A Systems Biology Approach towards Deciphering the Unfolded Protein Response in Huntington's Disease

Although the disease causing gene huntingtin has been known for some time, the exact cause of neuronal cell death during _Huntington's disease_ (HD) remains unknown. One potential mechanism contributing to the massive loss of neurons in HD brains might be the _Unfolded Protein Response_ (UPR) which is activated by accumulation of misfolded proteins in the endoplasmic reticulum (ER). As an adaptive response, UPR upregulates transcription of chaperones, temporarily attenuating new translation and activates protein degradation via the proteasome. However, at high levels of ER stress, UPR signalling can contribute to neuronal apoptosis.

Our primary aims include (a) construction of the UPR signalling network, (b) curation and bioinformatical identification of UPR target genes and finally (c) examination of HD gene expression data sets for UPR transcriptional signatures and differential regulation of UPR pathways.

The UPR signalling pathway is reconstructed based on literature review and using the "Unified Interactome database":http://www.unihi.org. Lists of UPR target genes detected by previous experiments or as predicted by computational analysis are compiled. This allows us to perform enrichment analysis for differential HD gene expression and to assess whether UPR expression signatures are prominent during HD pathogenesis.

Results: The canonical UPR pathway is complemented with additional protein interaction data allowing us to assess its embedding into the cellular context and to identify potential modifiers as well as novel drug targets.

Conclusions: The in depth systems biology analysis can give us valuable insights about the involvement of the UPR in HD.
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The Unfolded Protein Response and its potential role in Huntington's disease

Huntington's disease (HD) is a progressive, neurodegenerative disease with fatal outcome. Although the disease-causing gene (huntingtin) has been known for some time, the exact cause of neuronal cell death is still unknown. One potential mechanism contributing to the massive loss of neurons in the brain of HD patients might be the unfolded protein response (UPR), which is activated by accumulation of misfolded proteins in the endoplasmatic reticulum (ER). As an adaptive response to counter-balance accumulation of un- or misfolded proteins, the UPR upregulates transcription of chaperones, temporarily attenuates new translation, and activates protein degradation via the proteasome. However, it is known that persistent ER stress and activated UPR can cause cell death by triggering of apoptosis. Nevertheless, the evidence linking UPR with HD progression remains inconclusive. Here, we present first analyses of UPR activation during HD based on available expression data. To elucidate the potential role of UPR as a disease-relevant process, we examine its connection to cell death and inflammatory processes. Due to the complexity of these molecular mechanisms, a systems biology approach was pursued

StemCellNet: an interactive platform for network-oriented investigations in stem cell biology.

Stem cells are characterized by their potential for self-renewal and their capacity to differentiate into mature cells. These two key features emerge through the interplay of various factors within complex molecular networks. To provide researchers with a dedicated tool to investigate these networks, we have developed StemCellNet, a versatile web server for interactive network analysis and visualization. It rapidly generates focused networks based on a large collection of physical and regulatory interactions identified in human and murine stem cells. The StemCellNet web-interface has various easy-to-use tools for selection and prioritization of network components, as well as for integration of expression data provided by the user. As a unique feature, the networks generated can be screened against a compendium of stemness-associated genes. StemCellNet can also indicate novel candidate genes by evaluating their connectivity patterns. Finally, an optional dataset of generic interactions, which provides large coverage of the human and mouse proteome, extends the versatility of StemCellNet to other biomedical research areas in which stem cells play important roles, such as in degenerative diseases or cancer. The StemCellNet web server is freely accessible at http://stemcellnet.sysbiolab.eu

Novel Human Embryonic Stem Cell Regulators Identified by Conserved and Distinct CpG Island Methylation State

Human embryonic stem cells (hESCs) undergo epigenetic changes in vitro which may compromise function, so an epigenetic pluripotency "signature" would be invaluable for line validation. We assessed Cytosine-phosphate-Guanine Island (CGI) methylation in hESCs by genomic DNA hybridisation to a CGI array, and saw substantial variation in CGI methylation between lines. Comparison of hESC CGI methylation profiles to corresponding somatic tissue data and hESC mRNA expression profiles identified a conserved hESC-specific methylation pattern associated with expressed genes. Transcriptional repressors and activators were over-represented amongst genes whose associated CGIs were methylated or unmethylated specifically in hESCs, respectively. Knockdown of candidate transcriptional regulators (HMGA1, GLIS2, PFDN5) induced differentiation in hESCs, whereas ectopic expression in fibroblasts modulated iPSC colony formation. Chromatin immunoprecipitation confirmed interaction between the candidates and the core pluripotency transcription factor network. We thus identify novel pluripotency genes on the basis of a conserved and distinct epigenetic configuration in human stem cells

