Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

The human pancreatic islet transcriptome: impact of pro-inflammatory cytokines

ABSTRACT: We have used RNA-seq to identify transcripts, including splice variants, expressed in human islets of Langerhans under control condition or following exposure to the pro-inflammatory cytokines interleukin-1β (IL-1β) and interferon-γ (IFN-γ). A total of 29,776 transcripts were identified as expressed in human islets. Expression of around 20% of these transcripts was modified by pro-inflammatory cytokines, including apoptosis- and inflammation-related genes. Chemokines were among the transcripts most modified by cytokines. Interestingly, 35% of the genes expressed in human islets undergo alternative splicing as annotated in RefSeq, and cytokines caused substantial changes in spliced transcripts. Nova1, previously considered a brain-specific regulator of mRNA splicing, is expressed in islets. 25/41 of the candidate genes for type 1 diabetes are expressed in islets, and cytokines modified expression of several of these transcripts. 5 human islet of Langerhans preparations examined under 2 conditions (control and cytokine treatment)

ORGANISM(S): Homo sapiens

SUBMITTER: Guy Bottu

PROVIDER: E-GEOD-35296 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

ACCESS DATA

Publications

The human pancreatic islet transcriptome: expression of candidate genes for type 1 diabetes and the impact of pro-inflammatory cytokines.

Eizirik Décio L DL Sammeth Michael M Bouckenooghe Thomas T Bottu Guy G Sisino Giorgia G Igoillo-Esteve Mariana M Ortis Fernanda F Santin Izortze I Colli Maikel L ML Barthson Jenny J Bouwens Luc L Hughes Linda L Gregory Lorna L Lunter Gerton G Marselli Lorella L Marchetti Piero P McCarthy Mark I MI Cnop Miriam M

PLoS genetics 20120308 3

Type 1 diabetes (T1D) is an autoimmune disease in which pancreatic beta cells are killed by infiltrating immune cells and by cytokines released by these cells. Signaling events occurring in the pancreatic beta cells are decisive for their survival or death in diabetes. We have used RNA sequencing (RNA-seq) to identify transcripts, including splice variants, expressed in human islets of Langerhans under control conditions or following exposure to the pro-inflammatory cytokines interleukin-1β (IL- ...[more]

PMID: 22412385

Similar Datasets

Project description:Glycosylation is an abundant post-translational modification of both intracellular and extracellular proteins [1]. The majority of glycans are classified as N-linked chains, where the carbohydrate moiety is attached to asparagine residues, or O-linked chains, most commonly linked to a serine or threonine. N-linked glycosylation is initiated by the oligosaccharyltransferase complex with only two paralogs of the catalytic subunit, whereas O-glycan initiation is more complex. There are several types of O-linked glycosylation, but among the most diverse is the mucin or GalNAc type (hereafter referred to as O-glycosylation). O-glycosylation is initiated by 20 evolutionarily conserved polypeptide GalNAc-transferases (GalNAc-Ts), which catalyze the first step in the O-glycosylation of proteins by adding GalNAc residues to threonine, serine, and tyrosine amino acids (Fig 1A). Each of the GalNAc-Ts are differentially expressed in various tissues and have both distinct and overlapping peptide substrate specificities [2-12]. Thus, the repertoire of GalNAc-Ts expressed in a given cell determines the subset and O-glycosite pattern of glycosylated proteins [13]. Substantial efforts have been made to characterize and predict the substrate specificities of GalNAc-Ts in vitro, but understanding of the in vivo specificities of the individual GalNAc-Ts or their biological functions is limited [13-15]. This lack of insight prevents an understanding of how site-specific O-linked glycosylation affects diseases, such as metabolic disorders, cardiovascular disease, and various malignancies, that have been associated with GalNAc-Ts through genome-wide association studies and other linkage studies [16-26]. Therefore, it is imperative that we establish how O-glycosylation at specific sites in proteins affects protein function. A major task in achieving this goal is to identify the non-redundant biological functions of site-specific O-glycosylation. We and others recently developed new strategies for identifying specific sites on proteins that undergo O-glycosylation in different cell types and tissues [27-31]. Characterization of the O-glycoproteomic landscape in isolated human cells and multiple human cell lines suggests that more than 80 % of all proteins that traffic through the secretory pathway are O-glycoproteins [28, 30]. Probing the non-redundant contributions of individual GalNAc-Ts in cells with and without specific GalNAc-Ts [32-34] has revealed broad substrate specificities for some of the individual isoforms, whereas others seem to have very restricted substrate specificities [33-35]. Assessing all of the mapped O-glycosylation sites to identify associations between O-glycosites and protein annotations, we recently found that O-glycans are over-represented close to tandem repeat regions, protease cleavage sites, within propeptides, and on a select group of protein domains [28, 30, 36]. Although such general associations between the location of O-glycans and protein functions may direct future investigations, the strategy does not define the function of site-specific glycosylation. Further progress in discovering and defining novel functions of site-specific glycosylation events requires direct quantitative analysis of potential biological responses induced by the loss of distinct GalNAc-T isoforms, and such biological responses are not easily observed in single cell culture systems. Instead, more complex model systems can be used to examine and dissect the molecular mechanisms underlying the important biological functions of site-specific glycosylation. We previously used an organotypic tissue model equipped with genetically engineered cells to decipher the function of elongated O-glycans [29]. In the present study, we use the model combined with quantitative O-glycoproteomics and phosphoproteomics to perform open-ended discovery of the biological functions of site-specific glycosylation governed by GalNAc-Ts (Fig 1B). With this combinatorial strategy, we demonstrate that loss of individual GalNAc-T isoforms has distinct phenotypic consequences through their effect on distinct biological pathways, suggesting specific roles during epithelial formation.

Project description:Background RNA sequencing (RNA-seq) is a powerful technique for identifying and quantifying transcription and splicing events, both known and novel. However, given its recent development and the proliferation of library construction methods, understanding the bias it introduces is incomplete but critical to realizing its value. Results Here we present a method, in vitro transcription sequencing (IVT-seq), for identifying and assessing the technical biases in RNA-seq library generation and sequencing at scale. We created a pool of > 1000 in vitro transcribed (IVT) RNAs from a full-length human cDNA library and sequenced them with poly-A and total RNA-seq, the most common protocols. Because each cDNA is full length and we show IVT is incredibly processive, each base in each transcript should be equivalently represented. However, with common RNA-seq applications and platforms, we find ~50% of transcripts have > 2-fold and ~10% have > 10-fold differences in within-transcript sequence coverage. Strikingly, we also find > 6% of transcripts have regions of high, unpredictable sequencing coverage, where the same transcript varies dramatically in coverage between samples, confounding accurate determination of their expression. To get at causal factors, we used a combination of experimental and computational approaches to show that rRNA depletion is responsible for the most significant variability in coverage and that several sequence determinants also strongly influence representation. Conclusions In sum, these results show the utility of IVT-seq in promoting better understanding of bias introduced by RNA-seq and suggest caution in its interpretation. Furthermore, we find that rRNA-depletion is responsible for substantial, unappreciated biases in coverage. Perhaps most importantly, these coverage biases introduced during library preparation suggest exon level expression analysis may be inadvisable. 5 rRNA-depleted samples with duplicates, 1 polyA selected, 1 total RNA, and 1 plasmid library all without replicates.

Dataset Information

The human pancreatic islet transcriptome: impact of pro-inflammatory cytokines

Publications

The human pancreatic islet transcriptome: expression of candidate genes for type 1 diabetes and the impact of pro-inflammatory cytokines.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets