Assessing computational predictions of the phenotypic effect of cystathionine-beta-synthase variants.

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.

Systematically linking tranSMART, Galaxy and EGA for reusing human translational research data.

The availability of high-throughput molecular profiling techniques has provided more accurate and informative data for regular clinical studies. Nevertheless, complex computational workflows are required to interpret these data. Over the past years, the data volume has been growing explosively, requiring robust human data management to organise and integrate the data efficiently. For this reason, we set up an ELIXIR implementation study, together with the Translational research IT (TraIT) programme, to design a data ecosystem that is able to link raw and interpreted data. In this project, the data from the TraIT Cell Line Use Case (TraIT-CLUC) are used as a test case for this system. Within this ecosystem, we use the European Genome-phenome Archive (EGA) to store raw molecular profiling data; tranSMART to collect interpreted molecular profiling data and clinical data for corresponding samples; and Galaxy to store, run and manage the computational workflows. We can integrate these data by linking their repositories systematically. To showcase our design, we have structured the TraIT-CLUC data, which contain a variety of molecular profiling data types, for storage in both tranSMART and EGA. The metadata provided allows referencing between tranSMART and EGA, fulfilling the cycle of data submission and discovery; we have also designed a data flow from EGA to Galaxy, enabling reanalysis of the raw data in Galaxy. In this way, users can select patient cohorts in tranSMART, trace them back to the raw data and perform (re)analysis in Galaxy. Our conclusion is that the majority of metadata does not necessarily need to be stored (redundantly) in both databases, but that instead FAIR persistent identifiers should be available for well-defined data ontology levels: study, data access committee, physical sample, data sample and raw data file. This approach will pave the way for the stable linkage and reuse of data.

Working toward precision medicine: Predicting phenotypes from exomes in the Critical Assessment of Genome Interpretation (CAGI) challenges.

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.

Matching phenotypes to whole genomes: Lessons learned from four iterations of the personal genome project community challenges.

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.

Performance of in silico tools for the evaluation of p16INK4a (CDKN2A) variants in CAGI.

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 10 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.

Integration of EGA secure data access into Galaxy.

High-throughput molecular profiling techniques are routinely generating vast amounts of data for translational medicine studies. Secure access controlled systems are needed to manage, store, transfer and distribute these data due to its personally identifiable nature. The European Genome-phenome Archive (EGA) was created to facilitate access and management to long-term archival of bio-molecular data. Each data provider is responsible for ensuring a Data Access Committee is in place to grant access to data stored in the EGA. Moreover, the transfer of data during upload and download is encrypted. ELIXIR, a European research infrastructure for life-science data, initiated a project (2016 Human Data Implementation Study) to understand and document the ELIXIR requirements for secure management of controlled-access data. As part of this project, a full ecosystem was designed to connect archived raw experimental molecular profiling data with interpreted data and the computational workflows, using the CTMM Translational Research IT (CTMM-TraIT) infrastructure http://www.ctmm-trait.nl as an example. Here we present the first outcomes of this project, a framework to enable the download of EGA data to a Galaxy server in a secure way. Galaxy provides an intuitive user interface for molecular biologists and bioinformaticians to run and design data analysis workflows. More specifically, we developed a tool -- ega_download_streamer - that can download data securely from EGA into a Galaxy server, which can subsequently be further processed. This tool will allow a user within the browser to run an entire analysis containing sensitive data from EGA, and to make this analysis available for other researchers in a reproducible manner, as shown with a proof of concept study.  The tool ega_download_streamer is available in the Galaxy tool shed: https://toolshed.g2.bx.psu.edu/view/yhoogstrate/ega_download_streamer.

Association of gut microbiota with post-operative clinical course in Crohn's disease.

BACKGROUND: The gut microbiome is altered in Crohn's disease. Although individual taxa have been correlated with post-operative clinical course, global trends in microbial diversity have not been described in this context. METHODS: We collected mucosal biopsies from the terminal ileum and ascending colon during surgery and post-operative colonoscopy in 6 Crohn's patients undergoing ileocolic resection (and 40 additional Crohn's and healthy control patients undergoing either surgery or colonoscopy). Using next-generation sequencing technology, we profiled the gut microbiota in order to identify changes associated with remission or recurrence of inflammation. RESULTS: We performed 16S ribosomal profiling using 101 base-pair single-end sequencing on the Illumina GAIIx platform with deep coverage, at an average depth of 1.3 million high quality reads per sample. At the time of surgery, Crohn's patients who would remain in remission were more similar to controls and more species-rich than Crohn's patients with subsequent recurrence. Patients remaining in remission also exhibited greater stability of the microbiota through time. CONCLUSIONS: These observations permitted an association of gut microbial profiles with probability of recurrence in this limited single-center study. These results suggest that profiling the gut microbiota may be useful in guiding treatment of Crohn's patients undergoing surgery.

