Data integration, pathway analysis and mining for systems biology

Post-genomic molecular biology embodies high-throughput experimental techniques and hence is a data-rich field. The goal of this thesis is to develop bioinformatics methods to utilise publicly available data in order to produce knowledge and to aid mining of newly generated data. As an example of knowledge or hypothesis generation, consider function prediction of biological molecules. Assignment of protein function is a non-trivial task owing to the fact that the same protein may be involved in different biological processes, depending on the state of the biological system and protein localisation. The function of a gene or a gene product may be provided as a textual description in a gene or protein annotation database. Such textual descriptions lack in providing the contextual meaning of the gene function. Therefore, we need ways to represent the meaning in a formal way. Here we apply data integration approach to provide rich representation that enables context-sensitive mining of biological data in terms of integrated networks and conceptual spaces. Context-sensitive gene function annotation follows naturally from this framework, as a particular application. Next, knowledge that is already publicly available can be used to aid mining of new experimental data. We developed an integrative bioinformatics method that utilises publicly available knowledge of protein-protein interactions, metabolic networks and transcriptional regulatory networks to analyse transcriptomics data and predict altered biological processes. We applied this method to a study of dynamic response of Saccharomyces cerevisiae to oxidative stress. The application of our method revealed dynamically altered biological functions in response to oxidative stress, which were validated by comprehensive in vivo metabolomics experiments. The results provided in this thesis indicate that integration of heterogeneous biological data facilitates advanced mining of the data. The methods can be applied for gaining insight into functions of genes, gene products and other molecules, as well as for offering functional interpretation to transcriptomics and metabolomics experiments

Metabolite profiling of the carnivorous pitcher plants Darlingtonia and Sarracenia

Sarraceniaceae is a New World carnivorous plant family comprising three genera: Darlingtonia, Heliamphora, and Sarracenia. The plants occur in nutrient-poor environments and have developed insectivorous capability in order to supplement their nutrient uptake. Sarracenia flava contains the alkaloid coniine, otherwise only found in Conium maculatum, in which its biosynthesis has been studied, and several Aloe species. Its ecological role and biosynthetic origin in S. flava is speculative. The aim of the current research was to investigate the occurrence of coniine in Sarracenia and Darlingtonia and to identify common constituents of both genera, unique compounds for individual variants and floral scent chemicals. In this comprehensive metabolic profiling study, we looked for compound patterns that are associated with the taxonomy of Sarracenia species. In total, 57 different Sarracenia and D. californica accessions were used for metabolite content screening by gas chromatography-mass spectrometry. The resulting high-dimensional data were studied using a data mining approach. The two genera are characterized by a large number of metabolites and huge chemical diversity between different species. By applying feature selection for clustering and by integrating new biochemical data with existing phylogenetic data, we were able to demonstrate that the chemical composition of the species can be explained by their known classification. Although transcriptome analysis did not reveal a candidate gene for coniine biosynthesis, the use of a sensitive selected ion monitoring method enabled the detection of coniine in eight Sarracenia species, showing that it is more widespread in this genus than previously believed.Peer reviewe

From drug response profiling to target addiction scoring in cancer cell models

Deconvoluting the molecular target signals behind observed drug response phenotypes is an important part of phenotype-based drug discovery and repurposing efforts. We demonstrate here how our network-based deconvolution approach, named target addiction score (TAS), provides insights into the functional importance of druggable protein targets in cell-based drug sensitivity testing experiments. Using cancer cell line profiling data sets, we constructed a functional classification across 107 cancer cell models, based on their common and unique target addiction signatures. The pan-cancer addiction correlations could not be explained by the tissue of origin, and only correlated in part with molecular and genomic signatures of the heterogeneous cancer cells. The TAS-based cancer cell classification was also shown to be robust to drug response data resampling, as well as predictive of the transcriptomic patterns in an independent set of cancer cells that shared similar addiction signatures with the 107 cancers. The critical protein targets identified by the integrated approach were also shown to have clinically relevant mutation frequencies in patients with various cancer subtypes, including not only well-established pan-cancer genes, such as PTEN tumor suppressor, but also a number of targets that are less frequently mutated in specific cancer types, including ABL1 oncoprotein in acute myeloid leukemia. An application to leukemia patient primary cell models demonstrated how the target deconvolution approach offers functional insights into patient-specific addiction patterns, such as those indicative of their receptor-type tyrosine-protein kinase FLT3 internal tandem duplication (FLT3-ITD) status and co-addiction partners, which may lead to clinically actionable, personalized drug treatment developments. To promote its application to the future drug testing studies, we have made available an open-source implementation of the TAS calculation in the form of a stand-alone R package.Peer reviewe

