Search

Cancer is a predominant disease across animals. We applied a comparative genomics approach to systematically characterize genes whose conservation levels correlate positively (PC) or negatively (NC) with cancer resistance estimates across 193 vertebrates. Pathway analysis reveals that NC genes are enriched for metabolic functions and PC genes in cell cycle regulation, DNA repair, and immune response, pointing to their corresponding roles in mediating cancer risk. We find that PC genes are less tolerant to loss-of-function (LoF) mutations, are enriched in cancer driver genes, and are associated with germline mutations that increase human cancer risk. Their relevance to cancer risk is further supported via the analysis of mouse functional genomics and cancer mortality of zoo mammals’ data. In sum, our study describes a cross-species genomic analysis pointing to candidate genes that may mediate human cancer risk.

Introduction: PARP inhibitors (PARPi) are known to have a synthetic lethal (SL) relationship with BRCA1/2 mutated cancers (ie: BRCA loss confers sensitivity to PARPi), but heterogeneity in response exists. PARPi have also found utility in BRCA-non mutated cancers, suggesting other factors may contribute to response. Recently, PARPi trials in pediatric solid tumors have had mixed results. SL interactions have been shown to exist in a network relationship, as such the aim of this study was to identify a clinically relevant SL signature (including synthetic dosage lethality and synthetic rescue) that would predict response to PARPi in pediatric cancers. Methods: Pre-treatment genomic copy number alteration, RNA sequencing, and survival data from 519 pediatric cancer patients (INFORM Consortium, 91% solid tumor) were obtained. SL partner genes for PARPi were selected if co-alteration of the drug target (PARP1/2/3) and candidate gene was 1) found to be under-represented in the patient transcriptome, 2) associated with improved survival, and 3) the genes were phylogenetically similar to PARP1/2/3. The 100 gene signature was validated on two independent pediatric cancer drug screens: in-vivo orthotopic patient-derived xenograft (OPDX) mouse models (n=24) and in-vitro PRISM cell lines (n=54). It was also validated on a cisplatin (PARP surrogate) treated pediatric osteosarcoma clinical trial (n=37). Comparisons were made to 1) a signature generated with the same pipeline using adult TCGA data, 2) a validated adult synthetic lethality pipeline (SELECT), and 3) a published 60-gene PARPi response signature (BRCAness score). Results: The pediatric signature predicted response in all drug screens evaluated. Four cell line screens: Talazoparib (AUC 0.72, p = 0.003), Rucaparib (AUC 0.69, p = 0.01), Niraparib (AUC 0.67, p = 0.03), Olaparib (AUC 0.64, p = 0.06), and two OPDX screens: Talazoparib (AUC 0.74, p = 0.03), Olaparib (AUC 0.74, p = 0.03). The pediatric signature outperformed both adult signatures (AUCs 0.34-0.61, all p’s >0.05), and the BRCAness score (AUCs 0.41-0.53, p’s >0.05) except in the Olaparib cell line (BRCAness: AUC 0.68, p = 0.01). In the pediatric osteosarcoma dataset, the pediatric signature predicted death (AUC 0.68) and two-year recurrence (AUC 0.72), outperforming the other measures (death: AUC 0.49-0.66; two-year recurrence: AUCs 0.36-0.44). Discussion: We developed a pediatric derived, clinically relevant, synthetic lethal signature that predicts response to PARPi in pan-pediatric cancer drug screens and in a cisplatin treated osteosarcoma clinical trial. Our findings support the goal of identifying precision oncology frameworks to improve the care of children with cancer. Citation Format: Matthew Nagy, Fiorella Schischlik, Kun Wang, Nishanth Ulhas Nair, E. Michael Gertz, Lipika R. Pal, Natalie Jaeger, Christopher Previti, Dina ElHarouni, Stefan M. Pfister, Eytan Ruppin. Predicting response to PARP inhibitors in pediatric cancer via synthetic lethal networks [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3118.

Antibodies are fundamental effectors of humoral immunity, and have become a highly successful class of therapeutics. There is increasing evidence that antibodies utilize transient homotypic interactions to enhance function, and elucidation of such interactions can provide insights into their biology and new opportunities for their optimization as drugs. Yet the transitory nature of weak interactions makes them difficult to investigate. Capitalizing on their rich structural data and high conservation, we have characterized all the ways that antibody Fab regions interact crystallographically. This approach led to the discovery of previously unrealized interfaces between antibodies. While diverse interactions exist, β-sheet dimers and variable-constant elbow dimers are recurrent motifs. Disulfide engineering enabled interactions to be trapped and investigated structurally and functionally, providing experimental validation of the interfaces and illustrating their potential for optimization. This work provides first insight into previously undiscovered oligomeric interactions between antibodies, and enables new opportunities for their biotherapeutic optimization.

