Burak Kutlu

Detailed transcriptome atlas of the pancreatic beta cell

BackgroundGene expression patterns provide a detailed view of cellular functions. Comparison of profiles in disease vs normal conditions provides insights into the processes underlying disease progression. However, availability and integration of public gene expression datasets remains a major challenge. The aim of the present study was to explore the transcriptome of pancreatic islets and, based on this information, to prepare a comprehensive and open access inventory of insulin-producing beta cell gene expression, the Beta Cell Gene Atlas (BCGA).MethodsWe performed Massively Parallel Signature Sequencing (MPSS) analysis of human pancreatic islet samples and microarray analyses of purified rat beta cells, alpha cells and INS-1 cells, and compared the information with available array data in the literature.ResultsMPSS analysis detected around 7600 mRNA transcripts, of which around a third were of low abundance. We identified 2000 and 1400 transcripts that are enriched/depleted in beta cells compared to alpha cells and INS-1 cells, respectively. Microarray analysis identified around 200 transcription factors that are differentially expressed in either beta or alpha cells. We reanalyzed publicly available gene expression data and integrated these results with the new data from this study to build the BCGA. The BCGA contains basal (untreated conditions) gene expression level estimates in beta cells as well as in different cell types in human, rat and mouse pancreas. Hierarchical clustering of expression profile estimates classify cell types based on species while beta cells were clustered together.ConclusionOur gene atlas is a valuable source for detailed information on the gene expression distribution in beta cells and pancreatic islets along with insulin producing cell lines. The BCGA tool, as well as the data and code used to generate the Atlas are available at the T1Dbase website (T1DBase.org).

T1DBase: integration and presentation of complex data for type 1 diabetes research

T1DBase () [Smink et al. (2005) Nucleic Acids Res., 33, D544–D549; Burren et al. (2004) Hum. Genomics, 1, 98–109] is a public website and database that supports the type 1 diabetes (T1D) research community. T1DBase provides a consolidated T1D-oriented view of the complex data world that now confronts medical researchers and enables scientists to navigate from information they know to information that is new to them. Overview pages for genes and markers summarize information for these elements. The Gene Dossier summarizes information for a list of genes. GBrowse [Stein et al. (2002) Genome Res., 10, 1599–1610] displays genes and other features in their genomic context, and Cytoscape [Shannon et al. (2003) Genome Res., 13, 2498–2504] shows genes in the context of interacting proteins and genes. The Beta Cell Gene Atlas shows gene expression in β cells, islets, and related cell types and lines, and the Tissue Expression Viewer shows expression across other tissues. The Microarray Viewer shows expression from more than 20 array experiments. The Beta Cell Gene Expression Bank contains manually curated gene and pathway annotations for genes expressed in β cells. T1DMart is a query tool for markers and genotypes. PosterPages are ‘home pages’ about specific topics or datasets. The key challenge, now and in the future, is to provide powerful informatics capabilities to T1D scientists in a form they can use to enhance their research.

Limited Capacity of Human Adult Islets Expanded In Vitro to Redifferentiate Into Insulin-Producing β-Cells

Limited organ availability is an obstacle to the widespread use of islet transplantation in type 1 diabetic patients. To address this problem, many studies have explored methods for expanding functional human islets in vitro for diabetes cell therapy. We previously showed that islet cells replicate after monolayer formation under the influence of hepatocyte growth factor and selected extracellular matrices. However, under these conditions, senescence and loss of insulin expression occur after >15 doublings. In contrast, other groups have reported that islet cells expanded in monolayers for months progressed through a reversible epithelial-to-mesenchymal transition, and that on removal of serum from the cultures, islet-like structures producing insulin were formed (1). The aim of the current study was to compare the two methods for islet expansion using immunostaining, real-time quantitative PCR, and microarrays at the following time points: on arrival, after monolayer expansion, and after 1 week in serum-free media. At this time, cell aliquots were grafted into nude mice to study in vivo function. The two methods showed similar results in islet cell expansion. Attempts at cell differentiation after expansion by both methods failed to consistently recover a β-cell phenotype. Redifferentiation of β-cells after expansion is still a challenge in need of a solution.

