In vivo versus in silico assessment of potentially pathogenic missense variants in human reproductive genes.

Infertility is a heterogeneous condition, with genetic causes thought to underlie a substantial fraction of cases. Genome sequencing is becoming increasingly important for genetic diagnosis of diseases including idiopathic infertility; however, most rare or minor alleles identified in patients are variants of uncertain significance (VUS). Interpreting the functional impacts of VUS is challenging but profoundly important for clinical management and genetic counseling. To determine the consequences of these variants in key fertility genes, we functionally evaluated 11 missense variants in the genes ANKRD31, BRDT, DMC1, EXO1, FKBP6, MCM9, M1AP, MEI1, MSH4 and SEPT12 by generating genome-edited mouse models. Nine variants were classified as deleterious by most functional prediction algorithms, and two disrupted a protein-protein interaction (PPI) in the yeast two hybrid (Y2H) assay. Though these genes are essential for normal meiosis or spermiogenesis in mice, only one variant, observed in the MCM9 gene of a male infertility patient, compromised fertility or gametogenesis in the mouse models. To explore the disconnect between predictions and outcomes, we compared pathogenicity calls of missense variants made by ten widely used algorithms to 1) those annotated in ClinVar and 2) those evaluated in mice. All the algorithms performed poorly in terms of predicting the effects of human missense variants modeled in mice. These studies emphasize caution in the genetic diagnoses of infertile patients based primarily on pathogenicity prediction algorithms and emphasize the need for alternative and efficient in vitro or in vivo functional validation models for more effective and accurate VUS description to either pathogenic or benign categories.

Progranulin-derived granulin E and lysosome membrane protein CD68 interact to reciprocally regulate their protein homeostasis.

Progranulin (PGRN) is a glycoprotein implicated in several neurodegenerative diseases. It is highly expressed in microglia and macrophages and can be secreted or delivered to the lysosome compartment. PGRN comprises 7.5 granulin repeats and is processed into individual granulin peptides within the lysosome, but the functions of these peptides are largely unknown. Here, we identify CD68, a lysosome membrane protein mainly expressed in hematopoietic cells, as a binding partner of PGRN and PGRN-derived granulin E. Deletion analysis of CD68 showed that this interaction is mediated by the mucin-proline-rich domain of CD68. While CD68 deficiency does not affect the lysosomal localization of PGRN, it results in a specific decrease in the levels of granulin E but no other granulin peptides. On the other hand, the deficiency of PGRN, and its derivative granulin peptides, leads to a significant shift in the molecular weight of CD68, without altering CD68 localization within the cell. Our results support that granulin E and CD68 reciprocally regulate each other's protein homeostasis.

Human MLH1/3 variants causing aneuploidy, pregnancy loss, and premature reproductive aging.

Embryonic aneuploidy from mis-segregation of chromosomes during meiosis causes pregnancy loss. Proper disjunction of homologous chromosomes requires the mismatch repair (MMR) genes MLH1 and MLH3, essential in mice for fertility. Variants in these genes can increase colorectal cancer risk, yet the reproductive impacts are unclear. To determine if MLH1/3 single nucleotide polymorphisms (SNPs) in human populations could cause reproductive abnormalities, we use computational predictions, yeast two-hybrid assays, and MMR and recombination assays in yeast, selecting nine MLH1 and MLH3 variants to model in mice via genome editing. We identify seven alleles causing reproductive defects in mice including female subfertility and male infertility. Remarkably, in females these alleles cause age-dependent decreases in litter size and increased embryo resorption, likely a consequence of fewer chiasmata that increase univalents at meiotic metaphase I. Our data suggest that hypomorphic alleles of meiotic recombination genes can predispose females to increased incidence of pregnancy loss from gamete aneuploidy.

SAAMBE-SEQ: a sequence-based method for predicting mutation effect on protein-protein binding affinity.

