A transcription factor map as revealed by a genome-wide gene expression analysis of whole-blood mRNA transcriptome in multiple sclerosis

Transcriptional network analysis of peripheral blood leukocyte subsets in multiple sclerosis identifies a pathogenic role for a cytotoxicity-associated gene network in myeloid cells

Multiple sclerosis (MS) is an autoimmune disease of the central nervous system affecting predominantly adults. It is a complex disease associated with both environmental and genetic risk factors. Although over 230 risk single-nucleotide polymorphisms have been associated with MS, all are common human variants. The mechanisms by which they increase the risk of MS, however, remain elusive. We hypothesized that a complex genetic phenotype such as MS could be driven by coordinated expression of genes controlled by transcriptional regulatory networks. We, therefore, constructed a gene coexpression network from microarray expression analyses of five purified peripheral blood leukocyte subsets of 76 patients with relapsing remitting MS and 104 healthy controls. These analyses identified a major network (or module) of expressed genes associated with MS that play key roles in cell-mediated cytotoxicity which was downregulated in monocytes of patients with MS. Manipulation of the module gene expression was achieved in vitro through small interfering RNA gene knockdown of identified drivers. In a mouse model, network gene knockdown modulated the autoimmune inflammatory MS model disease-experimental autoimmune encephalomyelitis. This research implicates a cytotoxicity-associated gene network in myeloid cells in the pathogenesis of MS.

Transcription factor EGR2 controls homing and pathogenicity of TH17 cells in the central nervous system

CD4+ T helper 17 (TH17) cells protect barrier tissues but also trigger autoimmunity. The mechanisms behind these opposing processes remain unclear. Here, we found that the transcription factor EGR2 controlled the transcriptional program of pathogenic TH17 cells in the central nervous system (CNS) but not that of protective TH17 cells at barrier sites. EGR2 was significantly elevated in myelin-reactive CD4+ T cells from patients with multiple sclerosis and mice with autoimmune neuroinflammation. The EGR2 transcriptional program was intricately woven within the TH17 cell transcriptional regulatory network and showed high interconnectivity with core TH17 cell-specific transcription factors. Mechanistically, EGR2 enhanced TH17 cell differentiation and myeloid cell recruitment to the CNS by upregulating pathogenesis-associated genes and myelomonocytic chemokines. T cell-specific deletion of Egr2 attenuated neuroinflammation without compromising the host's ability to control infections. Our study shows that EGR2 regulates tissue-specific and disease-specific functions in pathogenic TH17 cells in the CNS.

Identification of ferroptosis-related gene signatures associated with multiple sclerosis using weighted gene co-expression network analysis

Multiple sclerosis (MS) is a chronic inflammatory disease of central nervous system leading to demyelination followed by neurological symptoms. Ferroptosis is a newly discovered pathogenic hallmark important for the progression of MS. However, the gene markers of ferroptosis in MS are still uncertain. In this study, mRNA expression profiles and clinical data of MS samples were retrieved from Gene Expression Omnibus database. Weighted gene co-expression network analysis and receiver operating characteristic curve analysis were utilized to identify ferroptosis-related gene (FRG) signatures of MS. Gene set enrichment analysis and gene set variation analysis were performed to explore the biological functions of single FRG signature. HMOX1, LPCAT3 and RPL8 were firstly identified as FRG signatures of MS with the predictive capacity confirmed. Gene set enrichment analysis and gene set variation analyses revealed that metabolism-related, immune and inflammation-related, microglia-related, oxidation-related, and mitochondria-related biological functions were enriched, providing implications of the mechanisms underlying ferroptosis in MS. This study presented a systematic analysis of FRG in MS and explored the potential ferroptosis targets for new interventional strategies in MS.

Ying Yang 1 engagement in brain pathology

Herein, we discuss data concerning the involvement of transcription factor Yin Yang 1 (YY1) in the development of brain diseases, highlighting mechanisms of its pathological actions. YY1 plays an important role in the developmental and adult pathology of the nervous system. YY1 is essential for neurulation as well as maintenance and differentiation of neuronal progenitor cells and oligodendrocytes regulating both neural and glial tissues of the brain. Lack of a YY1 gene causes many developmental abnormalities and anatomical malformations of the central nervous system (CNS). Once dysregulated, YY1 exerts multiple neuropathological actions being involved in the induction of many brain disorders like stroke, epilepsy, Alzheimer's and Parkinson's diseases, autism spectrum disorder, dystonia, and brain tumors. Better understanding of YY1's dysfunction in the nervous system may lead to the development of novel therapeutic strategies related to YY1's actions.

Understanding Abnormal c-JNK/p38MAPK Signaling Overactivation Involved in the Progression of Multiple Sclerosis: Possible Therapeutic Targets and Impact on Neurodegenerative Diseases

Demyelination, immune dysregulation, and neuroinflammation are the most common triggers of motor neuron disorders such as multiple sclerosis (MS). MS is a chronic demyelinating neurodegenerative disease of the central nervous system caused by abnormal immune activation, which causes myelin sheath damage. Cell signal transduction pathways are required for a variety of physiological and pathological processes in the brain. When these signaling systems become overactive, they can lead to disease progression. In various physiological conditions, abnormal mitogen-activated protein kinase (MAPK) activation is associated with several physiological dysfunctions that cause neurodegeneration. Previous research indicates that c-JNK and p38MAPK signaling play critical roles in neuronal growth and differentiation. c-JNK/p38MAPK is a member of the MAPK family, which regulates metabolic pathways, cell proliferation, differentiation, and apoptosis that control certain neurological activities. During brain injuries, c-JNK/p38MAPK also affects neuronal elastic properties, nerve growth, and cognitive processing. This review systematically linked abnormal c-JNK/p38MAPK signaling activation to multiple neuropathological pathways in MS and related neurological dysfunctions. MS progression is linked to genetic defects, oligodendrocyte destruction, glial overactivation, and immune dysregulation. We concluded that inhibiting both the c-JNK/p38MAPK signaling pathways can promote neuroprotection and neurotrophic effects against the clinical-pathological presentation of MS and influence other neurological disorders. As a result, the potential benefits of c-JNK/p38MAPK downregulation for the development of disease-modifying treatment interventions in the future could include MS prevention and related neurocomplications.

