PathPPI: an integrated dataset of human pathways and protein-protein interactions

Changes in social behavior with MAPK2 and KCTD13/CUL3 pathways alterations in two new outbred rat models for the 16p11.2 syndromes with autism spectrum disorders

Copy number variations (CNVs) of the human 16p11.2 locus are associated with several developmental/neurocognitive syndromes. Particularly, deletion and duplication of this genetic interval are found in patients with autism spectrum disorders, intellectual disability and other psychiatric traits. The high gene density associated with the region and the strong phenotypic variability of incomplete penetrance, make the study of the 16p11.2 syndromes extremely complex. To systematically study the effect of 16p11.2 CNVs and identify candidate genes and molecular mechanisms involved in the pathophysiology, mouse models were generated previously and showed learning and memory, and to some extent social deficits. To go further in understanding the social deficits caused by 16p11.2 syndromes, we engineered deletion and duplication of the homologous region to the human 16p11.2 genetic interval in two rat outbred strains, Sprague Dawley (SD) and Long Evans (LE). The 16p11.2 rat models displayed convergent defects in social behavior and in the novel object test in male carriers from both genetic backgrounds. Interestingly major pathways affecting MAPK1 and CUL3 were found altered in the rat 16p11.2 models with additional changes in males compared to females. Altogether, the consequences of the 16p11.2 genetic region dosage on social behavior are now found in three different species: humans, mice and rats. In addition, the rat models pointed to sexual dimorphism with lower severity of phenotypes in rat females compared to male mutants. This phenomenon is also observed in humans. We are convinced that the two rat models will be key to further investigating social behavior and understanding the brain mechanisms and specific brain regions that are key to controlling social behavior.

Proteins and metabolites fingerprints of gestational diabetes mellitus forming protein-metabolite interactomes are its potential biomarkers

Gestational diabetes mellitus (GDM) is a consequence of glucose intolerance with an inadequate production of insulin that happens during pregnancy and leads to adverse health consequences for both mother and fetus. GDM patients are at higher risk for preeclampsia, and developing diabetes mellitus type 2 in later life, while the child born to GDM mothers are more prone to macrosomia, and hypoglycemia. The universally accepted diagnostic criteria for GDM are lacking, therefore there is a need for a diagnosis of GDM that can identify GDM at its early stage (first trimester). We have reviewed the literature on proteins and metabolites fingerprints of GDM. Further, we have performed protein-protein, metabolite-metabolite, and protein-metabolite interaction network studies on GDM proteins and metabolites fingerprints. Notably, some proteins and metabolites fingerprints are forming strong interaction networks at high confidence scores. Therefore, we have suggested that those proteins and metabolites that are forming protein-metabolite interactomes are the potential biomarkers of GDM. The protein-metabolite biomarkers interactome may help in a deep understanding of the prognosis, pathogenesis of GDM, and also detection of GDM. The protein-metabolites interactome may be further applied in planning future therapeutic strategies to promote long-term health benefits in GDM mothers and their children.

Multiomic Metabolic Enrichment Network Analysis Reveals Metabolite-Protein Physical Interaction Subnetworks Altered in Cancer

Metabolism is recognized as an important driver of cancer progression and other complex diseases, but global metabolite profiling remains a challenge. Protein expression profiling is often a poor proxy since existing pathway enrichment models provide an incomplete mapping between the proteome and metabolism. To overcome these gaps, we introduce multiomic metabolic enrichment network analysis (MOMENTA), an integrative multiomic data analysis framework for more accurately deducing metabolic pathway changes from proteomics data alone in a gene set analysis context by leveraging protein interaction networks to extend annotated metabolic models. We apply MOMENTA to proteomic data from diverse cancer cell lines and human tumors to demonstrate its utility at revealing variation in metabolic pathway activity across cancer types, which we verify using independent metabolomics measurements. The novel metabolic networks we uncover in breast cancer and other tumors are linked to clinical outcomes, underscoring the pathophysiological relevance of the findings.

Multi-influential genetic interactions alter behaviour and cognition through six main biological cascades in Down syndrome mouse models

Down syndrome (DS) is the most common genetic form of intellectual disability caused by the presence of an additional copy of human chromosome 21 (Hsa21). To provide novel insights into genotype–phenotype correlations, we used standardized behavioural tests, magnetic resonance imaging and hippocampal gene expression to screen several DS mouse models for the mouse chromosome 16 region homologous to Hsa21. First, we unravelled several genetic interactions between different regions of chromosome 16 and how they contribute significantly to altering the outcome of the phenotypes in brain cognition, function and structure. Then, in-depth analysis of misregulated expressed genes involved in synaptic dysfunction highlighted six biological cascades centred around DYRK1A, GSK3β, NPY, SNARE, RHOA and NPAS4. Finally, we provide a novel vision of the existing altered gene–gene crosstalk and molecular mechanisms targeting specific hubs in DS models that should become central to better understanding of DS and improving the development of therapies.

dbSAP: single amino-acid polymorphism database for protein variation detection

Millions of human single nucleotide polymorphisms (SNPs) or mutations have been identified so far, and these variants could be strongly correlated with phenotypic variations of traits/diseases. Among these variants, non-synonymous ones can result in amino-acid changes that are called single amino-acid polymorphisms (SAPs). Although some studies have tried to investigate the SAPs, only a small fraction of SAPs have been identified due to inadequately inferred protein variation database and the low coverage of mass spectrometry (MS) experiments. Here, we present the dbSAP database for conveniently accessing the comprehensive information and relationships of spectra, peptides and proteins of SAPs, as well as related genes, pathways, diseases and drug targets. In order to fully explore human SAPs, we built a customized protein database that contained comprehensive variant proteins by integrating and annotating the human SNPs and mutations from eight distinct databases (UniProt, Protein Mutation Database, HPMD, MSIPI, MS-CanProVar, dbSNP, Ensembl and COSMIC). After a series of quality controls, a total of 16 854 SAP peptides involving in 439 537 spectra were identified with large scale MS datasets from various human tissues and cell lines. dbSAP is freely available at http://www.megabionet.org/dbSAP/index.html.