ESCRT-III subunits Snf7-1 and Snf7-2 differentially regulate transmembrane cargos in hESC-derived human neurons

<p>Abstract</p> <p>Backgrounds</p> <p>Endosomal sorting complex required for transport (ESCRT) is involved in several fundamental cellular processes and human diseases. Many mammalian ESCRT proteins have multiple isoforms but their precise functions remain largely unknown, especially in human neurons.</p> <p>Results</p> <p>In this study, we differentiated human embryonic stem cells (hESCs) into postmitotic neurons and characterized the functional properties of these neurons. Moreover, we found that among the three human paralogs of the yeast ESCRT-III subunit Snf7, hSnf7-1 and hSnf7-2 are most abundantly expressed in human neurons. Both hSnf7-1 and hSnf7-2 are required for the survival of human neurons, indicating a non-redundant essential function. Indeed, hSnf7-1 and hSnf7-2 are preferentially associated with CHMP2A and CHMP2B, respectively, and regulate the turnover of distinct transmembrane cargos such as neurotransmitter receptors in human neurons.</p> <p>Conclusion</p> <p>These findings indicate that different mammalian paralogs of the yeast ESCRT-III subunit Snf7 have non-redundant functions in human neurons, suggesting that ESCRT-III with distinct subunit compositions may preferentially regulate different cargo proteins.</p

Identifying differential exon splicing using linear models and correlation coefficients

Background: With the availability of the Affymetrix exon arrays a number of tools have been developed to enable the analysis. These however can be expensive or have several pre-installation requirements. This led us to develop an analysis workflow for analysing differential splicing using freely available software packages that are already being widely used for gene expression analysis. The workflow uses the packages in the standard installation of R and Bioconductor (BiocLite) to identify differential splicing. We use the splice index method with the LIMMA framework. The main drawback with this approach is that it relies on accurate estimates of gene expression from the probe-level data. Methods such as RMA and PLIER may misestimate when a large proportion of exons are spliced. We therefore present the novel concept of a gene correlation coefficient calculated using only the probeset expression pattern within a gene. We show that genes with lower correlation coefficients are likely to be differentially spliced.Results: The LIMMA approach was used to identify several tissue-specific transcripts and splicing events that are supported by previous experimental studies. Filtering the data is necessary, particularly removing exons and genes that are not expressed in all samples and cross-hybridising probesets, in order to reduce the false positive rate. The LIMMA approach ranked genes containing single or few differentially spliced exons much higher than genes containing several differentially spliced exons. On the other hand we found the gene correlation coefficient approach better for identifying genes with a large number of differentially spliced exons.Conclusion: We show that LIMMA can be used to identify differential exon splicing from Affymetrix exon array data. Though further work would be necessary to develop the use of correlation coefficients into a complete analysis approach, the preliminary results demonstrate their usefulness for identifying differentially spliced genes. The two approaches work complementary as they can potentially identify different subsets of genes (single/few spliced exons vs. large transcript structure differences)

Strengths and weaknesses of EST-based prediction of tissue-specific alternative splicing

BACKGROUND: Alternative splicing contributes significantly to the complexity of the human transcriptome and proteome. Computational prediction of alternative splice isoforms are usually based on EST sequences that also allow to approximate the expression pattern of the related transcripts. However, the limited number of tissues represented in the EST data as well as the different cDNA construction protocols may influence the predictive capacity of ESTs to unravel tissue-specifically expressed transcripts. METHODS: We predict tissue and tumor specific splice isoforms based on the genomic mapping (SpliceNest) of the EST consensus sequences and library annotation provided in the GeneNest database. We further ascertain the potentially rare tissue specific transcripts as the ones represented only by ESTs derived from normalized libraries. A subset of the predicted tissue and tumor specific isoforms are then validated via RT-PCR experiments over a spectrum of 40 tissue types. RESULTS: Our strategy revealed 427 genes with at least one tissue specific transcript as well as 1120 genes showing tumor specific isoforms. While our experimental evaluation of computationally predicted tissue-specific isoforms revealed a high success rate in confirming the expression of these isoforms in the respective tissue, the strategy frequently failed to detect the expected restricted expression pattern. The analysis of putative lowly expressed transcripts using normalized cDNA libraries suggests that our ability to detect tissue-specific isoforms strongly depends on the expression level of the respective transcript as well as on the sensitivity of the experimental methods. Especially splice isoforms predicted to be disease-specific tend to represent transcripts that are expressed in a set of healthy tissues rather than novel isoforms. CONCLUSIONS: We propose to combine the computational prediction of alternative splice isoforms with experimental validation for efficient delineation of an accurate set of tissue-specific transcripts

Role of T198 Modification in the Regulation of p27Kip1 Protein Stability and Function

The tumor suppressor gene p27Kip1 plays a fundamental role in human cancer progression. Its expression and/or functions are altered in almost all the different tumor histotype analyzed so far. Recently, it has been demonstrated that the tumor suppression function of p27 resides not only in the ability to inhibit Cyclins/CDKs complexes through its N-terminal domain but also in the capacity to modulate cell motility through its C-terminal portion. Particular interest has been raised by the last amino-acid, (Threonine 198) in the regulation of both protein stability and cell motility

Circulating MicroRNAs Are Not Eliminated by Hemodialysis

BACKGROUND: Circulating microRNAs are stably detectable in serum/plasma and other body fluids. In patients with acute kidney injury on dialysis therapy changes of miRNA patterns had been detected. It remains unclear if and how the dialysis procedure itself affects circulating microRNA level. METHODS: We quantified miR-21 and miR-210 by quantitative RT-PCR in plasma of patients with acute kidney injury requiring dialysis and measured pre- and post-dialyser miRNA levels as well as their amount in the collected spent dialysate. Single treatments using the following filters were studied: F60 S (1.3 m(2), Molecular Weight Cut Off (MWCO): 30 kDa, n = 8), AV 1000 S (1.8 m(2), MWCO: 30 kDa, n = 6) and EMiC 2 (1.8 m(2), MWCO: 40 kDa, n = 6). RESULTS: Circulating levels of miR-21 or -210 do not differ between pre- and post-dialyzer blood samples independently of the used filter surface and pore size: miR-21: F60S: p = 0.35, AV 1000 S p = 1.0, EMiC2 p = 1.0; miR-210: F60S: p = 0.91, AV 1000 S p = 0.09, EMiC2 p = 0.31. Correspondingly, only traces of both miRNAs could be found in the collected spent dialysate and ultrafiltrate. CONCLUSIONS: In patients with acute kidney injury circulating microRNAs are not removed by dialysis. As only traces of miR-21 and -210 are detected in dialysate and ultrafiltrate, microRNAs in the circulation are likely to be transported by larger structures such as proteins and/or microvesicles. As miRNAs are not affected by dialysis they might be more robust biomarkers of acute kidney injury

The Role of BOB1 in Chronic Lung Allograft Dysfunction

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