Systems biology and big data in asthma and allergy: recent discoveries and emerging challenges

Asthma is a common condition caused by immune and respiratory dysfunction, and it is often linked to allergy. A systems perspective may prove helpful in unravelling the complexity of asthma and allergy. Our aim is to give an overview of systems biology approaches used in allergy and asthma research. Specifically, we describe recent “omic”-level findings, and examine how these findings have been systematically integrated to generate further insight. Current research suggests that allergy is driven by genetic and epigenetic factors, in concert with environmental factors such as microbiome and diet, leading to early-life disturbance in immunological development and disruption of balance within key immuno-inflammatory pathways. Variation in inherited susceptibility and exposures causes heterogeneity in manifestations of asthma and other allergic diseases. Machine learning approaches are being used to explore this heterogeneity, and to probe the pathophysiological patterns or “endotypes” that correlate with subphenotypes of asthma and allergy. Mathematical models are being built based on genomic, transcriptomic, and proteomic data to predict or discriminate disease phenotypes, and to describe the biomolecular networks behind asthma. The use of systems biology in allergy and asthma research is rapidly growing, and has so far yielded fruitful results. However, the scale and multidisciplinary nature of this research means that it is accompanied by new challenges. Ultimately, it is hoped that systems medicine, with its integration of omics data into clinical practice, can pave the way to more precise, personalised and effective management of asthma.This work was supported by the National Health and Medical Research Council (NHMRC) of Australia via a postgraduate scholarship (ref. no. 1114753) to HHF Tang, research grant (1049539) to M Inouye and K Holt, and Fellowships (1061409) to K Holt and (1061435) to M Inouye. K Holt was further supported by a Senior Medical Research Fellowship from the Viertel Foundation of Australia

DPVis: Visual Analytics with Hidden Markov Models for Disease Progression Pathways

Clinical researchers use disease progression models to understand patient status and characterize progression patterns from longitudinal health records. One approach for disease progression modeling is to describe patient status using a small number of states that represent distinctive distributions over a set of observed measures. Hidden Markov models (HMMs) and its variants are a class of models that both discover these states and make inferences of health states for patients. Despite the advantages of using the algorithms for discovering interesting patterns, it still remains challenging for medical experts to interpret model outputs, understand complex modeling parameters, and clinically make sense of the patterns. To tackle these problems, we conducted a design study with clinical scientists, statisticians, and visualization experts, with the goal to investigate disease progression pathways of chronic diseases, namely type 1 diabetes (T1D), Huntington's disease, Parkinson's disease, and chronic obstructive pulmonary disease (COPD). As a result, we introduce DPVis which seamlessly integrates model parameters and outcomes of HMMs into interpretable and interactive visualizations. In this study, we demonstrate that DPVis is successful in evaluating disease progression models, visually summarizing disease states, interactively exploring disease progression patterns, and building, analyzing, and comparing clinically relevant patient subgroups.Comment: to appear at IEEE Transactions on Visualization and Computer Graphic

Deconstructing the carcinogenome: cancer genomics and exposome data generation, analysis, an tool development to further cancer prevention and therapy

The rise in large-scale cancer genomics data collection initiatives has paved the way for extensive research aimed at understanding the biology of human cancer. While the majority of this research is motivated by clinical applications aimed at advancing targeted therapy, cancer prevention initiatives are less emphasized. Many cancers are not attributable to known heritable genetic factors, making environmental exposure a main suspect in driving cancer risk. A major aspect of cancer prevention involves the identification of chemical carcinogens, substances linked to increased cancer susceptibility. Traditional methods for chemical carcinogens testing, including epidemiological studies and rodent bioassays, are expensive to conduct, not scalable to a large number of chemicals, and not capable of detecting specific mechanisms of actions of carcinogenicity. Thus, there exists a dire need for improvement in data generation and computational method development for chemical carcinogenicity testing. Here, we coin the term "carcinogenome" to denote the complete cancer genomic landscape encompassing both its repertoire of environmental chemical exposures, as well as its germ-line and somatic mutations and epi-genetic regulators. To study the carcinogenome, we analyze both the genomic behavior of real human tumors as well as profiles of the exposome, that is, data derived from chemical exposures in human, animal or cell line models. My thesis consists of two distinct projects that, through the generation and innovative analysis of multi-omics data, aim at advancing our understanding of the molecular mechanisms of cancer initiation and progression, and of the role environmental exposure plays in these processes. First, I detail our effort at data generation and method development for characterizing environmental contributions to carcinogenesis using transcriptional profiles of chemical perturbations. Second, I present the tool iEDGE (Integration of Epi-DNA and Gene Expression) and its applications to the integrative analysis of multi-level cancer genomics data from human primary tumors of multiple cancer types. These projects collectively further our understanding of the carcinogenome and will hopefully foster both cancer prevention, through the identification of environmental chemical carcinogens, and cancer therapy, through the discovery of novel cancer gene drivers and therapeutic targets

