Martin Oti

Predicting disease genes using protein–protein interactions

Background: The responsible genes have not yet been identified for many genetically mapped disease loci. Physically interacting proteins tend to be involved in the same cellular process, and mutations in their genes may lead to similar disease phenotypes. Objective: To investigate whether protein–protein interactions can predict genes for genetically heterogeneous diseases. Methods: 72 940 protein–protein interactions between 10 894 human proteins were used to search 432 loci for candidate disease genes representing 383 genetically heterogeneous hereditary diseases. For each disease, the protein interaction partners of its known causative genes were compared with the disease associated loci lacking identified causative genes. Interaction partners located within such loci were considered candidate disease gene predictions. Prediction accuracy was tested using a benchmark set of known disease genes. Results: Almost 300 candidate disease gene predictions were made. Some of these have since been confirmed. On average, 10% or more are expected to be genuine disease genes, representing a 10-fold enrichment compared with positional information only. Examples of interesting candidates are AKAP6 for arrythmogenic right ventricular dysplasia 3 and SYN3 for familial partial epilepsy with variable foci. Conclusions: Exploiting protein–protein interactions can greatly increase the likelihood of finding positional candidate disease genes. When applied on a large scale they can lead to novel candidate gene predictions.

Genome-wide profiling of p63 DNA-binding sites identifies an element that regulates gene expression during limb development in the 7q21 SHFM1 locus.

Heterozygous mutations in p63 are associated with split hand/foot malformations (SHFM), orofacial clefting, and ectodermal abnormalities. Elucidation of the p63 gene network that includes target genes and regulatory elements may reveal new genes for other malformation disorders. We performed genome-wide DNA–binding profiling by chromatin immunoprecipitation (ChIP), followed by deep sequencing (ChIP–seq) in primary human keratinocytes, and identified potential target genes and regulatory elements controlled by p63. We show that p63 binds to an enhancer element in the SHFM1 locus on chromosome 7q and that this element controls expression of DLX6 and possibly DLX5, both of which are important for limb development. A unique micro-deletion including this enhancer element, but not the DLX5/DLX6 genes, was identified in a patient with SHFM. Our study strongly indicates disruption of a non-coding cis-regulatory element located more than 250 kb from the DLX5/DLX6 genes as a novel disease mechanism in SHFM1. These data provide a proof-of-concept that the catalogue of p63 binding sites identified in this study may be of relevance to the studies of SHFM and other congenital malformations that resemble the p63-associated phenotypes.

De Novo Mutations in the Genome Organizer CTCF Cause Intellectual Disability

An increasing number of genes involved in chromatin structure and epigenetic regulation has been implicated in a variety of developmental disorders, often including intellectual disability. By trio exome sequencing and subsequent mutational screening we now identified two de novo frameshift mutations and one de novo missense mutation in CTCF in individuals with intellectual disability, microcephaly, and growth retardation. Furthermore, an individual with a larger deletion including CTCF was identified. CTCF (CCCTC-binding factor) is one of the most important chromatin organizers in vertebrates and is involved in various chromatin regulation processes such as higher order of chromatin organization, enhancer function, and maintenance of three-dimensional chromatin structure. Transcriptome analyses in all three individuals with point mutations revealed deregulation of genes involved in signal transduction and emphasized the role of CTCF in enhancer-driven expression of genes. Our findings indicate that haploinsufficiency of CTCF affects genomic interaction of enhancers and their regulated gene promoters that drive developmental processes and cognition.