Deregulation of hydroxybutyric acid metabolism provides diagnostic and prognostic biomarkers in ovarian high-grade serous carcinomas

Ovarian cancer is a heterogeneous disease of low prevalence but poor survival. Early diagnosis is critical for patients’ survival but often challenging because ovarian cancer symptoms are unclear and appear only in advanced stages of the disease. Finding more robust disease markers is therefore a clinical priority. Metabolomics is an emerging diagnostic tool that enables the detection of biomarkers reflecting alterations of tumor metabolism, a hallmark of cancer. We performed metabolomics profiling of serum and tumor tissue of high grade serous ovarian cancer (HGSOC) patients, and report novel diagnostic and prognostic biomarkers which indicate deregulation of hydroxybutyric acid (HBA) metabolism. The accumulation of HBA metabolites was associated with lower expression of succinic semialdehyde dehydrogenase (encoded by ALDH5A1), and with the presence of an epithelial-to-mesenchymal-transition (EMT) gene signature, implying a role for these metabolic alterations in cancer cell migration and invasion. In conclusion, our work reports the first comprehensive metabolomic investigation of HGSOC and proposes a new set of metabolites that can be used as diagnostic and prognostic biomarkers of the disease.This work was supported by the Academy of Finland post-doctoral fellow grants: MH decision 138201 and PG 265966. TA was supported by Academy of Finland (grants 272437, 269862, 279163, 292611) and Cancer Society of Finland. EIA is participant in the Charité Clinical Scientist Program funded by the Charité Universitätsmedizin Berlin and the Berlin Institute of Health

The gut microbiota modulates host energy and lipid metabolism in mice

Peer reviewe

Detection of Molecular Paths Associated with Insulitis and Type 1 Diabetes in Non-Obese Diabetic Mouse

Recent clinical evidence suggests important role of lipid and amino acid metabolism in early pre-autoimmune stages of type 1 diabetes pathogenesis. We study the molecular paths associated with the incidence of insulitis and type 1 diabetes in the Non-Obese Diabetic (NOD) mouse model using available gene expression data from the pancreatic tissue from young pre-diabetic mice. We apply a graph-theoretic approach by using a modified color coding algorithm to detect optimal molecular paths associated with specific phenotypes in an integrated biological network encompassing heterogeneous interaction data types. In agreement with our recent clinical findings, we identified a path downregulated in early insulitis involving dihydroxyacetone phosphate acyltransferase (DHAPAT), a key regulator of ether phospholipid synthesis. The pathway involving serine/threonine-protein phosphatase (PP2A), an upstream regulator of lipid metabolism and insulin secretion, was found upregulated in early insulitis. Our findings provide further evidence for an important role of lipid metabolism in early stages of type 1 diabetes pathogenesis, as well as suggest that such dysregulation of lipids and related increased oxidative stress can be tracked to beta cells

Association of Lipidome Remodeling in the Adipocyte Membrane with Acquired Obesity in Humans

The authors describe a new approach to studying cellular lipid profiles and propose a compensatory mechanism that may help maintain the normal membrane function of adipocytes in the context of obesity

Metabolic Regulation in Progression to Autoimmune Diabetes

Recent evidence from serum metabolomics indicates that specific metabolic disturbances precede β-cell autoimmunity in humans and can be used to identify those children who subsequently progress to type 1 diabetes. The mechanisms behind these disturbances are unknown. Here we show the specificity of the pre-autoimmune metabolic changes, as indicated by their conservation in a murine model of type 1 diabetes. We performed a study in non-obese prediabetic (NOD) mice which recapitulated the design of the human study and derived the metabolic states from longitudinal lipidomics data. We show that female NOD mice who later progress to autoimmune diabetes exhibit the same lipidomic pattern as prediabetic children. These metabolic changes are accompanied by enhanced glucose-stimulated insulin secretion, normoglycemia, upregulation of insulinotropic amino acids in islets, elevated plasma leptin and adiponectin, and diminished gut microbial diversity of the Clostridium leptum group. Together, the findings indicate that autoimmune diabetes is preceded by a state of increased metabolic demands on the islets resulting in elevated insulin secretion and suggest alternative metabolic related pathways as therapeutic targets to prevent diabetes