A chemogenomic screening identifies CK2 as a target for pro-senescence therapy in PTEN-deficient tumours

Enhancement of cellular senescence in tumours triggers a stable cell growth arrest and activation of an antitumour immune response that can be exploited for cancer therapy. Currently, there are only a limited number of targeted therapies that act by increasing senescence in cancers, but the majority of them are not selective and also target healthy cells. Here we developed a chemogenomic screening to identify compounds that enhance senescence in PTEN-deficient cells without affecting normal cells. By using this approach, we identified casein kinase 2 (CK2) as a pro-senescent target. Mechanistically, we show that Pten loss increases CK2 levels by activating STAT3. CK2 upregulation in Pten null tumours affects the stability of Pml, an essential regulator of senescence. However, CK2 inhibition stabilizes Pml levels enhancing senescence in Pten null tumours. Taken together, our screening strategy has identified a novel STAT3-CK2-PML network that can be targeted for pro-senescence therapy for cancer

The unfolded protein response and its potential role in Huntington's disease elucidated by a systems biology approach.

Huntington ´s disease (HD) is a progressive, neurodegenerative disease with a fatal outcome. Although the disease-causing gene (huntingtin) has been known for over 20 years, the exact mechanisms leading to neuronal cell death are still controversial. One potential mechanism contributing to the massive loss of neurons observed in the brain of HD patients could be the unfolded protein response (UPR) activated by accumulation of misfolded proteins in the endoplasmic reticulum (ER). As an adaptive response to counter-balance accumulation of un- or misfolded proteins, the UPR upregulates transcription of chaperones, temporarily attenuates new translation, and activates protein degradation via the proteasome. However, persistent ER stress and an activated UPR can also cause apoptotic cell death. Although different studies have indicated a role for the UPR in HD, the evidence remains inconclusive. Here, we present extensive bioinformatic analyses that revealed UPR activation in different experimental HD models based on transcriptomic data. Accordingly, we have identified 53 genes, including RAB5A, HMGB1, CTNNB1, DNM1, TUBB, TSG101, EEF2, DYNC1H1, SLC12A5, ATG5, AKT1, CASP7 and SYVN1 that provide a potential link between UPR and HD. To further elucidate the potential role of UPR as a disease-relevant process, we examined its connection to apoptosis based on molecular interaction data, and identified a set of 40 genes including ADD1, HSP90B1, IKBKB, IKBKG, RPS3A and LMNB1, which seem to be at the crossroads between these two important cellular processes. Remarkably, we also found strong correlation of UPR gene expression with the length of the polyglutamine tract of Huntingtin, which is a critical determinant of age of disease onset in human HD patients pointing to the UPR as a promising target for therapeutic intervention. The study is complemented by a newly developed web-portal called UPR-HD (http://uprhd.sysbiolab.eu) that enables visualization and interactive analysis of UPR-associated gene expression across various HD models

UniHI 7: an enhanced database for retrieval and interactive analysis of human molecular interaction networks.

Unified Human Interactome (UniHI) (http://www.unihi.org) is a database for retrieval, analysis and visualization of human molecular interaction networks. Its primary aim is to provide a comprehensive and easy-to-use platform for network-based investigations to a wide community of researchers in biology and medicine. Here, we describe a major update (version 7) of the database previously featured in NAR Database Issue. UniHI 7 currently includes almost 350,000 molecular interactions between genes, proteins and drugs, as well as numerous other types of data such as gene expression and functional annotation. Multiple options for interactive filtering and highlighting of proteins can be employed to obtain more reliable and specific network structures. Expression and other genomic data can be uploaded by the user to examine local network structures. Additional built-in tools enable ready identification of known drug targets, as well as of biological processes, phenotypes and pathways enriched with network proteins. A distinctive feature of UniHI 7 is its user-friendly interface designed to be utilized in an intuitive manner, enabling researchers less acquainted with network analysis to perform state-of-the-art network-based investigations

