Integrative methods for analyzing big data in precision medicine

We provide an overview of recent developments in big data analyses in the context of precision medicine and health informatics. With the advance in technologies capturing molecular and medical data, we entered the area of “Big Data” in biology and medicine. These data offer many opportunities to advance precision medicine. We outline key challenges in precision medicine and present recent advances in data integration-based methods to uncover personalized information from big data produced by various omics studies. We survey recent integrative methods for disease subtyping, biomarkers discovery, and drug repurposing, and list the tools that are available to domain scientists. Given the ever-growing nature of these big data, we highlight key issues that big data integration methods will face

Integrative methods for analysing big data in precision medicine

We provide an overview of recent developments in big data analyses in the context of precision medicine and health informatics. With the advance in technologies capturing molecular and medical data, we entered the area of “Big Data” in biology and medicine. These data offer many opportunities to advance precision medicine. We outline key challenges in precision medicine and present recent advances in data integration-based methods to uncover personalized information from big data produced by various omics studies. We survey recent integrative methods for disease subtyping, biomarkers discovery, and drug repurposing, and list the tools that are available to domain scientists. Given the ever-growing nature of these big data, we highlight key issues that big data integration methods will face

Data- and knowledge-based modeling of gene regulatory networks: an update

Gene regulatory network inference is a systems biology approach which predicts interactions between genes with the help of high-throughput data. In this review, we present current and updated network inference methods focusing on novel techniques for data acquisition, network inference assessment, network inference for interacting species and the integration of prior knowledge. After the advance of Next-Generation-Sequencing of cDNAs derived from RNA samples (RNA-Seq) we discuss in detail its application to network inference. Furthermore, we present progress for large-scale or even full-genomic network inference as well as for small-scale condensed network inference and review advances in the evaluation of network inference methods by crowdsourcing. Finally, we reflect the current availability of data and prior knowledge sources and give an outlook for the inference of gene regulatory networks that reflect interacting species, in particular pathogen-host interactions

Integrative statistical methods for decoding molecular responses to insect herbivory in Nicotiana attenuata

This work focuses on the development of statistical methods to select features (genes and metabolites) exhibiting induced local and systemic defense responses to insect attack in Nicotiana attenuata along with the extraction of additional information regarding their timing of action. To characterize the dynamics of activation in time and space of herbivory-induced responses, I designed a framework by combining methods previously developed for feature selection and extraction to identify activated network motifs. These motifs are the set of features that are differentially perturbed in local and systemic tissues in response to herbivory. The extraction of multifactorial statistical information in terms of time response variable simultaneously captured the dynamic response of a gene/metabolite in more than one tissue and therefore helped in identifying tissue-specific activation of biochemical pathways during herbivory, their transition points and shared patterns of regulation with other physiological processes and gene-metabolite interactions at the level of isolated motifs. I utilized this framework to evaluate the transcriptional and metabolic dynamics in the roots to investigate their role in aboveground stress responses. I discovered an emergent property of an inversion in root-specific semidiurnal (12h) rhythms in response to simulated leaf herbivory. In addition, I illustrated the benefits of our statistical framework, used for generating spatio-temporally resolved transcriptional/metabolic maps, by visualizing the chronology of the activation of pathways central to signaling, tolerance and defense in N. attenuata. The research described in this thesis, in addition to being valuable in deciphering dynamic responses to insect attack in a whole plant context, lays the foundation for future analyses in which statistical modeling of these networks assisted with experimental data could predict the logical rules governing these dynamic interactions

The need for an integrated multi‐OMICs approach in microbiome science in the food system

Microbiome science as an interdisciplinary research field has evolved rapidly over the past two decades, becoming a popular topic not only in the scientific community and among the general public, but also in the food industry due to the growing demand for microbiome-based technologies that provide added-value solutions. Microbiome research has expanded in the context of food systems, strongly driven by methodological advances in different -omics fields that leverage our understanding of microbial diversity and function. However, managing and integrating different complex -omics layers are still challenging. Within the Coordinated Support Action MicrobiomeSupport (https://www.microbiomesupport.eu/), a project supported by the European Commission, the workshop “Metagenomics, Metaproteomics and Metabolomics: the need for data integration in microbiome research” gathered 70 participants from different microbiome research fields relevant to food systems, to discuss challenges in microbiome research and to promote a switch from microbiome-based descriptive studies to functional studies, elucidating the biology and interactive roles ofmicrobiomes in food systems. A combination of technologies is proposed. This will reduce the biases resulting from each individual technology and result in a more comprehensive view of the biological system as a whole. Although combinations of different datasets are still rare, advanced bioinformatics tools and artificial intelligence approaches can contribute to understanding, prediction, and management of the microbiome, thereby providing the basis for the improvement of food quality and safety