Whole-genome sequencing (WGS) holds great potential as a diagnostic test. However, the majority of patients currently undergoing WGS lack a molecular diagnosis, largely due to the vast number of undiscovered disease genes and our inability to assess the pathogenicity of most genomic variants. The CAGI SickKids challenges attempted to address this knowledge gap by assessing state-of-the-art methods for clinical phenotype prediction from genomes. CAGI4 and CAGI5 participants were provided with WGS data and clinical descriptions of 25 and 24 undiagnosed patients from the SickKids Genome Clinic Project, respectively. Predictors were asked to identify primary and secondary causal variants. In addition, for CAGI5, groups had to match each genome to one of three disorder categories (neurologic, ophthalmologic, and connective), and separately to each patient. The performance of matching genomes to categories was no better than random but two groups performed significantly better than chance in matching genomes to patients. Two of the ten variants proposed by two groups in CAGI4 were deemed to be diagnostic, and several proposed pathogenic variants in CAGI5 are good candidates for phenotype expansion. We discuss implications for improving in silico assessment of genomic variants and identifying new disease genes.

Genetics play a key role in venous thromboembolism (VTE) risk, however established risk factors in European populations do not translate to individuals of African descent because of the differences in allele frequencies between populations. As part of the fifth iteration of the Critical Assessment of Genome Interpretation, participants were asked to predict VTE status in exome data from African American subjects. Participants were provided with 103 unlabeled exomes from patients treated with warfarin for non-VTE causes or VTE and asked to predict which disease each subject had been treated for. Given the lack of training data, many participants opted to use unsupervised machine learning methods, clustering the exomes by variation in genes known to be associated with VTE. The best performing method using only VTE related genes achieved an area under the ROC curve of 0.65. Here, we discuss the range of methods used in the prediction of VTE from sequence data and explore some of the difficulties of conducting a challenge with known confounders. In addition, we show that an existing genetic risk score for VTE that was developed in European subjects works well in African Americans.

Interpretation of genomic variation plays an essential role in the analysis of cancer and monogenic disease, and increasingly also in complex trait disease, with applications ranging from basic research to clinical decisions. Many computational impact prediction methods have been developed, yet the field lacks a clear consensus on their appropriate use and interpretation. The Critical Assessment of Genome Interpretation (CAGI, /'kā-jē/) is a community experiment to objectively assess computational methods for predicting the phenotypic impacts of genomic variation. CAGI participants are provided genetic variants and make blind predictions of resulting phenotype. Independent assessors evaluate the predictions by comparing with experimental and clinical data. CAGI has completed five editions with the goals of establishing the state of art in genome interpretation and of encouraging new methodological developments. This special issue (https://onlinelibrary.wiley.com/toc/10981004/2019/40/9) comprises reports from CAGI, focusing on the fifth edition that culminated in a conference that took place 5 to 7 July 2018. CAGI5 was comprised of 14 challenges and engaged hundreds of participants from a dozen countries. This edition had a notable increase in splicing and expression regulatory variant challenges, while also continuing challenges on clinical genomics, as well as complex disease datasets and missense variants in diseases ranging from cancer to Pompe disease to schizophrenia. Full information about CAGI is at https://genomeinterpretation.org.

Cancer is an evolutionarily conserved disease that occurs in a wide variety of species. We applied a comparative genomics approach to systematically characterize the genes whose conservation levels significantly correlates positively (PC) or negatively (NC) with a broad spectrum of cancer-resistance estimates, computed across almost 200 vertebrate species. PC genes are enriched in pathways relevant to tumor suppression including cell cycle, DNA repair, and immune response, while NC genes are enriched with a host of metabolic pathways. The conservation levels of the PC and NC genes in a species serve to build the first genomics-based predictor of its cancer resistance score. We find that PC genes are less tolerant to loss of function (LoF) mutations, are enriched in cancer driver genes and are associated with germline mutations that increase human cancer risk. Furthermore, their expression levels are associated with lifetime cancer risk across human tissues. Finally, their knockout in mice results in increased cancer incidence. In sum, we find that many genes associated with cancer resistance across species are implicated in human cancers, pointing to several additional candidate genes that may have a functional role in human cancer.

