Environmental Health Sciences

Školitel: Mgr. Eva Budinská, Ph.D.

Aims: The aim of the thesis is to build a framework for estimating effects of xenobiotics on human microbiome metabolic pathways.

Background and methods: Food with its variety of dietary compounds, environmental chemicals, pollutants, as well as medications can be considered as xenobiotics to the human microbiome. In homeostasis (the healthy state that is maintained by the constant adjustment of biochemical and physiological pathways), human microbiome provides an extra set of biochemical reactions. The intrusion of xenobiotics has the potential to introduce a departure from homeostasis in many ways seen from the perspective of human microbiome, but both human cells and microbial communities living in their surroundings have to cope with these perturbations: pollutants can trigger latent enzymatic activities changing the functional potential of these microbial consortia; other drug metabolites can block important enzymes or can be biotransformed making antibiotics or other medical interventions useless or ineffective. Such perturbations (blocks and diversions of normal enzymatic activities) can be modeled and explored in the context of metabolic network models. Computational System Biology approaches can model and explore consequences of changes in the structure of networks simulated by random or target attack to nodes/metabolites in the metabolic network of the microbiome of interest. The assembled metabolic network model for the community understudy will be dynamically “updated” based on selected computational approaches aimed at predicting latent enzymatic activities (edge insertion update), enzymatic inhibition (edge deletion update), or changes in kinetics (edge weight update).

Školitel: Stanislav Mazurenko

The recent advancements of Machine Learning (ML) techniques, coupled with growing protein data, provide promising directions for protein engineering. There are three types of protein data with an excellent ML potential: (i) in silico simulations, (ii) experimental measurements, and (iii) databases of protein sequences and structures. While ML has already leveraged some data from all the three sources in various applications in protein engineering, the field has only recently emerged, and much data remain unexplored. This project aims to explore the potential of machine learning methods in collecting protein data, reducing its dimensionality, performing data analysis, prediction, and optimization, to produce designs of improved proteins. The impact will primarily be (i) the new knowledge of the underlying mechanisms, (ii) promising protein variants, and (iii) user-friendly software tools that will provide access to the developed algorithms to the broader community of protein engineers.

Školitel: prof. RNDr. Luděk Bláha, Ph.D.

OBJECTIVES: The research aims to explore mechanisms (molecular toxicology, biomarkers of effects, toxicogenomic responses) triggered in humans and natural biota by organic pollutants, their metabolites and mixtures. The outcomes contribute to protection of the environment and health by providing scientific evidence and support to pragmatic risk assessment and management of chemicals.

FOCUS: Doctoral research projects focus on the effects of chemical groups that are broadly used in practice but their (eco)toxicological characterization is poor such as novel types of flame retardants, pharmaceuticals, pesticides and other potential endocrine-disrupters. Students benefit from outstanding research facilities of RECETOX that include high-end analytical instrumentations, molecular toxicology laboratories, alternative toxicological models - aquatic invertebrates and zebrafish.

EXAMPLES of potential student doctoral projects:
* Development of quantitative Adverse Outcome Pathways (AOPs) for liver toxicity and obesogenicity
* AOP networks beyond the male reproductive disorders
* In vitro toxicological investigations of novel flame retardants
* Molecular and biochemical effect biomarkers of low-dose mixture exposures in human cohort samples
* Automated text-mining approaches integrating toxicological data to toxicological knowledge

MORE INFORMATION: www.recetox.muni.cz

PLEASE NOTE: before initiating the formal application process to doctoral studies, all interested candidates are required to contact Prof. Ludek Blaha (blaha@sci.muni.cz) for informal discussion.

Školitel: Mgr. Hynek Pikhart, Ph.D., M.Sc.

The advertised positions will focus on environmental epidemiology. The successful candidates will investigate associations between environmental and social stressors and population health in the extensive prospective cohort studies (CELSPAC, HAPIEE) in the Czech Republic using multivariable regression techniques.

The selected Ph.D. students will join a newly established ERA Chair Team (https://www.recetox.muni.cz/erachair) supported by the competitive Horizon 2020 ERA Chair project R-EXPOSOME.

Školitel: PharmDr. Zdeněk Spáčil, Ph.D.

Alzheimerova choroba je nejběžnější příčinou demence (60-80% případů) u starší populace po celém světě. Současný výzkum podporuje myšlenku, že agregace bílkovin iniciuje nástup Alzheimerovy choroby. Navzdory velkému množství dostupné literatury a studií zůstává mechanismus patogeneze a potenciální léčba Alzheimerovy choroby nejasný. Cílem disertační práce je aplikovat moderní a vysoce specifické analytické techniky (ultra-účinná kapalinová chromatografie - UHPLC a tandemová hmotnostní spektrometrie – MS/MS) ke kvantitativnímu profilování biomarkerů, které jsou spojovány se vznikem této lidské neuropatologii. Konkrétně půjde o aplikaci cílených instrumentálních technik hmotnostní spektrometrie (např. selected reaction monitoring – SRM) a jejich využití ke kvantitativní charakterizaci složení membránových lipidů a proteinů v biologických vzorcích. Očekávaným výstupem jsou informace o změnách hladin jednotlivých tříd lipidů a proteinů v biologických membránách a sledování případných biologických a klinických důsledků těchto změn.

