A new method for evaluating the impacts of semantic similarity measures on the annotation of gene sets

PolyQ length co-evolution in neural proteins

Intermolecular co-evolution optimizes physiological performance in functionally related proteins, ultimately increasing molecular co-adaptation and evolutionary fitness. Polyglutamine (polyQ) repeats, which are over-represented in nervous system-related proteins, are increasingly recognized as length-dependent regulators of protein function and interactions, and their length variation contributes to intraspecific phenotypic variability and interspecific divergence. However, it is unclear whether polyQ repeat lengths evolve independently in each protein or rather co-evolve across functionally related protein pairs and networks, as in an integrated regulatory system. To address this issue, we investigated here the length evolution and co-evolution of polyQ repeats in clusters of functionally related and physically interacting neural proteins in Primates. We observed function-/disease-related polyQ repeat enrichment and evolutionary hypervariability in specific neural protein clusters, particularly in the neurocognitive and neuropsychiatric domains. Notably, these analyses detected extensive patterns of intermolecular polyQ length co-evolution in pairs and clusters of functionally related, physically interacting proteins. Moreover, they revealed both direct and inverse polyQ length co-variation in protein pairs, together with complex patterns of coordinated repeat variation in entire polyQ protein sets. These findings uncover a whole system of co-evolving polyQ repeats in neural proteins with direct implications for understanding polyQ-dependent phenotypic variability, neurocognitive evolution and neuropsychiatric disease pathogenesis.

A literature-based approach for curating gene signatures in multifaceted diseases

BACKGROUND AND AIMS

The task of identifying a representative and yet manageable target gene list for assessing the pathogenesis of complicated and multifaceted diseases is challenging. Using Inflammatory Bowel Disease (IBD) as an example, we conceived a bioinformatic approach to identify novel genes associated with the various disease subtypes, in combination with known clinical control genes.

METHODS

From the available literature, we used Acumenta Literature Lab^TM (LitLab), network analyses, and LitLab Gene Retriever to assemble a gene pool that has a high likelihood of representing immunity-related subtype-specific signatures of IBD.

RESULTS

We generated six relevant gene lists and 21 intersections that contain genes with unique literature associations to Crohn's Disease (n = 60), Ulcerative Colitis (n = 17), and unclassified (n = 45) subtypes of IBD. From this gene pool, we then filtered and constructed, using network analysis, a final list of 142 genes that are the most representative of the disease and its subtypes.

CONCLUSIONS

In this paper, we present the bioinformatic construction of a gene panel that putatively contains subtype signatures of IBD, a multifactorial disease. These gene signatures will be tested as biomarkers to classify patients with IBD, which has been a clinically challenging task. Such approach to diagnose and monitor complicated disease pathogenesis is a stepping-stone towards personalized care.

GSAn: an alternative to enrichment analysis for annotating gene sets

The revolution in new sequencing technologies is greatly leading to new understandings of the relations between genotype and phenotype. To interpret and analyze data that are grouped according to a phenotype of interest, methods based on statistical enrichment became a standard in biology. However, these methods synthesize the biological information by a priori selecting the over-represented terms and may suffer from focusing on the most studied genes that represent a limited coverage of annotated genes within a gene set. Semantic similarity measures have shown great results within the pairwise gene comparison by making advantage of the underlying structure of the Gene Ontology. We developed GSAn, a novel gene set annotation method that uses semantic similarity measures to synthesize a priori Gene Ontology annotation terms. The originality of our approach is to identify the best compromise between the number of retained annotation terms that has to be drastically reduced and the number of related genes that has to be as large as possible. Moreover, GSAn offers interactive visualization facilities dedicated to the multi-scale analysis of gene set annotations. Compared to enrichment analysis tools, GSAn has shown excellent results in terms of maximizing the gene coverage while minimizing the number of terms.