10 research outputs found
Proteomic Signature of Endothelial Dysfunction Identified in the Serum of Acute Ischemic Stroke Patients by the iTRAQ-Based LC–MS Approach
Acute
ischemic stroke (AIS) is a devastating cerebrovascular disorder
that leads to permanent physical and neurological disabilities in
adults worldwide. Proteins associated with stroke pathogenesis may
appear in the serum of AIS patients due to blood–brain barrier
dysfunction, thus permitting the development of blood-based biomarkers
for early diagnosis of stroke. These biomarkers could perhaps be an
adjunct to the existing imaging modalities and aid in better management
and therapeutic intervention during the course of the disease. For
this exploratory study, a combination of multiplexed isobaric tagging
using iTRAQ reagents and high resolution tandem mass spectrometry
was used to identify differentially expressed proteins in serum samples
from AIS patients. The quantitative proteomic analysis of serum from
both AIS and control subjects revealed 389 high confidence protein
identifications and their relative levels. Among them, 60 proteins
showed a ≥1.5-fold change in the AIS subjects. We verified
the altered serum levels of candidate proteins such as vWF, ADAMTS13,
S100A7, and DLG4 through ELISA, and the results also corroborate with
the experimental findings. vWF and ADAMTS13 are key players that regulate
blood hemostasis, and their altered concentration may contribute to
endothelial dysfunction. S100A7 is a novel candidate protein identified in this
study that is also known to mediate inflammation, endothelial proliferation,
and angiogenesis. The current study provided a potential and novel
biomarker panel that may in turn provide diagnostic aid to the existing
imaging modalities for the rapid diagnosis of ischemic strok
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Additional file 17: Figure S8. of A multi-omic analysis of human naĂŻve CD4+ T cells
Correlation between miRNA read count, transcript, and protein abundance. (A) Protein abundance versus miRNA read count. The x axis for each circle represents all miRNAs identified with a given read count while the y axis corresponds to the average iBAQ value of the genes targeted by those miRNAs with the specified read count. The dashed red line represents the iBAQ value of genes that are not targeted by any miRNAs identified in this study and serves as a background, reference level of protein expression. The black line is a linear regression of iBAQ values versus the miRNA read count. (B) Transcript abundance versus miRNA read count. The x axis for each circle represents all miRNAs identified with a given read count while the y axis corresponds to the average FPKM value of the genes targeted by those miRNAs with the specified read count. The dashed red line represents the FPKM levels of genes that are not targeted by any miRNAs identified in this study and serves as a background, reference level of transcript abundance. The black line is a linear regression of FPKM values versus miRNA read count. There is no obvious correlation between FPKM values and miRNA read counts. (PDF 150 kb
Additional file 5: Table S2. of A multi-omic analysis of human naĂŻve CD4+ T cells
A list of novel transcripts and novel spliced isoforms identified in naĂŻve CD4+ T cells. (XLSX 9927 kb
Additional file 23: Figure S12. of A multi-omic analysis of human naïve CD4+ T cells
Concordance of this study with Komori et al. (A) Each blue dot represents the methylation level of a gene. On the x-axis are the methylation differences between memory and naïve cells measured by Komori et al. and on the y-axis are TSS200 methylation levels measured in this study. The r2 value is a pearson’s correlation coefficient between the two datasets. (B) Each blue dot represents the methylation level of a gene. On the x-axis are the methylation differences between memory and naïve cells where Komori et al. provided quantitative values and on the y-axis are the TSS200 methylation levels measured in this study. The r2 value is a pearson’s correlation coefficient between the two datasets. (C) Genes identified in Komori et al. as displaying inverse correlation between methylation levels and transcription are plotted. On the left axis and shown in blue are the log2 transformed fold change of transcript levels between memory and naïve cells measured by Komori et al. and on the right axis and shown in green are log2 transformed fold change of transcript levels between memory and naïve cells measured in this study. (D) The correlation between the log2 transformed fold changes of genes between memory and naïve cells measured in each study is shown. Each blue dot represents a gene and the r2 is a pearson’s correlation coefficient. (PDF 1410 kb
Additional file 22: Table S11. of A multi-omic analysis of human naĂŻve CD4+ T cells
Pathways enriched from Gene Set Analysis of genes correlating with TCGA dataset 1 or dataset 2. (XLSX 12 kb
Additional file 15:Table S9. of A multi-omic analysis of human naĂŻve CD4+ T cells
A list of annotated pseudogenes that encode proteins in naĂŻve CD4+ T cells with the corresponding peptide evidence from proteomics data. (XLSX 9 kb
Additional file 25. of A multi-omic analysis of human naĂŻve CD4+ T cells
Supplemental methods. (DOC 41 kb