Machine Learning and Bioinformatics Models to Identify Pathways that Mediate Influences of Welding Fumes on Cancer Progression

Abstract: Welding generates and releases fumes that are hazardous to human health. Welding fumes (WFs) are a complex mix of metallic oxides, fluorides and silicates that can cause or exacerbate health problems in exposed individuals. In particular, WF inhalation over an extended period carries an increased risk of cancer, but how WFs may influence cancer behaviour or growth is unclear. To address this issue we employed a quantitative analytical framework to identify the gene expression effects of WFs that may affect the subsequent behaviour of the cancers. We examined datasets of transcript analyses made using microarray studies of WF-exposed tissues and of cancers, including datasets from colorectal cancer (CC), prostate cancer (PC), lung cancer (LC) and gastric cancer (GC). We constructed gene-disease association networks, identified signaling and ontological pathways, clustered protein-protein interaction network using multilayer network topology, and analyzed survival function of the significant genes using Cox proportional hazards (Cox PH) model and product-limit (PL) estimator. We observed that WF exposure causes altered expression of many genes (36, 13, 25 and 17 respectively) whose expression are also altered in CC, PC, LC and GC. Gene-disease association networks, signaling and ontological pathways, protein-protein interaction network, and survival functions of the significant genes suggest ways that WFs may influence the progression of CC, PC, LC and GC. This quantitative analytical framework has identified potentially novel mechanisms by which tissue WF exposure may lead to gene expression changes in tissue gene expression that affect cancer behaviour and, thus, cancer progression, growth or establishment

Machine Learning and Bioinformatics Models to Identify Pathways that Mediate Influences of Welding Fumes on Cancer Progression

Abstract: Welding generates and releases fumes that are hazardous to human health. Welding fumes (WFs) are a complex mix of metallic oxides, fluorides and silicates that can cause or exacerbate health problems in exposed individuals. In particular, WF inhalation over an extended period carries an increased risk of cancer, but how WFs may influence cancer behaviour or growth is unclear. To address this issue we employed a quantitative analytical framework to identify the gene expression effects of WFs that may affect the subsequent behaviour of the cancers. We examined datasets of transcript analyses made using microarray studies of WF-exposed tissues and of cancers, including datasets from colorectal cancer (CC), prostate cancer (PC), lung cancer (LC) and gastric cancer (GC). We constructed gene-disease association networks, identified signaling and ontological pathways, clustered protein-protein interaction network using multilayer network topology, and analyzed survival function of the significant genes using Cox proportional hazards (Cox PH) model and product-limit (PL) estimator. We observed that WF exposure causes altered expression of many genes (36, 13, 25 and 17 respectively) whose expression are also altered in CC, PC, LC and GC. Gene-disease association networks, signaling and ontological pathways, protein-protein interaction network, and survival functions of the significant genes suggest ways that WFs may influence the progression of CC, PC, LC and GC. This quantitative analytical framework has identified potentially novel mechanisms by which tissue WF exposure may lead to gene expression changes in tissue gene expression that affect cancer behaviour and, thus, cancer progression, growth or establishment

Mitochondria Mediated Outcomes of Developmental Exposure to Low-level Chemical Mixtures in Zebrafish Danio rerio

Exposure to drinking water contaminants has been linked to developmental outcomes in both epidemiological and model organism studies. However, low level mixture effects, on early development has yet to be explored. It is hypothesized that early chemical exposures may increase disease susceptibility later in life. This work aimed to investigate impacts of a variety of chemicals and concentrated on metals arsenic (As), cadmium (Cd), vanadium (V), and lead (Pb) due to their presence in drinking water and known developmental toxicity. To determine the effects of a metal and organic contaminant co-exposure, the ubiquitously used herbicide glyphosate was also explored. The zebrafish (Danio rerio) model was used to elucidate developmental impacts of lowlevel chemical mixture exposure with a focus on mitochondrial function. An in-depth analysis exploring embryonic oxygen consumption rate (eOCR) in response to all iterations of a 5-part chemical mixture of glyphosate, As, Cd, V, and Pb showed that mitochondria are highly sensitive to mixture toxicity, and that pre-exposure to a metal mixture leave the mitochondria more susceptible to acute chemical stress through depleted reserve capacity. Altered mitochondrial function, along with changes in gene expression and histology suggested that early mixture exposure may contribute to the endemic of chronic kidney disease of unknown etiology (CKDu). To investigate underlying molecular mechanisms that may contribute to CKDu susceptibility, RNA seq data from zebrafish embryos exposed to mixtures of As, Cd, V, and Pb, (+/- glyphosate) and glyphosate alone, suggest that exposure to metal and organic mixtures may be altering the extracellular matrix of kidney tissue. This combined with impaired mitochondrial function, could leave individuals more susceptible to kidney injury CKDu progression. To determine phenotypes associated with mixture exposure, changes in behavior after exposure to a large collection of water samples were explored. A cluster analysis of metals found in drinking water samples were coupled to changes in behavior and revealed that concentrations of Pb, Cd, As, Uranium (U) and Nickel (Ni), should be taken into special consideration when determining drinking water standards. These data suggest that impaired mitochondria, as a result of low-level mixture exposure, may function in the early onset of disease, such as CKDu, and further impair organism development

