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Practical matters How to find us P.L. (Peter) Horvatovich, Prof Dr

P.L. (Peter) Horvatovich, Prof Dr

Full Professor in Computational Mass Spectrometry, Head of Department of Analytical Biochemistry
Profielfoto van P.L. (Peter) Horvatovich, Prof Dr
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+31 50 36 33341 (Fixed office number.)
+31 6 2459 7203 (Mobile number)

Proteogenomics data integration of human lung tissue of COPD patients and controls.

Collaboration with Victor Guryev (ERIBA/UMCG), Dirkje Postma, Maarten van den Berge, Corry-Anke Brandsma, Wim Timens (Pulmonary Diseases, Pathology and Medical Biology departments of UMCG) and the laboratory of Gyorgy Marko Varga (Lund University).

Chronic obstructive pulmonary disease (COPD) is a common disease characterized by chronic progressive airflow obstruction. The prevalence of COPD increases with age to > 10% of adults over 65 years old. With no effective treatments, COPD has significant personal and economic impact which is expected to rise further. The ultimate goal of the project is to integrate data on the genomic, transcriptomic and proteomic levels, to identify the causal genetic variants, genes, and proteins responsible for the aberrant extracellular matrix turnover driving COPD development. In this project, we have developed a proteogenomics data integration pipeline for comprehensive data processing and analysis.

Determining Head and Neck Cancer protein profile difference between elderly and young patients.

This project is realized in collaboration with György Halmos and Renee Verhoeven at the Head and Neck Department, UMCG.

Aging of the Western society challenges health care professionals. Head and neck cancer is the sixth most common solid malignancy, with annual half million new cases worldwide, and almost 3000 new cases in the Netherlands. The aims of the study are (1) to develop an optimized tissue sample preparation protocol for larynx cancer tumors; (2) to identify protein profile differences between head and neck cancers of young and elderly patients and (3) to analyze differences between protein interaction network in cancer and healthy tissue between elderly and young head and neck cancer patients.

Identifying missing proteins and protein forms in Chromosome 5.

The project is performed in collaboration with the HUPO Chromosome Centric Human Proteome Project (Young-Ki Paik, Chris Overall and others).

The goals of the Chromosome 5 project are to identify the protein part list of Chromosome 5 including missing proteins (proteins which were not detected until today by mass spectrometry or other proteomics techniques) in biological samples, and to detect all possible genetic variants and post-translational modifications of proteins encoded in human chromosome 5. See further details at C-HPP Wiki:

Bioconjugation of Palladium-metallacages and chemical photo-cleavable tags for targeted delivery of drugs and mass spectrometry imaging of proteins spatial distribution.

This project is performed in collaboration with Angela Casini (Univ. Cardiff), Hjalmar Permentier (FSE/RUG), Geny Groothuis (FSE/RUG). The PhD candidate working on this project is Jiaying Han (CSC scholarship).

Palladium metallacages are capable to encapsulate drugs such as cisplatin. The aim of this project is to develop efficient bioconjugation of palladium metallacage and test the toxicity and targeted delivery properties of these compounds in precision cut liver and kidney slices. In the second part of this project, we aim to bioconjugate photo-cleavable mass tag for sensitive targeted detection of proteins using laser desorption ionisation mass spectrometry imaging (MSI) to measure the spatial resolution of target compounds in biological tissues.

Pre-processing and spatial correlation analyzis of compounds in mass spectrometry imaging data.

This project is performed in collaboration with Frank Suits (IBM), Gyorgy Marko Varga (Lund University), Melinda Rezeli (Lund University), Jonatan Eriksson (Lund University), Erika Amstalden van Hove (Vrije Universiteit Amsterdam) and Jeroen Kool (Vrije Universiteit Amsterdam).

Mass Spectrometry Imaging (MSI) allows measuring the spatial resolution of compounds in biological tissues. This project includes the development of data processing approach, which enables fast interaction of the user with large MSI data without any loss of acquired information.

Development of Threshold Avoiding Proteomics Pipeline (TAPP).

The project is performed by Alejandro Sanchez Brotons PhD student as main developer and in collaboration with Frank Suits (IBM). Ido Kema, Rebecca Heiner-Fokkema, Stephan Bakker and Folkert Kuipers are supporting the project. Andrei Barcaru (UMCG) and Karel Gerbrands as volunteer are working in this project.

This project has the aim to develop a comprehensive single-stage data-dependent LC-MS(/MS) data processing pipeline for accurate quantification of all mass spectrometry signal independent of the identification status. The pipeline can be applied to any separation technology (LC, CE, GC) coupled to mass spectrometry data, which deliver non-fragmented single-stage MS data.

Clinical Big Data for multifactorial diseases: from molecular profiles to precision medicine (Science and Systems Complexity, FSE Faculty Theme)

This project is performed in collaboration with Marco Grzegorczyk (RUG/JIB/FSE), Victor Guryev (ERIBA/UMCG). Funding: DSSC Faculty Theme and the group of Pharmaceutical Technology and Biopharmacy.

