We performed microarray analysis of peripheral blood mononuclear cells (PBMCs) from multiple sclerosis (MS) patients and detected a profile of immune cell activation, autoantigen upregulation, and enhanced E2F pathway transcription. Accordingly, E2f1-deficient mice manifested only mild disability upon induction of experimental autoimmune encephalomyelitis (EAE). Furthermore, PBMCs from Avonex-treated patients had lower expression of E2F targets. The profile was enriched in genes known to harbor MS-associated polymorphisms, or localized to MS susceptibility chromosomal regions. Our study shows that PBMC microarrays reflect MS pathobiology that can be validated in the EAE model.
"The gene ARF6, as showed by
, could be implicated in the disruptive effects of IL-1b, a gene recently associated with MS
. In addition, other group sensitive genes could be associated with MS
[54,55]. Considering the edges perturbed, the links between the gene 382 or ARF6 and the gene 1399 or CRKL resulted very interesting. "
[Show abstract][Hide abstract] ABSTRACT: Background
It is currently accepted that the perturbation of complex intracellular networks, rather than the dysregulation of a single gene, is the basis for phenotypical diversity. High-throughput gene expression data allow to investigate changes in gene expression profiles among different conditions. Recently, many efforts have been made to individuate which biological pathways are perturbed, given a list of differentially expressed genes (DEGs). In order to understand these mechanisms, it is necessary to unveil the variation of genes in relation to each other, considering the different phenotypes. In this paper, we illustrate a pipeline, based on Structural Equation Modeling (SEM) that allowed to investigate pathway modules, considering not only deregulated genes but also the connections between the perturbed ones.
The procedure was tested on microarray experiments relative to two neurological diseases: frontotemporal lobar degeneration with ubiquitinated inclusions (FTLD-U) and multiple sclerosis (MS). Starting from DEGs and dysregulated biological pathways, a model for each pathway was generated using databases information biological databases, in order to design how DEGs were connected in a causal structure. Successively, SEM analysis proved if pathways differ globally, between groups, and for specific path relationships. The results confirmed the importance of certain genes in the analyzed diseases, and unveiled which connections are modified among them.
We propose a framework to perform differential gene expression analysis on microarray data based on SEM, which is able to: 1) find relevant genes and perturbed biological pathways, investigating putative sub-pathway models based on the concept of disease module; 2) test and improve the generated models; 3) detect a differential expression level of one gene, and differential connection between two genes. This could shed light, not only on the mechanisms affecting variations in gene expression, but also on the causes of gene-gene relationship modifications in diseased phenotypes.
"However, it is more manageable and easier to obtain, ensuring that larger sample sizes can be collected and analyzed. The latter approach has been successfully applied to the study of multiple neurological, neurogenetic and psychiatric disorders, such as multiple sclerosis (Iglesias et al., 2004), Tourette syndrome (Du et al., 2006), neurofibromatosis type I, Down syndrome, tuberous sclerosis complex II (Tang et al., 2004), and bipolar disorder (Kakiuchi et al., 2003). "
[Show abstract][Hide abstract] ABSTRACT: Autism has long been thought to stem from abnormal neurodevelopment. Surprisingly, microarray-based genome-wide expression
studies, involving either postmortem brain tissue or lymphoblastoid cell lines, provide converging evidence supporting prominent
roles for the immune system in the pathogenesis of autism-spectrum disorders (ASDs). In particular, bioinformatic analyses,
employing biological databases and gene network prediction software, point toward the involvement of multiple genes interconnected
in immune-related pathways. Taken together, these findings suggest that a dysreactive immune process could derange neurodevelopment
during critical periods in a large subset of children with autism. These conclusions are also supported by neuropathological
and immunological studies, which are briefly summarized. Genome-wide expression studies can thus lead to a better understanding
of autism pathogenesis and facilitate the identification of subgroups of patients with a similar underlying pathophysiology
(“endophenotypes”), eventually leading to more effective therapeutic strategies. The characterization of peripheral gene-expression
patterns and immunological abnormalities can also contribute to design laboratory-based diagnostic tools for the early detection
KeywordsAutism-Autistic disorder-Immune system-Gene expression-Microarrays-Neurodevelopment-Neuroinflammation-Pervasive developmental disorders
"Proof-of-principle blood genomic studies of neurological diseases have been performed in animals . Subsequent studies have demonstrated characteristic blood genomic patterns for acute ischemic stroke   , migraine headache  , Tourette syndrome  , renal cell carcinoma , multiple sclerosis , benzene exposure , trauma , and neurogenetic disorders including neurofibromatosis type I, tuberous sclerosis type II, and Down syndrome  . This approach has been applied to bipolar disorder, for which XBP1 was identified as a genetic risk factor using gene expression profiling of lymphoblastoid cell lines from two sets of discordant twins . "
[Show abstract][Hide abstract] ABSTRACT: The objective of this study was to identify gene expression differences in blood differences in children with autism (AU) and autism spectrum disorder (ASD) compared to general population controls. Transcriptional profiles were compared with age- and gender-matched, typically developing children from the general population (GP). The AU group was subdivided based on a history of developmental regression (A-R) or a history of early onset (A-E without regression). Total RNA from blood was processed on human Affymetrix microarrays. Thirty-five children with AU (17 with early onset autism and 18 with autism with regression) and 14 ASD children (who did not meet criteria for AU) were compared to 12 GP children. Unpaired t tests (corrected for multiple comparisons with a false discovery rate of 0.05) detected a number of genes that were regulated more than 1.5-fold for AU versus GP (n=55 genes), for A-E versus GP (n=140 genes), for A-R versus GP (n=20 genes), and for A-R versus A-E (n=494 genes). No genes were significantly regulated for ASD versus GP. There were 11 genes shared between the comparisons of all autism subgroups to GP (AU, A-E, and A-R versus GP) and these genes were all expressed in natural killer cells and many belonged to the KEGG natural killer cytotoxicity pathway (p=0.02). A subset of these genes (n=7) was tested with qRT-PCR and all genes were found to be differentially expressed (p<0.05). We conclude that the gene expression data support emerging evidence for abnormalities in peripheral blood leukocytes in autism that could represent a genetic and/or environmental predisposition to the disorder.
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