Dataset Information

Meta-Analysis of Differential Connectivity in Gene Co-Expression Networks in Multiple Sclerosis.

ABSTRACT: Differential gene expression analyses to investigate multiple sclerosis (MS) molecular pathogenesis cannot detect genes harboring genetic and/or epigenetic modifications that change the gene functions without affecting their expression. Differential co-expression network approaches may capture changes in functional interactions resulting from these alterations. We re-analyzed 595 mRNA arrays from publicly available datasets by studying changes in gene co-expression networks in MS and in response to interferon (IFN)-? treatment. Interestingly, MS networks show a reduced connectivity relative to the healthy condition, and the treatment activates the transcription of genes and increases their connectivity in MS patients. Importantly, the analysis of changes in gene connectivity in MS patients provides new evidence of association for genes already implicated in MS by single-nucleotide polymorphism studies and that do not show differential expression. This is the case of amiloride-sensitive cation channel 1 neuronal (ACCN1) that shows a reduced number of interacting partners in MS networks, and it is known for its role in synaptic transmission and central nervous system (CNS) development. Furthermore, our study confirms a deregulation of the vitamin D system: among the transcription factors that potentially regulate the deregulated genes, we find TCF3 and SP1 that are both involved in vitamin D3-induced p27Kip1 expression. Unveiling differential network properties allows us to gain systems-level insights into disease mechanisms and may suggest putative targets for the treatment.

SUBMITTER: Creanza TM

PROVIDER: S-EPMC4926469 | biostudies-literature | 2016 Jun

REPOSITORIES: biostudies-literature

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Meta-Analysis of Differential Connectivity in Gene Co-Expression Networks in Multiple Sclerosis.

Creanza Teresa Maria TM Liguori Maria M Liuni Sabino S Nuzziello Nicoletta N Ancona Nicola N

International journal of molecular sciences 20160615 6

Differential gene expression analyses to investigate multiple sclerosis (MS) molecular pathogenesis cannot detect genes harboring genetic and/or epigenetic modifications that change the gene functions without affecting their expression. Differential co-expression network approaches may capture changes in functional interactions resulting from these alterations. We re-analyzed 595 mRNA arrays from publicly available datasets by studying changes in gene co-expression networks in MS and in response ...[more]

PMID: 27314336

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Project description:BackgroundMultiple Sclerosis (MS), a neurodegenerative and neuroinflammatory disease, causing lesions that disrupt the brain's anatomical and physiological connectivity networks, resulting in cognitive, visual and/or motor disabilities. Advanced imaging techniques like diffusion and functional MRI allow measurement of the brain's structural connectivity (SC) and functional connectivity (FC) networks, and can enable a better understanding of how their disruptions cause disability in people with MS (pwMS). However, advanced MRI techniques are used mainly for research purposes as they are expensive, time-consuming and require high-level expertise to acquire and process. As an alternative, the Network Modification (NeMo) Tool can be used to estimate SC and FC using lesion masks derived from pwMS and a reference set of controls' connectivity networks.ObjectiveHere, we test the hypothesis that estimated SC and FC (eSC and eFC) from the NeMo Tool, based only on an individual's lesion masks, can be used to classify pwMS into disability categories just as well as SC and FC extracted from advanced MRI directly in pwMS. We also aim to find the connections most important for differentiating between no disability vs evidence of disability groups.Materials and methodsOne hundred pwMS (age:45.5 ± 11.4 years, 66% female, disease duration: 12.97 ± 8.07 years) were included in this study. Expanded Disability Status Scale (EDSS) was used to assess disability, 67 pwMS had no disability (EDSS < 2). Observed SC and FC were extracted from diffusion and functional MRI directly in pwMS, respectively. The NeMo Tool was used to estimate the remaining structural connectome (eSC), by removing streamlines in a reference set of tractograms that intersected the lesion mask. The NeMo Tool's eSC was used then as input to a deep neural network to estimate the corresponding FC (eFC). Logistic regression with ridge regularization was used to classify pwMS into disability categories (no disability vs evidence of disability), based on demographics/clinical information (sex, age, race, disease duration, clinical phenotype, and spinal lesion burden) and either pairwise entries or regional summaries from one of the following matrices: SC, FC, eSC, and eFC. The area under the ROC curve (AUC) was used to assess the classification performance. Both univariate statistics and parameter coefficients from the classification models were used to identify features important to differentiating between the groups.ResultsThe regional eSC and eFC models outperformed their observed FC and SC counterparts (p-value < 0.05), while the pairwise eSC and SC performed similarly (p = 0.10). Regional eSC and eFC models had higher AUC (0.66-0.68) than the pairwise models (0.60-0.65), with regional eFC having highest classification accuracy across all models. Ridge regression coefficients for the regional eFC and regional observed FC models were significantly correlated (Pearson's r = 0.52, p-value < 10e-7). Decreased estimated SC node strength in default mode and ventral attention networks and increased eFC node strength in visual networks was associated with evidence of disability.DiscussionHere, for the first time, we use clinically acquired lesion masks to estimate both structural and functional connectomes in patient populations to better understand brain lesion-dysfunction mapping in pwMS. Models based on the NeMo Tool's estimates of SC and FC better classified pwMS by disability level than SC and FC observed directly in the individual using advanced MRI. This work provides a viable alternative to performing high-cost, advanced MRI in patient populations, bringing the connectome one step closer to the clinic.

