Gene family matters: expanding the HGNC resource

The HUGO Gene Nomenclature Committee (HGNC) assigns approved gene symbols to human loci. There are currently over 33,000 approved gene symbols, the majority of which represent protein-coding genes, but we also name other locus types such as non-coding RNAs, pseudogenes and phenotypic loci. Where relevant, the HGNC organise these genes into gene families and groups. The HGNC website http://www.genenames.org/ is an online repository of HGNC-approved gene nomenclature and associated resources for human genes, and includes links to genomic, proteomic and phenotypic information. In addition to this, we also have dedicated gene family web pages and are currently expanding and generating more of these pages using data curated by the HGNC and from information derived from external resources that focus on particular gene families. Here, we review our current online resources with a particular focus on our gene family data, using it to highlight our new Gene Symbol Report and gene family data downloads.     

The hierarchical basis of serial homology and evolutionary novelty

Given the pervasiveness of gene sharing in evolution and the extent of homology across the tree of life, why is everything not homologous with everything else? The continuity and overlapping genetic contributions to diverse traits across lineages seem to imply that no discrete determination of homology is possible. Although some argue that the widespread overlap in parts and processes should be acknowledged as “partial” homology, this threatens a broad base of presumed comparative morphological knowledge accepted by most biologists. Following a long scientific tradition, we advocate a strategy of “theoretical articulation” that introduces further distinctions to existing concepts to produce increased contrastive resolution among the labels used to represent biological phenomena. We pursue this strategy by drawing on successful patterns of reasoning from serial homology at the level of gene sequences to generate an enriched characterization of serial homology as a hierarchical, phylogenetic concept. Specifically, we propose that the concept of serial homology should be applied primarily to repeated but developmentally individualized body parts, such as cell types, differentiated body segments, or epidermal appendages. For these characters, a phylogenetic history can be reconstructed, similar to families of paralogous genes, endowing the notion of serial homology with a hierarchical, phylogenetic interpretation. On this basis, we propose a five-fold theoretical classification that permits a more fine-grained mapping of diverse trait-types. This facilitates answering the question of why everything is not homologous with everything else, as well as how novelty is possible given that any new character possesses evolutionary precursors. We illustrate the fecundity of our account by reference to debates over insect wing serial homologs and vertebrate paired appendages.     

The problem of the gene

During the early 20th century the diverse practices of genetics were unified by the concept of the gene. This classical gene was simultaneously a unit of structure, function, mutation, and recombination. Starting in the 1940s, however, the classical gene began to fragment. Today when we speak of a gene for some malady, a regulatory gene, a structural gene, or a gene frequency, it is entirely possible that we are deploying different gene concepts even though we are using the same term. The problem of the gene addresses the fragmentation of the classical gene concept by asking to what extent a comprehensive and unifying gene concept is possible or desirable. Fully comprehensive gene concepts seem untenable today, but, within different disciplinary domains, unifying, but non-comprehensive, gene concepts can be epistemically worthwhile. The problem of the gene persists, however, not because of its epistemic value, but because of its political value. Using both the arguments for newly proposed gene concepts and the historical dispute over the classical gene, I argue that the desirability of gene concepts rests in part on the political ramifications of their deployment and contestation.     

Genew: the Human Gene Nomenclature Database

Genew, the Human Gene Nomenclature Database, is the only resource that provides data for all human genes which have approved symbols. It is managed by the HUGO Gene Nomenclature Committee (HGNC) as a confidential database, containing over 16 000 records, 80% of which are represented on the Web by searchable text files. The data in Genew are highly curated by HGNC editors and gene records can be searched on the Web by symbol or name to directly retrieve information on gene symbol, gene name, cytogenetic location, OMIM number and PubMed ID. Data are integrated with other human gene databases, e.g. GDB, LocusLink and SWISS-PROT, and approved gene symbols are carefully co-ordinated with the Mouse Genome Database (MGD). Approved gene symbols are available for querying and browsing at http://www.gene.ucl.ac.uk/cgi-bin/nomenclature/searchgenes.pl.     