Constructing and validating a diagnostic nomogram for multiple sclerosis via bioinformatic analysis

The purpose of this study was to identify biomarkers and construct a diagnostic prediction model for multiple sclerosis (MS). Microarray datasets in the Gene Expression Omnibus (GEO) were downloaded. Weighted gene coexpression analysis (WGCNA) was used to search for hub modules and biomarkers related to MS. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were used to roughly define their biological functions and pathways. Least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic regression analysis were used to identify the diagnostic biomarkers and construct a nomogram. The calibration curve and receiver operating characteristic (ROC) curve were used to judge the diagnostic predictive ability. In addition, cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) algorithm was used to calculate the proportion of 22 kinds of immune cells. GSE41850 was used as the training set, and GSE17048 was used as the test set. WGCNA revealed one hub module containing 165 hub genes. Most of their biological functions and pathways are related to cell metabolism and immune cell activation. The diagnostic nomogram contained ARPC5, ROD1, UBQLN2, ZNF281, ABCA1 and FAS. The ROC curve and the calibration curve of the training set and test set confirmed that the nomogram had great prediction ability. In addition, monocytes and M0 macrophages were significantly different between MS patients and healthy people. The expression of ARPC5, ZNF281 and ABCA1 is correlated with M0 macrophages. The nomogram provides new insights and contributes to the accurate diagnosis of MS.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s13205-021-02675-1.

Single-cell transcriptomics analysis of mild cognitive impairment in World Trade Center disaster responders

INTRODUCTION

Recent research has found that World Trade Center (WTC) responders in their mid-50s have an elevated prevalence of mild cognitive impairment (MCI) that is associated with neural degeneration and subcortical thinning. This article extends our understanding of the molecular complexity of MCI through gene expression profiling of blood.

METHODS

The transcriptomics of 40 male WTC responders were profiled across two cohorts (discovery: nine MCI and nine controls; replication: 11 MCI and 11 controls) using CITE-Seq at single-cell resolution in blood.

RESULTS

Comparing the transcriptomic signatures across seven major cell subpopulations, the largest differences were observed in monocytes in which 226 genes were differentially expressed. Pathway analysis on the genes unique to monocytes identified processes associated with cerebral immune response.

DISCUSSION

Our findings suggested monocytes may constitute a key cell type to target in blood-based biomarker studies for early detection of risk of MCI and development of new interventions.

Dense module searching for gene networks associated with multiple sclerosis

BACKGROUND

Multiple sclerosis (MS) is a complex disease in which the immune system attacks the central nervous system. The molecular mechanisms contributing to the etiology of MS remain poorly understood. Genome-wide association studies (GWAS) of MS have identified a small number of genetic loci significant at the genome level, but they are mainly non-coding variants. Network-assisted analysis may help better interpret the functional roles of the variants with association signals and potential translational medicine application. The Dense Module Searching of GWAS tool (dmGWAS version 2.4) developed in our team is applied to 2 MS GWAS datasets (GeneMSA and IMSGC GWAS) using the human protein interactome as the reference network. A dual evaluation strategy is used to generate results with reproducibility.

RESULTS

Approximately 7500 significant network modules were identified for each independent GWAS dataset, and 20 significant modules were identified from the dual evaluation. The top modules included GRB2, HDAC1, JAK2, MAPK1, and STAT3 as central genes. Top module genes were enriched with functional terms such as "regulation of glial cell differentiation" (adjusted p-value = 2.58 × 10- 3), "T-cell costimulation" (adjusted p-value = 2.11 × 10- 6) and "virus receptor activity" (adjusted p-value = 1.67 × 10- 3). Interestingly, top gene networks included several MS FDA approved drug target genes HDAC1, IL2RA, KEAP1, and RELA, CONCLUSIONS: Our dmGWAS network analyses highlighted several genes (GRB2, HDAC1, IL2RA, JAK2, KEAP1, MAPK1, RELA and STAT3) in top modules that are promising to interpret GWAS signals and link to MS drug targets. The genes enriched with glial cell differentiation are important for understanding neurodegenerative processes in MS and for remyelination therapy investigation. Importantly, our identified genetic signals enriched in T cell costimulation and viral receptor activity supported the viral infection onset hypothesis for MS.

CSF microRNAs discriminate MS activity and share similarity to other neuroinflammatory disorders

Objective

To perform a comprehensive multicompartment analysis of microRNA (miRNA) expression in multiple sclerosis (MS) linked to disease activity and compared with other neuroinflammatory diseases through a retrospective cross-sectional study.

Methods

One hundred twenty-seven miRNAs were measured by PCR arrays on pooled CSF, serum, and peripheral blood mononuclear cell (PBMC) samples of 10 patients with relapsing MS and 10 controls. Sixty-four miRNAs were then measured by quantitative PCR on individual CSF samples of patients with relapsing or remitting MS and controls (n = 68). Fifty-seven miRNAs were analyzed in the CSF from a second cohort (n = 75), including patients with MS, neuroinfectious, or neuroinflammatory diseases and controls. MiRNAs significantly dysregulated in the CSF were analyzed on individual serum/PBMC samples (n = 59/48) of patients with relapsing or remitting MS and controls. Post hoc analysis consisted of principal component analysis (PCA), gene set, and pathway enrichment analysis.