Prediction of overall survival for patients with metastatic castration-resistant prostate cancer : development of a prognostic model through a crowdsourced challenge with open clinical trial data

Background Improvements to prognostic models in metastatic castration-resistant prostate cancer have the potential to augment clinical trial design and guide treatment strategies. In partnership with Project Data Sphere, a not-for-profit initiative allowing data from cancer clinical trials to be shared broadly with researchers, we designed an open-data, crowdsourced, DREAM (Dialogue for Reverse Engineering Assessments and Methods) challenge to not only identify a better prognostic model for prediction of survival in patients with metastatic castration-resistant prostate cancer but also engage a community of international data scientists to study this disease. Methods Data from the comparator arms of four phase 3 clinical trials in first-line metastatic castration-resistant prostate cancer were obtained from Project Data Sphere, comprising 476 patients treated with docetaxel and prednisone from the ASCENT2 trial, 526 patients treated with docetaxel, prednisone, and placebo in the MAINSAIL trial, 598 patients treated with docetaxel, prednisone or prednisolone, and placebo in the VENICE trial, and 470 patients treated with docetaxel and placebo in the ENTHUSE 33 trial. Datasets consisting of more than 150 clinical variables were curated centrally, including demographics, laboratory values, medical history, lesion sites, and previous treatments. Data from ASCENT2, MAINSAIL, and VENICE were released publicly to be used as training data to predict the outcome of interest-namely, overall survival. Clinical data were also released for ENTHUSE 33, but data for outcome variables (overall survival and event status) were hidden from the challenge participants so that ENTHUSE 33 could be used for independent validation. Methods were evaluated using the integrated time-dependent area under the curve (iAUC). The reference model, based on eight clinical variables and a penalised Cox proportional-hazards model, was used to compare method performance. Further validation was done using data from a fifth trial-ENTHUSE M1-in which 266 patients with metastatic castration-resistant prostate cancer were treated with placebo alone. Findings 50 independent methods were developed to predict overall survival and were evaluated through the DREAM challenge. The top performer was based on an ensemble of penalised Cox regression models (ePCR), which uniquely identified predictive interaction effects with immune biomarkers and markers of hepatic and renal function. Overall, ePCR outperformed all other methods (iAUC 0.791; Bayes factor >5) and surpassed the reference model (iAUC 0.743; Bayes factor >20). Both the ePCR model and reference models stratified patients in the ENTHUSE 33 trial into high-risk and low-risk groups with significantly different overall survival (ePCR: hazard ratio 3.32, 95% CI 2.39-4.62, p Interpretation Novel prognostic factors were delineated, and the assessment of 50 methods developed by independent international teams establishes a benchmark for development of methods in the future. The results of this effort show that data-sharing, when combined with a crowdsourced challenge, is a robust and powerful framework to develop new prognostic models in advanced prostate cancer.Peer reviewe

Crowdsourced assessment of common genetic contribution to predicting anti-TNF treatment response in rheumatoid arthritis

Correction: vol 7, 13205, 2016, doi:10.1038/ncomms13205Rheumatoid arthritis (RA) affects millions world-wide. While anti-TNF treatment is widely used to reduce disease progression, treatment fails in Bone-third of patients. No biomarker currently exists that identifies non-responders before treatment. A rigorous community-based assessment of the utility of SNP data for predicting anti-TNF treatment efficacy in RA patients was performed in the context of a DREAM Challenge (http://www.synapse.org/RA_Challenge). An open challenge framework enabled the comparative evaluation of predictions developed by 73 research groups using the most comprehensive available data and covering a wide range of state-of-the-art modelling methodologies. Despite a significant genetic heritability estimate of treatment non-response trait (h(2) = 0.18, P value = 0.02), no significant genetic contribution to prediction accuracy is observed. Results formally confirm the expectations of the rheumatology community that SNP information does not significantly improve predictive performance relative to standard clinical traits, thereby justifying a refocusing of future efforts on collection of other data.Peer reviewe