MOTIVATION: Vast majority of human genetic disorders are associated with mutations that affect protein-protein interactions by altering wild-type binding affinity. Therefore, it is extremely important to assess the effect of mutations on protein-protein binding free energy to assist the development of therapeutic solutions. Currently, the most popular approaches use structural information to deliver the predictions, which precludes them to be applicable on genome-scale investigations. Indeed, with the progress of genomic sequencing, researchers are frequently dealing with assessing effect of mutations for which there is no structure available. RESULTS: Here, we report a Gradient Boosting Decision Tree machine learning algorithm, the SAAMBE-SEQ, which is completely sequence-based and does not require structural information at all. SAAMBE-SEQ utilizes 80 features representing evolutionary information, sequence-based features and change of physical properties upon mutation at the mutation site. The approach is shown to achieve Pearson correlation coefficient (PCC) of 0.83 in 5-fold cross validation in a benchmarking test against experimentally determined binding free energy change (ΔΔG). Further, a blind test (no-STRUC) is compiled collecting experimental ΔΔG upon mutation for protein complexes for which structure is not available and used to benchmark SAAMBE-SEQ resulting in PCC in the range of 0.37-0.46. The accuracy of SAAMBE-SEQ method is found to be either better or comparable to most advanced structure-based methods. SAAMBE-SEQ is very fast, available as webserver and stand-alone code, and indeed utilizes only sequence information, and thus it is applicable for genome-scale investigations to study the effect of mutations on protein-protein interactions. AVAILABILITY AND IMPLEMENTATION: SAAMBE-SEQ is available at http://compbio.clemson.edu/saambe_webserver/indexSEQ.php#started. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Variants in RABL2A causing male infertility and ciliopathy.

Approximately 7% of men worldwide suffer from infertility, with sperm abnormalities being the most common defect. Though genetic causes are thought to underlie a substantial fraction of idiopathic cases, the actual molecular bases are usually undetermined. Because the consequences of most genetic variants in populations are unknown, this complicates genetic diagnosis even after genome sequencing of patients. Some patients with ciliopathies, including primary ciliary dyskinesia and Bardet-Biedl syndrome, also suffer from infertility because cilia and sperm flagella share several characteristics. Here, we identified two deleterious alleles of RABL2A, a gene essential for normal function of cilia and flagella. Our in silico predictions and in vitro assays suggest that both alleles destabilize the protein. We constructed and analyzed mice homozygous for these two single-nucleotide polymorphisms, Rabl2L119F (rs80006029) and Rabl2V158F (rs200121688), and found that they exhibit ciliopathy-associated disorders including male infertility, early growth retardation, excessive weight gain in adulthood, heterotaxia, pre-axial polydactyly, neural tube defects and hydrocephalus. Our study provides a paradigm for triaging candidate infertility variants in the population for in vivo functional validation, using computational, in vitro and in vivo approaches.

SAAMBE-3D: Predicting Effect of Mutations on Protein-Protein Interactions.

Maintaining wild type protein-protein interactions is essential for the normal function of cell and any mutation that alter their characteristics can cause disease. Therefore, the ability to correctly and quickly predict the effect of amino acid mutations is crucial for understanding disease effects and to be able to carry out genome-wide studies. Here, we report a new development of the SAAMBE method, SAAMBE-3D, which is a machine learning-based approach, resulting in accurate predictions and is extremely fast. It achieves the Pearson correlation coefficient ranging from 0.78 to 0.82 depending on the training protocol in benchmarking five-fold validation test against the SKEMPI v2.0 database and outperforms currently existing algorithms on various blind-tests. Furthermore, optimized and tested via five-fold cross-validation on the Cornell University dataset, the SAAMBE-3D achieves AUC of 1.0 and 0.96 on a homo and hereto-dimer test datasets. Another important feature of SAAMBE-3D is that it is very fast, it takes less than a fraction of a second to complete a prediction. SAAMBE-3D is available as a web server and as well as a stand-alone code, the last one being another important feature allowing other researchers to directly download the code and run it on their local computer. Combined all together, SAAMBE-3D is an accurate and fast software applicable for genome-wide studies to assess the effect of amino acid mutations on protein-protein interactions. The webserver and the stand-alone codes (SAAMBE-3D for predicting the change of binding free energy and SAAMBE-3D-DN for predicting if the mutation is disruptive or non-disruptive) are available.

Extensive disruption of protein interactions by genetic variants across the allele frequency spectrum in human populations.

Each human genome carries tens of thousands of coding variants. The extent to which this variation is functional and the mechanisms by which they exert their influence remains largely unexplored. To address this gap, we leverage the ExAC database of 60,706 human exomes to investigate experimentally the impact of 2009 missense single nucleotide variants (SNVs) across 2185 protein-protein interactions, generating interaction profiles for 4797 SNV-interaction pairs, of which 421 SNVs segregate at > 1% allele frequency in human populations. We find that interaction-disruptive SNVs are prevalent at both rare and common allele frequencies. Furthermore, these results suggest that 10.5% of missense variants carried per individual are disruptive, a higher proportion than previously reported; this indicates that each individual's genetic makeup may be significantly more complex than expected. Finally, we demonstrate that candidate disease-associated mutations can be identified through shared interaction perturbations between variants of interest and known disease mutations.