Alzheimer's disease is the most common cause of dementia (60-80% of cases) in the elderly population around the world. Current research supports the idea that protein aggregation initiates the onset of Alzheimer's disease. Despite a large number of available literature and studies, the mechanism of pathogenesis and the potential treatment for Alzheimer's disease remain elusive. The dissertation thesis aims to apply modern and highly specific analytical techniques (ultra-high performance liquid chromatography - UHPLC and tandem mass spectrometry - MS/MS) for the quantitative profiling of biomarkers associated with the development of human neuropathology. Specifically, the application of targeted instrumental techniques of mass spectrometry (i.e., selected reaction monitoring - SRM) will be used to quantitatively characterize the composition of membrane lipids and proteins in biological samples. The expected output is information on changes in individual lipid classes and protein levels in biological membranes and monitoring of potential biological or clinical consequences of these changes.

Školitel: Stanislav Mazurenko

Description:
Molecular dynamics (MD) simulations allow analyzing the physical movements of biomolecules. The generated data are sequences of frames (in 100 000s) captured at a predefined time step. Each frame consists of positions of all the atoms of a protein (in 10 000s), which are simulated using a molecular mechanics force field. The analysis of such a massive amount of data is often challenging especially for molecules with conformational heterogeneity, such as the disordered Abeta peptide relevant for Alzheimer's disease (AD). Abeta peptide is the hallmark of the disease and adopts diverse conformations. Understanding the dynamic properties of the Abeta protein is a key to determine the effects of drug candidates for potential AD treatment. The objective of this thesis is to build on recent advances in large-scale weakly and self-supervised learning for video sequences and develop new methods for automatic analysis of molecular dynamics simulations and more generally protein engineering.

(PLEASE NOTE: Before initiating formal application process, all potential candidates are required to contact the supervisor for informal discussion)

References:
S Mazurenko, Z Prokop, J Damborsky. Machine learning in enzyme engineering, In ACS Catalysis, 10 (2), 1210-1223, 2020.

A Miech, D Zhukov, JB Alayrac, M Tapaswi, I Laptev, J Sivic, Howto100m: Learning a text-video embedding by watching hundred million narrated video clips, In Proceedings of the IEEE/CVF International Conference on Computer Vision and Pattern Recognition, 2019.

J-B Alayrac, P Bojanowski, N Agrawal, J Sivic, I Laptev, S Lacoste-Julien, Learning from Narrated Instruction Videos, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017.

A Mardt, L Pasquali, H Wu, F Noe, VAMPnets for deep learning of molecular kinetics. In Nature Communications, 9, 5, 2018.

Links:
loschmidt.chemi.muni.cz/peg/
impact.ciirc.cvut.cz

Školitel: prof. RNDr. Zbyněk Prokop, Ph.D.

Miniaturization and automation are key trends in modern experimental methods in the natural sciences and biomedicine. Microfluidics makes it possible to perform thousands of experiments per second thanks to the precise handling of nano- to pico-liter volumes of solutions in the microenvironment of channels measuring tens of micrometers. The project will focus on the development and optimization of microfluidic systems for high-performance characterization and study of proteins obtained from genomic databases and constructed by protein engineering methods. The obtained data will be evaluated by artificial intelligence methods. The newly developed methods will be applied in the study of the mechanism of Alzheimer's disease and the development of new drugs for stroke. The project will be solved in cooperation with the research group Prof. Andrew deMello at ETH Zurich, Switzerland (https://www.demellogroup.ethz.ch/) and the International Center for Clinical Research, University Hospital at St. Anny in Brno

Školitel: Mgr. David Bednář, Ph.D.

Cílem práce bude studium enzymů pomocí výpočetních metod molekulového modelování a bioinformatiky. Výstupem analýz budou nejen nové poznatky v enzymologii, ale také varianty enzymů vytvořené metodami proteinového inženýrství, které budou mít potenciál v biotechnologických či biomedicínských aplikacích. Analýzy i design nových variant se zaměří na vylepšování stability molekul, které budou studovány metodami jako je Rosetta, FoldX nebo FireProt. Dále bude studována a optimalizována aktivita, specificita a selektivita enzymů metodami molekulového dokování, molekulové dynamiky, kvantová chemie a dalších. Poznatky ze studia enzymů budou také využity pro zlepšování softwarových nástrojů k analýze a designu proteinů, které tým v Loschmidtových laboratořích dlouhodobě vyvíjí.