Diseases with multifactorial origin and complex traits such as various types of cancer and chronic obstructive pulmonary disease (COPD) are among the leading causes of death in the Western society and form primary challenges of the current health system. Poor understanding of the complex molecular mechanisms of such diseases and current diagnostic approaches are often inadequate to find an effective treatment for a large proportion of patients leading to high mortality and high health-care costs. Personalized treatment of patients or identification of subgroups of patients where treatment is efficient using precision medicine approaches are pivotal to improve health and patient care. The main aim of this project is to develop a Machine Learning approach for large multi-omics datasets where parameters vary due to individual sampling in different patients. The developed Machine Learning approaches based on Bayesian network shall allow linking highly heterogeneous molecular profiles to diverse clinical parameters in order to identify for example patient sub-phenotypes, new key compounds as potential new drug targets for patient subgroups, or biomarker(s) that predict efficacy of treatment.

Translational research on human tumor heterogeneity to overcome recurrence and resistance to therapy (PROMETOV)

This project is performed in Groningen with Yang Zhang PhD candidate as joint project with Kathrin Thedieck (EMS/UMCG) and Eu consortium partners in Germany, UK, Estonia, Turkey, Slovenia and Israel. Project funded by Eu TRANSCAN-2, ZonNW and KWF.

Despite radical surgery and chemotherapy, ovarian cancer (OC) has a dismal prognosis due to therapy resistance or tumor recurrence. Tumor heterogeneity i.e differences between the primary tumor and metastases may be involved in resistance and recurrence of OC. Multi-level omics measurements of proteins and metabolites in primary tumors, metastases and plasma will enable an improved understanding of the contribution of tumor heterogeneity to OC resistance and recurrence than focusing solely on tumor heterogeneity at the genome level. Our primary aim is to determine the contribution of tumor heterogeneity to resistance and recurrence in ovarian cancer by multi-omics measurements of matched primary tumors, metastases and plasma. Secondary aims are to i) develop assays for markers of clinically relevant ovarian tumor heterogeneity and ii) evaluate treatments targeting the heterogeneous factors identified to contribute to resistance and recurrence in ovarian cancer cells. Our project will identify the contribution of OC tumor heterogeneity to chemotherapy resistance and recurrence and develop minimally-invasive methods for stratification of patients to therapies, therapy monitoring and detection of resistance or relapse.

Risk Assessment of Mycotoxin Exposure through dietary intake in Qatar

The project is performed in collaboration with Aishah Latif (ADLQ), Morana Jaganjac (ADQL). Andrei Barcaru (UMCG) was working on the project. Project funded by QNRP grant NPRP8-1472-3-290.

The mycotoxins have been identified as the most ubiquitous contaminant in the human food supply, which have adverse consequences on the human population, such as onset of hepatocellular carcinoma. Contamination of the mycotoxins in grains and nuts continues to be a problem not only in developing countries where storage of food and the weather are optimal for the growth of the Aspergillus fungus but also in parts of the world where global food trade exists e.g. in Qatar. Mycotoxins have been identified in rice, spices, cereals, fruits and vegetables, milk, eggs and corn-based products and have been the cause of rejection of food imports in many EU countries. This project has the aim to identify biomarkers of exposed individuals through analysis of urine and serum metabolome and focus on the identification of aflatoxins, fumonisins, ochratoxin, zearalenone, deoxynivalenol, T-2 and HT-2 toxins in biological samples and in high-risk foods. This project is supported by 2 PhD candidates Noof Nayef Al-Qasmi and Belqes Ahmad AlJaal and by Andrei Barcaru (RUG) working in a postdoctoral position.

Identification of metabolic changes in inborn error metabolic patients

Inborn error of metabolism are genetic mutations perturbing food and energy metabolism, which has a detrimental impact on patient’s health. The goal in this project is to develop data processing pipeline for pre-processing organic acid GC-MS data and develop statistical method, which is able to identify metabolic changes in inborn error metabolic profile compared to profiles of clinically matched controls. This work is performed in collaboration with Rebecca Heiner-Fokkema (main project leader, Clinical Chemistry, UMCG).

Cancer Moonshot for personalised diagnostic and treatment of melanoma patients.

Proteins are the key biomolecules actively involved biology. Cancer Moonshot project has the goal to integrate proteomics data into clinical cancer research to provide breakthrough in cancer diagnosis and treatment. Gyorgy Marko-Varga at the Centre of Excellence in Biological and Medical Mass Spectrometry (CEBMMS) at Lund University (Sweden) is leading a Cancer Moonshot project for melanoma, where Peter Horvatovich has received an honorary position. Cancer Moonshot project at CEBMMS received in 2016 aims to profile more than 4 000 samples from melanoma patients over the next 5 years. The role of our group is to support the high-throughput data analysis of LC-MS/MS proteomics data, proteogenomics data integration, to perform statistical analysis of the collected molecular profiles and clinical metadata and participation in the supervision of the PhD candidate Jonatan Erikson.

A chemoproteomic approach to study advanced glycation end-products

Saskia Sokoliova is a PhD student working on this project, which is performed in collaboration with Martin Witte (Stratingh Institute for Chemistry).

Glycolysis is one of the fundamental molecular cell processes, and dysfunctioning of this process leads to unregulated glycation of, among others, proteins. Glycation altered proteins are involved in multiple complex diseases such as cancer and COPD. In this project, we aim to develop a novel chemical tool and bioinformatic approach that identifies and quantifies advanced glycation end (AGE)-products of proteins produced by the methylglyoxal reaction at endogenously relevant concentrations. This project was recently funded by the FSE Faculty Theme of Molecular Life And Health and is a joint project with Martin Witte, leader of Chemical Biology research group at the Stratingh Institute (RUG).

Last modified:25 June 2022 2.44 p.m.