A computational framework for complex disease stratification from multiple large-scale datasets.

BACKGROUND: Multilevel data integration is becoming a major area of research in systems biology. Within this area, multi-'omics datasets on complex diseases are becoming more readily available and there is a need to set standards and good practices for integrated analysis of biological, clinical and environmental data. We present a framework to plan and generate single and multi-'omics signatures of disease states. METHODS: The framework is divided into four major steps: dataset subsetting, feature filtering, 'omics-based clustering and biomarker identification. RESULTS: We illustrate the usefulness of this framework by identifying potential patient clusters based on integrated multi-'omics signatures in a publicly available ovarian cystadenocarcinoma dataset. The analysis generated a higher number of stable and clinically relevant clusters than previously reported, and enabled the generation of predictive models of patient outcomes. CONCLUSIONS: This framework will help health researchers plan and perform multi-'omics big data analyses to generate hypotheses and make sense of their rich, diverse and ever growing datasets, to enable implementation of translational P4 medicine

How to handle big data for disease stratification in respiratory medicine?

This is the final version. Available from BMJ Publishing via the DOI in this record. Engineering and Physical Sciences Research Council (EPSRC)Medical Research CouncilMedical Research Counci

Precision medicine for the discovery of treatable mechanisms in severe asthma

Although the complex disease of asthma has been defined as being heterogeneous, the extent of its endophenotypes remain unclear. The pharmacological approach to initiating treatment has, until recently, been based on disease control and severity. The introduction of antibody therapies targeting the Type2 inflammation pathway for patients with severe asthma has resulted in the recognition of an allergic and an eosinophilic phenotype, which are not mutually exclusive. Concomitantly, molecular phenotyping based on a transcriptomic analysis of bronchial epithelial and sputum cells has identified a Type-2-high inflammation cluster characterised by eosinophilia and recurrent exacerbations, as well as Type-2-low clusters linked with IL-6 trans-signalling, interferon pathways, inflammasome activation and mitochondrial oxidative phosphorylation pathways. Systems biology approaches are establishing the links between these pathways or mechanisms, and clinical and physiologic features. Validation of these pathways contributes to defining endotypes and treatable mechanisms. Precision medicine approaches are necessary to link treatable mechanisms with treatable traits and biomarkers derived from clinical, physiologic and inflammatory features of clinical phenotypes. The deep molecular phenotyping of airway samples along with non-invasive biomarkers linked to bioinformatic and machine learning techniques will enable the rapid detection of molecular mechanisms that transgresses beyond the concept of treatable traits. This article is protected by copyright. All rights reserved

Machine Learning and Integrative Analysis of Biomedical Big Data.

Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues

COVID19 Disease Map, a computational knowledge repository of virus–host interaction mechanisms

