Base-resolution stratification of cancer mutations using functional variomics

Global fungal-host interactome mapping identifies host targets of candidalysin

Candidalysin, a cytolytic peptide toxin secreted by the human fungal pathogen Candida albicans, is critical for fungal pathogenesis. Yet, its intracellular targets have not been extensively mapped. Here, we performed a high-throughput enhanced yeast two-hybrid (HT-eY2H) screen to map the interactome of all eight Ece1 peptides with their direct human protein targets and identified a list of potential interacting proteins, some of which were shared between the peptides. CCNH, a regulatory subunit of the CDK-activating kinase (CAK) complex involved in DNA damage repair, was identified as one of the host targets of candidalysin. Mechanistic studies revealed that candidalysin triggers a significantly increased double-strand DNA breaks (DSBs), as evidenced by the formation of γ-H2AX foci and colocalization of CCNH and γ-H2AX. Importantly, candidalysin binds directly to CCNH to activate CAK to inhibit DNA damage repair pathway. Loss of CCNH alleviates DSBs formation under candidalysin treatment. Depletion of candidalysin-encoding gene fails to induce DSBs and stimulates CCNH upregulation in a murine model of oropharyngeal candidiasis. Collectively, our study reveals that a secreted fungal toxin acts to hijack the canonical DNA damage repair pathway by targeting CCNH and to promote fungal infection.

Widespread BRCA1/2-independent homologous recombination defects are caused by alterations in RNA-binding proteins

Defects in homologous recombination DNA repair (HRD) both predispose to cancer development and produce therapeutic vulnerabilities, making it critical to define the spectrum of genetic events that cause HRD. However, we found that mutations in BRCA1/2 and other canonical HR genes only identified 10%-20% of tumors that display genomic evidence of HRD. Using a networks-based approach, we discovered that over half of putative genes causing HRD originated outside of canonical DNA damage response genes, with a particular enrichment for RNA-binding protein (RBP)-encoding genes. These putative drivers of HRD were experimentally validated, cross-validated in an independent cohort, and enriched in cancer-associated genome-wide association study loci. Mechanistic studies indicate that some RBPs are recruited to sites of DNA damage to facilitate repair, whereas others control the expression of canonical HR genes. Overall, this study greatly expands the repertoire of known drivers of HRD, with implications for basic biology, genetic screening, and therapy stratification.

Gain-of-Function Variomics and Multi-omics Network Biology for Precision Medicine

Traditionally, disease causal mutations were thought to disrupt gene function. However, it becomes more clear that many deleterious mutations could exhibit a "gain-of-function" (GOF) behavior. Systematic investigation of such mutations has been lacking and largely overlooked. Advances in next-generation sequencing have identified thousands of genomic variants that perturb the normal functions of proteins, further contributing to diverse phenotypic consequences in disease. Elucidating the functional pathways rewired by GOF mutations will be crucial for prioritizing disease-causing variants and their resultant therapeutic liabilities. In distinct cell types (with varying genotypes), precise signal transduction controls cell decision, including gene regulation and phenotypic output. When signal transduction goes awry due to GOF mutations, it would give rise to various disease types. Quantitative and molecular understanding of network perturbations by GOF mutations may provide explanations for 'missing heritability" in previous genome-wide association studies. We envision that it will be instrumental to push current paradigm toward a thorough functional and quantitative modeling of all GOF mutations and their mechanistic molecular events involved in disease development and progression. Many fundamental questions pertaining to genotype-phenotype relationships remain unresolved. For example, which GOF mutations are key for gene regulation and cellular decisions? What are the GOF mechanisms at various regulation levels? How do interaction networks undergo rewiring upon GOF mutations? Is it possible to leverage GOF mutations to reprogram signal transduction in cells, aiming to cure disease? To begin to address these questions, we will cover a wide range of topics regarding GOF disease mutations and their characterization by multi-omic networks. We highlight the fundamental function of GOF mutations and discuss the potential mechanistic effects in the context of signaling networks. We also discuss advances in bioinformatic and computational resources, which will dramatically help with studies on the functional and phenotypic consequences of GOF mutations.

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.

