Community Structure Analysis of Gene Interaction Networks in Duchenne Muscular Dystrophy

Understanding the Variability in Graph Data Sets through Statistical Modeling on the Stiefel Manifold

Network analysis provides a rich framework to model complex phenomena, such as human brain connectivity. It has proven efficient to understand their natural properties and design predictive models. In this paper, we study the variability within groups of networks, i.e., the structure of connection similarities and differences across a set of networks. We propose a statistical framework to model these variations based on manifold-valued latent factors. Each network adjacency matrix is decomposed as a weighted sum of matrix patterns with rank one. Each pattern is described as a random perturbation of a dictionary element. As a hierarchical statistical model, it enables the analysis of heterogeneous populations of adjacency matrices using mixtures. Our framework can also be used to infer the weight of missing edges. We estimate the parameters of the model using an Expectation-Maximization-based algorithm. Experimenting on synthetic data, we show that the algorithm is able to accurately estimate the latent structure in both low and high dimensions. We apply our model on a large data set of functional brain connectivity matrices from the UK Biobank. Our results suggest that the proposed model accurately describes the complex variability in the data set with a small number of degrees of freedom.

Identification of Hub Genes in Duchenne Muscular Dystrophy: Evidence from Bioinformatic Analysis

The hub genes and signaling pathways associated with Duchenne muscular dystrophy (DMD) were predicted by bioinformatic methods to improve the therapeutic effect and quality of life of patients. Microarray data sets GSE465, GSE1004, and GSE1007 were downloaded from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were identified by GEO2R, and function enrichment analyses were performed by DAVID. The protein-protein interaction (PPI) network was constructed and the module analysis was performed using STRING and Cytoscape. A total of 195 DEGs were identified. The enriched functions and pathways of the DEGs include extracellular exosome, focal adhesion, extracellular matrix (ECM), focal adhesion, PI3K-Akt signaling pathway, calcium signaling pathway, and ECM-receptor interaction. Fifteen hub genes were identified. DEGs and hub genes identified in the present study help us understand the molecular mechanisms underlying the pathogenesis and progression of DMD, and provide candidate targets for treatment of DMD.

Minipuberty and Sexual Dimorphism in the Infant Human Thymus

AIRE expression in thymus is downregulated by estrogen after puberty, what probably renders women more susceptible to autoimmune disorders. Here we investigated the effects of minipuberty on male and female infant human thymic tissue in order to verify if this initial transient increase in sex hormones - along the first six months of life - could affect thymic transcriptional network regulation and AIRE expression. Gene co-expression network analysis for differentially expressed genes and miRNA-target analysis revealed sex differences in thymic tissue during minipuberty, but such differences were not detected in the thymic tissue of infants aged 7-18 months, i.e. the non-puberty group. AIRE expression was essentially the same in both sexes in minipuberty and in non-puberty groups, as assessed by genomic and immunohistochemical assays. However, AIRE-interactors networks showed several differences in all groups regarding gene-gene expression correlation. Therefore, minipuberty and genomic mechanisms interact in shaping thymic sexual dimorphism along the first six months of life.

Modular transcriptional repertoire and MicroRNA target analyses characterize genomic dysregulation in the thymus of Down syndrome infants

Trisomy 21-driven transcriptional alterations in human thymus were characterized through gene coexpression network (GCN) and miRNA-target analyses. We used whole thymic tissue--obtained at heart surgery from Down syndrome (DS) and karyotipically normal subjects (CT)--and a network-based approach for GCN analysis that allows the identification of modular transcriptional repertoires (communities) and the interactions between all the system's constituents through community detection. Changes in the degree of connections observed for hierarchically important hubs/genes in CT and DS networks corresponded to community changes. Distinct communities of highly interconnected genes were topologically identified in these networks. The role of miRNAs in modulating the expression of highly connected genes in CT and DS was revealed through miRNA-target analysis. Trisomy 21 gene dysregulation in thymus may be depicted as the breakdown and altered reorganization of transcriptional modules. Leading networks acting in normal or disease states were identified. CT networks would depict the "canonical" way of thymus functioning. Conversely, DS networks represent a "non-canonical" way, i.e., thymic tissue adaptation under trisomy 21 genomic dysregulation. This adaptation is probably driven by epigenetic mechanisms acting at chromatin level and through the miRNA control of transcriptional programs involving the networks' high-hierarchy genes.

Community structure analysis of transcriptional networks reveals distinct molecular pathways for early- and late-onset temporal lobe epilepsy with childhood febrile seizures

Age at epilepsy onset has a broad impact on brain plasticity and epilepsy pathomechanisms. Prolonged febrile seizures in early childhood (FS) constitute an initial precipitating insult (IPI) commonly associated with mesial temporal lobe epilepsy (MTLE). FS-MTLE patients may have early disease onset, i.e. just after the IPI, in early childhood, or late-onset, ranging from mid-adolescence to early adult life. The mechanisms governing early (E) or late (L) disease onset are largely unknown. In order to unveil the molecular pathways underlying E and L subtypes of FS-MTLE we investigated global gene expression in hippocampal CA3 explants of FS-MTLE patients submitted to hippocampectomy. Gene coexpression networks (GCNs) were obtained for the E and L patient groups. A network-based approach for GCN analysis was employed allowing: i) the visualization and analysis of differentially expressed (DE) and complete (CO) - all valid GO annotated transcripts - GCNs for the E and L groups; ii) the study of interactions between all the system's constituents based on community detection and coarse-grained community structure methods. We found that the E-DE communities with strongest connection weights harbor highly connected genes mainly related to neural excitability and febrile seizures, whereas in L-DE communities these genes are not only involved in network excitability but also playing roles in other epilepsy-related processes. Inversely, in E-CO the strongly connected communities are related to compensatory pathways (seizure inhibition, neuronal survival and responses to stress conditions) while in L-CO these communities harbor several genes related to pro-epileptic effects, seizure-related mechanisms and vulnerability to epilepsy. These results fit the concept, based on fMRI and behavioral studies, that early onset epilepsies, although impacting more severely the hippocampus, are associated to compensatory mechanisms, while in late MTLE development the brain is less able to generate adaptive mechanisms, what has implications for epilepsy management and drug discovery.

A Newtonian framework for community detection in undirected biological networks

Community detection is a key problem of interest in network analysis, with applications in a variety of domains such as biological networks, social network modeling, and communication pattern analysis. In this paper, we present a novel framework for community detection that is motivated by a physical system analogy. We model a network as a system of point masses, and drive the process of community detection, by leveraging the Newtonian interactions between the point masses. Our framework is designed to be generic and extensible relative to the model parameters that are most suited for the problem domain. We illustrate the applicability of our approach by applying the Newtonian Community Detection algorithm on protein-protein interaction networks of E. coli , C. elegans, and S. cerevisiae. We obtain results that are comparable in quality to those obtained from the Newman-Girvan algorithm, a widely employed divisive algorithm for community detection. We also present a detailed analysis of the structural properties of the communities produced by our proposed algorithm, together with a biological interpretation using E. coli protein network as a case study. A functional enrichment heat map is constructed with the Gene Ontology functional mapping, in addition to a pathway analysis for each community. The analysis illustrates that the proposed algorithm elicits communities that are not only meaningful from a topological standpoint, but also possess biological relevance. We believe that our algorithm has the potential to serve as a key computational tool for driving therapeutic applications involving targeted drug development for personalized care delivery.