BackgroundCoordinated efforts to collect large-scale data sets provide a basis for systems level understanding of complex diseases. In order to translate these fragmented and heterogeneous data sets into knowledge and medical benefits, advanced computational methods for data analysis, integration and visualization are needed.MethodsWe introduce a novel data integration framework, Anduril, for translating fragmented large-scale data into testable predictions. The Anduril framework allows rapid integration of heterogeneous data with state-of-the-art computational methods and existing knowledge in bio-databases. Anduril automatically generates thorough summary reports and a website that shows the most relevant features of each gene at a glance, allows sorting of data based on different parameters, and provides direct links to more detailed data on genes, transcripts or genomic regions. Anduril is open-source; all methods and documentation are freely available.ResultsWe have integrated multidimensional molecular and clinical data from 338 subjects having glioblastoma multiforme, one of the deadliest and most poorly understood cancers, using Anduril. The central objective of our approach is to identify genetic loci and genes that have significant survival effect. Our results suggest several novel genetic alterations linked to glioblastoma multiforme progression and, more specifically, reveal Moesin as a novel glioblastoma multiforme-associated gene that has a strong survival effect and whose depletion in vitro significantly inhibited cell proliferation. All analysis results are available as a comprehensive website.ConclusionsOur results demonstrate that integrated analysis and visualization of multidimensional and heterogeneous data by Anduril enables drawing conclusions on functional consequences of large-scale molecular data. Many of the identified genetic loci and genes having significant survival effect have not been reported earlier in the context of glioblastoma multiforme. Thus, in addition to generally applicable novel methodology, our results provide several glioblastoma multiforme candidate genes for further studies.Anduril is available at http://csbi.ltdk.helsinki.fi/anduril/The glioblastoma multiforme analysis results are available at http://csbi.ltdk.helsinki.fi/anduril/tcga-gbm/

The development of therapeutic strategies to combat immune-associated diseases requires the molecular mechanisms of human Th17 cell differentiation to be fully identified and understood. To investigate transcriptional control of Th17 cell differentiation, we used primary human CD4 T cells in small interfering RNA (siRNA)-mediated gene silencing and chromatin immunoprecipitation followed by massive parallel sequencing (ChIP-seq) to identify both the early direct and indirect targets of STAT3. The integrated dataset presented in this study confirms that STAT3 is critical for transcriptional regulation of early human Th17 cell differentiation. Additionally, we found that a number of SNPs from loci associated with immune-mediated disorders were located at sites where STAT3 binds to induce Th17 cell specification. Importantly, introduction of such SNPs alters STAT3 binding in DNA affinity precipitation assays. Overall, our study provides important insights for modulating Th17-mediated pathogenic immune responses in humans.

Defects in the mismatch repair system lead to microsatellite instability (MSI), a feature observed in~15% of all colorectal cancers (CRCs). Microsatellite mutations that drive tumourigenesis, typically inactivation of tumour suppressors, are selected for and are frequently detected in MSI cancers. Here, we evaluated somatic mutations in microsatellite repeats of 790 genes chosen based on reduced expression in MSI CRC and existence of a coding mononucleotide repeat of 6-10 bp in length. All the repeats were initially sequenced in 30 primary MSI CRC samples and whenever frameshift mutations were identified in >20%, additional 70 samples were sequenced. To distinguish driver mutations from passengers, we similarly analyzed the occurrence of frameshift mutations in 121 intronic control repeats and utilized a statistical regression model to determine cutoff mutation frequencies for repeats of all types (A/T and C/G, 6-10 bp). Along with several know target genes, including TGFBR2, ACVR2, and MSH3, six novel candidate driver genes emerged that harbored significantly more mutations than identical control repeats. The mutation frequencies in 100 MSI CRC samples were 51% in G8 of GLYR1, 47% in T9 of ABCC5, 43% in G8 of WDTC1, 33% in A8 of ROCK1, 30% in T8 of OR51E2, and 28% in A8 of TCEB3. Immunohistochemical staining of GLYR1 revealed defective protein expression in tumors carrying biallelic mutations, supporting a loss of function hypothesis. This is a large scale, unbiased effort to identify genes that when mutated are likely to contribute to MSI CRC development.DNA mismatch repair (MMR) system recognizes and removes misincorporations and slippage errors occurring in normal DNA replication. Defects in the MMR system lead to genetic instability referred to as microsatellite instability (MSI). MSI occurs as a consequence of a germline defect and a subsequent somatic inactivation of the wild-type allele of one of the key genes involved in this system, MLH1, MSH2, MSH6, and PMS2. 1 In sporadic CRC, MSI is typically caused

A common single nucleotide polymorphism (SNP), rs6983267, at 8q24.21 has recently been shown to associate with colorectal cancer (CRC). Three independent SNP association studies showed that rs6983267 contributes to CRC with odds ratios (OR) of 1.17 to 1.22. Here, we genotyped a populationbased series of 1,042 patients with CRC and 1,012 healthy controls for rs6983267 and determined the contribution of SNP to CRC in Finland, using germ line DNA, as well as the respective cancer DNA in heterozygous patients. The comprehensive clinical data available from the 1,042 patients and their first-degree relatives enabled us to thoroughly examine the possible association of this variant with different clinical features. As expected, a significant association between the G allele of rs6983267 and CRC [OR, 1.22; 95% confidence interval (CI), 1.08-1.38; P = 0.0018] was found, confirming the previous observations. A trend towards association of the G allele with microsatellite-stable cancer (OR, 1.37; 95% CI, 1.02-1.85; P = 0.04) and family history of cancers other than CRC was seen (OR, 1.20; 95% CI, 1-1.43; P = 0.05). Four hundred and sixty-six GT heterozygotes identified in this study were analyzed for allelic imbalance at rs6983267 in the respective cancer DNA. One hundred and one tumors showed allelic imbalance (22%). The risk allele G was favored in 67 versus 34 tumors (P = 0.0007). This finding implicates that the underlying germ line genetic defect in 8q24.21 is a target in the somatic evolution of CRC.

