Nicolas Simonis

Assessing computational tools for the discovery of transcription factor binding sites

The prediction of regulatory elements is a problem where computational methods offer great hope. Over the past few years, numerous tools have become available for this task. The purpose of the current assessment is twofold: to provide some guidance to users regarding the accuracy of currently available tools in various settings, and to provide a benchmark of data sets for assessing future tools.

High-quality binary protein interaction map of the yeast interactome network

Current yeast interactome network maps contain several hundred molecular complexes with limited and somewhat controversial representation of direct binary interactions. We carried out a comparative quality assessment of current yeast interactome data sets, demonstrating that high-throughput yeast two-hybrid (Y2H) screening provides high-quality binary interaction information. Because a large fraction of the yeast binary interactome remains to be mapped, we developed an empirically controlled mapping framework to produce a “second-generation” high-quality, high-throughput Y2H data set covering ∼20% of all yeast binary interactions. Both Y2H and affinity purification followed by mass spectrometry (AP/MS) data are of equally high quality but of a fundamentally different and complementary nature, resulting in networks with different topological and biological properties. Compared to co-complex interactome models, this binary map is enriched for transient signaling interactions and intercomplex connections with a highly significant clustering between essential proteins. Rather than correlating with essentiality, protein connectivity correlates with genetic pleiotropy.

Edgetic perturbation models of human inherited disorders

Cellular functions are mediated through complex systems of macromolecules and metabolites linked through biochemical and physical interactions, represented in interactome models as ‘nodes’ and ‘edges’, respectively. Better understanding of genotype‐to‐phenotype relationships in human disease will require modeling of how disease‐causing mutations affect systems or interactome properties. Here we investigate how perturbations of interactome networks may differ between complete loss of gene products (‘node removal’) and interaction‐specific or edge‐specific (‘edgetic’) alterations. Global computational analyses of ∼50 000 known causative mutations in human Mendelian disorders revealed clear separations of mutations probably corresponding to those of node removal versus edgetic perturbations. Experimental characterization of mutant alleles in various disorders identified diverse edgetic interaction profiles of mutant proteins, which correlated with distinct structural properties of disease proteins and disease mechanisms. Edgetic perturbations seem to confer distinct functional consequences from node removal because a large fraction of cases in which a single gene is linked to multiple disorders can be modeled by distinguishing edgetic network perturbations. Edgetic network perturbation models might improve both the understanding of dissemination of disease alleles in human populations and the development of molecular therapeutic strategies.

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.

Viral perturbations of host networks reflect disease etiology.

Many human diseases, arising from mutations of disease susceptibility genes (genetic diseases), are also associated with viral infections (virally implicated diseases), either in a directly causal manner or by indirect associations. Here we examine whether viral perturbations of host interactome may underlie such virally implicated disease relationships. Using as models two different human viruses, Epstein-Barr virus (EBV) and human papillomavirus (HPV), we find that host targets of viral proteins reside in network proximity to products of disease susceptibility genes. Expression changes in virally implicated disease tissues and comorbidity patterns cluster significantly in the network vicinity of viral targets. The topological proximity found between cellular targets of viral proteins and disease genes was exploited to uncover a novel pathway linking HPV to Fanconi anemia.

Host-Pathogen Interactome Mapping for HTLV-1 and -2 Retroviruses

BackgroundHuman T-cell leukemia virus type 1 (HTLV-1) and type 2 both target T lymphocytes, yet induce radically different phenotypic outcomes. HTLV-1 is a causative agent of Adult T-cell leukemia (ATL), whereas HTLV-2, highly similar to HTLV-1, causes no known overt disease. HTLV gene products are engaged in a dynamic struggle of activating and antagonistic interactions with host cells. Investigations focused on one or a few genes have identified several human factors interacting with HTLV viral proteins. Most of the available interaction data concern the highly investigated HTLV-1 Tax protein. Identifying shared and distinct host-pathogen protein interaction profiles for these two viruses would enlighten how they exploit distinctive or common strategies to subvert cellular pathways toward disease progression.ResultsWe employ a scalable methodology for the systematic mapping and comparison of pathogen-host protein interactions that includes stringent yeast two-hybrid screening and systematic retest, as well as two independent validations through an additional protein interaction detection method and a functional transactivation assay. The final data set contained 166 interactions between 10 viral proteins and 122 human proteins. Among the 166 interactions identified, 87 and 79 involved HTLV-1 and HTLV-2 -encoded proteins, respectively. Targets for HTLV-1 and HTLV-2 proteins implicate a diverse set of cellular processes including the ubiquitin-proteasome system, the apoptosis, different cancer pathways and the Notch signaling pathway.ConclusionsThis study constitutes a first pass, with homogeneous data, at comparative analysis of host targets for HTLV-1 and -2 retroviruses, complements currently existing data for formulation of systems biology models of retroviral induced diseases and presents new insights on biological pathways involved in retroviral infection.

