perturbation: connecting genotype to phenotype in cancer.

Proteins interact with other macromolecules in complex cellular networks for signal transduction and biological function. In cancer, genetic aberrations have been traditionally thought to disrupt the entire gene function. It has been increasingly appreciated that each mutation of a gene could have a subtle but unique effect on protein function or network rewiring, contributing to diverse phenotypic consequences across cancer patient populations. In this Review, we discuss the current understanding of cancer genetic variants, including the broad spectrum of mutation classes and the wide range of mechanistic effects on gene function in the context of signalling networks. We highlight recent advances in computational and experimental strategies to study the diverse functional and phenotypic consequences of mutations at the base-pair resolution. Such information is crucial to understanding the complex pleiotropic effect of cancer genes and provides a possible link between genotype and phenotype in cancer.

e-MutPath: computational modeling reveals the functional landscape of genetic mutations rewiring interactome networks.

Understanding the functional impact of cancer somatic mutations represents a critical knowledge gap for implementing precision oncology. It has been increasingly appreciated that the interaction profile mediated by a genomic mutation provides a fundamental link between genotype and phenotype. However, specific effects on biological signaling networks for the majority of mutations are largely unknown by experimental approaches. To resolve this challenge, we developed e-MutPath (edgetic Mutation-mediated Pathway perturbations), a network-based computational method to identify candidate 'edgetic' mutations that perturb functional pathways. e-MutPath identifies informative paths that could be used to distinguish disease risk factors from neutral elements and to stratify disease subtypes with clinical relevance. The predicted targets are enriched in cancer vulnerability genes, known drug targets but depleted for proteins associated with side effects, demonstrating the power of network-based strategies to investigate the functional impact and perturbation profiles of genomic mutations. Together, e-MutPath represents a robust computational tool to systematically assign functions to genetic mutations, especially in the context of their specific pathway perturbation effect.

Gene Regulatory Network Perturbation by Genetic and Epigenetic Variation.

Gene regulatory networks underlie biological function and cellular physiology. Alternative splicing (AS) is a fundamental step in gene regulatory networks and plays a key role in development and disease. In addition to the identification of aberrant AS events, an increasing number of studies are focusing on molecular determinants of AS, including genetic and epigenetic regulators. We review here recent efforts to identify various deregulated AS events as well as their molecular determinants that alter biological functions, and discuss clinical features of AS and their druggable potential.

Pathway perturbations in signaling networks: Linking genotype to phenotype.

Genes and gene products interact with each other to form signal transduction networks in the cell. The interactome networks are under intricate regulation in physiological conditions, but could go awry upon genome instability caused by genetic mutations. In the past decade with next-generation sequencing technologies, an increasing number of genomic mutations have been identified in a variety of disease patients and healthy individuals. As functional and systematic studies on these mutations leap forward, they begin to reveal insights into cellular homeostasis and disease mechanisms. In this review, we discuss recent advances in the field of network biology and signaling pathway perturbations upon genomic changes, and highlight the success of various omics datasets in unraveling genotype-to-phenotype relationships.

Systematic screening reveals a role for BRCA1 in the response to transcription-associated DNA damage.

BRCA1 is a breast and ovarian tumor suppressor. Given its numerous incompletely understood functions and the possibility that more exist, we performed complementary systematic screens in search of new BRCA1 protein-interacting partners. New BRCA1 functions and/or a better understanding of existing ones were sought. Among the new interacting proteins identified, genetic interactions were detected between BRCA1 and four of the interactors: TONSL, SETX, TCEANC, and TCEA2. Genetic interactions were also detected between BRCA1 and certain interactors of TONSL, including both members of the FACT complex. From these results, a new BRCA1 function in the response to transcription-associated DNA damage was detected. Specifically, new roles for BRCA1 in the restart of transcription after UV damage and in preventing or repairing damage caused by stabilized R loops were identified. These roles are likely carried out together with some of the newly identified interactors. This new function may be important in BRCA1 tumor suppression, since the expression of several interactors, including some of the above-noted transcription proteins, is repeatedly aberrant in both breast and ovarian cancers.

MERIT: Systematic Analysis and Characterization of Mutational Effect on RNA Interactome Topology.

The interaction between RNA-binding proteins (RBPs) and RNA plays an important role in regulating cellular function. However, decoding genome-wide protein-RNA regulatory networks as well as how cancer-related mutations impair RNA regulatory activities in hepatocellular carcinoma (HCC) remains mostly undetermined. We explored the genetic alteration patterns of RBPs and found that deleterious mutations are likely to occur on the surface of RBPs. We then constructed protein-RNA interactome networks by integration of target binding screens and expression profiles. Network analysis highlights regulatory principles among interacting RBPs. In addition, somatic mutations selectively target functionally important genes (cancer genes, core fitness genes, or conserved genes) and perturb the RBP-gene regulatory networks in cancer. These regulatory patterns were further validated using independent data. A computational method (Mutational Effect on RNA Interactome Topology) and a web-based, user-friendly resource were further proposed to analyze the RBP-gene regulatory networks across cancer types. Pan-cancer analysis also suggests that cancer cells selectively target "vulnerability" genes to perturb protein-RNA interactome that is involved in cancer hallmark-related functions. Specifically, we experimentally validated four pairs of RBP-gene interactions perturbed by mutations in HCC, which play critical roles in cell proliferation. Based on the expression of perturbed RBP and target genes, we identified three subtypes of HCC with different survival rates. Conclusion: Our results provide a valuable resource for characterizing somatic mutation-perturbed protein-RNA regulatory networks in HCC, yielding valuable insights into the genotype-phenotype relationships underlying human cancer, and potential biomarkers for precision medicine.