Results

Twenty-one miRNAs were differentially expressed, mainly upregulated in the CSF during MS relapses. Relapsing MS and neuroinfectious/inflammatory diseases exhibited a partially overlapping CSF miRNA expression profile. Besides confirming the association of miR-146a-5p/150-5p/155-5p with MS, 7 miRNAs uncharacterized for MS emerged (miR-15a-3p/124-5p/149-3p/29c-3p/33a-3p/34c-5p/297). PCA showed that distinct miRNA sets segregated MS from controls and relapse from remission. In silico analysis predicted the involvement of these miRNAs in cell cycle, immunoregulation, and neurogenesis, but also revealed that the signaling pathway pattern of remitting MS is more akin to controls rather than patients with relapsing MS.

Conclusions

This study highlights the CSF-predominant dysregulation of miRNAs in MS by identifying a signature of disease activity and intrathecal inflammation among neuroinflammatory disorders.

Cumulative influence of parity-related genomic changes in multiple sclerosis

Pregnancy reduces the frequency of relapses in Multiple Sclerosis (MS) and parity also has a beneficial long term effect on disease outcome. We aimed to uncover the biological mechanisms underlying the beneficial long-term effects of parity in MS. Genome-wide gene expression revealed 574 genes associated with parity; 38.3% showed significant DNA methylation changes (enrichment p = 0.029). These genes overlapped with previous MS genes in humans and a rat MS model and were overrepresented within axon guidance (P = 1.6e-05), developmental biology (P = 0.0094) and cell-cell communication (P = 0.019) pathways. This gene regulation could provide a basis for a protective effect of parity on the long-term outcome of MS.

Identification of key target genes and biological pathways in multiple sclerosis brains using microarray data obtained from the Gene Expression Omnibus database

OBJECTIVE

The purpose of this study was to investigate critical genes in multiple sclerosis (MS) using microarray data from brain tissue in MS.

MATERIALS

The expression profile data set of MS (GSE38010) downloaded from the Gene Expression Omnibus database contained gene information from five plaque tissues from MS brains and two white matter tissues from healthy controls. An R package was applied to process these raw chip data. Gene Ontology (GO) functional analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis and protein-protein interaction (PPI) network analysis were performed to investigate interactions between differentially expressed genes (DEGs) in MS brain tissues.

RESULTS

This study identified a total of 1065 DEGs, including 530 up-regulated genes and 535 down-regulated genes, in MS brain tissue samples compared to those in normal white matter tissue samples. GO and KEGG pathway enrichment analyses showed that the up-regulated DEGs were mainly related to neuron development, neuron projection morphogenesis and neuron differentiation. Furthermore, the down-regulated DEGs were largely related to axon ensheathment, ensheathment of neurons and nervous system development. Seven key genes were found as hub genes in the maintenance of the PPI network.

CONCLUSION

Several key target genes and their GO and KEGG pathway enrichment identified in the present study may serve as feasible targets for MS therapies.

Computer-Aided Breast Cancer Diagnosis with Optimal Feature Sets: Reduction Rules and Optimization Techniques

This chapter introduces a new method for knowledge extraction from databases for the purpose of finding a discriminative set of features that is also a robust set for within-class classification. Our method is generic and we introduce it here in the field of breast cancer diagnosis from digital mammography data. The mathematical formalism is based on a generalization of the k-Feature Set problem called (α, β)-k-Feature Set problem, introduced by Cotta and Moscato (J Comput Syst Sci 67(4):686-690, 2003). This method proceeds in two steps: first, an optimal (α, β)-k-feature set of minimum cardinality is identified and then, a set of classification rules using these features is obtained. We obtain the (α, β)-k-feature set in two phases; first a series of extremely powerful reduction techniques, which do not lose the optimal solution, are employed; and second, a metaheuristic search to identify the remaining features to be considered or disregarded. Two algorithms were tested with a public domain digital mammography dataset composed of 71 malignant and 75 benign cases. Based on the results provided by the algorithms, we obtain classification rules that employ only a subset of these features.

Analysis of microRNA and Gene Expression Profiles in Multiple Sclerosis: Integrating Interaction Data to Uncover Regulatory Mechanisms

MicroRNAs (miRNAs) have been reported to contribute to the pathophysiology of multiple sclerosis (MS), an inflammatory disorder of the central nervous system. Here, we propose a new consensus-based strategy to analyse and integrate miRNA and gene expression data in MS as well as other publically available data to gain a deeper understanding of the role of miRNAs in MS and to overcome the challenges posed by studies with limited patient sample sizes. We processed and analysed microarray datasets, and compared the expression of genes and miRNAs in the blood of MS patients and controls. We then used our consensus and integration approach to construct two molecular networks dysregulated in MS: a miRNA- and a gene-based network. We identified 18 differentially expressed (DE) miRNAs and 128 DE genes that may contribute to the regulatory alterations behind MS. The miRNAs were linked to immunological and neurological pathways, and we exposed let-7b-5p and miR-345-5p as promising blood-derived disease biomarkers in MS. The results suggest that DE miRNAs are more informative than DE genes in uncovering pathways potentially involved in MS. Our findings provide novel insights into the regulatory mechanisms and networks underlying MS.

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

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)-β 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.