Tremendous progress has been made to control the COVID‐19 pandemic caused by the SARS‐CoV‐2 virus. However, effective therapeutic options are still rare. Drug repurposing and combination represent practical strategies to address this urgent unmet medical need. Viruses, including coronaviruses, are known to hijack host metabolism to facilitate viral proliferation, making targeting host metabolism a promising antiviral approach. Here, we describe an integrated analysis of 12 published in vitro and human patient gene expression datasets on SARS‐CoV‐2 infection using genome‐scale metabolic modeling (GEM), revealing complicated host metabolism reprogramming during SARS‐CoV‐2 infection. We next applied the GEM‐based metabolic transformation algorithm to predict anti‐SARS‐CoV‐2 targets that counteract the virus‐induced metabolic changes. We successfully validated these targets using published drug and genetic screen data and by performing an siRNA assay in Caco‐2 cells. Further generating and analyzing RNA‐sequencing data of remdesivir‐treated Vero E6 cell samples, we predicted metabolic targets acting in combination with remdesivir, an approved anti‐SARS‐CoV‐2 drug. Our study provides clinical data‐supported candidate anti‐SARS‐CoV‐2 targets for future evaluation, demonstrating host metabolism targeting as a promising antiviral strategy., Metabolic modeling of 12 SARS‐CoV‐2 datasets identifies novel single or combinatory antiviral targets by reverting the virus‐induced host metabolic reprogramming.

Introduction: Despite the development of two mRNA vaccines, there is an urgent unmet need of finding new antiviral strategies. One such potential antiviral strategy is to target the synthetic lethal (SL) partners of transcriptionally altered genes in infected host cells, thereby selectively killing them to halt the infection at its heels (Mast FD, JCB, 2020). Methods: Here we conduct a first proof-of-concept SL inference approach to predict anti-SARS-CoV-2 targets in a systematic genome-wide manner. This effort capitalizes on our recently published pipeline for inferring clinically relevant SL interactions in cancer (Lee et al, Cell, 2021). Based on the latter, we comprehensively analyzed multiple in vitro and in vivo bulk and single-cell RNA-sequencing datasets of SARS-CoV-2 infection to predict candidate antiviral targets that are SL with altered host genes. Importantly, as our predictions are fine-tuned based on the analysis of patients’ data, they are more likely to be of translational value. Results: Our key results are twofold:1) The predicted SL-based targets are highly enriched for genes that are reported in four SARS-CoV-2 CRISPR-Cas9 genome-wide genetic screens to inhibit growth of infected cells. 2) A subset of top predicted 26 genes were experimentally tested in a targeted siRNA screen conducted in both infected and non-infected human Caco-2 cells. Remarkably, as expected given that these targets were predicted to be SL specific with genes upregulated in infected cells, indeed, knocking down these targets reduced viral replication and cell viability only under the infected condition without harming non-infected cells. Conclusion: In summary, this study is the first to demonstrate the potential of a synthetic lethality approach to identify viral (specifically anti-SARS-CoV-2) targets. Importantly, as both single cell and bulk transcriptomics patients’ data is considered from both infected people and controls, they are more likely to be of clinical relevance. Targeting host genes identified via an SL-based approach is probably more suitable when the infection is at the early stage and host can still tolerate the loss of infected host cells. Citation Format: Lipika R. Pal, Kuoyuan Cheng, Nishanth Ulhas Nair, Laura Martin-Sancho, Sanju Sinha, Yuan Pu, Laura Riva, Xin Yin, Fiorella Schischlik, Joo Sang Lee, Sumit Chanda, Eytan Ruppin. Identifying and testing cancer-derived synthetic-lethal anti-SARS-CoV-2 targets [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3583.

Novel strategies are needed to identify drug targets and treatments for the COVID-19 pandemic. The altered gene expression of virus-infected host cells provides an opportunity to specifically inhibit viral propagation via targeting the synthetic lethal (SL) partners of such altered host genes. Pursuing this antiviral strategy, here we comprehensively analyzed multiple in vitro and in vivo bulk and single-cell RNA-sequencing datasets of SARS-CoV-2 infection to predict clinically relevant candidate antiviral targets that are SL with altered host genes. The predicted SL-based targets are highly enriched for infected cell inhibiting genes reported in four SARS-CoV-2 CRISPR-Cas9 genome-wide genetic screens. Integrating our predictions with the results of these screens, we further selected a focused subset of 26 genes that we experimentally tested in a targeted siRNA screen using human Caco-2 cells. Notably, as predicted, knocking down these targets reduced viral replication and cell viability only under the infected condition without harming non-infected cells. Our results are made publicly available, to facilitate their in vivo testing and further validation.

Understanding the basis of complex trait disease is a fundamental problem in human genetics. The CAGI Crohn’s Exome challenges are providing insight into the adequacy of current disease models by requiring participants to identify which of a set of individuals has been diagnosed with the disease, given exome data. For the CAGI4 round, we developed a method that used the genotypes from exome sequencing data only to impute the status of Genome Wide Association Studies (GWAS) marker single nucleotide polymorphisms (SNPs). We then used the imputed genotypes as input to several machine learning methods that had been trained to predict disease status from marker SNP information. We achieved the best performance using Naïve Bayes and with a consensus machine learning method, obtaining an area under the curve (AUC) of 0.72, larger than other methods used in CAGI4. We also developed a model that incorporated the contribution from rare missense variants in the exome data, but this performed less well. Future progress is expected to come from the use of whole genome data rather than exomes.

