Arye Harel

GeneCards Version 3: the human gene integrator

GeneCards (www.genecards.org) is a comprehensive, authoritative compendium of annotative information about human genes, widely used for nearly 15 years. Its gene-centric content is automatically mined and integrated from over 80 digital sources, resulting in a web-based deep-linked card for each of >73 000 human gene entries, encompassing the following categories: protein coding, pseudogene, RNA gene, genetic locus, cluster and uncategorized. We now introduce GeneCards Version 3, featuring a speedy and sophisticated search engine and a revamped, technologically enabling infrastructure, catering to the expanding needs of biomedical researchers. A key focus is on gene-set analyses, which leverage GeneCards’ unique wealth of combinatorial annotations. These include the GeneALaCart batch query facility, which tabulates user-selected annotations for multiple genes and GeneDecks, which identifies similar genes with shared annotations, and finds set-shared annotations by descriptor enrichment analysis. Such set-centric features address a host of applications, including microarray data analysis, cross-database annotation mapping and gene-disorder associations for drug targeting. We highlight the new Version 3 database architecture, its multi-faceted search engine, and its semi-automated quality assurance system. Data enhancements include an expanded visualization of gene expression patterns in normal and cancer tissues, an integrated alternative splicing pattern display, and augmented multi-source SNPs and pathways sections. GeneCards now provides direct links to gene-related research reagents such as antibodies, recombinant proteins, DNA clones and inhibitory RNAs and features gene-related drugs and compounds lists. We also portray the GeneCards Inferred Functionality Score annotation landscape tool for scoring a gene’s functional information status. Finally, we delineate examples of applications and collaborations that have benefited from the GeneCards suite. Database URL: www.genecards.org

GeneDecks: paralog hunting and gene-set distillation with GeneCards annotation.

Sophisticated genomic navigation strongly benefits from a capacity to establish a similarity metric among genes. GeneDecks is a novel analysis tool that provides such a metric by highlighting shared descriptors between pairs of genes, based on the rich annotation within the GeneCards compendium of human genes. The current implementation addresses information about pathways, protein domains, Gene Ontology (GO) terms, mouse phenotypes, mRNA expression patterns, disorders, drug relationships, and sequence-based paralogy. GeneDecks has two modes: (1) Paralog Hunter, which seeks functional paralogs based on combinatorial similarity of attributes; and (2) Set Distiller, which ranks descriptors by their degree of sharing within a given gene set. GeneDecks enables the elucidation of unsuspected putative functional paralogs, and a refined scrutiny of various gene-sets (e.g., from high-throughput experiments) for discovering relevant biological patterns.

GIFtS: annotation landscape analysis with GeneCards

BackgroundGene annotation is a pivotal component in computational genomics, encompassing prediction of gene function, expression analysis, and sequence scrutiny. Hence, quantitative measures of the annotation landscape constitute a pertinent bioinformatics tool. GeneCards® is a gene-centric compendium of rich annotative information for over 50,000 human gene entries, building upon 68 data sources, including Gene Ontology (GO), pathways, interactions, phenotypes, publications and many more.ResultsWe present the GeneCards Inferred Functionality Score (GIFtS) which allows a quantitative assessment of a genes annotation status, by exploiting the unique wealth and diversity of GeneCards information. The GIFtS tool, linked from the GeneCards home page, facilitates browsing the human genome by searching for the annotation level of a specified gene, retrieving a list of genes within a specified range of GIFtS value, obtaining random genes with a specific GIFtS value, and experimenting with the GIFtS weighting algorithm for a variety of annotation categories. The bimodal shape of the GIFtS distribution suggests a division of the human gene repertoire into two main groups: the high-GIFtS peak consists almost entirely of protein-coding genes; the low-GIFtS peak consists of genes from all of the categories. Cluster analysis of GIFtS annotation vectors provides the classification of gene groups by detailed positioning in the annotation arena. GIFtS also provide measures which enable the evaluation of the databases that serve as GeneCards sources. An inverse correlation is found (for GIFtS>25) between the number of genes annotated by each source, and the average GIFtS value of genes associated with that source. Three typical source prototypes are revealed by their GIFtS distribution: genome-wide sources, sources comprising mainly highly annotated genes, and sources comprising mainly poorly annotated genes. The degree of accumulated knowledge for a given gene measured by GIFtS was correlated (for GIFtS>30) with the number of publications for a gene, and with the seniority of this entry in the HGNC database.ConclusionGIFtS can be a valuable tool for computational procedures which analyze lists of large set of genes resulting from wet-lab or computational research. GIFtS may also assist the scientific community with identification of groups of uncharacterized genes for diverse applications, such as delineation of novel functions and charting unexplored areas of the human genome.

