Research projects

This section introduces all current projects of the Environmental Cheminformatics group.

Environmental Cheminformatics to Identify Unknown Chemicals and their Effects (ECHIDNA)

ECHIDNA is a five year ATTRACT fellowship from the Luxembourg National Research Fund (FNR) starting late 2018 to build computational methods suitable for investigating and elucidating unknowns and causes of effects using HRMS of small molecules. Computational and experimental developments will improve structure elucidation, including cheminformatics approaches as well as stable and dynamic labelling of samples. New cheminformatics methods will improve our understanding of the fundamentals of HRMS and work towards the “holy grail” of a full Computer- Assisted Structure Elucidation (CASE) system for HRMS. A microbiome-PD cohort study will yield complex samples with patient/early stage/control information allowing a discovery-based prioritization of potential neurotoxins amongst these samples. Single cell biological systems will yield additional information on known and unknown metabolites in well-understood systems. This project involves several internal partners within LCSB, including the Enzymology and Metabolism Group, Eco-Systems Biology Group and the Bioinformatics Core.

Non-target Screening with High Resolution Mass Spectrometry

Analytical and computational methods for high throughput non-target high resolution mass spectrometry methods (NT-HR-MS) will be developed at LCSB in conjunction with the Metabolomics Platform, the Enzymology and Metabolism Group and the Bioinformatics Core. A large focus will be on developing open computational methods, primarily using the programming language R. This will incorporate the workflows developed during the SOLUTIONS project and the collaborative trial run by the US EPA (ENTACT). Open packages such as RMassBank will be integrated with other packages under development (ReSOLUTION, RChemMass) and connected with initiatives from the NORMAN Network (see below). MetFrag, developed in collaboration with the IPB within SOLUTIONS, will form the basis for non-target identification. The ECI group are collaborating with Rick Helmus at the University of Amsterdam for the development of patRoon and are actively developing Shinyscreen, led by Todor Kondic. Keep an eye on our GitLab pages for ECI code developments!

Open Data Exchange, the NORMAN Network and NORMAN-SLE

Ensuring the open availability of high quality data is critical to improving computational methods and this group will continue several initiatives within the NORMAN Network and beyond. We will continue to coordinate the NORMAN Suspect List Exchange, ensuring our datasets are FAIR, archived on Zenodo and integrated into public resources such as PubChem and the CompTox Chemicals Dashboard. The NORMAN-SLE is also available as a Classification Browser and Data Source in PubChem, with annotation content added progressively, and as lists in CompTox. Our work with PubChem is part of the ECI R3 workflows on GitLab. The NORMAN-SLE is tightly connected with unique European initiatives launched within several NORMAN working groups (Prioritization, EDA and Non-target screening) such as NormaNEWS, the NORMAN Digital Sample Freezing Platform and MassBank.EU (see below). We gratefully acknowledge our many collaborators contributing to these Open Science initiatives, especially the PubChem, CompTox and NORMAN members working closely with us.

Mass Spectral Libraries: MassBank, RMassBank

This group will continue collaborative activities in the European MassBank server, initiated within the NORMAN Network in 2012. This includes the contribution of high quality mass spectral data as well as uploading of external contributions and continuing input into server and functional developments. Collaborative development on the spectral processing software “RMassBank” will continue for automated processing, calibration and annotation of mass spectra using online services such as CACTUS, CTS and PubChem for chemical annotation. These methods are applicable for MassBank and computational mass spectrometry workflows in general. Our group will focus on pushing the boundaries of the possible with tentative libraries annotated with appropriate confidence levels and structural information to develop methods to deal with complex mixtures in real samples in a robust manner. Initiatives such as the SPectraL hASH (SPLASH) and the transition of the entire MassBank.EU to a GitHub-based collaborative project ensures improved access to data, independent of the data upload point.