The multiple sclerosis drug fingolimod (FTY720) stimulates neuronal gene expression, axonal growth and regeneration

Fingolimod (FTY720) is a new generation oral treatment for multiple sclerosis (MS). So far, FTY720 was mainly considered to target trafficking of immune cells but not brain cells such as neurons. Herein, we analyzed FTY720's potential to directly alter neuronal function. In CNS neurons, we identified a FTY720 governed gene expression response. FTY720 upregulated immediate early genes (IEGs) encoding for neuronal activity associated transcription factors such as c-Fos, FosB, Egr1 and Egr2 and induced actin cytoskeleton associated genes (actin isoforms, tropomyosin, calponin). Stimulation of primary neurons with FTY720 enhanced neurite growth and altered growth cone morphology. In accordance, FTY720 enhanced axon regeneration in mice upon facial nerve axotomy. We identified components of a FTY720 engaged signaling cascade including S1P receptors, G12/13G-proteins, RhoA-GTPases and the transcription factors SRF/MRTF. In summary, we uncovered a broader cellular and therapeutic operation mode of FTY720, suggesting beneficial FTY720 effects also on CNS neurons during MS therapy and for treatment of other neurodegenerative diseases requiring neuroprotective and neurorestorative processes.

Meta-Analysis of Multiple Sclerosis Microarray Data Reveals Dysregulation in RNA Splicing Regulatory Genes

Abnormalities in RNA metabolism and alternative splicing (AS) are emerging as important players in complex disease phenotypes. In particular, accumulating evidence suggests the existence of pathogenic links between multiple sclerosis (MS) and altered AS, including functional studies showing that an imbalance in alternatively-spliced isoforms may contribute to disease etiology. Here, we tested whether the altered expression of AS-related genes represents a MS-specific signature. A comprehensive comparative analysis of gene expression profiles of publicly-available microarray datasets (190 MS cases, 182 controls), followed by gene-ontology enrichment analysis, highlighted a significant enrichment for differentially-expressed genes involved in RNA metabolism/AS. In detail, a total of 17 genes were found to be differentially expressed in MS in multiple datasets, with CELF1 being dysregulated in five out of seven studies. We confirmed CELF1 downregulation in MS (p = 0.0015) by real-time RT-PCRs on RNA extracted from blood cells of 30 cases and 30 controls. As a proof of concept, we experimentally verified the unbalance in alternatively-spliced isoforms in MS of the NFAT5 gene, a putative CELF1 target. In conclusion, for the first time we provide evidence of a consistent dysregulation of splicing-related genes in MS and we discuss its possible implications in modulating specific AS events in MS susceptibility genes.

A New Combinatorial Optimization Approach for Integrated Feature Selection Using Different Datasets: A Prostate Cancer Transcriptomic Study

Background

The joint study of multiple datasets has become a common technique for increasing statistical power in detecting biomarkers obtained from smaller studies. The approach generally followed is based on the fact that as the total number of samples increases, we expect to have greater power to detect associations of interest. This methodology has been applied to genome-wide association and transcriptomic studies due to the availability of datasets in the public domain. While this approach is well established in biostatistics, the introduction of new combinatorial optimization models to address this issue has not been explored in depth. In this study, we introduce a new model for the integration of multiple datasets and we show its application in transcriptomics.

Methods

We propose a new combinatorial optimization problem that addresses the core issue of biomarker detection in integrated datasets. Optimal solutions for this model deliver a feature selection from a panel of prospective biomarkers. The model we propose is a generalised version of the (α,β)-k-Feature Set problem. We illustrate the performance of this new methodology via a challenging meta-analysis task involving six prostate cancer microarray datasets. The results are then compared to the popular RankProd meta-analysis tool and to what can be obtained by analysing the individual datasets by statistical and combinatorial methods alone.

Results

Application of the integrated method resulted in a more informative signature than the rank-based meta-analysis or individual dataset results, and overcomes problems arising from real world datasets. The set of genes identified is highly significant in the context of prostate cancer. The method used does not rely on homogenisation or transformation of values to a common scale, and at the same time is able to capture markers associated with subgroups of the disease.

Changes in winter depression phenotype correlate with white blood cell gene expression profiles: a combined metagene and gene ontology approach

In the present study we evaluate the feasibility of gene expression in white blood cells as a peripheral marker for winter depression. Sixteen patients with winter type seasonal affective disorder were included in the study. Blood was taken by venous puncture at three time points; in winter prior and following bright light therapy and in summer. RNA was isolated, converted into cRNA, amplified and hybridized on Illumina® gene expression arrays. The raw optical array data were quantile normalized and thereafter analyzed using a metagene approach, based on previously published Affymetrix gene array data. The raw data were also subjected to a secondary analysis focusing on circadian genes and genes involved in serotonergic neurotransmission. Differences between the conditions were analyzed, using analysis of variance on the principal components of the metagene score matrix. After correction for multiple testing no statistically significant differences were found. Another approach uses the correlation between metagene factor weights and the actual expression values, averaged over conditions. When comparing the correlations of winter vs. summer and bright light therapy vs. summer significant changes for several metagenes were found. Subsequent gene ontology analyses (DAVID and GeneTrail) of 5 major metagenes suggest an interaction between brain and white blood cells. The hypothesis driven analysis with a smaller group of genes failed to demonstrate any significant effects. The results from the combined metagene and gene ontology analyses support the idea of communication between brain and white blood cells. Future studies will need a much larger sample size to obtain information at the level of single genes.

Autologous haematopoietic stem cell transplantation reduces abnormalities in the expression of immune genes in multiple sclerosis

Autologous haematopoietic stem-cell transplantation (AHSCT) has been experimented as a treatment in patients affected by severe forms of multiple sclerosis (MS) who failed to respond to standard immunotherapy. The rationale of AHSCT is to 'reboot' the immune system and reconstitute a new adaptive immunity. The aim of our study was to identify, through a robust and unbiased transcriptomic analysis, any changes of gene expression in T-cells potentially underlying the treatment effect in patients who underwent non-myeloablative AHSCT for treatment of MS. We evaluated by microarray DNA-chip technology the gene expression of peripheral CD4+ and CD8+ T-cell subsets sorted from patients with MS patients before AHSCT, at 6 months, 1 year and 2 years after AHSCT and from healthy control subjects. Hierarchical clustering analysis revealed that reconstituted CD8+ T-cells of MS patients at 2 years post-transplantation, aggregated together with healthy controls, suggesting a normalization of gene expression in CD8+ cells post-therapy. When we compared the gene expression in MS patients before and after therapy, we detected a large number of differentially expressed genes (DEG) in both CD8+ and CD4+ T-cell subsets at all time points after transplantation. We catalogued the biological function of DEG and we selected 27 genes known to be involved in immune function for accurate quantification of gene expression by real-time PCR. The analysis confirmed and extended with quantitative data, a number of significant changes in both the CD4+ and CD8+ T-cells subsets from MS post-transplant. Notably, CD8+ T-cells revealed more extensive changes in the expression of genes involved in effector immune responses.