The availability of disease-specific genomic data is critical for developing new computational methods that predict the pathogenicity of human variants and advance the field of precision medicine. However, the lack of gold standards to properly train and benchmark such methods is one of the greatest challenges in the field. In response to this challenge, the scientific community is invited to participate in the Critical Assessment for Genome Interpretation (CAGI), where unpublished disease variants are available for classification by in silico methods. As part of the CAGI-5 challenge, we evaluated the performance of 18 submissions and three additional methods in predicting the pathogenicity of single nucleotide variants (SNVs) in checkpoint kinase 2 (CHEK2) for cases of breast cancer in Hispanic females. As part of the assessment, the efficacy of the analysis method and the setup of the challenge were also considered. The results indicated that though the challenge could benefit from additional participant data, the combined generalized linear model analysis and odds of pathogenicity analysis provided a framework to evaluate the methods submitted for SNV pathogenicity identification and for comparison to other available methods. The outcome of this challenge and the approaches used can help guide further advancements in identifying SNV-disease relationships.

Precise identification of causative variants from whole-genome sequencing data, including both coding and non-coding variants, is challenging. The CAGI5 SickKids clinical genome challenge provided an opportunity to assess our ability to extract such information. Participants in the challenge were required to match each of 24 whole-genome sequences to the correct phenotypic profile and to identify the disease class of each genome. These are all rare disease cases that have resisted genetic diagnosis in a state-of-the-art pipeline. The patients have a range of eye, neurological, and connective-tissue disorders. We used a gene-centric approach to address this problem, assigning each gene a multi-phenotype-matching score. Mutations in the top scoring genes for each phenotype profile were ranked on a six-point scale of pathogenicity probability, resulting in an approximately equal number of top ranked coding and non-coding candidate variants overall. We were able to assign the correct disease class for 12 cases and the correct genome to a clinical profile for five cases. The challenge assessor found genes in three of these five cases as likely appropriate. In the post-submission phase, after careful screening of the genes in the correct genome we identified additional potential diagnostic variants, a high proportion of which are non-coding.

The Critical Assessment of Genome Interpretation-5 intellectual disability challenge asked to use computational methods to predict patient clinical phenotypes and the causal variant(s) based on an analysis of their gene panel sequence data. Sequence data for 74 genes associated with intellectual disability (ID) and/or autism spectrum disorders (ASD) from a cohort of 150 patients with a range of neurodevelopmental manifestations (i.e. ID, autism, epilepsy, microcephaly, macrocephaly, hypotonia, ataxia) have been made available for this challenge. For each patient, predictors had to report the causative variants and which of the seven phenotypes were present. Since neurodevelopmental disorders are characterized by strong comorbidity, tested individuals often present more than one pathological condition. Considering the overall clinical manifestation of each patient, the correct phenotype has been predicted by at least one group for 93 individuals (62%). ID and ASD were the best predicted among the seven phenotypic traits. Also, causative or potentially pathogenic variants were predicted correctly by at least one group. However, the prediction of the correct causative variant seems to be insufficient to predict the correct phenotype. In some cases, the correct prediction has been supported by rare or common variants in genes different from the causative one.

Thermodynamic stability is a fundamental property shared by all proteins. Changes in stability due to mutation are a widespread molecular mechanism in genetic diseases. Methods for the prediction of mutation-induced stability change have typically been developed and evaluated on incomplete and/or biased data sets. As part of the Critical Assessment of Genome Interpretation (CAGI), we explored the utility of high-throughput variant stability profiling (VSP) assay data as an alternative for the assessment of computational methods and evaluated state-of-the-art predictors against over 7,000 non-synonymous variants from two proteins. We found that predictions were modestly correlated with actual experimental values. Predictors fared better when evaluated as classifiers of extreme stability effects. While different methods emerged as top-performers depending on the metric, it is non-trivial to draw conclusions on their adoption or improvement. Our analyses revealed that only 16% of all variants in VSP assays could be confidently defined as stability-affecting. Furthermore, it is unclear to what extent VSP abundance scores were reasonable proxies for the stability-related quantities that participating methods were designed to predict. Overall, our observations underscore the need for clearly defined objectives when developing and using both computational and experimental methods in the context of measuring variant impact.