Temporal and Causal Inference with Longitudinal Multi-omics Microbiome Data

Microbiomes are communities of microbes inhabiting an environmental niche. Thanks to next generation sequencing technologies, it is now possible to study microbial communities, their impact on the host environment, and their role in specific diseases and health. Technology has also triggered the increased generation of multi-omics microbiome data, including metatranscriptomics (quantitative survey of the complete metatranscriptome of the microbial community), metabolomics (quantitative profile of the entire set of metabolites present in the microbiome\u27s environmental niche), and host transcriptomics (gene expression profile of the host). Consequently, another major challenge in microbiome data analysis is the integration of multi-omics data sets and the construction of unified models. Finally, since microbiomes are inherently dynamic, to fully understand the complex interactions that take place within these communities, longitudinal studies are critical. Although the analysis of longitudinal microbiome data has been attempted, these approaches do not attempt to probe interactions between taxa, do not offer holistic analyses, and do not investigate causal relationships. In this work we propose approaches to address all of the above challenges. We propose novel analysis pipelines to analyze multi-omic longitudinal microbiome data, and to infer temporal and causal relationships between the different entities involved. As a first step, we showed how to deal with longitudinal metagenomic data sets by building a pipeline, PRIMAL, which takes microbial abundance data as input and outputs a dynamic Bayesian network model that is highly predictive, suggests significant interactions between the different microbes, and proposes important connections from clinical variables. A significant innovation of our work is its ability to deal with differential rates of the internal biological processes in different individuals. Second, we showed how to analyze longitudinal multi-omic microbiome datasets. Our pipeline, PALM, significantly extends the previous state of the art by allowing for the integration of longitudinal metatranscriptomics, host transcriptomics, and metabolomics data in additional to longitudinal metagenomics data. PALM achieves prediction powers comparable to the PRIMAL pipeline while discovering a web of interactions between the entities of far greater complexity. An important innovation of PALM is the use of a multi-omic Skeleton framework that incorporates prior knowledge in the learning of the models. Another major innovation of this work is devising a suite of validation methods, both in silico and in vitro, enhancing the utility and validity of PALM. Finally, we propose a suite of novel methods (unrolling and de-confounding), called METALICA, consisting of tools and techniques that make it possible to uncover significant details about the nature of microbial interactions. We also show methods to validate such interactions using ground truth databases. The proposed methods were tested using an IBD multi-omics dataset

Carcinogenic toxicants and emerging pollutants. A comprehensive case-study on toxicant interactions in vivo and in vitro: from Molecular Toxicology to Environmental Risk Assessment

Noxious chemicals have serious repercussions for wildlife and human health, not just due to incidental pollution, but also to chronic exposure to mixed pollutants. To the span of legacy toxicants such as aromatic hydrocarbons we persistently add new, like pharmaceuticals, that become reclassified as ‘of emerging concern’. Taking diclofenac (DFC), one of the most toxic non-steroid anti-inflammatory drugs (NSAIDs) to wildlife, and the carcinogenic PAH benzo[a]pyrene (B[a]P) as model compounds, we disclosed that binary combinations cause interaction effects in vivo and in vitro even at realistically low concentrations. Comparing both, DFC caused high zebrafish embryo mortality and more acute morphoanatomical lesions and B[a]P was more cytotoxic for human hepatoma HepG2 cells, however all this effects seemed to be diminished or delayed by co-exposure. The observed genotoxicity in vivo and in vitro indicated antagonism in short exposures. Bioinformatics and RNAseq yielded about 300 genes differentially expressed only in co-exposed zebrafish embryos and indicated that pathways related to cell cycle control may partly explain antagonism. The findings indicate serious chronic effects even when toxicopathological and genotoxicity-related responses seem to be reduced by co-exposure, which was confirmed by carryover of effects from parental generations (exposed to toxicants during embryonic development) to their offspring, even though individual exposures continue to have higher implications than mixtures. The overall complexity of effects and mechanisms was dependent of dose and duration of exposure. Altogether, mechanistic aspects and toxicopathology of co-exposure indicate significant risk of chronic disease along the life cycle of organisms. In vitro assays, in turn, were paramount to test multiple binary mixtures and enabled refining research towards potential adverse outcomes and molecular pathways, which is nowadays acknowledged as paramount to quantify risk under realistic scenarios and associate environment health and human occupational exposure.As substâncias nocivas têm graves repercussões para a vida selvagem e para a saúde humana, não apenas devido à poluição incidental, mas também à exposição crónica a misturas de poluentes. À gama de poluentes tradicionais, como os hidrocarbonetos aromáticos, acrescentamos continuamente novos produtos, como os fármacos, que são reclassificados como “emergentes”. Tendo o diclofenac (DFC), um dos anti-inflamatórios não esteroides (AINEs) mais tóxicos para a vida selvagem, e o PAH cancerígeno benzo[a]pireno (B[a]P) como substâncias modelo, revelamos que as misturas binárias causam efeitos de interação in vivo e in vitro, mesmo em concentrações realisticamente baixas. Comparando ambos, o DFC causou alta mortalidade embrionária no peixe- zebra e lesões morfológicas mais agudas e o B[a]P foi mais citotóxico para as células do hepatoma humano HepG2, no entanto todos estes efeitos pareceram diminuir ou retardar na co-exposição. A genotoxicidade foi inferior à aditiva, in vivo e in vitro, indicando antagonismo em exposições curtas. A bioinformática e o RNAseq geraram cerca de 300 genes diferencialmente expressos apenas em embriões de peixe-zebra co-expostos e indicaram que as vias relacionadas ao controle do ciclo celular podem parcialmente explicar o antagonismo. As descobertas indicam efeitos crónicos graves, mesmo quando as respostas toxicopatológicas e relacionadas com a genotoxicidade parecem ser reduzidas pela co-exposição, o que foi confirmado pela transferência de efeitos das gerações parentais (expostas aos poluentes durante o desenvolvimento embrionário) para os seus descendentes, embora as exposições individuais continuem a ter implicações mais elevadas do que as misturas. A complexidade geral dos efeitos e mecanismos dependeu da dose e da duração da exposição. Em conjunto, os aspetos mecanicistas e a toxicopatologia nas misturas indicam um risco significativo de doenças crónicas ao longo do ciclo de vida dos organismos. Os ensaios in vitro, por sua vez, foram fundamentais para testar múltiplas misturas binárias e permitiram refinar a pesquisa em direção a potenciais resultados adversos e vias moleculares, o que hoje é reconhecido como fundamental para quantificar o risco em cenários realistas e associar a saúde ambiental e a exposição ocupacional humana