FACER: comprehensive molecular and functional characterization of epigenetic chromatin regulators.

Epigenetic alterations, a well-recognized cancer hallmark, are driven by chromatin regulators (CRs). However, little is known about the extent of CR deregulation in cancer, and less is known about their common and specialized roles across various cancers. Here, we performed genome-wide analyses and constructed molecular signatures and network profiles of functional CRs in over 10 000 tumors across 33 cancer types. By integration of DNA mutation, genome-wide methylation, transcriptional/post-transcriptional regulation, and protein interaction networks with clinical outcomes, we identified CRs associated with cancer subtypes and clinical prognosis as potential oncogenic drivers. Comparative network analysis revealed principles of CR regulatory specificity and functionality. In addition, we identified common and specific CRs by assessing their prevalence across cancer types. Common CRs tend to be histone modifiers and chromatin remodelers with fundamental roles, whereas specialized CRs are involved in context-dependent functions. Finally, we have made a user-friendly web interface-FACER (Functional Atlas of Chromatin Epigenetic Regulators) available for exploring clinically relevant CRs for the development of CR biomarkers and therapeutic targets. Our integrative analysis reveals specific determinants of CRs across cancer types and presents a resource for investigating disease-associated CRs.

LncMAP: Pan-cancer atlas of long noncoding RNA-mediated transcriptional network perturbations.

Gene regulatory network perturbations contribute to the development and progression of cancer, however, molecular determinants that mediate transcriptional perturbations remain a fundamental challenge for cancer biology. We show that transcriptional perturbations are widely mediated by long noncoding RNAs (lncRNAs) via integration of genome-wide transcriptional regulation with paired lncRNA and gene expression profiles. Systematic construction of an LncRNA Modulator Atlas in Pan-cancer (LncMAP) reveals distinct types of lncRNA regulatory molecules, which are expressed in multiple tissues, exhibit higher conservation. Strikingly, cancers with similar tissue origin share lncRNA modulators which perturb the regulation of cell cycle and immune response-related functions. Furthermore, we identified a large number of pan-cancer lncRNA modulators with potential clinical significance, which are differentially expressed in cancer or are strongly correlated with drug sensitivity across cell lines. Further stratification of cancer patients based on lncRNA-mediated transcriptional perturbations identifies subtypes with distinct survival rates. Finally, we made a user-friendly web interface available for exploring lncRNA-mediated transcriptional perturbations across cancer types. Our study provides a systems-level dissection of lncRNA-mediated regulatory perturbations in cancer, and also presents a valuable tool and resource for investigating the function of lncRNAs in cancer.

An extended set of yeast-based functional assays accurately identifies human disease mutations.

We can now routinely identify coding variants within individual human genomes. A pressing challenge is to determine which variants disrupt the function of disease-associated genes. Both experimental and computational methods exist to predict pathogenicity of human genetic variation. However, a systematic performance comparison between them has been lacking. Therefore, we developed and exploited a panel of 26 yeast-based functional complementation assays to measure the impact of 179 variants (101 disease- and 78 non-disease-associated variants) from 22 human disease genes. Using the resulting reference standard, we show that experimental functional assays in a 1-billion-year diverged model organism can identify pathogenic alleles with significantly higher precision and specificity than current computational methods.

A disease module in the interactome explains disease heterogeneity, drug response and captures novel pathways and genes in asthma.

Recent advances in genetics have spurred rapid progress towards the systematic identification of genes involved in complex diseases. Still, the detailed understanding of the molecular and physiological mechanisms through which these genes affect disease phenotypes remains a major challenge. Here, we identify the asthma disease module, i.e. the local neighborhood of the interactome whose perturbation is associated with asthma, and validate it for functional and pathophysiological relevance, using both computational and experimental approaches. We find that the asthma disease module is enriched with modest GWAS P-values against the background of random variation, and with differentially expressed genes from normal and asthmatic fibroblast cells treated with an asthma-specific drug. The asthma module also contains immune response mechanisms that are shared with other immune-related disease modules. Further, using diverse omics (genomics, gene-expression, drug response) data, we identify the GAB1 signaling pathway as an important novel modulator in asthma. The wiring diagram of the uncovered asthma module suggests a relatively close link between GAB1 and glucocorticoids (GCs), which we experimentally validate, observing an increase in the level of GAB1 after GC treatment in BEAS-2B bronchial epithelial cells. The siRNA knockdown of GAB1 in the BEAS-2B cell line resulted in a decrease in the NFkB level, suggesting a novel regulatory path of the pro-inflammatory factor NFkB by GAB1 in asthma.

Pooled-matrix protein interaction screens using Barcode Fusion Genetics.

High-throughput binary protein interaction mapping is continuing to extend our understanding of cellular function and disease mechanisms. However, we remain one or two orders of magnitude away from a complete interaction map for humans and other major model organisms. Completion will require screening at substantially larger scales with many complementary assays, requiring further efficiency gains in proteome-scale interaction mapping. Here, we report Barcode Fusion Genetics-Yeast Two-Hybrid (BFG-Y2H), by which a full matrix of protein pairs can be screened in a single multiplexed strain pool. BFG-Y2H uses Cre recombination to fuse DNA barcodes from distinct plasmids, generating chimeric protein-pair barcodes that can be quantified via next-generation sequencing. We applied BFG-Y2H to four different matrices ranging in scale from ~25 K to 2.5 M protein pairs. The results show that BFG-Y2H increases the efficiency of protein matrix screening, with quality that is on par with state-of-the-art Y2H methods.