(GTG)5 MSP-PCR fingerprinting as a technique for discrimination of wine associated yeasts?

In microbiology, identification of all isolates by sequencing is still unfeasible in small research laboratories. Therefore, many yeast diversity studies follow a screening procedure consisting of clustering the yeast isolates using MSP-PCR fingerprinting, followed by identification of one or a few selected representatives of each cluster by sequencing. Although this procedure has been widely applied in the literature, it has not been properly validated. We evaluated a standardized protocol using MSP-PCR fingerprinting with the primers (GTG)₅ and M13 for the discrimination of wine associated yeasts in South Brazil. Two datasets were used: yeasts isolated from bottled wines and vineyard environments. We compared the discriminatory power of both primers in a subset of 16 strains, choosing the primer (GTG)₅ for further evaluation. Afterwards, we applied this technique to 245 strains, and compared the results with the identification obtained by partial sequencing of the LSU rRNA gene, considered as the gold standard. An array matrix was constructed for each dataset and used as input for clustering with two methods (hierarchical dendrograms and QAPGrid layout). For both yeast datasets, unrelated species were clustered in the same group. The sensitivity score of (GTG)₅ MSP-PCR fingerprinting was high, but specificity was low. As a conclusion, the yeast diversity inferred in several previous studies may have been underestimated and some isolates were probably misidentified due to the compliance to this screening procedure.

Sox5 and c-Maf cooperatively induce Th17 cell differentiation via RORγt induction as downstream targets of Stat3

A novel isoform of Sox5, Sox5t, and c-Maf activate RORγt to induce Th17 cells. Sox5^−/− mice exhibit impaired Th17 differentiation and are thus resistant to EAE and delayed-type hypersensitivity.

Stat3 signaling is essential for the induction of RORγt and subsequent Th17 cell differentiation. However, the downstream targets of Stat3 for RORγt expression remain largely unknown. We show here that a novel isoform of Sox5, named Sox5t, is induced in Th17 cells in a Stat3-dependent manner. In vivo, T cell–specific Sox5-deficient mice exhibit impaired Th17 cell differentiation and are resistant to experimental autoimmune encephalomyelitis and delayed-type hypersensitivity. Retrovirus-mediated induction of Sox5 together with c-Maf induces Th17 cell differentiation even in Stat3-deficient CD4⁺ T cells but not in RORγt-deficient CD4⁺ T cells, indicating that Sox5 and c-Maf induce Th17 cell differentiation as downstream effectors of Stat3 and as upstream inducers of RORγt. Moreover, Sox5 physically associates with c-Maf via the HMG domain of Sox5 and DNA-binding domain of c-Maf, and Sox5 together with c-Maf directly activates the promoter of RORγt in CD4⁺ T cells. Collectively, our results suggest that Sox5 and c-Maf cooperatively induce Th17 cell differentiation via the induction of RORγt as downstream targets of Stat3.

Gene expression in superior temporal cortex of schizophrenia patients

We investigated gene expression pattern obtained from microarray data of 10 schizophrenia patients and 10 control subjects. Brain tissue samples were obtained postmortem; thus, the different ages of the patients at death also allowed a study of the dynamic behavior of the expression patterns over a time frame of many years. We used statistical tests and dimensionality reduction methods to characterize the subset of genes differentially expressed in the two groups. A set of 10 genes were significantly downregulated, and a larger set of 40 genes were upregulated in the schizophrenia patients. Interestingly, the set of upregulated genes includes a large number of genes associated with gene transcription (zinc finger proteins and histone methylation) and apoptosis. We furthermore identified genes with a significant trend correlating with age in the control (MLL3) or the schizophrenia group (SOX5, CTRL). Assessments of correlations of other genes with the disorder (RRM1) or with the duration of medication could not be resolved, because all patients were medicated. This hypothesis-free approach uncovered a series of genes differentially expressed in schizophrenia that belong to a number of distinct cell functions, such as apoptosis, transcriptional regulation, cell motility, energy metabolism and hypoxia.

Increasing consistency of disease biomarker prediction across datasets

Microarray studies with human subjects often have limited sample sizes which hampers the ability to detect reliable biomarkers associated with disease and motivates the need to aggregate data across studies. However, human gene expression measurements may be influenced by many non-random factors such as genetics, sample preparations, and tissue heterogeneity. These factors can contribute to a lack of agreement among related studies, limiting the utility of their aggregation. We show that it is feasible to carry out an automatic correction of individual datasets to reduce the effect of such ‘latent variables’ (without prior knowledge of the variables) in such a way that datasets addressing the same condition show better agreement once each is corrected. We build our approach on the method of surrogate variable analysis but we demonstrate that the original algorithm is unsuitable for the analysis of human tissue samples that are mixtures of different cell types. We propose a modification to SVA that is crucial to obtaining the improvement in agreement that we observe. We develop our method on a compendium of multiple sclerosis data and verify it on an independent compendium of Parkinson's disease datasets. In both cases, we show that our method is able to improve agreement across varying study designs, platforms, and tissues. This approach has the potential for wide applicability to any field where lack of inter-study agreement has been a concern.