HCOP: a searchable database of human orthology predictions

The HUGO Gene Nomenclature Committee (HGNC) Comparison of Orthology Predictions (HCOP) search tool combines the human, mouse, rat and chicken orthology assertions made by PhIGs, HomoloGene, Ensembl, Inparanoid, Mouse Genome Informatics (MGI) and HGNC, enabling users to identify predicted ortholog pairs for a specified gene or genes. The HCOP resource provides a useful method to integrate, compare and access a variety of disparate sources of human orthology data. The HCOP search tool, data and documentation are available at http://www.gene.ucl.ac.uk/hcop.     

Purifying and directional selection in overlapping prokaryotic genes

In overlapping genes, the same DNA sequence codes for two proteins using different reading frames. Analysis of overlapping genes can help in understanding the mode of evolution of a coding region from noncoding DNA. We identified 71 pairs of convergent genes, with overlapping 3' ends longer than 15 nucleotides, that are conserved in at least two prokaryotic genomes. Among the overlap regions, we observed a statistically significant bias towards the 123:132 phase (i.e. the second codon base in one gene facing the degenerate third position in the second gene). This phase ensures the least mutual constraint on nonconservative amino acid replacements in both overlapping coding sequences. The excess of this phase is compatible with directional (positive) selection acting on the overlapping coding regions. This could be a general evolutionary mode for genes emerging from noncoding sequences, in which the protein sequence has not been subject to selection.     

Pattern of nucleotide substitution in the overlapping nonstructural genes of influenza A virus and implication for the genetic diversity of the H5N1 subtype

In viruses under strong pressure to minimize genome size, overlapping genes represent a fine strategy to condense a maximum amount of information into short nucleotide sequences. Here, we investigated the evolution of the genes encoding the nonstructural proteins NS1 and NS2 of influenza A virus (IAV), which are one of the best characterized cases of gene overlap. By a detailed analysis of about four hundred sequences grouped into 11 IAV subtypes, we found that the overlapping coding region of the NS1 gene shows a significant increase of the rate of nonsynonymous change, with respect to its nonoverlapping counterpart. The same feature was observed in the overlapping coding region of the NS2 gene. Such a variation pattern, which implies the occurrence of several amino acid substitutions in the protein regions encoded by overlapping frames, is different from the pattern of constrained evolution typical of other viral overlapping-gene systems. Amino acid sequence analysis of the NS1 and NS2 proteins revealed that some nonsynonymous substitutions, located in the region of gene overlap, play a critical role in shaping the genetic diversity of the highly pathogenic subtype H5N1. Since both proteins contribute to disease pathogenesis by affecting many virus and host-cell processes, information provided by this study should be useful to highlight the impact of nonstructural gene variation on the pathogenicity of H5N1 viruses.     

Detecting overlapping coding sequences with pairwise alignments

MOTIVATION: Overlapping gene coding sequences (CDSs) are particularly common in viruses but also occur in more complex genomes. Detecting such genes with conventional gene-finding algorithms can be difficult for several reasons. If an overlapping CDS is on the same read-strand as a known CDS, then there may not be a distinct promoter or mRNA. Furthermore, the constraints imposed by double-coding can result in atypical codon biases. However, these same constraints lead to particular mutation patterns that may be detectable in sequence alignments. RESULTS: In this paper, we investigate several statistics for detecting double-coding sequences with pairwise alignments--including a new maximum-likelihood method. We also develop a model for double-coding sequence evolution. Using simulated sequences generated with the model, we characterize the distribution of each statistic as a function of sequence composition, length, divergence time and double-coding frame. Using these results, we develop several algorithms for detecting overlapping CDSs. The algorithms were tested on known overlapping CDSs and other overlapping open reading frames (ORFs) in the hepatitis B virus (HBV), Escherichia coli and Salmonella typhimurium genomes. The algorithms should prove useful for detecting novel overlapping genes--especially short coding ORFs in viruses. AVAILABILITY: Programs may be obtained from the authors. SUPPLEMENTARY INFORMATION: http://biochem.otago.ac.nz/double.html.     

