Nicolas Thierry-Mieg

Proto-genes and de novo gene birth

Novel protein-coding genes can arise either through re-organization of pre-existing genes or de novo. Processes involving re-organization of pre-existing genes, notably after gene duplication, have been extensively described. In contrast, de novo gene birth remains poorly understood, mainly because translation of sequences devoid of genes, or ‘non-genic’ sequences, is expected to produce insignificant polypeptides rather than proteins with specific biological functions. Here we formalize an evolutionary model according to which functional genes evolve de novo through transitory proto-genes generated by widespread translational activity in non-genic sequences. Testing this model at the genome scale in Saccharomyces cerevisiae, we detect translation of hundreds of short species-specific open reading frames (ORFs) located in non-genic sequences. These translation events seem to provide adaptive potential, as suggested by their differential regulation upon stress and by signatures of retention by natural selection. In line with our model, we establish that S. cerevisiae ORFs can be placed within an evolutionary continuum ranging from non-genic sequences to genes. We identify ∼1,900 candidate proto-genes among S. cerevisiae ORFs and find that de novo gene birth from such a reservoir may be more prevalent than sporadic gene duplication. Our work illustrates that evolution exploits seemingly dispensable sequences to generate adaptive functional innovation.

A protein–protein interaction map of the Caenorhabditis elegans 26S proteasome

The ubiquitin‐proteasome proteolytic pathway is pivotal in most biological processes. Despite a great level of information available for the eukaryotic 26S proteasome—the protease responsible for the degradation of ubiquitylated proteins—several structural and functional questions remain unanswered. To gain more insight into the assembly and function of the metazoan 26S proteasome, a two‐hybrid‐based protein interaction map was generated using 30 Caenorhabditis elegans proteasome subunits. The results recapitulate interactions reported for other organisms and reveal new potential interactions both within the 19S regulatory complex and between the 19S and 20S subcomplexes. Moreover, novel potential proteasome interactors were identified, including an E3 ubiquitin ligase, transcription factors, chaperone proteins and other proteins not yet functionally annotated. By providing a wealth of novel biological hypotheses, this interaction map constitutes a framework for further analysis of the ubiquitin‐proteasome pathway in a multicellular organism amenable to both classical genetics and functional genomics.

Open-reading-frame sequence tags (OSTs) support the existence of at least 17,300 genes in C. elegans

The genome sequences of Caenorhabditis elegans, Drosophila melanogaster and Arabidopsis thaliana have been predicted to contain 19,000, 13,600 and 25,500 genes, respectively. Before this information can be fully used for evolutionary and functional studies, several issues need to be addressed. First, the gene number estimates obtained in silico and not yet supported by any experimental data need to be verified. For example, it seems biologically paradoxical that C. elegans would have 50% more genes than Drosophilia. Second, intron/exon predictions need to be tested experimentally. Third, complete sets of open reading frames (ORFs), or “ORFeomes,” need to be cloned into various expression vectors. To address these issues simultaneously, we have designed and applied to C. elegans the following strategy. Predicted ORFs are amplified by PCR from a highly representative cDNA library using ORF-specific primers, cloned by Gateway recombination cloning and then sequenced to generate ORF sequence tags (OSTs) as a way to verify identity and splicing. In a sample (n=1,222) of the nearly 10,000 genes predicted ab initio (that is, for which no expressed sequence tag (EST) is available so far), at least 70% were verified by OSTs. We also observed that 27% of these experimentally confirmed genes have a structure different from that predicted by GeneFinder. We now have experimental evidence that supports the existence of at least 17,300 genes in C. elegans. Hence we suggest that gene counts based primarily on ESTs may underestimate the number of genes in human and in other organisms.

MatrixDB, the extracellular matrix interaction database

MatrixDB (http://matrixdb.ibcp.fr) is a freely available database focused on interactions established by extracellular proteins and polysaccharides. Only few databases report protein–polysaccharide interactions and, to the best of our knowledge, there is no other database of extracellular interactions. MatrixDB takes into account the multimeric nature of several extracellular protein families for the curation of interactions, and reports interactions with individual polypeptide chains or with multimers, considered as permanent complexes, when appropriate. MatrixDB is a member of the International Molecular Exchange consortium (IMEx) and has adopted the PSI-MI standards for the curation and the exchange of interaction data. MatrixDB stores experimental data from our laboratory, data from literature curation, data imported from IMEx databases, and data from the Human Protein Reference Database. MatrixDB is focused on mammalian interactions, but aims to integrate interaction datasets of model organisms when available. MatrixDB provides direct links to databases recapitulating mutations in genes encoding extracellular proteins, to UniGene and to the Human Protein Atlas that shows expression and localization of proteins in a large variety of normal human tissues and cells. MatrixDB allows researchers to perform customized queries and to build tissue- and disease-specific interaction networks that can be visualized and analyzed with Cytoscape or Medusa.