We need to effectively combine the knowledge from surging literature with complex datasets to propose mechanistic models of SARS-CoV-2 infection, improving data interpretation and predicting key targets of intervention. Here, we describe a large-scale community effort to build an open access, interoperable and computable repository of COVID-19 molecular mechanisms. The COVID-19 Disease Map (C19DMap) is a graphical, interactive representation of disease-relevant molecular mechanisms linking many knowledge sources. Notably, it is a computational resource for graph-based analyses and disease modelling. To this end, we established a framework of tools, platforms and guidelines necessary for a multifaceted community of biocurators, domain experts, bioinformaticians and computational biologists. The diagrams of the C19DMap, curated from the literature, are integrated with relevant interaction and text mining databases. We demonstrate the application of network analysis and modelling approaches by concrete examples to highlight new testable hypotheses. This framework helps to find signatures of SARS-CoV-2 predisposition, treatment response or prioritisation of drug candidates. Such an approach may help deal with new waves of COVID-19 or similar pandemics in the long-term perspective. Co-authors include: Anna Niarakis, Alexander Mazein, Inna Kuperstein, Robert Phair, Aurelio Orta-Resendiz, Vidisha Singh, Sara Sadat Aghamiri, Marcio Luis Acencio, Enrico Glaab, Andreas Ruepp, Gisela Fobo, Corinna Montrone, Barbara Brauner, Goar Frishman, Luis Cristóbal Monraz Gómez, Julia Somers, Matti Hoch, Shailendra Kumar Gupta, Julia Scheel, Hanna Borlinghaus, Tobias Czauderna, Falk Schreiber, Arnau Montagud, Miguel Ponce de Leon, Akira Funahashi, Yusuke Hiki, Noriko Hiroi, Takahiro G Yamada, Andreas Dräger, Alina Renz, Muhammad Naveez, Zsolt Bocskei, FrancescoMessina, Daniela Börnigen, Liam Fergusson, Marta Conti, Marius Rameil, Vanessa Nakonecnij, Jakob Vanhoefer, Leonard Schmiester, Muying Wang, Emily E Ackerman, Jason E Shoemaker, Jeremy Zucker, Kristie Oxford, Jeremy Teuton, Ebru Kocakaya, Gökçe Yağmur Summak, Kristina Hanspers, Martina Kutmon, Susan Coort, Lars Eijssen, Friederike Ehrhart, Devasahayam Arokia Balaya Rex, Denise Slenter, Marvin Martens, Nhung Pham, Robin Haw, Bijay Jassal, Lisa Matthews, Marija Orlic-Milacic, Andrea Senff-Ribeiro, Karen Rothfels, Veronica Shamovsky, Ralf Stephan, Cristoffer Sevilla, Thawfeek Varusai, Jean-Marie Ravel, Rupsha Fraser, Vera Ortseifen, Silvia Marchesi, Piotr Gawron, Ewa Smula, Laurent Heirendt, Venkata Satagopam, Guanming Wu, Anders Riutta, Martin Golebiewski, Stuart Owen, Carole Goble, Xiaoming Hu, Rupert W Overall, Dieter Maier, Angela Bauch, Benjamin M Gyori, John A Bachman, Carlos Vega, Valentin Grouès, Miguel Vazquez, Pablo Porras, Luana Licata, Marta Iannuccelli, Francesca Sacco, Anastasia Nesterova, Anton Yuryev, Anita de Waard, Denes Turei, Augustin Luna, Ozgun Babur, Sylvain Soliman, Alberto Valdeolivas, Marina Esteban-Medina, Maria Peña-Chilet, Kinza Rian, Tomáš Helikar, Bhanwar Lal Puniya, Dezso Modos, Agatha Treveil, Marton Olbei, Bertrand De Meulder, Stephane Ballereau, Aurélien Dugourd, Aurélien Naldi, Vincent Noël, Laurence Calzone, Chris Sander, Emek Demir, Tamas Korcsmaros, Tom C Freeman, Franck Augé, Jacques S Beckmann, Jan Hasenauer, Olaf Wolkenhauer, Egon L Willighagen, Alexander R Pico, Chris T Evelo, Marc E Gillespie, Lincoln D Stein, Henning Hermjakob, Peter D’Eustachio, Julio Saez-Rodriguez, Joaquin Dopazo, Alfonso Valencia, Hiroaki Kitano, Emmanuel Barillot, Charles Auffray, Rudi Balling, Reinhard Schneide

Quantification of inflammatory mediators to explore molecular mechanisms and sub-phenotypes of asthma

This thesis summarizes a series of studies using liquid chromatography coupled to mass spectrometry methodologies to quantify metabolites of fatty acids (i.e., oxylipins) and histamine in different samples from experimental models and clinical studies with the overall aim to define mechanisms and identify biomarkers for improved sub-phenotyping of asthma. Asthma is characterized by variable airflow obstruction, hyperresponsiveness and chronic inflammation in the airways. The substantial overlap among clinical descriptors has resulted in difficulties to establish diagnosis and predict response to treatment. Instead, a shift in focus towards identifying specific cellular and molecular mechanisms has emerged, aiming to define new treatable traits based on specific cellular and molecular pathways (defined as endotypes). Important pathobiological components involve the release of potent inflammatory mediators, such as histamine, prostaglandins (PGs) and leukotrienes (LTs), that cause bronchoconstriction and airway inflammation. A rapid hydrophilic interaction chromatography method failed to quantify the major histamine metabolite 1,4-methyl-5-imidazoleacetic acid (tele-MIAA) due to ion suppression from inorganic salts present in urine. Ion-pairing chromatography was therefore employed and the resulting increase in precision enabled the detection of higher baseline levels of tele-MIAA in females compared to males (3.0 vs. 2.1 μmol/mmol creatinine, respectively) (Paper I). In addition, levels of tele-MIAA reached up to 30 μmol/mmol creatinine in spot urine samples from mastocytosis patients. Three liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) methods quantified 130 oxylipins and were able to define kinetic release and enzymatic contribution of mast cell-derived mediators to smooth muscle contraction using isolated and intact airways from humans and guinea pigs in vitro. PGD2 levels were elevated 24-hour post anti-IgE stimulation of human bronchus, suggesting a prolonged mast cell activation (Paper II). Furthermore, exposure to house dust mite (HDM) induced strong release of lipoxygenase-derived LTB4, 5,15-DiHETE, 15-HETE and 15-HEDE along with eosinophilic infiltration in a C57BL/6 murine model of asthma. Interestingly, high levels of cysteinyl-leukotrienes (CysLTs) remained unchanged suggesting a different role of CysLTs in mice (Paper III). Urinary profiles of 11 eicosanoid metabolites in 100 healthy control subjects and 497 asthmatics defined normal baseline levels and revealed increased concentration of PGs, LTE4 and isoprostanes with asthma severity. Consensus clustering of 497 asthmatics identified a five-cluster model with distinct clinical characteristics, which included two new phenotypes, U1 and U5, with low levels of thromboxanes and PGs respectively (Paper IV). At the 12 to 18-month longitudinal time point for the 302 subjects with severe asthma, z-scored eicosanoid concentrations retained the five-cluster profile, despite technical and intra-subject variability. In conclusion, the developed bioanalytical methods were applied to define levels of histamine and eicosanoid metabolites in urine from healthy subjects. In addition, release of multiple oxylipins following mast cell-mediated bronchoconstriction and HDM-induced airway inflammation in model systems were explored to relate functions to levels of lipid mediators. For the first time, grouping of asthmatics according to profiles of eicosanoid metabolites in urine was performed and demonstrated sufficient resolution to identify five sub-phenotypes of asthma possessing distinct clinical characteristics. The presented approaches, for both in vitro and in vivo respiratory research, offer an opportunity to progress the development of new treatment options and suggests a panel of PGs, LTE4 and isoprostanes to be further validated as diagnostic markers in patients with asthma