Network-driven strategies to integrate and exploit biomedical data

[eng] In the quest for understanding complex biological systems, the scientific community has been delving into protein, chemical and disease biology, populating biomedical databases with a wealth of data and knowledge. Currently, the field of biomedicine has entered a Big Data era, in which computational-driven research can largely benefit from existing knowledge to better understand and characterize biological and chemical entities. And yet, the heterogeneity and complexity of biomedical data trigger the need for a proper integration and representation of this knowledge, so that it can be effectively and efficiently exploited. In this thesis, we aim at developing new strategies to leverage the current biomedical knowledge, so that meaningful information can be extracted and fused into downstream applications. To this goal, we have capitalized on network analysis algorithms to integrate and exploit biomedical data in a wide variety of scenarios, providing a better understanding of pharmacoomics experiments while helping accelerate the drug discovery process. More specifically, we have (i) devised an approach to identify functional gene sets associated with drug response mechanisms of action, (ii) created a resource of biomedical descriptors able to anticipate cellular drug response and identify new drug repurposing opportunities, (iii) designed a tool to annotate biomedical support for a given set of experimental observations, and (iv) reviewed different chemical and biological descriptors relevant for drug discovery, illustrating how they can be used to provide solutions to current challenges in biomedicine.[cat] En la cerca d’una millor comprensió dels sistemes biològics complexos, la comunitat científica ha estat aprofundint en la biologia de les proteïnes, fàrmacs i malalties, poblant les bases de dades biomèdiques amb un gran volum de dades i coneixement. En l’actualitat, el camp de la biomedicina es troba en una era de “dades massives” (Big Data), on la investigació duta a terme per ordinadors se’n pot beneficiar per entendre i caracteritzar millor les entitats químiques i biològiques. No obstant, la heterogeneïtat i complexitat de les dades biomèdiques requereix que aquestes s’integrin i es representin d’una manera idònia, permetent així explotar aquesta informació d’una manera efectiva i eficient. L’objectiu d’aquesta tesis doctoral és desenvolupar noves estratègies que permetin explotar el coneixement biomèdic actual i així extreure informació rellevant per aplicacions biomèdiques futures. Per aquesta finalitat, em fet servir algoritmes de xarxes per tal d’integrar i explotar el coneixement biomèdic en diferents tasques, proporcionant un millor enteniment dels experiments farmacoòmics per tal d’ajudar accelerar el procés de descobriment de nous fàrmacs. Com a resultat, en aquesta tesi hem (i) dissenyat una estratègia per identificar grups funcionals de gens associats a la resposta de línies cel·lulars als fàrmacs, (ii) creat una col·lecció de descriptors biomèdics capaços, entre altres coses, d’anticipar com les cèl·lules responen als fàrmacs o trobar nous usos per fàrmacs existents, (iii) desenvolupat una eina per descobrir quins contextos biològics corresponen a una associació biològica observada experimentalment i, finalment, (iv) hem explorat diferents descriptors químics i biològics rellevants pel procés de descobriment de nous fàrmacs, mostrant com aquests poden ser utilitzats per trobar solucions a reptes actuals dins el camp de la biomedicina