Baudot, A. | Search bioRxiv

bioRxiv2018

Transcriptomic metaanalyses of autistic brains reveals shared gene expression and biological pathway abnormalities with cancer.

Epidemiological and clinical evidence points to cancer as a comorbidity in people with autism spectrum disorders (ASD). A significant overlap of genes and biological processes between both diseases has also been reported. Here, for the first time, we compared the gene expression profiles of ASD frontal cortex tissues and 22 cancer types obtained by differential expression meta-analysis. Four cancer types (brain, thyroid, kidney, and pancreatic cancers) presented a significant overlap in gene expression deregulations in the same direction as ASD whereas two cancer types (lung and prostate cancers) showed differential expression profiles significantly deregulated in the opposite direction from ASD. Functional enrichment and LINCS L1000 based drug set enrichment analyses revealed the implication of several biological processes and pathways that were affected jointly in both diseases, including impairments of the immune system, and impairments in oxidative phosphorylation and ATP synthesis among others. Our data also suggest that brain and kidney cancer have patterns of transcriptomic dysregulation in the PI3K/AKT/MTOR axis that are similar to those found in ASD. These shared transcriptomic alterations could help explain epidemiological observations suggesting direct and inverse comorbid associations between ASD and particular cancer types.

systems biology↗

bioRxiv2017

Random Walk With Restart On Multiplex And Heterogeneous Biological Networks

Recent years have witnessed an exponential growth in the number of identified interactions between biological molecules. These interactions are usually represented as large and complex networks, calling for the development of appropriated tools to exploit the functional information they contain. Random walk with restart is the state-of-the-art guilt-by-association approach. It explores the network vicinity of gene/protein seeds to study their functions, based on the premise that nodes related to similar functions tend to lie close to each others in the networks.\n\nIn the present study, we extended the random walk with restart algorithm to multiplex and heterogeneous networks. The walk can now explore different layers of physical and functional interactions between genes and proteins, such as protein-protein interactions and co-expression associations. In addition, the walk can also jump to a network containing different sets of edges and nodes, such as phenotype similarities between diseases.\n\nWe devised a leave-one-out cross-validation strategy to evaluate the algorithms abilities to predict disease-associated genes. We demonstrate the increased performances of the multiplex-heterogeneous random walk with restart as compared to several random walks on monoplex or heterogeneous networks. Overall, our framework is able to leverage the different interaction sources to outperform current approaches.\n\nFinally, we applied the algorithm to predict genes candidate for being involved in the Wiedemann-Rautenstrauch syndrome, and to explore the network vicinity of the SHORT syndrome.\n\nThe source code and the software are freely available at: https://github.com/alberto-valdeolivas/RWR-MH.

systems biology↗