Project description:BackgroundWe previously reported that expression of a miR-138 mimic or knockdown of SIN3A in primary cultures of cystic fibrosis (CF) airway epithelia increased ΔF508-CFTR mRNA and protein levels, and partially restored CFTR-dependent chloride transport. Global mRNA transcript profiling in ΔF508-CFBE cells treated with miR-138 mimic or SIN3A siRNA identified two genes, SYVN1 and NEDD8, whose inhibition significantly increased ΔF508-CFTR trafficking, maturation, and function. Little is known regarding the dynamic changes in the CFTR gene network during such rescue events. We hypothesized that analysis of condition-specific gene networks from transcriptomic data characterizing ΔF508-CFTR rescue could help identify dynamic gene modules associated with CFTR biogenesis.MethodsWe applied a computational method, termed M-module, to analyze multiple gene networks, each of which exhibited differential activity compared to a baseline condition. In doing so, we identified both unique and shared gene pathways across multiple differential networks. To construct differential networks, gene expression data from CFBE cells were divided into three groups: (1) siRNA inhibition of NEDD8 and SYVN1; (2) miR-138 mimic and SIN3A siRNA; and (3) temperature (27 °C for 24 h, 40 °C for 24 h, and 27 °C for 24 h followed by 40 °C for 24 h).ResultsInterrogation of individual networks (e.g., NEDD8/SYVN1 network), combinations of two networks (e.g., NEDD8/SYVN1 + temperature networks), and all three networks yielded sets of 1-modules, 2-modules, and 3-modules, respectively. Gene ontology analysis revealed significant enrichment of dynamic modules in pathways including translation, protein metabolic/catabolic processes, protein complex assembly, and endocytosis. Candidate CFTR effectors identified in the analysis included CHURC1, GZF1, and RPL15, and siRNA-mediated knockdown of these genes partially restored CFTR-dependent transepithelial chloride current to ΔF508-CFBE cells.ConclusionsThe ability of the M-module to identify dynamic modules involved in ΔF508 rescue provides a novel approach for studying CFTR biogenesis and identifying candidate suppressors of ΔF508.

Project description:Bipolar disorder (BPD) is a severe mental illness characterized by episodes of depression and mania. To investigate the molecular mechanisms underlying the pathophysiology of bipolar disorder, we performed transcriptome studies using RNA-seq data from the prefrontal cortex (PFC) of individuals with BPD and matched controls, as well as data from cell culture and animal model studies. We found 879 differentially expressed genes that were also replicated in an independent cohort of post-mortem samples. Genes involving the mechanistic target of rapamycine (mTOR) pathway were down-regulated, while genes interrelated with the mTOR pathway such as Janus kinase (JAK)-signal transducer and activator of transcription (STAT) pathway were up-regulated. Gene co-expression network analyses identified a module related to the mTOR pathway that was up-regulated in BPD and also enriched for markers of endothelial cells. We also found a down-regulated co-expression module enriched for genes involved in mTOR signalling and in mTOR related pathways and enriched with neuronal markers. The mTOR related modules were also replicated in the independent cohort of samples. To investigate whether the expression of the modules related to mTOR signalling pathway could be differentially regulated in different cell types we performed comparative network analyses in experimental models. We found both up-regulated modules in the PFC significantly overlapped with an up-regulated module in the brain endothelial cells from mice treated with lipopolysaccharides (LPS) and mTOR related pathways such as JAK-STAT, PI3K-Akt and ribosome were enriched in the common genes. In addition, the down-regulated module in the PFC significantly overlapped with a down-regulated module from neurons treated with the mTOR inhibitor, Torin1 and mTOR signalling, autophagy, and synaptic vesicle cycles were significantly enriched in the common genes. These results suggest that co-expression networks related to mTOR signalling pathways may be up- or down-regulated in different cell types in the PFC of BPD. These results provide novel insights into the molecular mechanisms underlying the pathophysiology of BPD.

Dataset Information

Meta-Analysis of Differential Connectivity in Gene Co-Expression Networks in Multiple Sclerosis.

Publications

Meta-Analysis of Differential Connectivity in Gene Co-Expression Networks in Multiple Sclerosis.

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