An interactome perturbation framework prioritizes damaging missense mutations for developmental disorders.

Identifying disease-associated missense mutations remains a challenge, especially in large-scale sequencing studies. Here we establish an experimentally and computationally integrated approach to investigate the functional impact of missense mutations in the context of the human interactome network and test our approach by analyzing ~2,000 de novo missense mutations found in autism subjects and their unaffected siblings. Interaction-disrupting de novo missense mutations are more common in autism probands, principally affect hub proteins, and disrupt a significantly higher fraction of hub interactions than in unaffected siblings. Moreover, they tend to disrupt interactions involving genes previously implicated in autism, providing complementary evidence that strengthens previously identified associations and enhances the discovery of new ones. Importantly, by analyzing de novo missense mutation data from six disorders, we demonstrate that our interactome perturbation approach offers a generalizable framework for identifying and prioritizing missense mutations that contribute to the risk of human disease.

Interactome INSIDER: a structural interactome browser for genomic studies.

We present Interactome INSIDER, a tool to link genomic variant information with structural protein-protein interactomes. Underlying this tool is the application of machine learning to predict protein interaction interfaces for 185,957 protein interactions with previously unresolved interfaces in human and seven model organisms, including the entire experimentally determined human binary interactome. Predicted interfaces exhibit functional properties similar to those of known interfaces, including enrichment for disease mutations and recurrent cancer mutations. Through 2,164 de novo mutagenesis experiments, we show that mutations of predicted and known interface residues disrupt interactions at a similar rate and much more frequently than mutations outside of predicted interfaces. To spur functional genomic studies, Interactome INSIDER (http://interactomeinsider.yulab.org) enables users to identify whether variants or disease mutations are enriched in known and predicted interaction interfaces at various resolutions. Users may explore known population variants, disease mutations, and somatic cancer mutations, or they may upload their own set of mutations for this purpose.

Extracting Complementary Insights from Molecular Phenotypes for Prioritization of Disease-Associated Mutations.

Rapid advances in next-generation sequencing technology have resulted in an explosion of whole-exome/genome sequencing data, providing an unprecedented opportunity to identify disease- and trait-associated variants in humans on a large scale. To date, the long-standing paradigm has leveraged fitness-based approximations to translate this ever-expanding sequencing data into causal insights in disease. However, while this approach robustly identifies variants under evolutionary constraint, it fails to provide molecular insights. Moreover, complex disease phenomena often violate standard assumptions of a direct organismal phenotype to overall fitness effect relationship. Here we discuss the potential of a molecular phenotype-oriented paradigm to uniquely identify candidate disease-causing mutations from the human genetic background. By providing a direct connection between single nucleotide mutations and observable organismal and cellular phenotypes associated with disease, we suggest that molecular phenotypes can readily incorporate alongside established fitness-based methodologies to provide complementary insights to the functional impact of human mutations. Lastly, we discuss how integrated approaches between molecular phenotypes and fitness-based perspectives facilitate new insights into the molecular mechanisms underlying disease-associated mutations while also providing a platform for improved interpretation of epistasis in human disease.

A Proteome-wide Fission Yeast Interactome Reveals Network Evolution Principles from Yeasts to Human.

Here, we present FissionNet, a proteome-wide binary protein interactome for S. pombe, comprising 2,278 high-quality interactions, of which ∼ 50% were previously not reported in any species. FissionNet unravels previously unreported interactions implicated in processes such as gene silencing and pre-mRNA splicing. We developed a rigorous network comparison framework that accounts for assay sensitivity and specificity, revealing extensive species-specific network rewiring between fission yeast, budding yeast, and human. Surprisingly, although genes are better conserved between the yeasts, S. pombe interactions are significantly better conserved in human than in S. cerevisiae. Our framework also reveals that different modes of gene duplication influence the extent to which paralogous proteins are functionally repurposed. Finally, cross-species interactome mapping demonstrates that coevolution of interacting proteins is remarkably prevalent, a result with important implications for studying human disease in model organisms. Overall, FissionNet is a valuable resource for understanding protein functions and their evolution.