Computational hypothesis generation with genome-side metabolic reconstructions: in-silico prediction of metabolic changes in the freshwater model organism Daphnia to environmental stressors

Computational toxicology is an emerging, multidisciplinary field that uses in-silico modelling techniques to predict and understand how biological organisms interact with pollutants and environmental stressors. Genome-wide metabolic reconstruction (GWMR) is an in-silico modelling technique that aims to represent the metabolic capabilities of an organism. Daphnia is an emerging model species for environmental omics whose underlying biology is still being uncovered. Creating a metabolic reconstruction of Daphnia and applying it in an environmental computational toxicology setting has the potential to aid in understanding its interaction with environmental stressors. Here, the fist GWMR of D. magna is presented, which is built using METRONOME, a newly developed tool for automated GWMR of new genome sequences. Active module identification allows for omics data sets to be integrated into in-silico models and uses optimisation algorithms to find hot-spots within networks that represent areas that are significantly impacted based on a toxicogenomic transcriptomics dataset. Here, a method that uses the active modules approach in a predictive capacity for computational hypothesis generation is introduced to predict unknown metabolic responses to environmentally relevant human-induced stressors. A computational workflow is presented that takes a new genome sequence, builds a GWMR and integrates gene expression data to make predictions of metabolic effects. The aim is to introduce an element of hypothesis generation into the untargeted metabolomics experimental workflow. A study to validate this approach using D. magna as the target organism is presented, which uses untargeted Liquid-Chromatography Mass Spectrometry (LC-MS) to make metabolomics measurements. A software tool MUSCLE is presented that uses multi-objective closed-loop evolutionary optimisation to automatically develop LC-MS instrument methods and is used here to develop the analytical method

Parkinson’s Disease: Insights from Drosophila Model

Parkinson’s disease (PD) is a medical condition that has been known since ancient times. It is the second most common neurodegenerative disorder affecting approximately 1% of the population over 50 years. It is characterized by both motor and non-motor symptoms. Most of PD cases are sporadic while 5–10% cases are familial. Environment factors such as exposure to pesticides, herbicides and other heavy metals are expected to be the main cause of sporadic form of the disease. Mutation of the susceptible genes such as SNCA, PINK1, PARKIN, DJ1, and others are considered to be the main cause of the familial form of disease. Drosophila offers many advantages for studying human neurodegenerative diseases and their underlying molecular and cellular pathology. Shorter life span; large number of progeny; conserved molecular mechanism(s) among fly, mice and human; availability of many techniques, and tools to manipulate gene expression makes drosophila a potential model system to understand the pathology associated with PD and to unravel underlying molecular mechanism(s) responsible for dopaminergic neurodegeneration in PD—understanding of which will be of potential assistance to develop therapeutic strategies to PD. In the present review, we made an effort to discuss the contribution of fly model to understand pathophysiology of PD, in understanding the biological functions of genes implicated in PD; to understand the gene-environment interaction in PD; and validation of clues that are generated through genome-wide association studies (GWAS) in human through fly; further to screen and develop potential therapeutic molecules for PD. In nutshell, fly has been a great model system which has immensely contributed to the biomedical research relating to understand and addressing the pathology of human neurological diseases in general and PD in particular

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

A Systems-Level Approach to Understand The Seasonal Factors Of Early Development With Clinical and Pharmacological Applications

Major developmental defects occur in 100,000 to 200,000 children born each year in the United States of America. 97% of these defects are from unidentified causes. Many fetal outcomes (e.g., developmental defects), result from interactions between genetic and environmental factors. The lifetime effects from prenatal exposures with low impact (e.g., air pollution) are often understudied. Even when these exposures are studied, the focus is often placed on immediate effects of the exposure (e.g., fetal anomalies, miscarriage rates) leaving lifetime effects largely unexplored. This makes prolonged (or lifetime) effects of low-impact exposures an understudied research area. Included in this set of low-impact exposures is seasonal variance at birth. This thesis measures the effects of seasonal variance at birth on lifetime disease risk at both the population-level and molecular-levels. Four aims, comprising this thesis study, were conducted that utilize data from pharmacology, clinical care (Electronic Health Records) and genetics. These aims included: 1.) Development of an Algorithm to Reveal Diseases with a Prenatal/Perinatal Seasonality Component (described in chapter 2); 2.) Investigation of Climate Variables that Affect Lifetime Disease Risk By Altering Environmental Drivers (described in chapters 3 and 4); 3.) Discovery of Genes Involved in Birth Season – Disease Effects (described in chapter 5) and 4.) Investigation of Pharmacological Inhibitors As Phenocopies of the Birth Season – Disease Effect (described in chapters 6 and 7). Knowledge gained from these four areas, through seven distinct studies, establishes that birth season is a causal risk factor in a number of common diseases including cardiovascular diseases