The Biological Coherence of Human Phenome Databases

Disease networks are increasingly explored as a complement to networks centered around interactions between genes and proteins. The quality of disease networks is heavily dependent on the amount and quality of phenotype information in phenotype databases of human genetic diseases. We explored which aspects of phenotype database architecture and content best reflect the underlying biology of disease. We used the OMIM-based HPO, Orphanet, and POSSUM phenotype databases for this purpose and devised a biological coherence score based on the sharing of gene ontology annotation to investigate the degree to which phenotype similarity in these databases reflects related pathobiology. Our analyses support the notion that a fine-grained phenotype ontology enhances the accuracy of phenome representation. In addition, we find that the OMIM database that is most used by the human genetics community is heavily underannotated. We show that this problem can easily be overcome by simply adding data available in the POSSUM database to improve OMIM phenotype representations in the HPO. Also, we find that the use of feature frequency estimates--currently implemented only in the Orphanet database--significantly improves the quality of the phenome representation. Our data suggest that there is much to be gained by improving human phenome databases and that some of the measures needed to achieve this are relatively easy to implement. More generally, we propose that curation and more systematic annotation of human phenome databases can greatly improve the power of the phenotype for genetic disease analysis.

Transcription factor p63 bookmarks and regulates dynamic enhancers during epidermal differentiation

The transcription factor p63 plays a pivotal role in keratinocyte proliferation and differentiation in the epidermis. However, how p63 regulates epidermal genes during differentiation is not yet clear. Using epigenome profiling of differentiating human primary epidermal keratinocytes, we characterized a catalog of dynamically regulated genes and p63‐bound regulatory elements that are relevant for epithelial development and related diseases. p63‐bound regulatory elements occur as single or clustered enhancers, and remarkably, only a subset is active as defined by the co‐presence of the active enhancer mark histone modification H3K27ac in epidermal keratinocytes. We show that the dynamics of gene expression correlates with the activity of p63‐bound enhancers rather than with p63 binding itself. The activity of p63‐bound enhancers is likely determined by other transcription factors that cooperate with p63. Our data show that inactive p63‐bound enhancers in epidermal keratinocytes may be active during the development of other epithelial‐related structures such as limbs and suggest that p63 bookmarks genomic loci during the commitment of the epithelial lineage and regulates genes through temporal‐ and spatial‐specific active enhancers.

Conservation of divergent transcription in fungi

The comparison of fully sequenced genomes enables the study of selective constraints that determine genome organisation. We show that, in fungi, adjacent divergently transcribed (<---->) genes are more conserved in orientation than convergent (--><--) or co-oriented (-->-->) gene pairs. Furthermore, the time divergent orientation of two genes is conserved correlates with the degree of their co-expression and with the likelihood of them being functionally related. The functional interactions of the proteins encoded by the conserved divergent gene pairs indicate a potential for protein function prediction in eukaryotes.

Conserved co-expression for candidate disease gene prioritization

BackgroundGenes that are co-expressed tend to be involved in the same biological process. However, co-expression is not a very reliable predictor of functional links between genes. The evolutionary conservation of co-expression between species can be used to predict protein function more reliably than co-expression in a single species. Here we examine whether co-expression across multiple species is also a better prioritizer of disease genes than is co-expression between human genes alone.ResultsWe use co-expression data from yeast (S. cerevisiae), nematode worm (C. elegans), fruit fly (D. melanogaster), mouse and human and find that the use of evolutionary conservation can indeed improve the predictive value of co-expression. The effect that genes causing the same disease have higher co-expression than do other genes from their associated disease loci, is significantly enhanced when co-expression data are combined across evolutionarily distant species. We also find that performance can vary significantly depending on the co-expression datasets used, and just using more data does not necessarily lead to better prioritization. Instead, we find that dataset quality is more important than quantity, and using a consistent microarray platform per species leads to better performance than using more inclusive datasets pooled from various platforms.ConclusionWe find that evolutionarily conserved gene co-expression prioritizes disease candidate genes better than human gene co-expression alone, and provide the integrated data as a new resource for disease gene prioritization tools.