Interaction networks: From protein functions to drug discovery. A review

Most genes, proteins and other components carry out their functions within a complex network of interactions and a single molecule can affect a wide range of other cell components. A global, integrative, approach has been developed for several years, including protein-protein interaction networks (interactomes). In this review, we describe the high-throughput methods used to identify new interactions and to build large interaction datasets. The minimum information required for reporting a molecular interaction experiment (MIMIx) has been defined as a standard for storing data in publicly available interaction databases. Several examples of interaction networks from molecular machines (proteasome) or organelles (phagosome, mitochondrion) to whole organisms (viruses, bacteria, yeast, fly, and worm) are given and attempts to cover the entire human interaction network are discussed. The methods used to perform the topological analysis of interaction networks and to extract biological information from them are presented. These investigations have provided clues on protein functions, signalling and metabolic pathways, and physiological processes, unraveled the molecular basis of some diseases (cancer, infectious diseases), and will be very useful to identify new therapeutic targets and for drug discovery. A major challenge is now to integrate data from different sources (interactome, transcriptome, phenome, localization) to switch from static to dynamic interaction networks. The merging of a viral interactome and the human interactome has been used to simulate viral infection, paving the way for future studies aiming at providing molecular basis of human diseases.

MatrixDB, a database focused on extracellular protein–protein and protein–carbohydrate interactions

Summary: MatrixDB (http://matrixdb.ibcp.fr) is a database reporting mammalian protein–protein and protein–carbohydrate interactions involving extracellular molecules. It takes into account the full interaction repertoire of the extracellular matrix involving full-length molecules, fragments and multimers. The current version of MatrixDB contains 1972 interactions corresponding to 4412 experiments and involving 259 extracellular biomolecules. Availability: MatrixDB is freely available at http://matrixdb.ibcp.fr Contact: nicolas.thierry-mieg@imag.fr; s.ricard-blum@ibcp.fr Supplementary information: Supplementary data are available at Bioinformatics online.

MatrixDB, the extracellular matrix interaction database: updated content, a new navigator and expanded functionalities

MatrixDB (http://matrixdb.ibcp.fr) is a freely available database focused on interactions established by extracellular proteins and polysaccharides. It is an active member of the International Molecular Exchange (IMEx) consortium and has adopted the PSI-MI standards for annotating and exchanging interaction data, either at the MIMIx or IMEx level. MatrixDB content has been updated by curation and by importing extracellular interaction data from other IMEx databases. Other major changes include the creation of a new website and the development of a novel graphical navigator, iNavigator, to build and expand interaction networks. Filters may be applied to build sub-networks based on a list of biomolecules, a specified interaction detection method and/or an expression level by tissue, developmental stage, and health state (UniGene data). Any molecule of the network may be selected and its partners added to the network at any time. Networks may be exported under Cytoscape and tabular formats and as images, and may be saved for subsequent re-use.

A Model of Elegance

GenBank, http://www.ncbi.nlm.nih.gov/Web/GenbankGenome Sequencing Center, University of Washington School of Medicine, http://genome.wustl.edu/gsc/gschmpg.htmlThe Sanger Center, http://www.sanger.ac.ukXREFdb, http://www.ncbi.nlm.nih.gov/Bassett/modelorgs

Interaction networks as a tool to investigate the mechanisms of aging.

Biological systems are made up of very large numbers of different components interacting at various scales. Most genes, proteins and other cell components carry out their functions within a complex network of interactions and a single component can affect a wide range of other components. Interactions involved in biological processes have been first characterized individually but this “reductionist” approach suffers from a lack of information about time, space, and context in which the interactions occur in vivo. A global, integrative, approach has been developed for several years, focusing on the building of protein–protein interaction maps or interactomes. These interaction networks are complex systems, where new properties arise. They are part of the emergent field of systems biology, which focuses on studying complex biological systems such as a cell or organism, viewed as an integrated and interacting network of genes, proteins and biochemical reactions. Aging is associated with many diseases, such as cancer, diabetes, cardiovascular and neurodegenerative disorders and this limits the investigation of the mechanisms underlying the aging process when focusing on a single gene or a single biochemical pathway. The integration of existing intracellular interaction networks with the extracellular interaction network we have developed (MatrixDB, http://matrixdb.ibcp.fr) will contribute to provide further insights into the global mechanisms of aging.

SPINK2 deficiency causes infertility by inducing sperm defects in heterozygotes and azoospermia in homozygotes

Azoospermia, characterized by the absence of spermatozoa in the ejaculate, is a common cause of male infertility with a poorly characterized etiology. Exome sequencing analysis of two azoospermic brothers allowed the identification of a homozygous splice mutation in SPINK2, encoding a serine protease inhibitor believed to target acrosin, the main sperm acrosomal protease. In accord with these findings, we observed that homozygous Spink2 KO male mice had azoospermia. Moreover, despite normal fertility, heterozygous male mice had a high rate of morphologically abnormal spermatozoa and a reduced sperm motility. Further analysis demonstrated that in the absence of Spink2, protease‐induced stress initiates Golgi fragmentation and prevents acrosome biogenesis leading to spermatid differentiation arrest. We also observed a deleterious effect of acrosin overexpression in HEK cells, effect that was alleviated by SPINK2 coexpression confirming its role as acrosin inhibitor. These results demonstrate that SPINK2 is necessary to neutralize proteases during their cellular transit toward the acrosome and that its deficiency induces a pathological continuum ranging from oligoasthenoteratozoospermia in heterozygotes to azoospermia in homozygotes.