Gene expression analysis of relapsing-remitting, primary progressive and secondary progressive multiple sclerosis

BACKGROUND

Previous studies of multiple sclerosis (MS) have indicated differences in the pathogenesis in relapsing-remitting (RRMS), secondary progressive (SPMS) and primary progressive (PPMS) disease.

OBJECTIVE

We hypothesized that different MS subtypes would show differences in gene expression that could be traced to specific subsets of peripheral blood mononuclear cells (PBMCs).

METHODS

Gene expression in RRMS, SPMS, PPMS and healthy control (HC) PBMCs was analyzed on Affymetrix arrays. In addition, we studied gene expression in pools of purified PBMC subsets.

RESULTS

We found 380 genes that were differentially expressed in RRMS, PPMS, SPMS and HCs (false discovery rate < 5%). There were no major differences between the subtypes of MS. The genes showing most prominent expression changes in RRMS were associated with adaptive immune pathways, while genes in PPMS were associated with innate immune system pathways. SPMS patients shared pathways with RRMS and PPMS patients. Gene expression changes were most prominent in B cells, CD8+ T cells and monocytes.

CONCLUSION

Differences in gene expression, which could be traced to B cells, CD8+ T cells and monocytes, were found between MS patients and HCs but only minor differences were observed between MS subgroups.

Differences and similarities in the transcriptional profile of peripheral whole blood in early and late-onset preeclampsia: insights into the molecular basis of the phenotype of preeclampsiaa

OBJECTIVE

Preeclampsia (PE) can be sub-divided into early- and late-onset phenotypes. The pathogenesis of these two phenotypes has not been elucidated. To gain insight into the mechanisms of disease, the transcriptional profiles of whole blood from women with early- and late-onset PE were examined.

METHODS

A cross-sectional study was conducted to include women with: i) early-onset PE (diagnosed prior to 34 weeks, n=25); ii) late-onset PE (after 34 weeks, n=47); and iii) uncomplicated pregnancy (n=61). Microarray analysis of mRNA expression in peripheral whole blood was undertaken using Affymetrix microarrays. Differential gene expression was evaluated using a moderated t-test (false discovery rate <0.1 and fold change >1.5), adjusting for maternal white blood cell count and gestational age. Validation by real-time qRT-PCR was performed in a larger sample size [early PE (n=31), late PE (n=72) and controls (n=99)] in all differentially expressed genes. Gene ontology analysis and pathway analysis were performed.

RESULTS

i) 43 and 28 genes were differentially expressed in early- and late-onset PE compared to the control group, respectively; ii) qRT-PCR confirmed the microarray results for early and late-onset PE in 77% (33/43) and 71% (20/28) of genes, respectively; iii) 20 genes that are involved in coagulation (SERPINI2), immune regulation (VSIG4, CD24), developmental process (H19) and inflammation (S100A10) were differentially expressed in early-onset PE alone. In contrast, only seven genes that encoded proteins involved in innate immunity (LTF, ELANE) and cell-to-cell recognition in the nervous system (CNTNAP3) were differentially expressed in late-onset PE alone. Thirteen genes that encode proteins involved in host defense (DEFA4, BPI, CTSG, LCN2), tight junctions in blood-brain barrier (EMP1) and liver regeneration (ECT2) were differentially expressed in both early- and late-onset PE.

CONCLUSION

Early- and late-onset PE are characterized by a common signature in the transcriptional profile of whole blood. A small set of genes were differentially regulated in early- and late-onset PE. Future studies of the biological function, expression timetable and protein expression of these genes may provide insight into the pathophysiology of PE.

Blood RNA profiling in a large cohort of multiple sclerosis patients and healthy controls

Multiple sclerosis (MS) is the most common autoimmune disease of the central nervous system (CNS). It is characterized by the infiltration of autoreactive immune cells into the CNS, which target the myelin sheath, leading to the loss of neuronal function. Although it is accepted that MS is a multifactorial disorder with both genetic and environmental factors influencing its development and course, the molecular pathogenesis of MS has not yet been fully elucidated. Here, we studied the longitudinal gene expression profiles of whole-blood RNA from a cohort of 195 MS patients and 66 healthy controls. We analyzed these transcriptomes at both the individual transcript and the biological pathway level. We found 62 transcripts to be significantly up-regulated in MS patients; the expression of 11 of these genes was counter-regulated by interferon treatment, suggesting partial restoration of a 'healthy' gene expression profile. Global pathway analyses linked the proteasome and Wnt signaling to MS disease processes. Since genotypes from a subset of individuals were available, we were able to identify expression quantitative trait loci (eQTL), a number of which involved two genes of the MS gene signature. However, all these eQTL were also present in healthy controls. This study highlights the challenge posed by analyzing transcripts from whole blood and how these can be mitigated by using large, well-characterized cohorts of patients with longitudinal follow-up and multi-modality measurements.

Dendritic cells and multiple sclerosis: disease, tolerance and therapy

Multiple sclerosis (MS) is a devastating neurological disease that predominantly affects young adults resulting in severe personal and economic impact. The majority of therapies for this disease were developed in, or are beneficial in experimental autoimmune encephalomyelitis (EAE), the animal model of MS. While known to target adaptive anti-CNS immune responses, they also target, the innate immune arm. This mini-review focuses on the role of dendritic cells (DCs), the professional antigen presenting cells of the innate immune system. The evidence for a role for DCs in the appropriate regulation of anti-CNS autoimmune responses and their role in MS disease susceptibility and possible therapeutic utility are discussed. Additionally, the current controversy regarding the evidence for the presence of functional DCs in the normal CNS is reviewed. Furthermore, the role of CNS DCs and potential routes of their intercourse between the CNS and cervical lymph nodes are considered. Finally, the future role that this nexus between the CNS and the cervical lymph nodes might play in site directed molecular and cellular therapy for MS is outlined.

An intronic variant associated with systemic lupus erythematosus changes the binding affinity of Yinyang1 to downregulate WDFY4

Two recent genome-wide association studies of East Asian populations revealed three genetic variants in WDFY4/LRRC18 associated with systemic lupus erythematosus (SLE). To identify the gene contributing to this disease susceptibility, we examined the mRNA expression of WDFY4 and LRRC18 in patients with SLE and healthy controls. WDFY4 was significantly downregulated in SLE patients as compared with controls. We used allelic expression and dual-luciferase assays to identify the functional variant. Transcriptional activity was lower for the rs877819A than -G allele. Electrophoretic mobility shift and supershift assays revealed that the transcription factor Yinyang1 (YY1) binds to rs877819, with lower affinity to the A allele, which explained the reduced transcriptional activity. This effect was further confirmed by YY1 small interfering RNA knockdown, overexpression and chromatin immunoprecipitation experiments. rs877819 in WDFY4 might be the functional site associated with SLE by reduced binding of YY1 and downregulating WDFY4 expression.

Whole miRNome-wide differential co-expression of microRNAs

Co-regulation of genes has been extensively analyzed, however, rather limited knowledge is available on co-regulations within the miRNome. We investigated differential co-expression of microRNAs (miRNAs) based on miRNome profiles of whole blood from 540 individuals. These include patients suffering from different cancer and non-cancer diseases, and unaffected controls. Using hierarchical clustering, we found 9 significant clusters of co-expressed miRNAs containing 2–36 individual miRNAs. Through analyzing multiple sequencing alignments in the clusters, we found that co-expression of miRNAs is associated with both sequence similarity and genomic co-localization. We calculated correlations for all 371,953 pairs of miRNAs for all 540 individuals and identified 184 pairs of miRNAs with high correlation values. Out of these 184 pairs of miRNAs, 16 pairs (8.7%) were differentially co-expressed in unaffected controls, cancer patients and patients with non-cancer diseases. By computing correlated and anti-correlated miRNA pairs, we constructed a network with 184 putative co-regulations as edges and 100 miRNAs as nodes. Thereby, we detected specific clusters of miRNAs with high and low correlation values. Our approach represents the most comprehensive co-regulation analysis based on whole miRNome-wide expression profiling. Our findings further decrypt the interactions of miRNAs in normal and human pathological processes.

MS risk genes are transcriptionally regulated in CSF leukocytes at relapse

Background:

Infiltrating T-helper cells, cytotoxic T-cells, B-cells and monocytes are thought to mediate the damage to myelin, oligodendrocytes and axons in multiple sclerosis (MS), which results in progressive disability.

Objective:

The objective of this paper is to explore gene expression profiles of leukocytes in the cerebrospinal fluid (CSF) compartment of MS patients during relapse.

Methods:

Global gene expression was analyzed by DNA microarray analysis of cells in CSF from MS patients and controls, and verifications were performed with real-time polymerase chain reaction (PCR) and enzyme-linked immunosorbent assay (ELISA).

Results:

Fifty percent of the recently described risk genes for MS and 28% of non-risk genes were differently expressed in MS patients compared to controls (χ2-test, p=7.7 × 10−5). Genes involved in T- and NK-cell processes were up-regulated, and genes involved in processes targeting innate immunity or B-cells were down-regulated in MS. Increased expression of EDN1 and CXCL11 and decreased expression of HMOX1 was verified with real-time PCR and increased expression of CXCL13 was verified with ELISA in CSF.

Conclusion:

DNA microarray analysis is useful in identifying differently expressed genes in CSF leukocytes, which may be important in MS in vivo. Our findings suggest that many of the risk genes for MS are differently expressed in the disease-mediating leukocytes that penetrate the blood-brain barrier.

Comparison of brain and blood gene expression in an animal model of negative symptoms in schizophrenia

OBJECTIVES

To investigate the potential of white blood cells as probes for central processes we have measured gene expression in both the anterior cingulate cortex and white blood cells using a putative animal model of negative symptoms in schizophrenia.

METHODS

The model is based on the capability of ketamine to induce psychotic symptoms in healthy volunteers and to worsen such symptoms in schizophrenic patients. Classical fear conditioning is used to assess emotional processing and cognitive function in animals exposed to sub-chronic ketamine vs. controls. Gene expression was measured using a commercially sourced whole genome rat gene array. Data analyses were performed using ANOVA (Systat 11).

RESULTS

In both anterior cingulate cortex and white blood cells a significant interaction between ketamine and fear conditioning could be observed. The outcome is largely supported by our subsequent metagene analysis. Moreover, the correlation between gene expression in brain and blood is about constant when no ketamine is present (r~0.4). With ketamine, however, the correlation becomes very low (r~0.2) when there is no fear, but it increases to ~0.6 when fear and ketamine are both present. Our results show that under normal conditions ketamine lowers gene expression in the brain, but this effect is completely reversed in combination with fear conditioning, indicating a stimulatory action.

CONCLUSION

This paradoxical outcome indicates that extreme care must be taken when using gene expression data from white blood cells as marker for psychiatric disorders, especially when pharmacological and environmental interactions are at play.