FGFR1 mutations cause Hartsfield syndrome, the unique association of holoprosencephaly and ectrodactyly

Background Harstfield syndrome is the rare and unique association of holoprosencephaly (HPE) and ectrodactyly, with or without cleft lip and palate, and variable additional features. All the reported cases occurred sporadically. Although several causal genes of HPE and ectrodactyly have been identified, the genetic cause of Hartsfield syndrome remains unknown. We hypothesised that a single key developmental gene may underlie the co-occurrence of HPE and ectrodactyly. Methods We used whole exome sequencing in four isolated cases including one case-parents trio, and direct Sanger sequencing of three additional cases, to investigate the causative variants in Hartsfield syndrome. Results We identified a novel FGFR1 mutation in six out of seven patients. Affected residues are highly conserved and are located in the extracellular binding domain of the receptor (two homozygous mutations) or the intracellular tyrosine kinase domain (four heterozygous de novo variants). Strikingly, among the six novel mutations, three are located in close proximity to the ATPs phosphates or the coordinating magnesium, with one position required for kinase activity, and three are adjacent to known mutations involved in Kallmann syndrome plus other developmental anomalies. Conclusions Dominant or recessive FGFR1 mutations are responsible for Hartsfield syndrome, consistent with the known roles of FGFR1 in vertebrate ontogeny and conditional Fgfr1-deficient mice. Our study shows that, in humans, lack of accurate FGFR1 activation can disrupt both brain and hand/foot midline development, and that FGFR1 loss-of-function mutations are responsible for a wider spectrum of clinical anomalies than previously thought, ranging in severity from seemingly isolated hypogonadotropic hypogonadism, through Kallmann syndrome with or without additional features, to Hartsfield syndrome at its most severe end.

Combining pattern discovery and discriminant analysis to predict gene co-regulation

MOTIVATION Several pattern discovery methods have been proposed to detect over-represented motifs in upstream sequences of co-regulated genes, and are for example used to predict cis-acting elements from clusters of co-expressed genes. The clusters to be analyzed are often noisy, containing a mixture of co-regulated and non-co-regulated genes. We propose a method to discriminate co-regulated from non-co-regulated genes on the basis of counts of pattern occurrences in their non-coding sequences. METHODS String-based pattern discovery is combined with discriminant analysis to classify genes on the basis of putative regulatory motifs. RESULTS The approach is evaluated by comparing the significance of patterns detected in annotated regulons (positive control), random gene selections (negative control) and high-throughput regulons (noisy data) from the yeast Saccharomyces cerevisiae. The classification is evaluated on the annotated regulons, and the robustness and rejection power is assessed with mixtures of co-regulated and random genes.

tRNA Methyltransferase Homolog Gene TRMT10A Mutation in Young Onset Diabetes and Primary Microcephaly in Humans

We describe a new syndrome of young onset diabetes, short stature and microcephaly with intellectual disability in a large consanguineous family with three affected children. Linkage analysis and whole exome sequencing were used to identify the causal nonsense mutation, which changed an arginine codon into a stop at position 127 of the tRNA methyltransferase homolog gene TRMT10A (also called RG9MTD2). TRMT10A mRNA and protein were absent in lymphoblasts from the affected siblings. TRMT10A is ubiquitously expressed but enriched in brain and pancreatic islets, consistent with the tissues affected in this syndrome. In situ hybridization studies showed that TRMT10A is expressed in human embryonic and fetal brain. TRMT10A is the mammalian ortholog of S. cerevisiae TRM10, previously shown to catalyze the methylation of guanine 9 (m1G9) in several tRNAs. Consistent with this putative function, in silico topology prediction indicated that TRMT10A has predominant nuclear localization, which we experimentally confirmed by immunofluorescence and confocal microscopy. TRMT10A localizes to the nucleolus of β- and non-β-cells, where tRNA modifications occur. TRMT10A silencing induces rat and human β-cell apoptosis. Taken together, we propose that TRMT10A deficiency negatively affects β-cell mass and the pool of neurons in the developing brain. This is the first study describing the impact of TRMT10A deficiency in mammals, highlighting a role in the pathogenesis of microcephaly and early onset diabetes. In light of the recent report that the type 2 diabetes candidate gene CDKAL1 is a tRNA methylthiotransferase, the findings in this family suggest broader relevance of tRNA methyltransferases in the pathogenesis of type 2 diabetes.

Two novel CCDC88C mutations confirm the role of DAPLE in autosomal recessive congenital hydrocephalus

Background Human congenital non-syndromic hydrocephalus is a vastly heterogeneous condition. A subgroup of cases are not secondary to a specific cause (eg, a neural tube defect), and within this subgroup, autosomal recessive inheritance has been described. One homozygous mutation in the DAPLE (Dvl-associating protein with a high frequency of leucine residues) protein-encoding gene CCDC88C (coiled-coil domain containing 88C) has recently been reported in a single family. The role of this gene has not been validated in another family, and no other autosomal recessive gene has been reported. Methods We used homozygosity mapping and whole exome sequencing in two families with primary, non-syndromic congenital hydrocephalus from two different ethnic backgrounds. Results In each family, we identified a novel homozygous mutation of CCDC88C. One mutation produced a premature stop codon at position 312 of the protein, while the second mutation induced a frameshift in the last exon, producing a stop codon that truncated the extreme C-terminus of DAPLE, including the 2026-2028 Gly-Cys-Val motif known to bind the post synaptic density protein (PSD95), Drosophila disc large tumor suppressor (Dlg1), and zonula occludens-1 protein (zo-1) (PDZ) domain of Dishevelled. Conclusions Our data validate CCDC88C as causing autosomal recessive, primary non-syndromic congenital hydrocephalus, suggesting this gene may be an important cause of congenital hydrocephalus, and underscore the important role of the C-terminal PDZ domain-binding motif in the DAPLE protein.