HCOP: The HGNC comparison of orthology predictions search tool

The HGNC Comparison of Orthology Predictions search tool, HCOP (), enables users to compare predicted human and mouse orthologs for a specified gene, or set of genes, from either species according to the ortholog assertions from the Ensembl, HGNC, Homologene, Inparanoid, MGI and PhIGs databases. Users can assess the reliability of the prediction from the number of these different sources that identify a particular orthologous pair. HCOP provides a useful one-stop resource to summarise, compare and access various sources of human and mouse orthology data.     

On the integrated frameworks of species concepts: Mayden’s hierarchy of species concepts and de Queiroz’s unified concept of species

Richard L. Mayden and Kevin de Queiroz have devised and developed ‘a hierarchy of species concepts’ and ‘a unified species concept’, respectively. Although their integrated frameworks of species concepts are rather different as to how to integrate the diverse modern concepts of species, the end result is that they are likely to agree on species recognition in nature, because they virtually share the same major components (i.e. evolutionary or lineage concept of species; same way of delimiting species), and have the same important consequences. Both the hierarchical and unified frameworks, however, are interpreted to have shortcoming regarding the way of integrating the modern species concepts. I reformulate these ideas into a framework of species concepts as follows: It treats the idea of species as population‐level evolutionary lineages (sensu Wiley 1978 ) as the concept for species category, and it adopts the contingent biological properties of species (e.g. internal reproductive isolation, diagnosability, monophyly) as operational criteria in delimiting species. I also suggest that existing and revised versions of the integrated framework of species concepts all are not new species concepts, but versions of the evolutionary species concept, because they treat the evolutionary (or lineage) species concept as the concept for species category.     

A short guide to long non-coding RNA gene nomenclature

The HUGO Gene Nomenclature Committee (HGNC) is the only organisation authorised to assign standardised nomenclature to human genes. Of the 38,000 approved gene symbols in our database (http://www.genenames.org), the majority represent protein-coding (pc) genes; however, we also name pseudogenes, phenotypic loci, some genomic features, and to date have named more than 8,500 human non-protein coding RNA (ncRNA) genes and ncRNA pseudogenes. We have already established unique names for most of the small ncRNA genes by working with experts for each class. Small ncRNAs can be defined into their respective classes by their shared homology and common function. In contrast, long non-coding RNA (lncRNA) genes represent a disparate set of loci related only by their size, more than 200 bases in length, share no conserved sequence homology, and have variable functions. As with pc genes, wherever possible, lncRNAs are named based on the known function of their product; a short guide is presented herein to help authors when developing novel gene symbols for lncRNAs with characterised function. Researchers must contact the HGNC with their suggestions prior to publication, to check whether the proposed gene symbol can be approved. Although thousands of lncRNAs have been predicted in the human genome, for the vast majority their function remains unresolved. lncRNA genes with no known function are named based on their genomic context. Working with lncRNA researchers, the HGNC aims to provide unique and, wherever possible, meaningful gene symbols to all lncRNA genes.     

Detection of Signature Sequences in Overlapping Genes and Prediction of a Novel Overlapping Gene in Hepatitis G Virus