Use of Microarray Analysis to Unveil Transcription Factor and Gene Networks Contributing to β Cell Dysfunction and Apoptosis

Abstract: The β cell fate following immune‐mediated damage depends on an intricate pattern of dozens of genes up‐ or downregulated in parallel and/or sequentially. We are utilizing microarray analysis to clarify the pattern of gene expression in primary rat β cells exposed to the proapoptotic cytokines, IL‐1β and/or IFN‐γ. The picture emerging from these experiments is that β cells are not passive bystanders of their own destruction. On the contrary, β cells respond to damage by activating diverse networks of transcription factors and genes that may either lead to apoptosis or preserve viability. Of note, cytokine‐exposed β cells produce and release chemokines that may contribute to the homing and activation of T cells and macrophages during insulitis. Several of the effects of cytokines depend on the activation of the transcription factor, NF‐κB. NF‐κB blocking prevents cytokine‐induced β cell death, and characterization of NF‐κB‐dependent genes by microarray analysis indicated that this transcription factor controls diverse networks of transcription factors and effector genes that are relevant for maintenance of β cell differentiated status, cytosolic and ER calcium homeostasis, attraction of mononuclear cells, and apoptosis. Identification of this and additional “transcription factor networks” is being pursued by cluster analysis of gene expression in insulin‐producing cells exposed to cytokines for different time periods. Identification of complex gene patterns poses a formidable challenge, but is now technically feasible. These accumulating evidences may finally unveil the molecular mechanisms regulating the β cell “decision” to undergo or not apoptosis in early T1DM.

Evolutionary etiology of high-grade astrocytomas

Significance High-grade astrocytomas (HGAs), including glioblastomas (GBMs), are the most common human brain tumors, and they remain fatal with no effective treatment. The most prevalent form, primary GBM, presents clinically as advanced disease, thus providing no access to or understanding of early stages. We report a comprehensive study in the mouse that establishes causal relationships and an evolutionary etiology in HGA development. Events yielding disease, both engineered and spontaneous, indicate grade-specific roles culminating in the development of GBMs with characteristics of primary GBMs, including molecular alignment with the mesenchymal subclass, asymptomatic early disease, and rapid emergence of high-grade aggressive cancer. These genetically engineered models provide a path to basic understanding of disease etiology and a window into diagnostic and therapeutic discovery. Glioblastoma (GBM), the most common brain malignancy, remains fatal with no effective treatment. Analyses of common aberrations in GBM suggest major regulatory pathways associated with disease etiology. However, 90% of GBMs are diagnosed at an advanced stage (primary GBMs), providing no access to early disease stages for assessing disease progression events. As such, both understanding of disease mechanisms and the development of biomarkers and therapeutics for effective disease management are limited. Here, we describe an adult-inducible astrocyte-specific system in genetically engineered mice that queries causation in disease evolution of regulatory networks perturbed in human GBM. Events yielding disease, both engineered and spontaneous, indicate ordered grade-specific perturbations that yield high-grade astrocytomas (anaplastic astrocytomas and GBMs). Impaired retinoblastoma protein RB tumor suppression yields grade II histopathology. Additional activation of v-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog (KRAS) network drives progression to grade III disease, and further inactivation of phosphatase and tensin homolog (PTEN) yields GBM. Spontaneous missense mutation of tumor suppressor Trp53 arises subsequent to KRAS activation, but before grade III progression. The stochastic appearance of mutations identical to those observed in humans, particularly the same spectrum of p53 amino acid changes, supports the validity of engineered lesions and the ensuing interpretations of etiology. Absence of isocitrate dehydrogenase 1 (IDH1) mutation, asymptomatic low grade disease, and rapid emergence of GBM combined with a mesenchymal transcriptome signature reflect characteristics of primary GBM and provide insight into causal relationships.

Temporal profiling of cytokine-induced genes in pancreatic β-cells by meta-analysis and network inference.