Imprinted DLK1-DIO3 region of 14q32 defines a schizophrenia-associated miRNA signature in peripheral blood mononuclear cells

MicroRNAs (miRNAs) regulate gene expression at the post-transcriptional level and are important for coordinating nervous system development and neuronal function in the mature brain. We have recently identified schizophrenia-associated alteration of cortical miRNA biogenesis and expression in post-mortem brain tissue with implications for the dysregulation of schizophrenia candidate genes. Although these changes were observed in the central nervous system, it is plausible that schizophrenia-associated miRNA expression signatures may also be detected in non-neural tissue. To explore this possibility, we investigated the miRNA expression profile of peripheral blood mononuclear cells (PBMCs) from 112 patients with schizophrenia and 76 non-psychiatric controls. miRNA expression analysis of total RNA conducted using commercial miRNA arrays revealed that 33 miRNAs were significantly downregulated after correction for multiple testing with a false discovery rate (FDR) of 0%, which increased to 83 when we considered miRNA with an FDR<5%. Seven miRNAs altered in microarray analysis of schizophrenia were also confirmed to be downregulated by quantitative real-time reverse transcription-polymerase chain reaction. A large subgroup consisting of 17 downregulated miRNAs is transcribed from a single imprinted locus at the maternally expressed DLK1-DIO3 region on chromosome 14q32. This pattern of differentially expressed miRNA in PBMCs may be indicative of significant underlying genetic or epigenetic alteration associated with schizophrenia.

Integration of MicroRNA databases to study MicroRNAs associated with multiple sclerosis

MicroRNAs (miRNAs) are small non-coding RNAs which regulate many genes post-transcriptionally. In various contexts of medical science, miRNAs gained increasing attention over the last few years. Analyzing the functions, interactions and cellular effects of miRNAs is a very complex and challenging task. Many miRNA databases with diverse data contents have been developed. Here, we demonstrate how to integrate their information in a reasonable way on a set of miRNAs that were found to be dysregulated in the blood of patients with multiple sclerosis (MS). Using the miR2Disease database, we retrieved 16 miRNAs associated with MS according to four different studies. We studied the predicted and experimentally validated target genes of these miRNAs, their expression profiles in different blood cell types and brain tissues, the pathways and biological processes affected by these miRNAs as well as their regulation by transcription factors. Only miRNA-mRNA interactions that were predicted by at least seven different prediction algorithms were considered. This resulted in a network of 1,498 target genes. In this network, the MS-associated miRNAs hsa-miR-20a-5p and hsa-miR-20b-5p occurred as central hubs regulating about 500 genes each. Strikingly, many of the putative target genes play a role in T cell activation and signaling, and many have transcription factor activity. The latter suggests that miRNAs often act as regulators of regulators with many secondary effects on gene expression. Our present work provides a guideline on how information of different databases can be integrated in the analysis of miRNAs. Future investigations of miRNAs shall help to better understand the mechanisms underlying different diseases and their treatments.

Brain transcriptome perturbations in the Hfe(-/-) mouse model of genetic iron loading

Severe disruption of brain iron homeostasis can cause fatal neurodegenerative disease, however debate surrounds the neurologic effects of milder, more common iron loading disorders such as hereditary hemochromatosis, which is usually caused by loss-of-function polymorphisms in the HFE gene. There is evidence from both human and animal studies that HFE gene variants may affect brain function and modify risks of brain disease. To investigate how disruption of HFE influences brain transcript levels, we used microarray and real-time reverse transcription polymerase chain reaction to assess the brain transcriptome in Hfe(-/-) mice relative to wildtype AKR controls (age 10 weeks, n≥4/group). The Hfe(-/-) mouse brain showed numerous significant changes in transcript levels (p<0.05) although few of these related to proteins directly involved in iron homeostasis. There were robust changes of at least 2-fold in levels of transcripts for prominent genes relating to transcriptional regulation (FBJ osteosarcoma oncogene Fos, early growth response genes), neurotransmission (glutamate NMDA receptor Grin1, GABA receptor Gabbr1) and synaptic plasticity and memory (calcium/calmodulin-dependent protein kinase IIα Camk2a). As previously reported for dietary iron-supplemented mice, there were altered levels of transcripts for genes linked to neuronal ceroid lipofuscinosis, a disease characterized by excessive lipofuscin deposition. Labile iron is known to enhance lipofuscin generation which may accelerate brain aging. The findings provide evidence that iron loading disorders can considerably perturb levels of transcripts for genes essential for normal brain function and may help explain some of the neurologic signs and symptoms reported in hemochromatosis patients.

CCAAT/enhancer binding protein-δ expression by dendritic cells regulates CNS autoimmune inflammatory disease

CCAAT enhancer binding protein-delta (C/EBPδ) is a transcription factor that regulates inflammatory processes mediating bystander neuronal injury and CNS autoimmune inflammatory disease. The mechanism of the involvement of C/EBPδ in these processes remains to be determined. Here, we examined the cellular source(s) and mechanisms by which C/EBPδ may be involved in an animal model of multiple sclerosis. Mice deficient in C/EBPδ expression exhibited less severe clinical disease than wild-type littermates in response to induction of experimental autoimmune encephalomyelitis (EAE) by vaccination with a myelin oligodendrocyte glycoprotein (MOG) fragment. This reduction in EAE severity was associated with a significant alteration in the complement of major CNS T-helper (Th) cell subtypes throughout disease, manifest as reduced ratios of Th17 cells to regulatory T-cells (Tregs). Studies in bone marrow chimeric mice indicated that C/EBPδ expression by peripherally derived immune cells mediates C/EBPδ involvement in EAE. Follow up in vitro and in vivo examination of dendritic cell (DC) mediated Th-cell development suggests that C/EBPδ suppresses DC expression of interleukin-10 (IL-10), favoring Th17 over Treg development. In vitro and in vivo blockade of IL-10 signaling attenuated the effect of reduced C/EBPδ expression by DCs on Th17:Treg ratios. These findings identify C/EBPδ as an important DC transcription factor in CNS autoimmune inflammatory disease by virtue of its capacity to alter the Th17:Treg balance in an IL-10 dependent fashion.