Towards an Interoperable Ecosystem of Research Cohort and Real-world Data Catalogues Enabling Multi-center Studies

Objectives : Existing individual-level human data cover large populations on many dimensions such as lifestyle, demography, laboratory measures, clinical parameters, etc. Recent years have seen large investments in data catalogues to FAIRify data descriptions to capitalise on this great promise, i.e. make catalogue contents more Findable, Accessible, Interoperable and Reusable. However, their valuable diversity also created heterogeneity, which poses challenges to optimally exploit their richness. Methods : In this opinion review, we analyse catalogues for human subject research ranging from cohort studies to surveillance, administrative and healthcare records. Results : We observe that while these catalogues are heterogeneous, have various scopes, and use different terminologies, still the underlying concepts seem potentially harmonizable. We propose a unified framework to enable catalogue data sharing, with catalogues of multi-center cohorts nested as a special case in catalogues of real-world data sources. Moreover, we list recommendations to create an integrated community of metadata catalogues and an open catalogue ecosystem to sustain these efforts and maximise impact. Conclusions : We propose to embrace the autonomy of motivated catalogue teams and invest in their collaboration via minimal standardisation efforts such as clear data licensing, persistent identifiers for linking same records between catalogues, minimal metadata ‘common data elements’ using shared ontologies, symmetric architectures for data sharing (push/pull) with clear provenance tracks to process updates and acknowledge original contributors. And most importantly, we encourage the creation of environments for collaboration and resource sharing between catalogue developers, building on international networks such as OpenAIRE and research data alliance, as well as domain specific ESFRIs such as BBMRI and ELIXIR

Network and systems medicine: Position paper of the European Collaboration on Science and Technology action on Open Multiscale Systems Medicine

Introduction: Network and systems medicine has rapidly evolved over the past decade, thanks to computational and integrative tools, which stem in part from systems biology. However, major challenges and hurdles are still present regarding validation and translation into clinical application and decision making for precision medicine. Methods: In this context, the Collaboration on Science and Technology Action on Open Multiscale Systems Medicine (OpenMultiMed) reviewed the available advanced technologies for multidimensional data generation and integration in an open-science approach as well as key clinical applications of network and systems medicine and the main issues and opportunities for the future. Results: The development of multi-omic approaches as well as new digital tools provides a unique opportunity to explore complex biological systems and networks at different scales. Moreover, the application of findable, applicable, interoperable, and reusable principles and the adoption of standards increases data availability and sharing for multiscale integration and interpretation. These innovations have led to the first clinical applications of network and systems medicine, particularly in the field of personalized therapy and drug dosing. Enlarging network and systems medicine application would now imply to increase patient engagement and health care providers as well as to educate the novel generations of medical doctors and biomedical researchers to shift the current organ- and symptom-based medical concepts toward network- and systems-based ones for more precise diagnoses, interventions, and ideally prevention. Conclusion: In this dynamic setting, the health care system will also have to evolve, if not revolutionize, in terms of organization and management

Estimating multiple greenspace exposure types and their associations with neighbourhood premature mortality: A socioecological study.

BACKGROUND: Greenspace exposures are often measured using single exposure metrics, which can lead to conflicting results. Existing methodologies are limited in their ability to estimate greenspace exposure comprehensively. We demonstrate new methods for estimating single and combined greenspace exposure metrics, representing multiple exposure types that combine impacts at various scales. We also investigate the association between those greenspace exposure types and premature mortality. METHODS: We used geospatial data and spatial analytics to model and map greenspace availability, accessibility and eye-level visibility exposure metrics. These were harmonised and standardised to create a novel composite greenspace exposure index (CGEI). Using these metrics, we investigated associations between greenspace exposures and years of potential life lost (YPLL) for 1673 neighbourhoods applying spatial autoregressive models. We also investigated the variations in these associations in conjunction with levels of socioeconomic deprivation based on the index of multiple deprivations. RESULTS: Our new CGEI metric provides the opportunity to estimate spatially explicit total greenspace exposure. We found that a 1-unit increase in neighbourhood CGEI was associated with approximately a 10-year reduction in YPLL. Meaning a 0.1 increment or 10% increase in the CGEI is associated with an approximately one year lower premature mortality value. A single 1-unit increase in greenspace availability was associated with a YPLL reduction of 9.8 years, whereas greenness visibility related to a reduction of 6.14 years. We found no significant association between greenspace accessibility and YPLL. Our results further identified divergent trends in the relations between greenspace exposure types (e.g. availability vs. accessibility) and levels of socioeconomic deprivation (e.g. least vs. most). CONCLUSION: Our methods and metrics provide a novel approach to the assessment of multiple greenspace exposure types, and can be linked to the broader exposome framework. Our results showed that a higher composite greenspace exposure is associated with lower premature mortality

