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Bioinformatics Analyses Determined the Distinct CNS and Peripheral Surrogate Biomarker Candidates Between Two Mouse Models for Progressive Multiple Sclerosis.

ABSTRACT: Previously, we have established two distinct progressive multiple sclerosis (MS) models by induction of experimental autoimmune encephalomyelitis (EAE) with myelin oligodendrocyte glycoprotein (MOG) in two mouse strains. A.SW mice develop ataxia with antibody deposition, but no T cell infiltration, in the central nervous system (CNS), while SJL/J mice develop paralysis with CNS T cell infiltration. In this study, we determined biomarkers contributing to the homogeneity and heterogeneity of two models. Using the CNS and spleen microarray transcriptome and cytokine data, we conducted computational analyses. We identified up-regulation of immune-related genes, including immunoglobulins, in the CNS of both models. Pro-inflammatory cytokines, interferon (IFN)-γ and interleukin (IL)-17, were associated with the disease progression in SJL/J mice, while the expression of both cytokines was detected only at the EAE onset in A.SW mice. Principal component analysis (PCA) of CNS transcriptome data demonstrated that down-regulation of prolactin may reflect disease progression. Pattern matching analysis of spleen transcriptome with CNS PCA identified 333 splenic surrogate markers, including Stfa2l1, which reflected the changes in the CNS. Among them, we found that two genes (PER1/MIR6883 and FKBP5) and one gene (SLC16A1/MCT1) were also significantly up-regulated and down-regulated, respectively, in human MS peripheral blood, using data mining.

SUBMITTER: Omura S

PROVIDER: S-EPMC6434997 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Bioinformatics Analyses Determined the Distinct CNS and Peripheral Surrogate Biomarker Candidates between Two Mouse Models for Progressive Multiple Sclerosis

Project description:Previously, we have established two distinct progressive multiple sclerosis (MS) models by induction of experimental autoimmune encephalomyelitis (EAE) with myelin oligodendrocyte glycoprotein (MOG) in two mouse strains. A.SW mice develop ataxia with antibody deposition, but no T cell infiltration, in the central nervous system (CNS), while SJL/J mice develop paralysis with CNS T cell infiltration. In this study, we determined biomarkers contributing to the heterogeneities of the two models. Using the CNS and spleen microarray transcriptome and cytokine data, we conducted computational supervised analyses (volcano plot, heat map, and pathway analysis) and unsupervised principal component analysis (PCA) with pattern matching analysis (PMA).

2019-04-09 | GSE99300 | GEO

Bioinformatics Multivariate Analysis Determined a Set of Phase-Specific Biomarker Candidates in a Novel Mouse Model for Viral Myocarditis

Project description:Myocarditis is an inflammatory disease in the heart and is mainly caused by viral infections. Viral myocarditis has been proposed to be divided into three phases; the acute viral phase, the subacute immune phase, and the chronic cardiac remodeling phase. Although individualized therapy should be applied depending on the phase, no clinical or experimental studies have found biomarkers that distinguish between the three phases of myocarditis. Theiler’s murine encephalomyelitis virus (TMEV) belongs to the genus Cardiovirus, and can cause myocarditis in susceptible mouse strains. Using this novel model for viral myocarditis induced with TMEV, we conducted multivariate analysis including echocardiography, serum troponin and viral RNA titration, and microarray for identifying the biomarker candidates that discriminate the three phases. Using C3H mice infected with TMEV on 4, 7, and 60 days post infection (p.i.), we conducted bioinformatics analyses, including principal component analysis (PCA) of microarray data, since our traditional cardiac and serum assays, including two-way comparison of microarray data, did not lead to the identification of a single biomarker. PCA separated heart samples clearly between the groups of 4, 7, and 60 days p.i. Representative genes contributing to the separation were as follows: 4 and 7 days p.i., innate immunity-related genes, such as Irf7, and Cxcl9; 7 and 60 days p.i., acquired immunity-related genes, such as Cd3g and H2-Aa; and cardiac remodeling-related genes, such as Mmp12 and Gpnmb. Here, sets of molecules, but not a single molecule, identified by the unsupervised PCA, were found to be useful as the phase-specific biomarkers. Five mice were infected with TMEV intraperitoneally and hearvested their hearts on 4, 7, and 60 days post infection, respectively, for RNA isolation and hybridization on Affymetrix microarrays. As the control groups, age-matched uninfected mice were prepared.