The circadian clock regulates daily variations in physiologic processes. CLOCK acts as a regulator in the circadian apparatus controlling the expression of other clock genes, including PER1. Clock genes have been implicated in cancer-related functions; in this work, we investigated CLOCK as a possible target of somatic mutations in microsatellite unstable colorectal cancers. Combining microarray gene expression data and public gene sequence information, we identified CLOCK as 1 of 790 putative novel microsatellite instability (MSI) target genes. A total of 101 MSI colorectal carcinomas (CRC) were sequenced for a coding microsatellite in CLOCK. The effect of restoring CLOCK expression was studied in LS180 cells lacking wild-type CLOCK by stably expressing GST-CLOCK or glutathione S-transferase empty vector and testing the effects of UV-induced apoptosis and radiation by DNA content analysis using flow cytometry. Putative novel CLOCK target genes were searched by using ChIP-seq. CLOCK mutations occurred in 53% of MSI CRCs. Restoring CLOCK expression in cells with biallelic CLOCK inactivation resulted in protection against UV-induced apoptosis and decreased G 2 -M arrest in response to ionizing radiation. Using ChIP-Seq, novel CLOCK-binding elements were identified near DNA damage genes p21, NBR1, BRCA1, and RAD50. CLOCK is shown to be mutated in cancer, and altered response to DNA damage provides one plausible mechanism of tumorigenesis. Mol Cancer Res; 8(7); 952-60. ©2010 AACR.

The transcriptional network and protein regulators that govern T helper 17 (Th17) cell differentiation have been studied extensively using advanced genomic approaches. For a better understanding of these biological processes, we have moved a step forward, from gene- to protein-level characterization of Th17 cells. Mass spectrometry–based label-free quantitative (LFQ) proteomics analysis were made of in vitro differentiated murine Th17 and induced regulatory T (iTreg) cells. More than 4,000 proteins, covering almost all subcellular compartments, were detected. Quantitative comparison of the protein expression profiles resulted in the identification of proteins specifically expressed in the Th17 and iTreg cells. Importantly, our combined analysis of proteome and gene expression data revealed protein expression changes that were not associated with changes at the transcriptional level. Our dataset provides a valuable resource, with new insights into the proteomic characteristics of Th17 and iTreg cells, which may prove useful in developing treatment of autoimmune diseases and developing tumor immunotherapy.

BackgroundBone morphogenetic proteins (BMPs) are members of the TGF-beta superfamily of growth factors. They are known for their roles in regulation of osteogenesis and developmental processes and, in recent years, evidence has accumulated of their crucial functions in tumor biology. BMP4 and BMP7, in particular, have been implicated in breast cancer. However, little is known about BMP target genes in the context of tumor. We explored the effects of BMP4 and BMP7 treatment on global gene transcription in seven breast cancer cell lines during a 6-point time series, using a whole-genome oligo microarray. Data analysis included hierarchical clustering of differentially expressed genes, gene ontology enrichment analyses and model based clustering of temporal data.ResultsBoth ligands had a strong effect on gene expression, although the response to BMP4 treatment was more pronounced. The cellular functions most strongly affected by BMP signaling were regulation of transcription and development. The observed transcriptional response, as well as its functional outcome, followed a temporal sequence, with regulation of gene expression and signal transduction leading to changes in metabolism and cell proliferation. Hierarchical clustering revealed distinct differences in the response of individual cell lines to BMPs, but also highlighted a synexpression group of genes for both ligands. Interestingly, the majority of the genes within these synexpression groups were shared by the two ligands, probably representing the core molecular responses common to BMP4 and BMP7 signaling pathways.ConclusionsAll in all, we show that BMP signaling has a remarkable effect on gene transcription in breast cancer cells and that the functions affected follow a logical temporal pattern. Our results also uncover components of the common cellular transcriptional response to BMP4 and BMP7. Most importantly, this study provides a list of potential novel BMP target genes relevant in breast cancer.

Motivation: Mechanistic models based on ordinary differential equations provide powerful and accurate means to describe the dynamics of molecular machinery which orchestrates gene regulation. When combined with appropriate statistical techniques, mechanistic models can be calibrated using experimental data and, in many cases, also the model structure can be inferred from time–course measurements. However, existing mechanistic models are limited in the sense that they rely on the assumption of static network structure and cannot be applied when transient phenomena affect, or rewire, the network structure. In the context of gene regulatory network inference, network rewiring results from the net impact of possible unobserved transient phenomena such as changes in signaling pathway activities or epigenome, which are generally difficult, but important, to account for.Results: We introduce a novel method that can be used to infer dynamically evolving regulatory networks from time–course data. Our method is based on the notion that all mechanistic ordinary differential equation models can be coupled with a latent process that approximates the network structure rewiring process. We illustrate the performance of the method using simulated data and, further, we apply the method to study the regulatory interactions during T helper 17 (Th17) cell differentiation using time–course RNA sequencing data. The computational experiments with the real data show that our method is capable of capturing the experimentally verified rewiring effects of the core Th17 regulatory network. We predict Th17 lineage specific subnetworks that are activated sequentially and control the differentiation process in an overlapping manner.Availability and Implementation: An implementation of the method is available at http://research.ics.aalto.fi/csb/software/lem/.Contacts: jukka.intosalmi@aalto.fi or harri.lahdesmaki@aalto.fi