Evolutionary history of redox metal-binding domains across the tree of life

Significance Oxidoreductases mediate the biological production of chemical energy and regulate the flow of essential elements in all organisms and ecosystems, yet their evolutionary history is poorly understood. Here we present a network analysis of all known metal-containing oxidoreductases across the tree of life. Members of this network seem to have driven microbial metabolism in the Archean oceans. Our analysis reveals that oxidoreductases are polyphyletic and derived from a minimum of 10 different ancient protein families with distantly related domains. However, we find substantial evidence that two apparently distinct and ubiquitous iron-containing families of oxidoreductases containing Fe2S2 and hemes arose from a single common ancestor. Oxidoreductases mediate electron transfer (i.e., redox) reactions across the tree of life and ultimately facilitate the biologically driven fluxes of hydrogen, carbon, nitrogen, oxygen, and sulfur on Earth. The core enzymes responsible for these reactions are ancient, often small in size, and highly diverse in amino acid sequence, and many require specific transition metals in their active sites. Here we reconstruct the evolution of metal-binding domains in extant oxidoreductases using a flexible network approach and permissive profile alignments based on available microbial genome data. Our results suggest there were at least 10 independent origins of redox domain families. However, we also identified multiple ancient connections between Fe2S2- (adrenodoxin-like) and heme- (cytochrome c) binding domains. Our results suggest that these two iron-containing redox families had a single common ancestor that underwent duplication and divergence. The iron-containing protein family constitutes ∼50% of all metal-containing oxidoreductases and potentially catalyzed redox reactions in the Archean oceans. Heme-binding domains seem to be derived via modular evolutionary processes that ultimately form the backbone of redox reactions in both anaerobic and aerobic respiration and photosynthesis. The empirically discovered network allows us to peer into the ancient history of microbial metabolism on our planet.

Discovering the electronic circuit diagram of life: structural relationships among transition metal binding sites in oxidoreductases

Oxidoreductases play a central role in catalysing enzymatic electron-transfer reactions across the tree of life. To first order, the equilibrium thermodynamic properties of these proteins are governed by protein folds associated with specific transition metals and ligands at the active site. A global analysis of holoenzyme structures and functions suggests that there are fewer than approximately 500 fundamental oxidoreductases, which can be further clustered into 35 unique groups. These catalysts evolved in prokaryotes early in the Earths history and are largely responsible for the emergence of non-equilibrium biogeochemical cycles on the planets surface. Although the evolutionary history of the amino acid sequences in the oxidoreductases is very difficult to reconstruct due to gene duplication and horizontal gene transfer, the evolution of the folds in the catalytic sites can potentially be used to infer the history of these enzymes. Using a novel, yet simple analysis of the secondary structures associated with the ligands in oxidoreductases, we developed a structural phylogeny of these enzymes. The results of this ‘composome’ analysis suggest an early split from a basal set of a small group of proteins dominated by loop structures into two families of oxidoreductases, one dominated by α-helices and the second by β-sheets. The structural evolutionary patterns in both clades trace redox gradients and increased hydrogen bond energy in the active sites. The overall pattern suggests that the evolution of the oxidoreductases led to decreased entropy in the transition metal folds over approximately 2.5 billion years, allowing the enzymes to use increasingly oxidized substrates with high specificity.

Deciphering Primordial Cyanobacterial Genome Functions from Protein Network Analysis