A massively parallel pipeline to clone DNA variants and examine molecular phenotypes of human disease mutations.

Understanding the functional relevance of DNA variants is essential for all exome and genome sequencing projects. However, current mutagenesis cloning protocols require Sanger sequencing, and thus are prohibitively costly and labor-intensive. We describe a massively-parallel site-directed mutagenesis approach, "Clone-seq", leveraging next-generation sequencing to rapidly and cost-effectively generate a large number of mutant alleles. Using Clone-seq, we further develop a comparative interactome-scanning pipeline integrating high-throughput GFP, yeast two-hybrid (Y2H), and mass spectrometry assays to systematically evaluate the functional impact of mutations on protein stability and interactions. We use this pipeline to show that disease mutations on protein-protein interaction interfaces are significantly more likely than those away from interfaces to disrupt corresponding interactions. We also find that mutation pairs with similar molecular phenotypes in terms of both protein stability and interactions are significantly more likely to cause the same disease than those with different molecular phenotypes, validating the in vivo biological relevance of our high-throughput GFP and Y2H assays, and indicating that both assays can be used to determine candidate disease mutations in the future. The general scheme of our experimental pipeline can be readily expanded to other types of interactome-mapping methods to comprehensively evaluate the functional relevance of all DNA variants, including those in non-coding regions.

Elucidating common structural features of human pathogenic variations using large-scale atomic-resolution protein networks.

With the rapid growth of structural genomics, numerous protein crystal structures have become available. However, the parallel increase in knowledge of the functional principles underlying biological processes, and more specifically the underlying molecular mechanisms of disease, has been less dramatic. This notwithstanding, the study of complex cellular networks has made possible the inference of protein functions on a large scale. Here, we combine the scale of network systems biology with the resolution of traditional structural biology to generate a large-scale atomic-resolution interactome-network comprising 3,398 interactions between 2,890 proteins with a well-defined interaction interface and interface residues for each interaction. Within the framework of this atomic-resolution network, we have explored the structural principles underlying variations causing human-inherited disease. We find that in-frame pathogenic variations are enriched at both the interface and in the interacting domain, suggesting that variations not only at interface "hot-spots," but in the entire interacting domain can result in alterations of interactions. Further, the sites of pathogenic variations are closely related to the biophysical strength of the interactions they perturb. Finally, we show that biochemical alterations consequent to these variations are considerably more disruptive than evolutionary changes, with the most significant alterations at the protein interaction interface.

Exploring mechanisms of human disease through structurally resolved protein interactome networks.

The study of the molecular basis of human disease has gained increasing attention over the past decade. With significant improvements in sequencing efficiency and throughput, a wealth of genotypic data has become available. However the translation of this information into concrete advances in diagnostic and clinical setups has proved far more challenging. Two major reasons for this are the lack of functional annotation for genomic variants and the complex nature of genotype-to-phenotype relationships. One fundamental approach to bypass these issues is to examine the effects of genetic variation at the level of proteins as they are directly involved in carrying out biological functions. Within the cell, proteins function by interacting with other proteins as a part of an underlying interactome network. This network can be determined using interactome mapping - a combination of high-throughput experimental toolkits and curation from small-scale studies. Integrating structural information from co-crystals with the network allows generation of a structurally resolved network. Within the context of this network, the structural principles of disease mutations can be examined and used to generate reliable mechanistic hypotheses regarding disease pathogenesis.

Integrative annotation of variants from 1092 humans: application to cancer genomics.

Interpreting variants, especially noncoding ones, in the increasing number of personal genomes is challenging. We used patterns of polymorphisms in functionally annotated regions in 1092 humans to identify deleterious variants; then we experimentally validated candidates. We analyzed both coding and noncoding regions, with the former corroborating the latter. We found regions particularly sensitive to mutations ("ultrasensitive") and variants that are disruptive because of mechanistic effects on transcription-factor binding (that is, "motif-breakers"). We also found variants in regions with higher network centrality tend to be deleterious. Insertions and deletions followed a similar pattern to single-nucleotide variants, with some notable exceptions (e.g., certain deletions and enhancers). On the basis of these patterns, we developed a computational tool (FunSeq), whose application to ~90 cancer genomes reveals nearly a hundred candidate noncoding drivers.