Accurate prediction of the impact of genomic variation on phenotype is a major goal of computational biology and an important contributor to personalized medicine. Computational predictions can lead to a better understanding of the mechanisms underlying genetic diseases, including cancer, but their adoption requires thorough and unbiased assessment. Cystathionine-beta-synthase (CBS) is an enzyme that catalyzes the first step of the transsulfuration pathway, from homocysteine to cystathionine, and in which variations are associated with human hyperhomocysteinemia and homocystinuria. We have created a computational challenge under the CAGI framework to evaluate how well different methods can predict the phenotypic effect(s) of CBS single amino acid substitutions using a blinded experimental data set. CAGI participants were asked to predict yeast growth based on the identity of the mutations. The performance of the methods was evaluated using several metrics. The CBS challenge highlighted the difficulty of predicting the phenotype of an ex vivo system in a model organism when classification models were trained on human disease data. We also discuss the variations in difficulty of prediction for known benign and deleterious variants, as well as identify methodological and experimental constraints with lessons to be learned for future challenges. ispartof: HUMAN MUTATION vol:40 issue:9 pages:1530-1545 ispartof: location:United States status: published

Rare diseases are not rare, and many of them have an underlying genetic cause. This chapter provides an overview of the history of sequencing technologies and how they are used in rare disease research. More specifically, challenges and opportunities for the use of genomics analyses in rare disease research and clinical practice are discussed. Last but not least, organizational efforts in rare disease genomics have had great successes and such collaborative efforts are the key to success in implementing genomics in rare disease research and diagnostics.

The nature of the product to be discovered guides the reasoning to discover it. Biologists and medical researchers often search for mechanisms. The “new mechanistic philosophy of science” provides resources about the nature of biological mechanisms that aid the discovery of mechanisms. Here, we apply these resources to the discovery of mechanisms in medicine. A new diagrammatic representation of a disease mechanism chain indicates both what is known and, most significantly, what is not known at a given time, thereby guiding the researcher and collaborators in discovery. Mechanisms of genetic diseases provide the examples.

© 2017 Wiley Periodicals, Inc. The CAGI-4 Hopkins clinical panel challenge was an attempt to assess state-of-the-art methods for clinical phenotype prediction from DNA sequence. Participants were provided with exonic sequences of 83 genes for 106 patients from the Johns Hopkins DNA Diagnostic Laboratory. Five groups participated in the challenge, predicting both the probability that each patient had each of the 14 possible classes of disease, as well as one or more causal variants. In cases where the Hopkins laboratory reported a variant, at least one predictor correctly identified the disease class in 36 of the 43 patients (84%). Even in cases where the Hopkins laboratory did not find a variant, at least one predictor correctly identified the class in 39 of the 63 patients (62%). Each prediction group correctly diagnosed at least one patient that was not successfully diagnosed by any other group. We discuss the causal variant predictions by different groups and their implications for further development of methods to assess variants of unknown significance. Our results suggest that clinically relevant variants may be missed when physicians order small panels targeted on a specific phenotype. We also quantify the false-positive rate of DNA-guided analysis in the absence of prior phenotypic indication.

Precision medicine aims to predict a patient's disease risk and best therapeutic options by using that individual's genetic sequencing data. The Critical Assessment of Genome Interpretation (CAGI) is a community experiment consisting of genotype–phenotype prediction challenges; participants build models, undergo assessment, and share key findings. For CAGI 4, three challenges involved using exome-sequencing data: Crohn's disease, bipolar disorder, and warfarin dosing. Previous CAGI challenges included prior versions of the Crohn's disease challenge. Here, we discuss the range of techniques used for phenotype prediction as well as the methods used for assessing predictive models. Additionally, we outline some of the difficulties associated with making predictions and evaluating them. The lessons learned from the exome challenges can be applied to both research and clinical efforts to improve phenotype prediction from genotype. In addition, these challenges serve as a vehicle for sharing clinical and research exome data in a secure manner with scientists who have a broad range of expertise, contributing to a collaborative effort to advance our understanding of genotype–phenotype relationships.

Correct phenotypic interpretation of variants of unknown significance for cancer-associated genes is a diagnostic challenge as genetic screenings gain in popularity in the next-generation sequencing era. The Critical Assessment of Genome Interpretation (CAGI) experiment aims to test and define the state of the art of genotype-phenotype interpretation. Here, we present the assessment of the CAGI p16INK4a challenge. Participants were asked to predict the effect on cellular proliferation of ten variants for the p16INK4a tumor suppressor, a cyclin-dependent kinase inhibitor encoded by the CDKN2A gene. Twenty-two pathogenicity predictors were assessed with a variety of accuracy measures for reliability in a medical context. Different assessment measures were combined in an overall ranking to provide more robust results. The R scripts used for assessment are publicly available from a GitHub repository for future use in similar assessment exercises. Despite a limited test-set size, our findings show a variety of results, with some methods performing significantly better. Methods combining different strategies frequently outperform simpler approaches. The best predictor, Yang&Zhou lab, uses a machine learning method combining an empirical energy function measuring protein stability with an evolutionary conservation term. The p16INK4a challenge highlights how subtle structural effects can neutralize otherwise deleterious variants. This article is protected by copyright. All rights reserved.