Type 1 Diabetes (T1D) is an autoimmune disease where local release of cytokines such as IL-1β and IFN-γ contributes to β-cell apoptosis. To identify relevant genes regulating this process we performed a meta-analysis of 8 datasets of β-cell gene expression after exposure to IL-1β and IFN-γ. Two of these datasets are novel and contain time-series expressions in human islet cells and rat INS-1E cells. Genes were ranked according to their differential expression within and after 24 h from exposure, and characterized by function and prior knowledge in the literature. A regulatory network was then inferred from the human time expression datasets, using a time-series extension of a network inference method. The two most differentially expressed genes previously unknown in T1D literature (RIPK2 and ELF3) were found to modulate cytokine-induced apoptosis. The inferred regulatory network is thus supported by the experimental validation, providing a proof-of-concept for the proposed statistical inference approach.

Meta-analysis of gene expression in human pancreatic islets after in vitro expansion

Pancreatic islet transplantation as a potential cure for type 1 diabetes (T1D) cannot be scaled up due to a scarcity of human pancreas donors. In vitro expansion of beta-cells from mature human pancreatic islets provides an alternative source of insulin-producing cells. The exact nature of the expanded cells produced by diverse expansion protocols and their potential for differentiation into functional beta-cells remain elusive. We performed a large-scale meta-analysis of gene expression in human pancreatic islet cells, which were processed using three different previously described protocols for expansion and for which redifferentiation was attempted. All three expansion protocols induced dramatic changes in the expression profiles of pancreatic islets; many of these changes are shared among the three protocols. Attempts at redifferentiation of expanded cells induce a limited number of gene expression changes. Nevertheless, these fail to restore a pancreatic islet-like gene expression pattern. Comparison with a collection of public microarray datasets confirmed that expanded cells are highly comparable to mesenchymal stem cells. Genes induced in expanded cells are also enriched for targets of transcription factors important for pluripotency induction. The present data increase our understanding of the active pathways in expanded and redifferentiated islets. Knowledge of the mesenchymal stem cell potential may help development of drug therapeutics to restore beta-cell mass in T1D patients.

New Approaches for in Silico Identification of Cytokine‐Modified β Cell Gene Networks

Abstract: Beta cell dysfunction and death in type 1 diabetes mellitus (T1DM) is caused by direct contact with activated macrophages and T lymphocytes and by exposure to soluble mediators secreted by these cells, such as cytokines and nitric oxide. Cytokine‐induced apoptosis depends on the expression of pro‐ and anti‐apoptotic genes that remain to be characterized. Using microarray analyses, we identified several transcription factor and “effector” gene networks regulated by interleukin‐1β and/or interferon‐γ in β cells. This suggests that β cell fate following exposure to cytokines is a complex and highly regulated process, depending on the duration and severity of perturbation of key gene networks. In order to draw correct conclusions from these massive amounts of data, we need to utilize novel bioinformatics and statistical tools. Thus, we are presently performing in silico analysis for the localization of binding sites for the transcription factor NF‐κB (previously shown to be pivotal for β cell apoptosis) in 15 temporally related gene clusters, identified by time‐course microarray analysis. In silico analysis is based on a broad range of computational techniques used to detect motifs in a DNA sequence corresponding to the binding site of a transcription factor. These computer‐based findings must be validated by use of positive and negative controls, and by “ChIP on chip” analysis. Moreover, new statistical approaches are required to decrease false positive findings. These novel approaches will constitute a “proof of principle” for the integrated use of bioinformatics and functional genomics in the characterization of relevant cytokine‐regulated β cell gene networks leading to β cell apoptosis in T1DM.