The Great Oxidation Event (GOE) ∼2.4 billion years ago resulted from the accumulation of oxygen by the ancestors of cyanobacteria [1-3]. Cyanobacteria continue to play a significant role in primary production [4] and in regulating the global marine and limnic nitrogen cycles [5, 6]. Relatively little is known, however, about the evolutionary history and gene content of primordial cyanobacteria [7, 8]. To address these issues, we used protein similarity networks [9], containing proteomes from 48 cyanobacteria as the test group, and reference proteomes from 84 microbes representing four distinct metabolic groups from most reducing to most oxidizing: methanogens, obligate anaerobes (nonmethanogenic), facultative aerobes, and obligate aerobes. These four metabolic groups represent extant bioinformatic proxies for ancient redox chemistries, extending from an anoxic origin through the GOE and ultimately to obligate aerobes [10-13]. Analysis of the network metric degree showed a strong relationship between cyanobacteria and obligate anaerobes, from which cyanobacteria presumably arose, for core functions that include translation, photosynthesis, energy conservation, and environmental interactions. These data were used to reconstruct primordial functions in cyanobacteria that included nine gene families involved in photosynthesis, hydrogenases, and proteins involved in defense from environmental stress. The presence of 60% of these genes in both reaction center I (RC-I) and RC-II-type bacteria may be explained by selective loss of either RC in the evolutionary history of some photosynthetic lineages. Finally, the network reveals that cyanobacteria occupy a unique position among prokaryotes as a hub between anaerobes and obligate aerobes.

Omics Data Management and Annotation

Technological Omics breakthroughs, including next generation sequencing, bring avalanches of data which need to undergo effective data management to ensure integrity, security, and maximal knowledge-gleaning. Data management system requirements include flexible input formats, diverse data entry mechanisms and views, user friendliness, attention to standards, hardware and software platform definition, as well as robustness. Relevant solutions elaborated by the scientific community include Laboratory Information Management Systems (LIMS) and standardization protocols facilitating data sharing and managing. In project planning, special consideration has to be made when choosing relevant Omics annotation sources, since many of them overlap and require sophisticated integration heuristics. The data modeling step defines and categorizes the data into objects (e.g., genes, articles, disorders) and creates an application flow. A data storage/warehouse mechanism must be selected, such as file-based systems and relational databases, the latter typically used for larger projects. Omics project life cycle considerations must include the definition and deployment of new versions, incorporating either full or partial updates. Finally, quality assurance (QA) procedures must validate data and feature integrity, as well as system performance expectations. We illustrate these data management principles with examples from the life cycle of the GeneCards Omics project (http://www.genecards.org), a comprehensive, widely used compendium of annotative information about human genes. For example, the GeneCards infrastructure has recently been changed from text files to a relational database, enabling better organization and views of the growing data. Omics data handling benefits from the wealth of Web-based information, the vast amount of public domain software, increasingly affordable hardware, and effective use of data management and annotation principles as outlined in this chapter.

Evolution of prokaryotic respiratory molybdoenzymes and the frequency of their genomic co-occurrence

Molybdoenzymes are an ancient protein family found in phylogenetically and ecologically diverse prokaryotes. Under anaerobic conditions, respiratory molybdoenzymes catalyze redox reactions that transfer electrons to a variety of substrates that act as terminal electron acceptors for energy generation. Here, we used probe sequences to conduct an extensive genomic survey and phylogenetic inference for NarG, DmsA, TorA and nine other respiratory molybdoenzyme subfamilies. Our analysis demonstrates their abundance in 60% of prokaryotic phyla. In contrast to many other autonomic genetic units in prokaryotes, the major route of evolution of their predominant subfamilies is vertical gene transfer, gene duplication and divergence. Our results show the robustness of genomic co-occurrence of respiratory molybdoenzymes genes, found in the majority of studied species, for most of the enzyme subfamilies. Genomes which encode for multiple respiratory molybdoenzymes are also enriched in genes regulating replication, recombination and mobility of genetic elements. Respiratory molybdoenzymes were found in prokaryotes associated with diverse environments occupying terrestrial, aquatic, food and host-related habitats, emphasizing their essential role in adaptation of prokaryotes to changing environments. Interestingly, host-associated prokaryotes such as human pathogens more frequently carry multiple respiratory molybdoenzyme genes compared with non-host-associated prokaryotes, highlighting the importance of metabolic flexibility in host-microbiome environments.

Selenate reductase activity in Escherichia coli requires Isc iron–sulfur cluster biosynthesis genes

The selenate reductase in Escherichia coli is a multi-subunit enzyme predicted to bind Fe-S clusters. In this study, we examined the iron-sulfur cluster biosynthesis genes that are required for selenate reductase activity. Mutants devoid of either the iscU or hscB gene in the Isc iron-sulfur cluster biosynthesis pathway lost the ability to reduce selenate. Genetic complementation by the wild-type sequences restored selenate reductase activity. The results indicate the Isc biosynthetic system plays a key role in selenate reductase Fe-S cofactor assembly and is essential for enzyme activity.