CAGI (Critical Assessment of Genome Interpretation) conducts community experiments to determine the state of the art in relating genotype to phenotype. Here we report results obtained using newly-developed ensemble methods to address two CAGI4 challenges: enzyme activity for population missense variants found in NAGLU (Human N-acetyl-glucosaminidase) and random missense mutations in Human UBE2I (Human SUMO E2 ligase), assayed in a high throughput competitive yeast complementation procedure. The ensemble methods are effective, ranked 2nd for SUMO-ligase and 3rd for NAGLU, according to the CAGI independent assessors. However, in common with other methods used in CAGI, there are large discrepancies between predicted and experimental activities for a subset of variants. Analysis of the structural context provides some insight into these. Post-challenge analysis shows the ensemble methods are also effective at assigning pathogenicity for the NAGLU variants. In the clinic, providing an estimate of the reliability of pathogenic assignments is key. We have also used the NAGLU dataset to show that ensemble methods have considerable potential for this task, and are already reliable enough for use with a subset of mutations. This article is protected by copyright. All rights reserved

Compared with earlier more restricted sequencing technologies, identification of rare disease variants using whole-genome sequence has the possibility of finding all causative variants, but issues of data quality and an overwhelming level of background variants complicate the analysis. The CAGI4 SickKids clinical genome challenge provided an opportunity to assess the landscape of variants found in a difficult set of 25 unsolved rare disease cases. To address the challenge, we developed a three-stage pipeline, first carefully analyzing data quality, then classifying high-quality gene-specific variants into seven categories, and finally examining each candidate variant for compatibility with the often complex phenotypes of these patients for final prioritization. Variants consistent with the phenotypes were found in 24 out of the 25 cases, and in a number of these, there are prioritized variants in multiple genes. Data quality analysis suggests that some of the selected variants are likely incorrect calls, complicating interpretation. The data providers followed up on three suggested variants with Sanger sequencing, and in one case, a prioritized variant was confirmed as likely causative by the referring physician, providing a diagnosis in a previously intractable case.

The use of gene panel sequence for diagnostic and prognostic testing is now widespread, but there are so far few objective tests of methods to interpret these data. We describe the design and implementation of a gene panel sequencing data analysis pipeline (VarP) and its assessment in a CAGI4 community experiment. The method was applied to clinical gene panel sequencing data of 106 patients, with the goal of determining which of 14 disease classes each patient has and the corresponding causative variant(s). The disease class was correctly identified for 36 cases, including 10 where the original clinical pipeline did not find causative variants. For a further seven cases, we found strong evidence of an alternative disease to that tested. Many of the potentially causative variants are missense, with no previous association with disease, and these proved the hardest to correctly assign pathogenicity or otherwise. Post analysis showed that three-dimensional structure data could have helped for up to half of these cases. Over-reliance on HGMD annotation led to a number of incorrect disease assignments. We used a largely ad hoc method to assign probabilities of pathogenicity for each variant, and there is much work still to be done in this area.

The advent of next-generation sequencing has dramatically decreased the cost for whole-genome sequencing and increased the viability for its application in research and clinical care. The Personal Genome Project (PGP) provides unrestricted access to genomes of individuals and their associated phenotypes. This resource enabled the Critical Assessment of Genome Interpretation (CAGI) to create a community challenge to assess the bioinformatics community's ability to predict traits from whole genomes. In the CAGI PGP challenge, researchers were asked to predict whether an individual had a particular trait or profile based on their whole genome. Several approaches were used to assess submissions, including ROC AUC (area under receiver operating characteristic curve), probability rankings, the number of correct predictions, and statistical significance simulations. Overall, we found that prediction of individual traits is difficult, relying on a strong knowledge of trait frequency within the general population, whereas matching genomes to trait profiles relies heavily upon a small number of common traits including ancestry, blood type, and eye color. When a rare genetic disorder is present, profiles can be matched when one or more pathogenic variants are identified. Prediction accuracy has improved substantially over the last 6 years due to improved methodology and a better understanding of features.