A large-scale evaluation of computational protein function prediction.

Automated annotation of protein function is challenging. As the number of sequenced genomes rapidly grows, the overwhelming majority of protein products can only be annotated computationally. If computational predictions are to be relied upon, it is crucial that the accuracy of these methods be high. Here we report the results from the first large-scale community-based critical assessment of protein function annotation (CAFA) experiment. Fifty-four methods representing the state of the art for protein function prediction were evaluated on a target set of 866 proteins from 11 organisms. Two findings stand out: (i) today's best protein function prediction algorithms substantially outperform widely used first-generation methods, with large gains on all types of targets; and (ii) although the top methods perform well enough to guide experiments, there is considerable need for improvement of currently available tools.

Somatic mutations of ErbB4: selective loss-of-function phenotype affecting signal transduction pathways in cancer.

Cancer drugs targeting ErbB receptors, such as epidermal growth factor receptor and ErbB2, are currently in clinical use. However, the role of ErbB4 as a potential cancer drug target has remained controversial. Recently, somatic mutations altering the coding region of ErbB4 were described in patients with breast, gastric, colorectal, or non-small cell lung cancer, but the functional significance of these mutations is unknown. Here we demonstrate that 2 of 10 of the cancer-associated mutations of ErbB4 lead to loss of ErbB4 kinase activity due to disruption of functionally important structural features. Interestingly, the kinase-dead ErbB4 mutants were as efficient as wild-type ErbB4 in forming a heterodimeric neuregulin receptor with ErbB2 and promoting phosphorylation of Erk1/2 and Akt in an ErbB2 kinase-dependent manner. However, the mutant ErbB4 receptors failed to phosphorylate STAT5 and suppressed differentiation of MDA-MB-468 mammary carcinoma cells. These findings suggest that the somatic ErbB4 mutations have functional consequences and lead to selective changes in ErbB4 signaling.

Phylogenetic molecular function annotation.

It is now easier to discover thousands of protein sequences in a new microbial genome than it is to biochemically characterize the specific activity of a single protein of unknown function. The molecular functions of protein sequences have typically been predicted using homology-based computational methods, which rely on the principle that homologous proteins share a similar function. However, some protein families include groups of proteins with different molecular functions. A phylogenetic approach for predicting molecular function (sometimes called "phylogenomics") is an effective means to predict protein molecular function. These methods incorporate functional evidence from all members of a family that have functional characterizations using the evolutionary history of the protein family to make robust predictions for the uncharacterized proteins. However, they are often difficult to apply on a genome-wide scale because of the time-consuming step of reconstructing the phylogenies of each protein to be annotated. Our automated approach for function annotation using phylogeny, the SIFTER (Statistical Inference of Function Through Evolutionary Relationships) methodology, uses a statistical graphical model to compute the probabilities of molecular functions for unannotated proteins. Our benchmark tests showed that SIFTER provides accurate functional predictions on various protein families, outperforming other available methods.

Ligand specificity of constitutive androstane receptor as probed by induced-fit docking and mutagenesis.

Constitutive androstane receptor (CAR, NR1I3) belongs to the nuclear receptor family of transcription factors and acts as a chemical sensor of drugs and endogenous compounds. The ligand-binding preferences of CAR are diverse, and more importantly, there are significant species differences in ligand specificity. Here, we show that while certain residues are critical for the basal activity of mouse CAR (mCAR) and/or affect the binding of all tested ligands, mutation of some ligand-binding pocket (LBP) residues (e.g., F171 and Y336) paradoxically decreased the activity of a specific ligand while increasing that of others. Comparisons to previously reported human CAR (hCAR) residues indicated that the function of key CAR residues (e.g., N175, L253) is dramatically different between species. The docking results provide some mechanistic rationale for the ability of 17alpha-ethinyl-3,17beta-estradiol (EE2) to both activate mCAR and repress hCAR.

Functional classification of amino acid decarboxylases from the alanine racemase structural family by phylogenetic studies.

Arginine decarboxylase (ADC) and ornithine decarboxylase (ODC) are involved in the biosynthesis of putrescine, which is the precursor of other polyamines in animals, plants, and bacteria. These pyridoxal-5'-phosphate-dependent decarboxylases belong to the alanine racemase (AR) structural family together with diaminopimelate decarboxylase (DapDC), which catalyzes the final step of lysine biosynthesis in bacteria. We have constructed a multiple-sequence alignment of decarboxylases in the AR structural family and, based on the alignment, inferred phylogenetic trees. The phylogenetic tree consists of 3 distinct clades formed by ADC, DapDC, and ODC that diverged from an ancestral decarboxylase. The ancestral decarboxylase probably was able to recognize several substrates, and in archaea and bacteria, ODC may have retained the ability to bind other amino acids. Previously, a paralogue of ODC has been proposed to account for ADC activity detected in mammalian cells. According to our results, this appears unlikely, emphasizing the need for more caution in functional assignment made using sequence data and illustrating the continuing value of phylogenetic analysis in clarifying relationships and putative functions.

