Exposome Lecture Series
The Exposome Lecture Series hosts seminars held by invited international experts reflecting current status and recent developments in Exposome relevant research fields. The lectures aim at stimulating scientific exchange and discussion of ideas and knowledge. These (irregular) events take place at the UFZ Leipzig.
19 September 2017, 10:00 h, Lecture Hall Buliding 1.0 (VTS)
Jana Asselman, Ghent University, Belgium
Gene co-expression networks drive and predict reproductive effects in response to multiple stress in aquatic invertebrates
Increasing events of environmental disturbances in aquatic environments have intensified the need to understand, study and predict the effects on aquatic ecosystems. Both natural and anthropogenic-induced changes can significantly affect survival and reproduction, key drivers of population growth. Underlying these life history parameters are coordinated interactions and changes in gene networks and pathways that trigger a cascade of responses at higher functional levels. Some of the first indications of environmental disturbances can therefore be detected at gene expression level. Gene expression patterns can thus be used as causal links between environmental disturbances and demographic parameters. As such, gene expression data can provide a powerful basis to develop predictive models. Here, we generated a dataset of natural and chemical stressors, including their binary mixtures to identify gene expression networks driving life history responses in the aquatic crustacean, Daphnia. Based on the identified gene networks, we developed generalized additive models in which these gene expression networks can be used as predictors for life history parameters, including prediction of potential interactions between multiple stressors. Surprisingly, the gene expression networks of exposures to individual stressors are more informative and better predictors for reproduction under multiple stress conditions then the expression profiles of exposures to multiple stressors. These gene networks were small, often containing less than 200 genes but densely interconnected. They were not biased towards specific gene functions but rather contained a diverse set of genes representing different metabolic pathways. These results highlight the potential of gene expression data to clearly anchor phenotypic changes and can be used to characterize the cascade of events happening in response to stress.
26 April 2017, 10:00 h, KUBUS Lecture Hall 1B
Luigi Margiotta-Casaluci, Brunel University London, UK
Mechanistic toxicology and chemical safety assessment: bridging the gap between science and decision-making
Chemical safety assessment is currently ongoing a fast-paced revolution centred on the growing availability of in vitro and in silico approaches able to shed light on the complex set of chemical-induced mechanisms underlying toxic effects. This knowledge has great potential to drive animal-free toxicity predictions; however, its current ability to inform decision-making is often limited by the difficulty faced by regulators to interpret the high complexity of pathway-based predictions compared to the simple outcomes generated by traditional in vivo regulatory studies. The raising question is: what can we do to facilitate the implementation of pathway-based toxicology in the regulatory arena? Answering this question is critically important if we want to live in safer chemical world. In fact, traditional environmental risk assessment frameworks rarely incorporate mechanistic rationales, making the process heavily dependent on case-by-case evaluations, and therefore unfit to embrace the predictive and animal-free vision of modern toxicology. The challenge is not only limited to pragmatic aspects, but it goes deeper in the biological essence of the problem. Chronic perturbations of molecular pathways can lead to a variety of subtle multi-scale effects that can have important implications for the long-term maintenance of human and environmental health. Traditional chemical testing falls short to detect those effects. The development of reliable and fit-for-purpose mechanistic models is therefore of paramount importance to bring chemical safety assessment beyond the current limitations and guide the development of safer chemicals. In this talk I will present my recent research on the effects of human pharmaceuticals in fish models to highlight some of the major challenges we encountered in these years, and discuss possible solutions to move forward.
9 November 2016, 10:00 h, KUBUS Hall 1 A
Prof. PAOLO VINEIS, Imperial College London, UK
Systems perspectives of the exposome
Systems biology has been driven by technology (the development of omics) and by statistical modelling and bioinformatics. It is time to bring biological thinking back. We need to make at least three traditions of thought compatible: (a) causality in epidemiology, e.g. the “sufficient-component-cause framework”, and causality in other sciences, e.g. the Salmon and Dowe approach; (b) new acquisitions about disease pathogenesis, e.g. the “branched model” in cancer, and the role of biomarkers in this process; (c) the burgeoning of omic research, with a large number of “signals” that need to be interpreted. To address the new challenges of epidemiology, the concept of the “exposome” has been proposed, initially by Wild , with more recent detailed development in relation to its application to population-based studies [Wild, 2012]. The original concept was expanded by others, particularly Rappaport and Smith  who functionalized the exposome in terms of chemical signals detectable in biospecimens. The canonical exposome concept refers to the totality of exposures from a variety of sources including chemical agents, biological agents, radiation, and psychosocial components from conception onward, over a complete lifetime [Rappaport and Smith, 2010; Wild, 2012]. I will try to offer a unifying framework to incorporate omic data into causal models, referring to a position called “evidential pluralism”, according to which causal reasoning is based on both “difference-making” and the underlying biological mechanisms (Russo and Williamson). I will show examples from recent projects in the field, namely: new omic approaches such as adductomics; new long-term methylation biomarkers (in relation to smoking and air pollution); markers related to early life exposure and the role of socio-economic differentials. In particular, Illari and Russo suggest to conceptualize the detecting and tracing of signals in terms of information transmission, which is a development of Salmon’s and Dowe’s mark transmission theory. One advantage of information transmission is that it is potentially widely applicable and capable of explaining how heterogeneous factors such as micro and macro – biological and social – are linked; this is arguably a pressing issue in the light of results of omic studies and also for the design of public health policies.