Applying Data Mining Techniques Over Big Data

With rapid development of information technology, data flows in different variety of formats - sensors data, tweets, photos, raw data, and unstructured data. Statistics show that there were 800,000 Petabytes stored in the world in 2000. Today Internet is about 1.8 Zettabytes (Zettabytes is 10^21), and this number will reach 35 Zettabytes by 2020. With that, data management systems are not able to scale to this huge amount of raw, unstructured data, which what is called today big data. In this present study, we show the basic concept and design of big data tools, algorthims [sic] and techniques. We compare the classical data mining algorithms with big data algorthims [sic] by using hadoop/MapReuce [sic] as the core implemention [sic] of big-data for scalable algorthims. [sic] We implemented K-means and A-priori algorthim [sic] by using Hadoop/MapReduce on 5 nodes cluster of hadoop. We also show their performance for Gigabytes of data. Finally, we explore NoSQL (Not Only SQL) databases for semi-structured, massively large-scale of data using MongoDB as an example. Then, we show the performance between HDFS (Hadoop Distributed File System) and MongoDB data stores for these two algorithms.This research work is part of a full scholarship fund of a Master degree through Minisrty of Higher Education and Scientific Research (MOHESR), Republic of Iraq (Fund 17004)

A novel phosphatidylinositol 3-kinase (PI3K) inhibitor directs a potent FOXO-dependent, p53-independent cell cycle arrest phenotype characterized by the differential induction of a subset of FOXO-regulated genes.

INTRODUCTION: The activation of the phosphoinositide 3-kinase (PI3K)/AKT signalling pathway is one the most frequent genetic events in breast cancer, consequently the development of PI3K inhibitors has attracted much attention. Here we evaluate the effect of PI3K inhibition on global gene expression in breast cancer cells. METHODS: We used a range of methodologies that include in silico compound analysis, in vitro kinase assays, cell invasion assays, proliferation assays, genome-wide transcription studies (Agilent Technologies full genome arrays), gene set enrichment analysis, quantitative real-time PCR, immunoblotting in addition to chromatin immunoprecipitation. RESULTS: We defined the physico-chemical and the biological properties of ETP-45658, a novel potent PI3K inhibitor. We demonstrated that ETP-45658 potently inhibited cell proliferation within a broad range of human cancer cells, most potently suppressing the growth of breast cancer cells via inhibiting cell cycle. We show that this response is Forkhead box O (FOXO) protein dependent and p53 independent. Our genome-wide microarray analysis revealed that the cell cycle was the most affected biological process after exposure to ETP-45658 (or our control PI3K inhibitor PI-103), that despite the multiple transcription factors that are regulated by the PI3K/AKT signalling cascade, only the binding sites for FOXO transcription factors were significantly enriched and only a subset of all FOXO-dependent genes were induced. This disparity in gene transcription was not due to differential FOXO promoter recruitment. CONCLUSIONS: The constitutive activation of PI3Ks and thus the exclusion of FOXO transcription factors from the nucleus is a key feature of breast cancer. Our results presented here highlight that PI3K inhibition activates specific FOXO-dependent genes that mediate cell cycle arrest in breast cancer cells

Greenhouse Gas Sensors Fabricated with New Materials for Climatic Usage: A Review

With the increasing utilization of fossil fuels in today’s technological world, the atmosphere’s concentration of greenhouse gases is increasing and needs to be controlled. In order to achieve this goal, it is imperative to have sensors that can provide data on the greenhouse gases in the environment. The recent literature contains a few publications that detail the use of new methods and materials for sensing these gases. The first part of this review is focused on the possible effects of greenhouse gases in the atmosphere, and the second part surveys the developments of sensors for greenhouse gases with coverage on carbon nano-materials and composites directed towards sensing gases like CO2, CH4, and NOx. With carbon dioxide measurements, due consideration is given to the dissolved carbon dioxide gas in water (moisture). The density functional calculations project that Pd-doped single-walled carbon nanotubes are ideal for the development of NOx sensors. The current trend is to make sensors using 3D printing or inkjet printing in order to allow for the achievement of ppb levels of sensitivity that have not been realized before. This review is to elaborate on the need for the development of greenhouse gas sensors for climatic usage by using selected examples