Background There are now over 2000 loci in the human genome where genome wide association studies (GWAS) have found one or more SNPs to be associated with altered risk of a complex trait disease. At each of these loci, there must be some molecular level mechanism relevant to the disease. What are these mechanisms and how do they contribute to disease? Results Here we consider the roles of three primary mechanism classes: changes that directly alter protein function (missense SNPs), changes that alter transcript abundance as a consequence of variants close-by in sequence, and changes that affect splicing. Missense SNPs are divided into those predicted to have a high impact on in vivo protein function, and those with a low impact. Splicing is divided into SNPs with a direct impact on splice sites, and those with a predicted effect on auxiliary splicing signals. The analysis was based on associations found for seven complex trait diseases in the classic Wellcome Trust Case Control Consortium (WTCCC1) GWA study and subsequent studies and meta-analyses, collected from the GWAS catalog. Linkage disequilibrium information was used to identify possible candidate SNPs for involvement in disease mechanism in each of the 356 loci associated with these seven diseases. With the parameters used, we find that 76% of loci have at least of these mechanisms. Overall, except for the low incidence of direct impact on splice sites, the mechanisms are found at similar frequencies, with changes in transcript abundance the most common. But the distribution of mechanisms over diseases varies markedly, as does the fraction of loci with assigned mechanisms. Many of the implicated proteins have previously been suggested as relevant, but the specific mechanism assignments are new. In addition, a number of new disease relevant proteins are proposed. Conclusions The high fraction of GWAS loci with proposed mechanisms suggests that these classes of mechanism play a major role. Other mechanism types, such as variants affecting expression of genes remote in the DNA sequence, will contribute in other loci. Each of the identified putative mechanisms provides a hypothesis for further investigation.

Recent genome-wide association studies (GWAS) have led to the reliable identification of single nucleotide polymorphisms (SNPs) at a number of loci associated with increased risk of specific common human diseases. Each such locus implicates multiple possible candidate SNPs for involvement in disease mechanism. A variety of mechanisms may link the presence of an SNP to altered in vivo gene product function and hence contribute to disease risk. Here, we report an analysis of the role of one of these mechanisms, missense SNPs (msSNPs) in proteins in seven complex trait diseases. Linkage disequilibrium information was used to identify possible candidate msSNPs associated with increased disease risk at each of 356 loci for the seven diseases. Two computational methods were used to estimate which of these SNPs has a significant impact on in vivo protein function. 69% of the loci have at least one candidate msSNP and 33% have at least one predicted high-impact msSNP. In some cases, these SNPs are in well-established disease-related proteins, such as MST1 (macrophage stimulating 1) for Crohn's disease. In others, they are in proteins identified by GWAS as likely candidates for disease relevance, but previously without known mechanism, such as ADAMTS13 (ADAM metallopeptidase with thrombospondin type 1 motif, 13) for coronary artery disease. In still other cases, the missense SNPs are in proteins not previously suggested as disease candidates, such as TUBB1 (tubulin, beta 1, class VI) for hypertension. Together, these data support a substantial role for this class of SNPs in susceptibility to common human disease.

High throughput genome wide associations studies (GWAS) are now identifying a large number of genome loci related to risk of common human disease. Each such locus presents a challenge in identifying the relevant underlying mechanism. Here we report the experimental characterization of a proposed causal single nucleotide polymorphism (SNP) in a locus related to risk of Crohn's disease and ulcerative colitis. The SNP lies in the MST1 gene encoding Macrophage Stimulating Protein (MSP), and results in an R689C amino acid substitution within the β-chain of MSP (MSPβ). MSP binding to the RON receptor tyrosine kinase activates signaling pathways involved in the inflammatory response. We have purified wild-type and mutant MSPβ proteins and compared biochemical and biophysical properties that might impact the MSP/RON signaling pathway. Surface plasmon resonance (SPR) binding studies showed that MSPβ R689C affinity to RON is approximately 10-fold lower than that of the wild-type MSPβ and differential scanning fluorimetry (DSF) showed that the thermal stability of the mutant MSPβ was slightly lower than that of wild-type MSPβ, by 1.6 K. The substitution was found not to impair the specific Arg483-Val484 peptide bond cleavage by matriptase-1, required for MSP activation, and mass spectrometry of tryptic fragments of the mutated protein showed that the free thiol introduced by the R689C mutation did not form an aberrant disulfide bond. Together, the studies indicate that the missense SNP impairs MSP function by reducing its affinity to RON and perhaps through a secondary effect on in vivo concentration arising from reduced thermodynamic stability, resulting in down-regulation of the MSP/RON signaling pathway.

