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Gene expression profiles for the human pancreas and purified islets in Type 1 diabetes: new findings at clinical onset and in long-standing diabetes

TLDR
In this paper, the authors reported whole genome transcript analysis validated by quantitative reverse transcription-polymerase chain reaction (qRT-PCR) and correlated with immunohistological observations for four Type 1 diabetes patients and for purified islets from two of them.
Abstract
Type 1 diabetes (T1D) is caused by the selective destruction of the insulin-producing beta cells of the pancreas by an autoimmune response. Due to ethical and practical difficulties, the features of the destructive process are known from a small number of observations, and transcriptomic data are remarkably missing. Here we report whole genome transcript analysis validated by quantitative reverse transcription-polymerase chain reaction (qRT-PCR) and correlated with immunohistological observations for four T1D pancreases (collected 5 days, 9 months, 8 and 10 years after diagnosis) and for purified islets from two of them. Collectively, the expression profile of immune response and inflammatory genes confirmed the current views on the immunopathogenesis of diabetes and showed similarities with other autoimmune diseases; for example, an interferon signature was detected. The data also supported the concept that the autoimmune process is maintained and balanced partially by regeneration and regulatory pathway activation, e.g. non-classical class I human leucocyte antigen and leucocyte immunoglobulin-like receptor, subfamily B1 (LILRB1). Changes in gene expression in islets were confined mainly to endocrine and neural genes, some of which are T1D autoantigens. By contrast, these islets showed only a few overexpressed immune system genes, among which bioinformatic analysis pointed to chemokine (C-C motif) receptor 5 (CCR5) and chemokine (CXC motif) receptor 4) (CXCR4) chemokine pathway activation. Remarkably, the expression of genes of innate immunity, complement, chemokines, immunoglobulin and regeneration genes was maintained or even increased in the long-standing cases. Transcriptomic data favour the view that T1D is caused by a chronic inflammatory process with a strong participation of innate immunity that progresses in spite of the regulatory and regenerative mechanisms.

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Citations
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Journal ArticleDOI

Peripheral and Islet Interleukin-17 Pathway Activation Characterizes Human Autoimmune Diabetes and Promotes Cytokine-Mediated β-Cell Death

TL;DR: It is shown that IL-17 mediates significant and reproducible enhancement of IL-1β/interferon (IFN)-γ–induced and tumor necrosis factor (TNF)-α/IFN-γ-induced apoptosis in human islets, rat β-cells, and INS-1E cells, in association with significant upregulation of β-cell IL17RA expression via activation of the transcription factors STAT1 and nuclear factor (NF)-κB.
Journal ArticleDOI

Insulitis in human type 1 diabetes: The quest for an elusive lesion.

TL;DR: Recent new insights into the regenerative capacity of the β-cell mass in the pre-clinical stages of the disease are discussed and these findings to the inflammatory processes within the islet tissue are related.
Journal ArticleDOI

Islet inflammation and CXCL10 in recent-onset type 1 diabetes.

TL;DR: The data point to CXCL10 as an important cytokine in distressed islets that may contribute to inflammation leading to insulitis and β cell destruction, regardless of local viral infection.
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Plasma-derived exosome characterization reveals a distinct microRNA signature in long duration Type 1 diabetes.

TL;DR: The potential of EXOs miRNA profiling in the diagnosis of type 1 diabetes mellitus is highlighted as well as new insights into the molecular mechanisms involved in T1DM are revealed.
Journal ArticleDOI

The role of the complement system in metabolic organs and metabolic diseases

TL;DR: The link between complement and metabolic organs, focusing on the pancreas, adipose tissue, and liver and the diverse effects of complement system on metabolic disorders are discussed.
References
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Journal ArticleDOI

Analysis of relative gene expression data using real-time quantitative pcr and the 2(-delta delta c(t)) method

TL;DR: The 2-Delta Delta C(T) method as mentioned in this paper was proposed to analyze the relative changes in gene expression from real-time quantitative PCR experiments, and it has been shown to be useful in the analysis of realtime, quantitative PCR data.
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Controlling the false discovery rate: a practical and powerful approach to multiple testing

TL;DR: In this paper, a different approach to problems of multiple significance testing is presented, which calls for controlling the expected proportion of falsely rejected hypotheses -the false discovery rate, which is equivalent to the FWER when all hypotheses are true but is smaller otherwise.
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Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes

TL;DR: The normalization strategy presented here is a prerequisite for accurate RT-PCR expression profiling, which opens up the possibility of studying the biological relevance of small expression differences.
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Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments

TL;DR: The hierarchical model of Lonnstedt and Speed (2002) is developed into a practical approach for general microarray experiments with arbitrary numbers of treatments and RNA samples and the moderated t-statistic is shown to follow a t-distribution with augmented degrees of freedom.
Journal ArticleDOI

Exploration, normalization, and summaries of high density oligonucleotide array probe level data

TL;DR: There is no obvious downside to using RMA and attaching a standard error (SE) to this quantity using a linear model which removes probe-specific affinities, and the exploratory data analyses of the probe level data motivate a new summary measure that is a robust multi-array average (RMA) of background-adjusted, normalized, and log-transformed PM values.
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