2013-12-24 | E-GEOD-53607 | biostudies-arrayexpress

Bioinformatics Multivariate Analysis Determined a Set of Phase-Specific Biomarker Candidates in a Novel Mouse Model for Viral Myocarditis

2013-12-24 | GSE53607 | GEO

Bioinformatics multivariate analysis determined a set of phase-specific biomarker candidates in a novel mouse model for viral myocarditis.

Project description:Myocarditis is an inflammatory disease of the cardiac muscle and is mainly caused by viral infections. Viral myocarditis has been proposed to be divided into 3 phases: the acute viral phase, the subacute immune phase, and the chronic cardiac remodeling phase. Although individualized therapy should be applied depending on the phase, no clinical or experimental studies have found biomarkers that distinguish between the 3 phases. Theiler's murine encephalomyelitis virus belongs to the genus Cardiovirus and can cause myocarditis in susceptible mouse strains.Using this novel model for viral myocarditis induced with Theiler's murine encephalomyelitis virus, we conducted multivariate analysis including echocardiography, serum troponin and viral RNA titration, and microarray to identify the biomarker candidates that can discriminate the 3 phases. Using C3H mice infected with Theiler's murine encephalomyelitis virus on 4, 7, and 60 days post infection, we conducted bioinformatics analyses, including principal component analysis and k-means clustering of microarray data, because our traditional cardiac and serum assays, including 2-way comparison of microarray data, did not lead to the identification of a single biomarker. Principal component analysis separated heart samples clearly between the groups of 4, 7, and 60 days post infection. Representative genes contributing to the separation were as follows: 4 and 7 days post infection, innate immunity-related genes, such as Irf7 and Cxcl9; 7 and 60 days post infection, acquired immunity-related genes, such as Cd3g and H2-Aa; and cardiac remodeling-related genes, such as Mmp12 and Gpnmb.Sets of molecules, not single molecules, identified by unsupervised principal component analysis, were found to be useful as phase-specific biomarkers.

| S-EPMC4332820 | biostudies-literature

Peripheral Biomarker Candidates of Posttraumatic Stress Disorder.

Project description:There is high variability in the manifestation of physical and mental health problems following exposure to trauma and disaster. Although most people may show a range of acute symptoms in the aftermath of traumatic events, chronic and persistent mental disorders may not be developed in all individuals who were exposed to traumatic events. The most common long-term pathological consequence after trauma exposure is posttraumatic stress disorder (PTSD). However, comorbid conditions including depression, anxiety disorder, substance use-related problems, and a variety of other symptoms may frequently be observed in individuals with trauma exposure. Post-traumatic syndrome (PTS) is defined collectively as vast psychosocial problems that could be experienced in response to traumatic events. It is important to predict who will continue to suffer from physical and mental health problems and who will recover following trauma exposure. However, given the heterogeneity and variability in symptom manifestations, it is difficult to find identify biomarkers which predict the development of PTSD. In this review, we will summarize the results of recent studies with regard to putative biomarkers of PTSD and suggest future research directions for biomarker discovery for PTSD.

| S-EPMC4580745 | biostudies-literature

A bioinformatics filtering strategy for identifying radiation response biomarker candidates.

Project description:The number of biomarker candidates is often much larger than the number of clinical patient data points available, which motivates the use of a rational candidate variable filtering methodology. The goal of this paper is to apply such a bioinformatics filtering process to isolate a modest number (<10) of key interacting genes and their associated single nucleotide polymorphisms involved in radiation response, and to ultimately serve as a basis for using clinical datasets to identify new biomarkers. In step 1, we surveyed the literature on genetic and protein correlates to radiation response, in vivo or in vitro, across cellular, animal, and human studies. In step 2, we analyzed two publicly available microarray datasets and identified genes in which mRNA expression changed in response to radiation. Combining results from Step 1 and Step 2, we identified 20 genes that were common to all three sources. As a final step, a curated database of protein interactions was used to generate the most statistically reliable protein interaction network among any subset of the 20 genes resulting from Steps 1 and 2, resulting in identification of a small, tightly interacting network with 7 out of 20 input genes. We further ranked the genes in terms of likely importance, based on their location within the network using a graph-based scoring function. The resulting core interacting network provides an attractive set of genes likely to be important to radiation response.

| S-EPMC3387230 | biostudies-literature

Lymphoid Aggregates in the CNS of Progressive Multiple Sclerosis Patients Lack Regulatory T Cells.

Project description:In gray matter pathology of multiple sclerosis, neurodegeneration associates with a high degree of meningeal inflammatory activity. Importantly, ectopic lymphoid follicles (eLFs) were identified at the inflamed meninges of patients with progressive multiple sclerosis. Besides T lymphocytes, they comprise B cells and might elicit germinal center (GC)-like reactions. GC reactions are controlled by FOXP3+ T-follicular regulatory cells (TFR), but it is unknown if they participate in autoantibody production in eLFs. Receiving human post-mortem material, gathered from autopsies of progressive multiple sclerosis patients, indeed, distinct inflammatory infiltrates enriched with B cells could be detected in perivascular areas and deep sulci. CD35+ cells, parafollicular CD138+ plasma cells, and abundant expression of the homing receptor for GCs, CXCR5, on lymphocytes defined some of them as eLFs. However, they resembled GCs only in varying extent, as T cells did not express PD-1, only few cells were positive for the key transcriptional regulator BCL-6 and ongoing proliferation, whereas a substantial number of T cells expressed high NFATc1 like GC-follicular T cells. Then again, predominant cytoplasmic NFATc1 and an enrichment with CD3+CD27+ memory and CD4+CD69+ tissue-resident cells implied a chronic state, very much in line with PD-1 and BCL-6 downregulation. Intriguingly, FOXP3+ cells were almost absent in the whole brain sections and CD3+FOXP3+ TFRs were never found in the lymphoid aggregates. This also points to less controlled humoral immune responses in those lymphoid aggregates possibly enabling the occurrence of CNS-specific autoantibodies in multiple sclerosis patients.

| S-EPMC6974514 | biostudies-literature

Progressive multiple sclerosis

Project description:Progressive multiple sclerosis

| PRJNA966951 | ENA

A type 2 biomarker separates relapsing-remitting from secondary progressive multiple sclerosis.

Project description:ObjectiveWe tested whether it is possible to differentiate relapsing-remitting (RR) from secondary progressive (SP) disease stages in patients with multiple sclerosis (MS) using a combination of nuclear magnetic resonance (NMR) metabolomics and partial least squares discriminant analysis (PLS-DA) of biofluids, which makes no assumptions on the underlying mechanisms of disease.MethodsSerum samples were obtained from patients with primary progressive MS (PPMS), SPMS, and RRMS; patients with other neurodegenerative conditions; and age-matched controls. Samples were analyzed by NMR and PLS-DA models were derived to separate disease groups.ResultsThe PLS-DA models for serum samples from patients with MS enabled reliable differentiation between RRMS and SPMS. This approach also identified significant differences between the metabolite profiles of each of the MS groups (PP, SP, and RR) and the healthy controls, as well as predicting disease group membership with high specificity and sensitivity.ConclusionsNMR metabolomics analysis of serum is a sensitive and robust method for differentiating between different stages of MS, yielding diagnostic markers without a priori knowledge of disease pathogenesis. Critically, this study identified and validated a type II biomarker for the RR to SP transition in patients with MS. This approach may be of considerable benefit in categorizing patients for treatment and as an outcome measure in future clinical trials.Classification of evidenceThis study provides Class II evidence that serum metabolite profiles accurately distinguish patients with different subtypes and stages of MS.

| S-EPMC4222850 | biostudies-literature

Functional Roles and Targets of COVID-19 in Blood Cells Determined Using Bioinformatics Analyses.

Project description:BackgroundCoronavirus disease 2019 (COVID-19), caused by novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is a global epidemic with a high mortality rate. In this study, our goal was to identify the function and associated targets of SARS-CoV-2 from circulating monocytes in the blood and peripheral blood mononuclear cell (PBMC) dataset of patients with COVID-19.MethodsThe Gene Expression Omnibus database (GSE164805 and GSE180594) was used to identify differentially expressed genes (DEGs). Gene ontology function analysis and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses of the DEGs were performed using the DAVID database.ResultsGene ontology analysis of DEG revealed that GSE164805 and GSE180594 were involved in the regulation of cell migration, upregulation of cell proliferation, and in the activation of the mitogen-activated protein kinase signaling pathway. Kyoto Encyclopedia of Genes and Genomes analysis of GSE164805 revealed that the DEGs were enriched in peroxisome, melanogenesis, and actin regulation. Peroxisome genes were highly expressed in patients with mild and severe COVID-19. Bioinformatics analysis to compare GSE180594 and public data for the single-cell atlas of the peripheral immune response in patients with COVID-19 showed that interferon-associated genes were highly increased in acute COVID-19 PBMC and in CD14+ and CD16+ monocytes from patients with COVID-19.ConclusionsWe comprehensively analyzed the blood cell gene expression profile data of patients with COVID-19 using bioinformatics methods to preliminary understand the functions and associated targets of DEGs in the blood cells of these patients. Thus, our data provide targets for potential therapies against COVID-19.

| S-EPMC8874192 | biostudies-literature

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