Computationally Linking Chemical Exposure to Molecular Effects with Complex Data: Comparing Methods to Disentangle Chemical Drivers in Environmental Mixtures and Knowledge-based Deep Learning for Predictions in Environmental Toxicology

Chemical exposures affect the environment and may lead to adverse outcomes in its organisms. Omics-based approaches, like standardised microarray experiments, have expanded the toolbox to monitor the distribution of chemicals and assess the risk to organisms in the environment. The resulting complex data have extended the scope of toxicological knowledge bases and published literature. A plethora of computational approaches have been applied in environmental toxicology considering systems biology and data integration. Still, the complexity of environmental and biological systems given in data challenges investigations of exposure-related effects. This thesis aimed at computationally linking chemical exposure to biological effects on the molecular level considering sources of complex environmental data. The first study employed data of an omics-based exposure study considering mixture effects in a freshwater environment. We compared three data-driven analyses in their suitability to disentangle mixture effects of chemical exposures to biological effects and their reliability in attributing potentially adverse outcomes to chemical drivers with toxicological databases on gene and pathway levels. Differential gene expression analysis and a network inference approach resulted in toxicologically meaningful outcomes and uncovered individual chemical effects — stand-alone and in combination. We developed an integrative computational strategy to harvest exposure-related gene associations from environmental samples considering mixtures of lowly concentrated compounds. The applied approaches allowed assessing the hazard of chemicals more systematically with correlation-based compound groups. This dissertation presents another achievement toward a data-driven hypothesis generation for molecular exposure effects. The approach combined text-mining and deep learning. The study was entirely data-driven and involved state-of-the-art computational methods of artificial intelligence. We employed literature-based relational data and curated toxicological knowledge to predict chemical-biomolecule interactions. A word embedding neural network with a subsequent feed-forward network was implemented. Data augmentation and recurrent neural networks were beneficial for training with curated toxicological knowledge. The trained models reached accuracies of up to 94% for unseen test data of the employed knowledge base. However, we could not reliably confirm known chemical-gene interactions across selected data sources. Still, the predictive models might derive unknown information from toxicological knowledge sources, like literature, databases or omics-based exposure studies. Thus, the deep learning models might allow predicting hypotheses of exposure-related molecular effects. Both achievements of this dissertation might support the prioritisation of chemicals for testing and an intelligent selection of chemicals for monitoring in future exposure studies.:Table of Contents ... I Abstract ... V Acknowledgements ... VII Prelude ... IX 1 Introduction 1.1 An overview of environmental toxicology ... 2 1.1.1 Environmental toxicology ... 2 1.1.2 Chemicals in the environment ... 4 1.1.3 Systems biological perspectives in environmental toxicology ... 7 Computational toxicology ... 11 1.2.1 Omics-based approaches ... 12 1.2.2 Linking chemical exposure to transcriptional effects ... 14 1.2.3 Up-scaling from the gene level to higher biological organisation levels ... 19 1.2.4 Biomedical literature-based discovery ... 24 1.2.5 Deep learning with knowledge representation ... 27 1.3 Research question and approaches ... 29 2 Methods and Data ... 33 2.1 Linking environmental relevant mixture exposures to transcriptional effects ... 34 2.1.1 Exposure and microarray data ... 34 2.1.2 Preprocessing ... 35 2.1.3 Differential gene expression ... 37 2.1.4 Association rule mining ... 38 2.1.5 Weighted gene correlation network analysis ... 39 2.1.6 Method comparison ... 41 Predicting exposure-related effects on a molecular level ... 44 2.2.1 Input ... 44 2.2.2 Input preparation ... 47 2.2.3 Deep learning models ... 49 2.2.4 Toxicogenomic application ... 54 3 Method comparison to link complex stream water exposures to effects on the transcriptional level ... 57 3.1 Background and motivation ... 58 3.1.1 Workflow ... 61 3.2 Results ... 62 3.2.1 Data preprocessing ... 62 3.2.2 Differential gene expression analysis ... 67 3.2.3 Association rule mining ... 71 3.2.4 Network inference ... 78 3.2.5 Method comparison ... 84 3.2.6 Application case of method integration ... 87 3.3 Discussion ... 91 3.4 Conclusion ... 99 4 Deep learning prediction of chemical-biomolecule interactions ... 101 4.1 Motivation ... 102 4.1.1Workflow ...105 4.2 Results ... 107 4.2.1 Input preparation ... 107 4.2.2 Model selection ... 110 4.2.3 Model comparison ... 118 4.2.4 Toxicogenomic application ... 121 4.2.5 Horizontal augmentation without tail-padding ...123 4.2.6 Four-class problem formulation ... 124 4.2.7 Training with CTD data ... 125 4.3 Discussion ... 129 4.3.1 Transferring biomedical knowledge towards toxicology ... 129 4.3.2 Deep learning with biomedical knowledge representation ...133 4.3.3 Data integration ...136 4.4 Conclusion ... 141 5 Conclusion and Future perspectives ... 143 5.1 Conclusion ... 143 5.1.1 Investigating complex mixtures in the environment ... 144 5.1.2 Complex knowledge from literature and curated databases predict chemical- biomolecule interactions ... 145 5.1.3 Linking chemical exposure to biological effects by integrating CTD ... 146 5.2 Future perspectives ... 147 S1 Supplement Chapter 1 ... 153 S1.1 Example of an estrogen bioassay ... 154 S1.2 Types of mode of action ... 154 S1.3 The dogma of molecular biology ... 157 S1.4 Transcriptomics ... 159 S2 Supplement Chapter 3 ... 161 S3 Supplement Chapter 4 ... 175 S3.1 Hyperparameter tuning results ... 176 S3.2 Functional enrichment with predicted chemical-gene interactions and CTD reference pathway genesets ... 179 S3.3 Reduction of learning rate in a model with large word embedding vectors ... 183 S3.4 Horizontal augmentation without tail-padding ... 183 S3.5 Four-relationship classification ... 185 S3.6 Interpreting loss observations for SemMedDB trained models ... 187 List of Abbreviations ... i List of Figures ... vi List of Tables ... x Bibliography ... xii Curriculum scientiae ... xxxix Selbständigkeitserklärung ... xlii