Prof. PAOLO VINEIS is Chair of Environmental Epidemiology at Imperial College, London. He leads the Exposome and Health theme of the MRC-PHE Centre for Environment and Health at Imperial College, please see here for further information.
DALMA MARTINOVIĆ-WEIGELT: "Integrating chemical monitoring data with high-content and high-throughput effects data", 26 October 2016
26 October 2016, 10:00 h, KUBUS Lecture Hall 2
Dr. DALMA MARTINOVIĆ-WEIGELT, University of St. Thomas, St. Paul, Minnesota, USA
Integrating chemical monitoring data with high-content and high-throughput effects data to prioritize contaminants and hazards in chemical mixtures
Determining ecological risks associated with exposures to complex chemical mixtures in the environment is one of the main challenges of chemical safety assessment and environmental protection. Effects-based monitoring tools that can measure the integrated biological activity of an entire mixture have been proposed as one of the solutions. An important limitation of the effects-based approaches is that they typically do not provide insight into which chemicals are causing the observed biological responses. Current approaches for integrating chemical monitoring with biological effects data will be discussed. More specifically, we will present and critically evaluate two approaches where prior knowledge regarding toxicity of individual contaminants is combined with empirical in situ assessment to predict toxicity of mixtures and prioritize contaminants. First approach involves development of knowledge assembly model (KAM; which is specific to the aquatic system of interest) based on chemical monitoring data and publically available chemical-gene interaction data. Follow-up fish transcriptomics studies and reverse causal reasoning approaches are then utilized to prioritize risks and contaminants. Second approach utilizes publically available high-throughput in vitro data to extract benchmark effect concentrations for hundreds of biological targets. From these data, exposure-to-activity ratios are calculated and integrated with empirical in vitro effects assessment to prioritize specific chemicals for further testing and to identify biological effects of interest. Observed/predicted molecular-level effects data generated by above approaches can be integrated with adverse outcome pathway knowledge to aid extrapolation into regulatory outcomes of concern (e.g., organism- and population-level effects).
Dr. DALMA MARTINOVIĆ-WEIGELT is Associate Professor at University of St. Thomas, St. Paul, MN. Her research focus is on development of AOPs and bioeffects-based monitoring approaches using high content and high throughput data.
14 October 2016, 09:00 h, KUBUS Hall 2 AB
Prof. XIANGDONG LI, Hong Kong Polytechnic University, Hong Kong, China
Airborne particulate matter pollution in urban areas: A chemical mixture perspective
Rapid urban and industrial development has resulted in severe air pollution problems in developing countries such as China, especially in highly industrialized and populous urban clusters. Dissecting the complex mixtures of airborne particulate matter (PM) has been a key scientific focus in the last two decades, leading to significant advances in understanding physicochemical compositions for comprehensive source apportionment. However, identifying causative components with an attributable link to population-based health outcomes remains a huge challenge. The microbiome, an integral dimension of the PM mixture, is an unexplored frontier in terms of identities and functions in atmospheric processes and human health. In this study, we identify the major gaps in addressing these issues, and recommend a holistic framework for evaluating the sources, processes, and impacts of atmospheric PM pollution. Such an approach and the knowledge generated will facilitate the formulation of regulatory measures to control PM pollution in China and elsewhere.
28 June 2016, 13:00 h, KUBUS Hall 1CD
Prof. PHILIPP MAYER, DTU-Environment, Denmark
The Partitioning Based Laboratory for Organic Pollutants
“Partitioning” is crucial for the fate, exposure and effects of organic chemicals in the environment. However, it can also be utilized in a wide range of partitioning based approaches within the environmental laboratory. The presentation will provide an overview of almost 20 years of research on partitioning based techniques with special emphasis on equilibrium sampling, passive dosing, sorptive bioaccessibility extractions & pull-push systems. Such approaches have been applied for studying and understanding “bioavailability”, co-transport, (mixture) toxicity, binding to molecular scale constituents, the thermodynamics of bioaccumulation and biotransformation.
More information about this research here.