Giardia lamblia fructose-1,6-bisphosphate aldolase (FBPA) is a member of the class II zinc-dependent aldolase family that catalyzes the cleavage of d-fructose 1,6-bisphosphate (FBP) into dihydroxyacetone phosphate (DHAP) and d-glyceraldehyde 3-phosphate (G3P). In addition to the active site zinc, the catalytic apparatus of FBPA employs an aspartic acid, Asp83 in the G. lamblia enzyme, which when replaced with an alanine residue renders the enzyme inactive. A comparison of the crystal structures of D83A FBPA in complex with FBP and of wild-type FBPA in the unbound state revealed a substrate-induced conformational transition of loops in the vicinity of the active site and a shift in the location of Zn(2+). When FBP binds, the Zn(2+) shifts up to 4.6 A toward the catalytic Asp83, which brings the metal within coordination distance of the Asp83 carboxylate group. In addition, the structure of wild-type FBPA was determined in complex with the competitive inhibitor d-tagatose 1,6-bisphosphate (TBP), a FBP stereoisomer. In this structure, the zinc binds in a site close to that previously seen in the structure of FBPA in complex with phosphoglycolohydroxamate, an analogue of the postulated DHAP ene-diolate intermediate. Together, the ensemble of structures suggests that the zinc mobility is necessary to orient the Asp83 side chain and to polarize the substrate for proton transfer from the FBP C(4) hydroxyl group to the Asp83 carboxyl group. In the absence of FBP, the alternative zinc position is too remote for coordinating the Asp83. We propose a modification of the catalytic mechanism that incorporates the novel features observed in the FBPA-FBP structure. The mechanism invokes coordination and coplanarity of the Zn(2+) with the FBP's O-C(3)-C(4)-O group concomitant with coordination of the Asp83 carboxylic group. Catalysis is accompanied by movement of Zn(2+) to a site coplanar with the O-C(2)-C(3)-O group of the DHAP. glFBPA exhibits strict substrate specificity toward FBP and does not cleave TBP. The active sites of FBPAs contain an aspartate residue equivalent to Asp255 of glFBPA, whereas tagatose-1,6-bisphosphate aldolase contains an alanine in this position. We and others hypothesized that this aspartic acid is a likely determinant of FBP versus TBP specificity. Replacement of Asp255 with an alanine resulted in an enzyme that possesses double specificity, now cleaving TBP (albeit with low efficacy; k(cat)/K(m) = 80 M(-1) s(-1)) while maintaining activity toward FBP at a 50-fold lower catalytic efficacy compared with that of wild-type FBPA. The collection of structures and sequence analyses highlighted additional residues that may be involved in substrate discrimination.

Knowledge of specific domain-domain interactions (DDIs) is essential to understand the functional significance of protein interaction networks. Despite the availability of an enormous amount of data on protein-protein interactions (PPIs), very little is known about specific DDIs occurring in them. Here, we present a top-down approach to accurately infer functionally relevant DDIs from PPI data. We created a comprehensive, non-redundant dataset of 209,165 experimentally-derived PPIs by combining datasets from five major interaction databases. We introduced an integrated scoring system that uses a novel combination of a set of five orthogonal scoring features covering the probabilistic, evolutionary, evidence-based, spatial and functional properties of interacting domains, which can map the interacting propensity of two domains in many dimensions. This method outperforms similar existing methods both in the accuracy of prediction and in the coverage of domain interaction space. We predicted a set of 52,492 high-confidence DDIs to carry out cross-species comparison of DDI conservation in eight model species including human, mouse, Drosophila, C. elegans, yeast, Plasmodium, E. coli and Arabidopsis. Our results show that only 23% of these DDIs are conserved in at least two species and only 3.8% in at least 4 species, indicating a rather low conservation across species. Pair-wise analysis of DDI conservation revealed a 'sliding conservation' pattern between the evolutionarily neighboring species. Our methodology and the high-confidence DDI predictions generated in this study can help to better understand the functional significance of PPIs at the modular level, thus can significantly impact further experimental investigations in systems biology research.

The functional repertoire of the human proteome is an incremental collection of functions accomplished by protein domains evolved along the Homo sapiens lineage. Therefore, knowledge on the origin of these functionalities provides a better understanding of the domain and protein evolution in human. The lack of proper comprehension about such origin has impelled us to study the evolutionary origin of human proteome in a unique way as detailed in this study. This study reports a unique approach for understanding the evolution of human proteome by tracing the origin of its constituting domains hierarchically, along the Homo sapiens lineage. The uniqueness of this method lies in subtractive searching of functional and conserved domains in the human proteome resulting in higher efficiency of detecting their origins. From these analyses the nature of protein evolution and trends in domain evolution can be observed in the context of the entire human proteome data. The method adopted here also helps delineate the degree of divergence of functional families occurred during the course of evolution. This approach to trace the evolutionary origin of functional domains in the human proteome facilitates better understanding of their functional versatility as well as provides insights into the functionality of hypothetical proteins present in the human proteome. This work elucidates the origin of functional and conserved domains in human proteins, their distribution along the Homo sapiens lineage, occurrence frequency of different domain combinations and proteome-wide patterns of their distribution, providing insights into the evolutionary solution to the increased complexity of the human proteome.