Data between environment and health : an epistemological study of the exposome

This thesis is a philosophical analysis of the epistemic role of scientific data in biomedical research. It is comprised of an introduction (Chapter 1), three articles (Chapter 2, 3, 4) and a conclusion (Chapter 5). I use a case study approach and focus on the epidemiology of the ‘exposome’, a new line of research based on a reconceptualisation of exposure and the use of new and diverse datasets. I argue that data can sustain the subject matter of exposome research by shaping concepts, strategies, techniques and what counts as evidence. Yet, the epistemic role of data is enacted by the ways in which it is used by epistemic agents and thus constantly connected to and mediated by other artefacts, components and features of scientific inquiry. In Chapter 2, I discuss the innovations and changes of the exposome. I argue that these should be framed as the establishment of a repertoire, as opposed to a paradigm. The exposome repertoire consists in many components transferred from other areas of the life and health sciences: thus, scientific change is the result of the alignment of these components and it is not due to only one of these factors, such as data. In Chapter 3, I discuss data practices in exposome research. I argue that researchers use evidential claims to specify the evidential and representational value of datasets. Three strategies for evidential claims can be distinguished, differing in terms of level of abstraction, lines of work and type of evidential claim and leading to a picture of evidence production as epistemic-intensive labour. In Chapter 4, I discuss how data is classified as evidence in exposome research, in the context of philosophical discussions of the types of evidence used for causal claims in biomedical research. I argue that molecular data collected in exposome research is used to study differences and dependences, as opposed to mechanisms; more generally, the classification of a dataset as a type of evidence is dependent on the ways in which the data is used, rather than its intrinsic properties

Association between fetal abdominal growth trajectories, maternal metabolite signatures early in pregnancy, and childhood growth and adiposity : prospective observational multinational INTERBIO-21st fetal study

Background Obesity predominantly affects populations in high-income countries and those countries facing epidemiological transition. The risk of childhood obesity is increased among infants who had overweight or obesity at birth, but in low-resource settings one in five infants are born small for gestational age. We aimed to study the relationships between: (1) maternal metabolite signatures; (2) fetal abdominal growth; and (3) postnatal growth, adiposity, and neurodevelopment. Methods In the prospective, multinational, observational INTERBIO-21st fetal study, conducted in maternity units in Pelotas (Brazil), Nairobi (Kenya), Karachi (Pakistan), Soweto (South Africa), Mae Sot (Thailand), and Oxford (UK), we enrolled women (≥18 years, with a BMI of less than 35 kg/m2, natural conception, and a singleton pregnancy) who initiated antenatal care before 14 weeks’ gestation. Ultrasound scans were performed every 5±1 weeks until delivery to measure fetal growth and feto–placental blood flow, and we used finite mixture models to derive growth trajectories of abdominal circumference. The infants’ health, growth, and development were monitored from birth to age 2 years. Early pregnancy maternal blood and umbilical cord venous blood samples were collected for untargeted metabolomic analysis. Findings From Feb 8, 2012, to Nov 30, 2019, we enrolled 3598 pregnant women and followed up their infants to 2 years of age. We identified four ultrasound-derived trajectories of fetal abdominal circumference growth that accelerated or decelerated within a crucial 20–25 week gestational age window: faltering growth, early accelerating growth, late accelerating growth, and median growth tracking. These distinct phenotypes had matching feto–placental blood flow patterns throughout pregnancy, and different growth, adiposity, vision, and neurodevelopment outcomes in early childhood. There were 709 maternal metabolites with positive effect for the faltering growth phenotype and 54 for the early accelerating growth phenotype; 31 maternal metabolites had a negative effect for the faltering growth phenotype and 76 for the early accelerating growth phenotype. Metabolites associated with the faltering growth phenotype had statistically significant odds ratios close to 1·5 (ie, suggesting upregulation of metabolic pathways of impaired fetal growth). The metabolites had a reciprocal relationship with the early accelerating growth phenotype, with statistically significant odds ratios close to 0.6 (ie, suggesting downregulation of fetal growth acceleration). The maternal metabolite signatures included 5-hydroxy-eicosatetraenoic acid, and 11 phosphatidylcholines linked to oxylipin or saturated fatty acid sidechains. The fungicide, chlorothalonil, was highly abundant in the early accelerating growth phenotype group. Interpretation Early pregnancy lipid biology associated with fetal abdominal growth trajectories is an indicator of patterns of growth, adiposity, vision, and neurodevelopment up to the age of 2 years. Our findings could contribute to the earlier identification of infants at risk of obesity. Funding Bill & Melinda Gates Foundation