Human Intellectual Disability Genes Form Conserved Functional Modules in Drosophila

Intellectual Disability (ID) disorders, defined by an IQ below 70, are genetically and phenotypically highly heterogeneous. Identification of common molecular pathways underlying these disorders is crucial for understanding the molecular basis of cognition and for the development of therapeutic intervention strategies. To systematically establish their functional connectivity, we used transgenic RNAi to target 270 ID gene orthologs in the Drosophila eye. Assessment of neuronal function in behavioral and electrophysiological assays and multiparametric morphological analysis identified phenotypes associated with knockdown of 180 ID gene orthologs. Most of these genotype-phenotype associations were novel. For example, we uncovered 16 genes that are required for basal neurotransmission and have not previously been implicated in this process in any system or organism. ID gene orthologs with morphological eye phenotypes, in contrast to genes without phenotypes, are relatively highly expressed in the human nervous system and are enriched for neuronal functions, suggesting that eye phenotyping can distinguish different classes of ID genes. Indeed, grouping genes by Drosophila phenotype uncovered 26 connected functional modules. Novel links between ID genes successfully predicted that MYCN, PIGV and UPF3B regulate synapse development. Drosophila phenotype groups show, in addition to ID, significant phenotypic similarity also in humans, indicating that functional modules are conserved. The combined data indicate that ID disorders, despite their extreme genetic diversity, are caused by disruption of a limited number of highly connected functional modules.

CTCF-mediated chromatin loops enclose inducible gene regulatory domains

BackgroundThe CCTC-binding factor (CTCF) protein is involved in genome organization, including mediating three-dimensional chromatin interactions. Human patient lymphocytes with mutations in a single copy of the CTCF gene have reduced expression of enhancer-associated genes involved in response to stimuli. We hypothesize that CTCF interactions stabilize enhancer-promoter chromatin interaction domains, facilitating increased expression of genes in response to stimuli. Here we systematically investigate this model using computational analyses.ResultsWe use CTCF ChIA-PET data from the ENCODE project to show that CTCF-associated chromatin loops have a tendency to enclose regions of enhancer-regulated stimulus responsive genes, insulating them from neighboring regions of constitutively expressed housekeeping genes. To facilitate cell type-specific CTCF loop identification, we develop an algorithm to predict CTCF loops from ChIP-seq data alone by exploiting the CTCF motif directionality in loop anchors. We apply this algorithm to a hundred ENCODE cell line datasets, confirming the universality of our observations as well as identifying a general distinction between primary and immortal cells in loop-enclosed gene content. Finally, we combine the existing evidence to propose a model for the formation of CTCF loops in which partner sites are brought together by chromatin template reeling through stationary RNA polymerases, consistent with the transcription factory hypothesis.ConclusionsWe provide computational evidence that CTCF-mediated chromatin interactions enclose domains of stimulus responsive enhancer-regulated genes, insulating them from nearby housekeeping genes.

Systematic analysis of copy number variants of a large cohort of orofacial cleft patients identifies candidate genes for orofacial clefts.

Orofacial clefts (OFCs) represent a large fraction of human birth defects and are one of the most common phenotypes affected by large copy number variants (CNVs). Due to the limited number of CNV patients in individual centers, CNV analyses of a large number of OFC patients are challenging. The present study analyzed 249 genomic deletions and 226 duplications from a cohort of 312 OFC patients reported in two publicly accessible databases of chromosome imbalance and phenotype in humans, DECIPHER and ECARUCA. Genomic regions deleted or duplicated in multiple patients were identified, and genes in these overlapping CNVs were prioritized based on the number of genes encompassed by the region and gene expression in embryonic mouse palate. Our analyses of these overlapping CNVs identified two genes known to be causative for human OFCs, SATB2 and MEIS2, and 12 genes (DGCR6, FGF2, FRZB, LETM1, MAPK3, SPRY1, THBS1, TSHZ1, TTC28, TULP4, WHSC1, WHSC2) that are associated with OFC or orofacial development. Additionally, we report 34 deleted and 24 duplicated genes that have not previously been associated with OFCs but are associated with the BMP, MAPK and RAC1 pathways. Statistical analyses show that the high number of overlapping CNVs is not due to random occurrence. The identified genes are not located in highly variable genomic regions in healthy populations and are significantly enriched for genes that are involved in orofacial development. In summary, we report a CNV analysis pipeline of a large cohort of OFC patients and identify novel candidate OFC genes.