Multi-parametric analysis of 57 SYNGAP1 variants reveal impacts on GTPase signaling, localization, and protein stability

SYNGAP1 is a neuronal Ras and Rap GTPase-activating protein with important roles in regulating excitatory synaptic plasticity. While many SYNGAP1 missense and nonsense mutations have been associated with intellectual disability, epilepsy, schizophrenia, and autism spectrum disorder (ASD), whether and how they contribute to individual disease phenotypes is often unknown. Here, we characterize 57 variants in seven assays that examine multiple aspects of SYNGAP1 function. Specifically, we used multiplex phospho-flow cytometry to measure variant impact on protein stability, pERK, pGSK3β, pp38, pCREB, and high-content imaging to examine subcellular localization. We find variants ranging from complete loss-of-function (LoF) to wild-type (WT)-like in their regulation of pERK and pGSK3β, while all variants retain at least partial ability to dephosphorylate pCREB. Interestingly, our assays reveal that a larger proportion of variants located within the disordered domain of unknown function (DUF) comprising the C-terminal half of SYNGAP1 exhibited higher LoF, compared to variants within the better studied catalytic domain. Moreover, we find protein instability to be a major contributor to dysfunction for only two missense variants, both located within the catalytic domain. Using high-content imaging, we find variants located within the C2 domain known to mediate membrane lipid interactions exhibit significantly larger cytoplasmic speckles than WT SYNGAP1. Moreover, this subcellular phenotype shows both correlation with altered catalytic activity and unique deviation from signaling assay results, highlighting multiple independent molecular mechanisms underlying variant dysfunction. Our multidimensional dataset allows clustering of variants based on functional phenotypes and provides high-confidence, multi-functional measures for making pathogenicity predictions.

Solution structure of Gaussia Luciferase with five disulfide bonds and identification of a putative coelenterazine binding cavity by heteronuclear NMR

Gaussia luciferase (GLuc) is a small luciferase (18.2 kDa; 168 residues) and is thus attracting much attention as a reporter protein, but the lack of structural information is hampering further application. Here, we report the first solution structure of a fully active, recombinant GLuc determined by heteronuclear multidimensional NMR. We obtained a natively folded GLuc by bacterial expression and efficient refolding using a Solubility Enhancement Petide (SEP) tag. Almost perfect assignments of GLuc’s ¹H, ¹³C and ¹⁵N backbone signals were obtained. GLuc structure was determined using CYANA, which automatically identified over 2500 NOEs of which > 570 were long-range. GLuc is an all-alpha-helix protein made of nine helices. The region spanning residues 10–18, 36–81, 96–145 and containing eight out of the nine helices was determined with a C_α-atom RMSD of 1.39 Å ± 0.39 Å. The structure of GLuc is novel and unique. Two homologous sequential repeats form two anti-parallel bundles made by 4 helices and tied together by three disulfide bonds. The N-terminal helix 1 is grabbed by these 4 helices. Further, we found a hydrophobic cavity where several residues responsible for bioluminescence were identified in previous mutational studies, and we thus hypothesize that this is a catalytic cavity, where the hydrophobic coelenterazine binds and the bioluminescence reaction takes place.

Gain-of-Function Mutations: An Emerging Advantage for Cancer Biology

Advances in next-generation sequencing have identified thousands of genomic variants that perturb the normal functions of proteins, further contributing to diverse phenotypic consequences in cancer. Elucidating the functional pathways altered by loss-of-function (LOF) or gain-of-function (GOF) mutations will be crucial for prioritizing cancer-causing variants and their resultant therapeutic liabilities. In this review, we highlight the fundamental function of GOF mutations and discuss the potential mechanistic effects in the context of signaling networks. We also summarize advances in experimental and computational resources, which will dramatically help with studies on the functional and phenotypic consequences of mutations. Together, systematic investigations of the function of GOF mutations will provide an important missing piece for cancer biology and precision therapy.

High-Throughput Odorant Receptor Deorphanization Via Phospho-S6 Ribosomal Protein Immunoprecipitation and mRNA Profiling

We describe an approach for the high-throughput surveying of odorant receptors (ORs) expressed in olfactory sensory neurons (OSNs) that have been activated by specific odorants. When OSNs are activated, there is a molecular signature in the form of a phosphorylated-S6 (pS6) ribosomal subunit. By the immunoprecipitation of the protein-RNA complex containing pS6, we identify the OR mRNA species expressed in these activated OSNs. The one neuron - one receptor rule (mature OSN expresses a single unique OR) allows for the identification of the collection of ORs that responded toward the tested odorant. Here we detail the procedure of (1) odor stimulation, (2) tissue harvesting, (3) immunoprecipitation, and (4) mRNA profiling for the high-throughput deorphanization of ORs in vivo.