A biomimetic natural sciences approach to understanding the mechanisms of ageing in burden of life style diseases

The worldwide landscape of an ageing population and age-related disease brings with it huge socio-economic and public healthcare concerns across nations. Correspondingly, monumental human and financial resources have been invested in biomedical research, with a mission to decode the mechanisms of ageing and how these contribute to age-related disease. Multiple hallmarks of ageing have been identified that are common across taxa, highlighting their fundamental importance. These include dysregulated mitochondrial metabolism and telomeres biology, epigenetic modifications, cell–matrix interactions, proteostasis, dysregulated nutrient sensing, stem cell exhaustion, inflammageing and immuno-senescence. While our understanding of the molecular basis of ageing is improving, it remains a complex and multifactorial process that remains to be fully understood. A key aspect of the shortfall in our understanding of the ageing process lies in translating data from standard animal models to humans. Consequently, we suggest that a ‘biomimetic’ and comparative approach, integrating knowledge from species in the wild, as opposed to inbred genetically homogenous laboratory animals, can provide powerful insights into human ageing processes. Here we discuss some particularities and comparative patterns among several species from the animal kingdom, endowed with longevity or short lifespans and unique metabolic profiles that could be potentially exploited to the understanding of ageing and age-related diseases. Based upon lessons from nature, we also highlight several avenues for renewed focus in the pathophysiology of ageing and age-related disease (i.e. diet-microbiome-health axis, oxidative protein damage, adaptive homoeostasis and planetary health). We propose that a biomimetic alliance with collaborative research from different disciplines can improve our understanding of ageing and age-related diseases with long-term sustainable utility

Advancing the Science of Cancer in Latinos

This open access book is a collection of articles based on presentations from the 2020 Advancing the Science of Cancer in Latinos conference that gives an overview of conference outcomes. The vision of the conference has been to unite researchers, scientists, physicians and other healthcare professionals, patient advocates, and students from across the world to discuss research advancements, identify gaps, and develop actionable goals to translate basic research findings into clinical best practices, effective community interventions, and professional training programs to decrease cancer risks and eliminate cancer disparities for Latinos. This conference comes at an especially important time when Latinos – the largest and youngest minority group in the U.S. – are expected to face a 142% rise in cancer cases in the coming years. Disparities continue to impact this population in critical areas: access to preventive and clinical care, changeable risk behaviors, quality of life, and mortality. Each chapter summarizes the presentation and includes current knowledge in the specific topic areas, identified gaps, and opportunities for future research. Topics explored include: Applying an Exposome-Wide (ExWAS) Approach to Latino Cancer Disparities Supportive Care Needs and Coping Strategies Used by Latino Men Cancer Survivors Optimizing Engagement of the Latino Community in Cancer Research Latino Population Growth and the Changing Demography of Cancer Implementation Science to Enhance the Value of Cancer Research in Latinos A Strength-Based Approach to Cancer Prevention in Latinxs Overcoming Clinical Research Disparities by Advancing Inclusive Research Advancing the Science of Cancer in Latinos: Building Collaboration for Action will appeal to a wide readership due to its comprehensive coverage of topics ranging from basic science and community prevention research to clinical practice to policy. The book is an essential resource for physicians and other medical professionals, researchers, scientists, academicians, patient advocates, and students. It also will appeal to policy-makers, NCI-designated cancer centers, academic centers, state health departments, and community organizations