Microbiomes in One Heath (MICROH)

MICROH, which stands for “Microbiomes in One Health”, is a competitive, interdisciplinary PhD training programme, supported by the PRIDE doctoral research funding scheme (PRIDE17/11823097) of the Luxembourg National Research Fund (FNR). MICROH aims to study interactions within and between microbiomes in relation to two major healthcare challenges of our time, i.e. the spread of antimicrobial resistance genes and the increasing prevalence of chronic diseases. MICROH bridges microbiology and big data analytics in a structured doctoral training environment.

By tackling frontier research questions of immediate public health relevance, the research-intensive programme responds to the unmet need of training the next generation of microbiome scientists in an interdisciplinary environment covering integration and analysis of multi-omics data, as well as basic and translational biomedical knowledge, and its practical application to the diagnosis of diseases and ultimately their treatment. The programme includes transferable skills training, support in career development, lectures and teaching by international experts and annual PhD symposia. PhD candidates will conduct their research projects either at the LCSB, LSRU, PHYMS-RU, LIH, LNS, IBBL and/or LIST.

For more information see the MICROH website.

Metabolite Repair and Fluorometabolite Annotation (SinFonia)

SinFonia, an EU funded H2020 project, aims to integrate the nonnative element fluorine into the metabolism of Pseudomonas putida to produce novel fluorinated polyhydroxyalkanoates (PHAs), ideally in such a way that bacterial growth will become dependent on this incorporation. Fluorine is common in industrial chemicals with versatile applications from electrical insulation to waterproofing, yet it is seldom present in biological systems. The current production processes for fluorochemicals often negatively affect the environment, and there is a great need for more sustainable and less harmful alternatives. If successful, SinFonia will provide a less hazardous and more sustainable solution to synthesizing fluorochemicals.

Our role, in collaboration with the Enzymology and Metabolism Group, is to identify fluorinated and undesirable metabolites in engineered bacteria using non-targeted mass spectrometry. Engineered systems may lack sufficient metabolite repair capacity, which we aim to counteract by screening for metabolite damages in our cell factories and envisaging strategies to repair them. Metabolite repair may be particularly important in SinFonia due to the load of adding heterologous pathways and a nonnative element to create new-to-nature products.

ELIXIR-Toxicology Community

The Environmental Cheminformatics group is part of the developing ELIXIR-Toxicology Community led by BiGCaT from the University of Maastricht. Keep an eye on the ELIXIR-Tox GitHub site and follow @egonwillighagen and @bigcat_um on Twitter for updates!

Luxembourg Time Machine (LuxTIME)

The “Luxemburg Time Machine” (LuxTIME) project is a pilot project in the University of Luxembourg’s Institute of Advanced Studies (IAS). LuxTIME will explore radically new ways for analysing and interpreting factual evidence of the past, fostering the energies and skills across two interdisciplinary centres (LCSB, C2 DH) at the University in cooperation with the Luxembourg Institute of Science and Technology (LIST). We intend to build an interdisciplinary framework for investigating “big data” of the past, inspired by the conceptual premises of the “European Time Machine” Flagship project. The LuxTIME digital dataset will include information from three different fields and scientific perspectives (eco-hydrology, medicine and history) and use a local showcase (i.e. the industrialisation of Belval / Minette region) as a testbed to study the impact of environmental changes on the health of the local population in a long term perspective. LuxTIME will study the past in completely new ways using evidence from eco-hydrological studies (water and pollutant sources, flow paths and transit times, topographic / geological transformations), medical records (describing disease patterns, mortality rates, social/psychological well-being), bio-chemical data (based on mass spectrometry techniques) and history (archival sources documenting economic, social, political and cultural changes. By mixing “contextual information” based on archival evidence with “scientific evidence” derived from chemical, biological, or medical investigations, the project explores new ground in interpreting “big data of the past” in a truly interdisciplinary setting. The Belval-case will critically test the analytical potential of a multi-layered research design which – this is the mid-term ambition – can be expanded into a national case study; that is the building of a real “Luxembourg Time Machine” including many different kinds of data from many different institutions.