The effects of the glial-specific, calcium-binding, S-100 protein on brain membrane and supernatant protein phosphorylation were assessed. S-100 concentrations as low as 5 micrograms/ml caused a marked inhibition of the phosphorylation of a soluble brain protein having a molecular weight of 73,000 daltons (73K). This protein was designated the S-100 protein-modulated phosphoprotein (SMP). Half-maximal inhibition of the phosphorylation of SMP by S-100 was obtained at concentrations of 12 micrograms/ml (0.57 microM). The inhibition of SMP phosphorylation by S-100 was calcium-dependent, with a calculated calcium Ka of 2.0 +/- 0.3 microM. SMP phosphorylation was also inhibited by calmodulin, but only partially and with a much lower potency. The inhibition of SMP phosphorylation by S-100 was not inhibited by fluphenazine, whereas the effect of calmodulin was. SMP was found in many brain areas, with the highest levels seen in the corpus callosum. Various peripheral tissues, such as kidney; liver; and pineal, pituitary, and adrenal glands, did not contain detectable SMP levels. At higher S-100 concentrations, greater than 10 micrograms/ml, the phosphorylation of several other soluble proteins was markedly inhibited. These proteins have molecular weights of 56K, 50K, and 47K. The phosphorylation of these proteins was enhanced by calmodulin. These data suggest that the S-100 protein may function to modulate the phosphorylation of brain proteins in a manner analogous to (although in a reciprocal fashion) that of calmodulin.
Total RNA was isolated using the SeraMir RNA isolation kit (SBI Biosciences). RNA quantification and quality were assessed by measuring the 260/280 ratio using a Nanophotometer (Implen, Munich, Germany). RNA integrity was evaluated using the Bioanalyzer 2100 (Agilent Technologies, Santa Clara, USA). The average (±S.D.) RIN value for islet samples utilized in this study was 9.8 ± 0.28. Next, 500 nanograms of RNA were used to prepare a single-indexed strand-specific cDNA library using a TruSeq Stranded mRNA Library Prep Kit (Illumina). The resulting libraries were assessed for quantity and size distribution using a Qubit and Agilent 2100 Bioanalyzer; 1.5 pM pooled libraries were sequenced with 2×75bp paired-end configuration on an Illumina NextSeq500 using a NextSeq 500/550 High Output Kit with an average of 52.6M reads. A Phred quality score (Q score) was used to measure the quality of sequencing; >90% of the sequencing reads reached Q30 (99.9% base call accuracy). RNA-Sequencing Alignment and Differential Expression AnalysisSequencing data were first assessed for quality using FastQC (Babraham Bioinformatics, Cambridge, UK). Next, all sequenced libraries were mapped to the human genome (GENCODE GRCh37) using the STAR RNA-seq aligner (1). Read distributions across the genome were assessed using bamutils (from ngsutils) (2). Differential expression analysis between cytokinetreated and untreated human islets was performed using edgeR v3.22.3 from the Bioconductor package (3). Biological coefficients of variation between the samples were estimated using an empirical Bayes approach under the assumption that data followed a negative binomial distribution. We filtered out low expression transcripts based on the percentage of samples (less than 50%) and using a count per million (CPM) cutoff of 0.5. A total of 15,892 mRNAs remained after filtering and were used in differential expression analysis. Age, donor sex, and BMI were adjusted as co-variates in our statistical model. Statistical significance was defined as an FDR P value ≤ 0.05 for comparison between cytokine-treated and untreated human islets. RNA Sequencing and Alternative Splicing AnalysisTo provide additional validation of our results, we analyzed one external dataset of human islets treated using the same cytokines (GSE108413)(4). We also analyzed alternative splicing patterns in islet from 4 donors with type 1 diabetes (GSE121863) and in islets from 12 non-diabetic control donors (5). These datasets were retrieved from NCBI GEO and analyzed using the same computational pipeline described in the Material and Methods. Overlapping events between GSE121863 and our RNA-Seq dataset were defined as those 1) with more than half of each comparison group replicate having a sum of inclusion junction counts and skipping junction counts per sample ≥10, 2) FDR ≤ 0.05, and 3) exhibiting consistent alternative exon change trend between the two datasets.
MicroRNAs (miRNAs) are small non-coding RNAs that play a crucial role in modulating gene expression and are enriched in cell-derived extracellular vesicles (EVs). We investigated whether miRNAs from human islets and islet-derived EVs could provide insight into β cell stress pathways activated during type 1 diabetes (T1D) evolution, therefore serving as potential disease biomarkers. We treated human islets from 10 cadaveric donors with IL-1β and IFN-γ to model T1D ex vivo. MicroRNAs were isolated from islets and islet-derived EVs, and small RNA sequencing was performed. We found 20 and 14 differentially expressed (DE) miRNAs in cytokine- versus control-treated islets and EVs, respectively. Interestingly, the miRNAs found in EVs were mostly different from those found in islets. Only two miRNAs, miR-155-5p and miR-146a-5p, were upregulated in both islets and EVs, suggesting selective sorting of miRNAs into EVs. We used machine learning algorithms to rank DE EV-associated miRNAs, and developed custom label-free Localized Surface Plasmon Resonance-based biosensors to measure top ranked EVs in human plasma. Results from this analysis revealed that miR-155, miR-146, miR-30c, and miR-802 were upregulated and miR-124-3p was downregulated in plasma-derived EVs from children with recent-onset T1D. In addition, miR-146 and miR-30c were upregulated in plasma-derived EVs of autoantibody positive (AAb+) children compared to matched non-diabetic controls, while miR-124 was downregulated in both T1D and AAb+ groups. Furthermore, single-molecule fluorescence in situ hybridization confirmed increased expression of the most highly upregulated islet miRNA, miR-155, in pancreatic sections from organ donors with AAb+ and T1D.
The emergence of labeling strategies and live cell imaging methods enables the imaging of chromatin in living cells at single digit nanometer resolution as well as milliseconds temporal resolution. These technical breakthroughs revolutionize our understanding of chromatin structure, dynamics and functions. Single molecule tracking algorithms are usually preferred to quantify the movement of these intranucleus elements to interpret the spatiotemporal evolution of the chromatin. In this review, we will first summarize the fluorescent labeling strategy of chromatin in live cells which will be followed by a systematic comparison of live cell imaging instrumentation. With the proper microscope, we will discuss the image analysis pipelines to extract the biophysical properties of the chromatin. Finally, we expect to give practical suggestions to broad biologists on how to select methods and link to the model properly according to different investigation purposes.
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