The aim of this thesis will be to study enzymes by in silico approaches of molecular modeling and bioinformatics. The outcomes of the project will be used both in understanding the basics of enzymology and also to design enzyme variants by methods of protein engineering which can be applicable in biotechnology or biomedicine. The analysis and design of new enzyme variants will focus on improving the protein stability by methods like Rosetta, FoldX, or FireProt. Morover, other enzyme properties like activity, selectivity, or specificity will be analysed and optimized by molecular docking, molecular dynamics, or quantum chemistry calculations. The knowledge obtained during the analysis and design of enzymes will be utilized to improve functionality of software tools for protein engineering which are developed in Loschmidt laboratories.

Školitel: doc. Mgr. Klára Hilscherová, Ph.D.

OBJECTIVES: The overall research goal is to develop efficient approaches for the characterization of specific toxic potentials of complex exposure mixtures that organisms including human are exposed to in the environment. This refers to the realistic exposure scenarios with possible joint action of a wide spectra of pollutants. The research aims namely on the ability of pollutants to interfere with hormonal regulation (endocrine disruption) and the role of this disruption in adverse effects, especially on the early (neuro)development and reproduction. The outcomes will help to prioritize the toxicity risk drivers, characterize the joint action of co-occurring chemicals and relationship of the mixture exposure with adverse effects in exposed organisms including human. This will provide scientific basis for effective risk and regulatory assessment and management of relevant exposure mixtures and chemical risk drivers and thus contribute to environmental and human health protection.

FOCUS: The doctoral research projects focus on development and implementation of diagnostic and predictive effect-based methods (in vitro, in silico) for the characterization of potential impact of internal and external exposure mixtures on environmental and human health and associated risks. This includes description of relevant exposure mixtures effects on key processes in hormonal regulation affecting early (neuro)development and reproduction in organisms. Adverse outcome pathways concept will be employed to relate the in vitro/in silico assessed endocrine disrupting potential with diagnostic approaches on the level of effect biomarkers or omics methods linking the mechanisms of action with these disorders. The projects will focus namely on the development and optimization of a battery of high-throughput bioassays covering wide range of modes of action relevant for neurodevelopment or endocrine disruption, and characterization of the endocrine disrupting potential of complex environmental exposure and effect-driver identification by a combination of progressive bioanalytical, molecular biology and non-/target high-resolution mass spectrometry methods.

EXAMPLES of potential student doctoral projects:

(1) Endocrine disrupting pollutants able to interfere with early development

(2) Adverse outcome pathways-based approaches in human exposure and epidemiological studies

(3) Human cell-based bioanalytical approaches for the assessment of endocrine disruptive potential including 2D and 3D-in vitro systems

(4) Assessment of biomarkers of endocrine disruption in biological matrices using advanced analytical methods and their relation to exposure

MORE INFORMATION: www.recetox.muni.cz

PLEASE NOTE: before initiating the formal application process to doctoral studies, all interested candidates are recommended to contact Assoc.Prof. Klára Hilscherová (hilscherova@recetox.muni.cz) for informal discussion and more information on the PhD studies and research topics.

Školitel: Ing. RNDr. Martin Marek, Ph.D.

Project summary: Enzymes catalyse most of the chemical reactions that occur in biological systems and can be given non-natural catalytic functions by protein engineering. However, despite their vast importance, we do not know how enzymes acquire the structural diversity and conformational flexibility that enables them to evolve towards new molecular functions. Our proof-of-concept data on three structurally similar but functionally distinct enzyme classes of haloalkane dehalogenases (EC 3.8.1.5), beta-lactone decarboxylases (EC 4.1.1.114), and light-emitting monooxygenases (EC 1.13.12.5) suggest that as-yet-underexplored molecular elements – access tunnels and flexible loops – play a pivotal role in their functional diversification.

The proposed PhD project will investigate the molecular structures of these model enzymes using an innovative multi-method biology approach to identify the key structural and dynamic elements that govern enzymes’ evolvability. This project will combine X-ray crystallography, single-particle cryo-electron microscopy, and advanced mass spectrometry techniques to capture unprecedented molecular details of the conformational sampling that is required for productive enzymatic biocatalysis. Complementary protein simulations, mutational and biochemical experiments will delineate the evolutionary trajectories that lead to the emergence of novel enzymatic functions. The resulting knowledge will extend our understanding of molecular evolution beyond the current state-of-the-art, particularly by revealing how the conformational diversity of proteins is associated with specific biocatalytic functions. The gained knowledge from this PhD project will pave the way for the development of new theoretical concepts and cutting-edge software tools for the rational engineering of tailor-made biocatalysts exploitable in biotechnology and biomedicine.

PLEASE NOTE: Before starting formal application/admission process, all applicants are requested to contact supervisor (martin.marek@recetox.muni.cz).