A computational framework for complex disease stratification from multiple large-scale datasets.

Background Multilevel data integration is becoming a major area of research in systems biology. Within this area, multi-‘omics datasets on complex diseases are becoming more readily available and there is a need to set standards and good practices for integrated analysis of biological, clinical and environmental data. We present a framework to plan and generate single and multi-‘omics signatures of disease states. Methods The framework is divided into four major steps: dataset subsetting, feature filtering, ‘omics-based clustering and biomarker identification. Results We illustrate the usefulness of this framework by identifying potential patient clusters based on integrated multi-‘omics signatures in a publicly available ovarian cystadenocarcinoma dataset. The analysis generated a higher number of stable and clinically relevant clusters than previously reported, and enabled the generation of predictive models of patient outcomes. Conclusions This framework will help health researchers plan and perform multi-‘omics big data analyses to generate hypotheses and make sense of their rich, diverse and ever growing datasets, to enable implementation of translational P4 medicine.<br/

A computational framework for complex disease stratification from multiple large-scale datasets

Background: Multilevel data integration is becoming a major area of research in systems biology. Within this area, multi-'omics datasets on complex diseases are becoming more readily available and there is a need to set standards and good practices for integrated analysis of biological, clinical and environmental data. We present a framework to plan and generate single and multi-'omics signatures of disease states.Methods: The framework is divided into four major steps: dataset subsetting, feature filtering, 'omics-based clustering and biomarker identification.Results: We illustrate the usefulness of this framework by identifying potential patient clusters based on integrated multi-'omics signatures in a publicly available ovarian cystadenocarcinoma dataset. The analysis generated a higher number of stable and clinically relevant clusters than previously reported, and enabled the generation of predictive models of patient outcomes. Conclusions: This framework will help health researchers plan and perform multi-'omics big data analyses to generate hypotheses and make sense of their rich, diverse and ever growing datasets, to enable implementation of translational P4 medicine.</p

A computational framework for complex disease stratification from multiple large-scale datasets.

BACKGROUND: Multilevel data integration is becoming a major area of research in systems biology. Within this area, multi-'omics datasets on complex diseases are becoming more readily available and there is a need to set standards and good practices for integrated analysis of biological, clinical and environmental data. We present a framework to plan and generate single and multi-'omics signatures of disease states. METHODS: The framework is divided into four major steps: dataset subsetting, feature filtering, 'omics-based clustering and biomarker identification. RESULTS: We illustrate the usefulness of this framework by identifying potential patient clusters based on integrated multi-'omics signatures in a publicly available ovarian cystadenocarcinoma dataset. The analysis generated a higher number of stable and clinically relevant clusters than previously reported, and enabled the generation of predictive models of patient outcomes. CONCLUSIONS: This framework will help health researchers plan and perform multi-'omics big data analyses to generate hypotheses and make sense of their rich, diverse and ever growing datasets, to enable implementation of translational P4 medicine