scholarly journals The Effect of Cognitive Rehabilitation on Peripheral Blood B Cell Distribution and Specific Gene Expression Levels in MS patients

2021 ◽  
Vol 1 (2) ◽  
pp. 32-39
Author(s):  
Ece Akbayır ◽  
Melis Şen ◽  
Erdil Arsoy ◽  
Recai Türkoğlu ◽  
Vuslat Yılmaz ◽  
...  
2020 ◽  
Author(s):  
Jonathan L. Hess ◽  
Samuel Chen ◽  
Thomas P. Quinn ◽  
Sek Won Kong ◽  
Murray Cairns ◽  
...  

AbstractEx vivo molecular analysis of the human brain is virtually impossible given major risks and ethical concerns. Transcriptome imputation offers a promising and non-invasive alternative for developing models (albeit imperfect) of brain gene expression in lieu of biopsying brain tissue. Popular tools such as FUSION (Gusev et al., 2016) and PrediXcan (Gamazon et al., 2015) use genotypes at common cis-expression quantitative trait loci (eQTLs) to predict tissue-specific gene expression levels. However, those tools cannot reliably predict expression levels for a majority of genes in the brain. This raises the question of whether an alternative modeling approach should be evaluated to capture greater variance in more genes in the brain that are not yet imputable with existing cis-eQTL imputation tools. To address this problem, we developed a novel transcriptome-imputation method called the Brain Gene Expression and Network Imputation Engine (BrainGENIE) that imputes brain-region-specific gene expression levels from peripheral blood gene expression. BrainGENIE predicted brain-region-specific expression levels for 1,733 – 11,569 genes (cross-validation R2≥0.01, false-discovery rate-adjusted p<0.05), few of which are imputable by PrediXcan. Disease-related transcriptome signals detected by BrainGENIE showed stronger agreement with known transcriptome signatures from postmortem brain when compared with findings from analyses of peripheral blood or S-PrediXcan. BrainGENIE complements and outperforms existing transcriptome-imputation tools, provides biologically meaningful predictions, and opens avenues for studying brain transcriptomes longitudinally. BrainGENIE was developed using R (v.3.6.3, tested in 4.0.2) and is freely available at: https://github.com/hessJ/BrainGENIE.


2006 ◽  
Vol 169 (2) ◽  
pp. 655-664 ◽  
Author(s):  
Christoph Renné ◽  
Jose Ignacio Martin-Subero ◽  
Maren Eickernjäger ◽  
Martin-Leo Hansmann ◽  
Ralf Küppers ◽  
...  

2015 ◽  
Vol 9s3 ◽  
pp. BBI.S29470 ◽  
Author(s):  
Mikhail G. Dozmorov ◽  
Nicolas Dominguez ◽  
Krista Bean ◽  
Susan R. Macwana ◽  
Virginia Roberts ◽  
...  

Systemic lupus erythematosus (SLE) is an autoimmune disease characterized by complex interplay among immune cell types. SLE activity is experimentally assessed by several blood tests, including gene expression profiling of heterogeneous populations of cells in peripheral blood. To better understand the contribution of different cell types in SLE pathogenesis, we applied the two methods in cell-type-specific differential expression analysis, csSAM and DSection, to identify cell-type-specific gene expression differences in heterogeneous gene expression measures obtained using RNA-seq technology. We identified B-cell-, monocyte-, and neutrophil-specific gene expression differences. Immunoglobulin-coding gene expression was altered in B-cells, while a ribosomal signature was prominent in monocytes. On the contrary, genes differentially expressed in the heterogeneous mixture of cells did not show any functional enrichment. Our results identify antigen binding and structural constituents of ribosomes as functions altered by B-cell- and monocyte-specific gene expression differences, respectively. Finally, these results position both csSAM and DSection methods as viable techniques for cell-type-specific differential expression analysis, which may help uncover pathogenic, cell-type-specific processes in SLE.


Blood ◽  
2004 ◽  
Vol 104 (11) ◽  
pp. 420-420
Author(s):  
Christian Flotho ◽  
Susana C. Raimondi ◽  
James R. Downing

Abstract We have demonstrated that expression profiling of leukemic blasts can accurately identify the known prognostic subtypes of ALL, including T-ALL, E2A-PBX1, TEL-AML1, MLL rearrangements, BCR-ABL, and hyperdiploid &gt;50 chromosomes (HD&gt;50). Interestingly, almost 70% of the genes that defined HD&gt;50 ALL localized to chromosome 21 or X. To further explore the relationship between gene expression and chromosome dosage, we compared the expression profiles obtained using the Affymetrix U133A&B microarrays of 17 HD&gt;50 ALLs to 78 diploid or pseudodiploid ALLs. Our analysis demonstrated that the average expression level for all genes on a chromosome could be used to predict chromosome copy numbers. Specifically, the copy number for each chromosome calculated by gene expression profiling predicted the numerical chromosomal abnormalities detected by standard cytogenetics. For chromosomes that were trisomic in HD&gt;50 ALL, the mean chromosome-specific gene expression level was increased approximately 1.5-fold compared to that observed in diploid or pseudodiploid ALL cases. Similarly, for chromosome 21 and X, the mean chromosome-specific gene expression levels were increased approximately 2-fold, consistent with a duplication of the active X chromosome and tetrasomy of chromosome 21, a finding verified by standard cytogenetics in &gt;90% of the HD&gt;50 cases. These finding indicate that the aberrant gene expression levels seen in HD&gt;50 ALL primarily reflect gene dosages. Importantly, we did not observe any clustering of aberrantly expressed genes across the duplicated chromosomes, making regional gain or loss of genomic material unlikely. Paradoxically, however, a more detailed analysis revealed a small but statistically significant number of genes on the trisomic/tetrasomic chromosomes whose expression levels were markedly reduced when compared to that seen in diploid or pseudodiploid leukemic samples. Using the Statistical Analysis of Microarrays (SAM) algorithm we identified 20 genes whose expression was reduced &gt;2-fold despite having an increase in copy number. Interestingly, included within this group are several known tumor suppressors, including AKAP12, which is specifically silenced by methylation in fos-transformed cells, and IGF2R and IGFBP7, negative regulators of insulin-like growth factor signaling. In addition to the silencing of a small subset of genes, we also identified 21 genes on these chromosomes whose expression levels were markedly higher (&gt;3-fold) than would be predicted solely based on copy number. Although the mechanism responsible for their increased expression remains unknown, included in this group are four genes involved in signal transduction (IL3RA, IL13RA1, SNX9, and GASP) and a novel cytokine, C17, whose expression is normally limited to CD34+ hematopoietic progenitors. Taken together, these data suggest that aberrant growth in HD&gt;50 ALL is in part driven by increased expression of a large number of genes secondary to chromosome duplications, coupled with a further enhanced expression of a limited number of growth promoting genes, and the specific silencing of a small subset of negative growth regulatory genes. Understanding the mechanisms responsible for the non-dosage related changes in gene expression should provide important insights into the pathology of HD&gt;50 ALL.


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