Optimal Scaling of Digital Transcriptomes

Deep sequencing of transcriptomes has become an indispensable tool for biology, enabling expression levels for thousands of genes to be compared across multiple samples. Since transcript counts scale with sequencing depth, counts from different samples must be normalized to a common scale prior to comparison. We analyzed fifteen existing and novel algorithms for normalizing transcript counts, and evaluated the effectiveness of the resulting normalizations. For this purpose we defined two novel and mutually independent metrics: (1) the number of “uniform” genes (genes whose normalized expression levels have a sufficiently low coefficient of variation), and (2) low Spearman correlation between normalized expression profiles of gene pairs. We also define four novel algorithms, one of which explicitly maximizes the number of uniform genes, and compared the performance of all fifteen algorithms. The two most commonly used methods (scaling to a fixed total value, or equalizing the expression of certain ‘housekeeping’ genes) yielded particularly poor results, surpassed even by normalization based on randomly selected gene sets. Conversely, seven of the algorithms approached what appears to be optimal normalization. Three of these algorithms rely on the identification of “ubiquitous” genes: genes expressed in all the samples studied, but never at very high or very low levels. We demonstrate that these include a “core” of genes expressed in many tissues in a mutually consistent pattern, which is suitable for use as an internal normalization guide. The new methods yield robustly normalized expression values, which is a prerequisite for the identification of differentially expressed and tissue-specific genes as potential biomarkers.

Abstract 1809: Analysis of molecular networks that drive astrocytoma initiation and progression.

High grade astrocytomas, anaplastic astrocytoma and glioblastoma multiforme (GBM) remain incurable in spite of advanced aggressive treatments including surgery, radiation and chemotherapy. To study pathways and mechanisms involved in the development of high grade astrocytomas, we used mouse models wherein key molecular pathways perturbed in human GBMs were inactivated or induced via Cre-driven adult astrocyte-specific system. Inhibition of Rb pathway via expression of T 121 , a N-terminal fragment of SV40 large T antigen (T: TgGZT 121, GFAP-CreER TM ) initiated diffuse grade II astrocytoma formation by 2 months after tamoxifen treatment which in some cases developed to grade III pathology 1.5 year after induction. Activation of KRas pathway (TR: TgGZT 121 , Kras +/lsl-G12D , GFAP-CreER TM ) facilitated progression to grade III anaplastic astrocytoma tumor masses 4-5 months post induction which in a few cases developed to grade IV glioblastoma with some features of human disease. Additional PTEN loss or haploinsufficiency (TRPhet: TgGZT 121 , Kras +/lsl-G12D , PTEN +/fl , GFAP-CreER TM ; TRPnull: TgGZT 121 , Kras +/lsl-G12D , PTEN fl/fl , GFAP-CreER TM ) led to rapid development of glioblastoma with characteristic features of angiogenesis and necrosis observed in human disease. Transcriptome analyses of GBM in our mouse models showed concordance with highly aggressive mesenchymal and proneural subclasses. To identify disease-specific gene networks involved in astrocytoma initiation and progression, we analyzed more than 300 brain samples from tamoxifen-induced T, TR, TRPhet and TRPnull mice and corresponding controls at different time points after induction for gene and miRNA expression by microarray and Nanostring technology. The genes that were induced early and gradually increased in expression with tumor grade belonged to several key molecular networks: DNA replication and repair, cell division and chromosome transmission fidelity, cell cycle progression, metabolism, and pathways important for embryonic stem cell biology. Pathways significantly induced at later stages of disease (grades III-IV) included Notch and Wnt signaling, inflammatory response genes, p53 signaling, and RNA processing. Significantly downregulated pathways were related to neuronal development and function. We have confirmed expression of several candidate genes in mouse tumor samples and cell lines derived from low and high grade tumors. Currently we are investigating potential role of the selected candidates in astrocytoma development by in vitro and in vivo functional analysis. In summary, we utilized mouse models to determine global molecular changes during astrocytoma initiation and progression to high grade. These studies are important for understanding the mechanisms of the disease and may facilitate the development of new therapies. Citation Format: Yaroslava Ruzankina, Burak Kutlu, Sophie Wang, Yurong Song, Deborah Householder, Philip Martin, Maureen Baran, Simone Difilippantonio, Leroy Hood, Terry Van Dyke. Analysis of molecular networks that drive astrocytoma initiation and progression. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 1809. doi:10.1158/1538-7445.AM2013-1809