In viruses an increased coding ability is provided by overlapping genes, in which two alternative open reading frames (ORFs) may be translated to yield two distinct proteins. The identification of signature sequences in overlapping genes is a topic of particular interest, since additional out-of-frame coding regions can be nested within known genes. In this work, a novel feature peculiar to overlapping coding regions is presented. It was detected by analysis of a sample set of 21 virus genomic sequences and consisted in the repeated occurrence of a cluster of basic amino acid residues, encoded by a frame, combined to a stretch of acidic residues, encoded by the corresponding overlapping frame. A computer scan of an additional set of virus sequences demonstrated that this feature is common to several other known overlapping ORFs and led to prediction of a novel overlapping gene in hepatitis G virus (HGV). The occurrence of a bifunctional coding region in HGV was also supported by its extremely lower rate of synonymous nucleotide substitutions compared to that observed in the other gene regions of the HGV genome. Analysis of the amino acid sequence that was deduced from the putative overlapping gene revealed a high content of basic residues and the presence of a nuclear targeting signal; these characteristics suggest that a core-like protein may be expressed by this novel ORF. Received: 21 July 1999 / Accepted: 26 October 1999     

Molecular evolution of the Paramyxoviridae and Rhabdoviridae multiple-protein-encoding P gene

Presented here is an analysis of the molecular evolutionary dynamics of the P gene among 76 representative sequences of the Paramyxoviridae and Rhabdoviridae RNA virus families. In a number of Paramyxoviridae taxa, as well as in vesicular stomatitis viruses of the Rhabdoviridae, the P gene encodes multiple proteins from a single genomic RNA sequence. These products include the phosphoprotein (P), as well as the C and V proteins. The complexity of the P gene makes it an intriguing locus to study from an evolutionary perspective. Amino acid sequence alignments of the proteins encoded at the P and N loci were used in independent phylogenetic reconstructions of the Paramyxoviridae and Rhabdoviridae families. P-gene-coding capacities were mapped onto the Paramyxoviridae phylogeny, and the most parsimonious path of multiple-coding-capacity evolution was determined. Levels of amino acid variation for Paramyxoviridae and Rhabdoviridae P-gene-encoded products were also analyzed. Proteins encoded in overlapping reading frames from the same nucleotides have different levels of amino acid variation. The nucleotide architecture that underlies the amino acid variation was determined in order to evaluate the role of selection in the evolution of the P gene overlapping reading frames. In every case, the evolution of one of the proteins encoded in the overlapping reading frames has been constrained by negative selection while the other has evolved more rapidly. The integrity of the overlapping reading frame that represents a derived state is generally maintained at the expense of the ancestral reading frame encoded by the same nucleotides. The evolution of such multicoding sequences is likely a response by RNA viruses to selective pressure to maximize genomic information content while maintaining small genome size. The ability to evolve such a complex genomic strategy is intimately related to the dynamics of the viral quasispecies, which allow enhanced exploration of the adaptive landscape.     

Stability and evolution of overlapping genes

Abstract.— When the same sequence of nucleotides codes for regions of more than one functional polypeptide, this sequence contains overlapping genes. Overlap is most common in rapidly evolving genomes with high mutation rates such as viruses, bacteria, and mitochondria. Overlap is thought to be important as: (1) a means of compressing a maximum amount of information into short sequences of structural genes; and (2) as a mechanism for regulating gene expression through translational coupling of functionally related polypeptides. The stability of overlapping codes is examined in relation to the information cost of overlap and the mutation rate of the genome. The degree of overlap in a given population will tend to become monomorphic. Evolution toward partial overlap of genes is shown to depend on a convex cost function of overlap. Overlap does not evolve when expression of overlapping genes is mutually exclusive and produced by rare mutations to the wild-type genome. Assuming overlap increases coupling between functionally related genes, the conditions favoring overlap are explored in relation to the kinetics of gene activation and decay. Coupling is most effective for genes in which the gene overlapping at its 5'end (leading gene) decays rapidly, while the gene overlapping at the 3'end (induced gene) decays slowly. If gene expression can feedback on itself (autocatalysis), then high rates of activation favor overlap.     

An update of the S100 nomenclature

The plethora of names given to S100 proteins resulted in considerable confusion. Here we present the official and updated nomenclature of this protein family, approved by the HGNC (HUGO gene nomenclature committee) and ECS (European Calcium Society).