Binding properties of HABA-type azo derivatives to avidin and avidin-related protein 4.

The chicken genome encodes several biotin-binding proteins, including avidin and avidin-related protein 4 (AVR4). In addition to D-biotin, avidin binds an azo dye compound, 4-hydroxyazobenzene-2-carboxylic acid (HABA), but the HABA-binding properties of AVR4 are not yet known. Differential scanning calorimetry, UV/visible spectroscopy, and molecular modeling were used to analyze the binding of 15 azo molecules to avidin and AVR4. Significant differences are seen in azo compound preferences for the two proteins, emphasizing the importance of the loop between strands beta3 and beta4 for azo ligand recognition; information on these loops is provided by the high-resolution (1.5 A) X-ray structure for avidin reported here. These results may be valuable in designing improved tools for avidin-based life science and nanobiotechnology applications.

Construction of hevein (Hev b 6.02) with reduced allergenicity for immunotherapy of latex allergy by comutation of six amino acid residues on the conformational IgE epitopes.

Recently we have established that IgE Abs bind to conformational epitopes in the N- and C-terminal regions of the major natural rubber latex allergen, hevein (Hev b 6.02). To identify the critical amino acid residues that interact with IgE, the hevein sequence was scanned by using site-specific mutations. Twenty-nine hevein mutants were designed and produced by a baculovirus expression system in insect cells and tested by IgE inhibition-ELISA using sera from 26 latex allergic patients. Six potential IgE-interacting residues of hevein (Arg(5), Lys(10), Glu(29), Tyr(30), His(35), and Gln(38)) were identified and characterized further in detail. Based on these six residues, two triple mutants (Hdelta3A, Hdelta3B) and hevein mutant where all six residues were mutated (Hdelta6), were designed, modeled, and produced. Structural and functional properties of these combinatory mutants were compared experimentally and in silico with those of recombinant hevein. The IgE-binding affinity of the mutants decreased by three to five orders of magnitude as compared with that of recombinant hevein. Skin prick test reactivity of the triple mutant HDelta3A was drastically reduced and that of the six-residue mutant Hdelta6 was completely abolished in all patients examined in this study. The approach presented in this paper offers tools for identification and modification of amino acid residues on conformational epitopes of allergens that interact with IgE. Hevein with a highly reduced ability to bind IgE should provide a valuable candidate molecule for immunotherapy of latex allergy and is anticipated to have a low risk of systemic side effects.

BODIL: a molecular modeling environment for structure-function analysis and drug design.

BODIL is a molecular modeling environment geared to help the user to quickly identify key features of proteins critical to molecular recognition, especially (1) in drug discovery applications, and (2) to understand the structural basis for function. The program incorporates state-of-the-art graphics, sequence and structural alignment methods, among other capabilities needed in modern structure-function-drug target research. BODIL has a flexible design that allows on-the-fly incorporation of new modules, has intelligent memory management, and fast multi-view graphics. A beta version of BODIL and an accompanying tutorial are available at http://www.abo.fi/fak/mnf/bkf/research/johnson/bodil.html.

The major conformational IgE-binding epitopes of hevein (Hev b6.02) are identified by a novel chimera-based allergen epitope mapping strategy.

A novel approach to localize and reconstruct conformational IgE-binding epitope regions of hevein (Hev b6.02), a major natural rubber latex allergen, is described. An antimicrobial protein (AMP) from the amaranth Amaranthus caudatus was used as an immunologically non-IgE-binding adaptor molecule to which terminal or central parts of hevein were fused. Hevein and AMP share a structurally identical core region but have different N-terminal and C-terminal regions. Only 1 of 16 hevein-allergic patients showed weak IgE binding to purified native or recombinant AMP. Chimeric AMP with the hevein N terminus was recognized by IgE from 14 (88%) patients, and chimeric AMP with the hevein C terminus was recognized by IgE from 6 (38%) patients. In contrast, chimeric AMP containing the hevein core region was recognized by IgE from only two patients. When both the N-terminal and C-terminal regions of hevein were fused with the AMP core, IgE from all 16 patients bound to the chimera. This chimera was also able to significantly inhibit (>70%) IgE binding to the native hevein. On the contrary, linear synthetic peptides corresponding to hevein regions in the AMP chimeras showed no significant IgE binding capacity in either enzyme-linked immunosorbent assay or inhibition enzyme-linked immunosorbent assay. These results suggest that the IgE binding ability of hevein is essentially determined by its N-terminal and C-terminal regions and that major IgE-binding epitopes of hevein are conformational. The chimera-based epitope mapping strategy described here provides a valuable tool for defining structural epitopes and creating specific reagents for allergen immunotherapy.