Research projects of the group
Genetic analysis of susceptibility to childhood tumours and pharmacogenetic analysis of response to therapy
At the Department of Genetics, Cell- and Immunobiology there is a human data- and DNA biobank consists of DNA from 626 childhood acute lymphoblastic leukaemia (ALL), 93 childhood osteosarcoma and 70 testicle tumour patients and hundreds of healthy controls. In the databank we collected the patients clinical (medical and laboratory diagnostic) data at the diagnosis, and during the treatment until the end of the therapy. We recorded certain drug levels, side effects (regarding the cardiovascular system, nervous system, etc.) and in the case of some side effects follow up data after the treatment. Genetic polymorphisms in genes of drug metabolic enzymes, drug transporters and drug targets can influence the efficacy and even the effect of the medication. Gene polymorphisms can modify the necessary medicine amount for the optimal response and contribute to the development of side effects. In our research we study the relationship between the genetic polymorphisms of the above mentioned genes and the clinical data of the patients in our databank. We search for polymorphisms significantly influencing the response to therapy and the course of the disease. One group of these studied genes is the adenosine triphosphate binding casette (ABC) transporter superfamily. Several members of this superfamily are known to be expressed also in tumor cells and they are important in the transport of chemotherapic drugs across the cell membrane. Among these the best-known are the ABCB1 (ATP-binding cassette, sub-family B, member 1 or MDR1: multiple drug resistance 1) and ABCG2 (ATP-binding cassette, sub-family G, member 2), but many other genes involved in this process belong to ABC-transporters. The polymorphisms of thecytochrome p450 (CYP) gene family and different gene variants of other drug metabolic enzymes are also relevant in the chemotherapy treatment response. One of the most crucial aims of our research is to contribute to the new, relevant pharmacogenetic results of personalized therapy. Before finding the adequate therapy method, the physicians should determine the genotype of the patients to get a better outcome, and reduce the toxic side effects.
We also examine the inheritable susceptibility of tumourigenesis (ALL, osteosarcoma, testicle cancer). It is known, that in some family lines definite tumour incidence is more prevalent. It confirms that the susceptibility of disease development has genetic components too. The above-mentioned genes (e.g. ABCB1, ABCG2, CYP) have significant role in the transport ofxenobiotics, that can cause mutation in genes. The dysfunction of the MTHFR(member of the folic acid metabolic pathway), and GST (glutathione S-transferases, genes of redox system) gene family can also contribute to the development of the disease.
Genomic investigation of asthma with animal experiments and examinations of human samples
The asthma genetics research group conducts research to gain insight into the genetic background of asthma and the molecular biological patterns influencing the asthma pathomechanism by use of human samples and animal disease models. In these studies the group has succeeded in proving numerous genes, genetic variations and biological pathways contributing to the asthma phenotype.
Since 2006 due to our infrastructural development we are able to carry out high-throughput screenings, population genetic tests and microarrayexperiments to validate the SNP results.
Our Institute stores Hungary's largest asthma-allergy biobank which consists of clinical, laboratory data and DNA samples of 425 asthmatic, 304 allergic and 500 healthy children. In the future we aim to continuously enlarge our biobank by involving foreign samples also.
One of our scientific goals is the detailed mapping of genomic regions previously identified in asthma pathomechanism and the consequent genotyping of the newly identified polymorhisms on our biobank. Apart from the already characterized 11q13 region we plan to thoroughly investigate the genomic regions 14q22 and 17q1. Furthermore, we aim to analyze the polymorphisms of candidate genes identified by animal studies or literature data mining. The SNP searching and selecting procedure is directed by software developed by our group.
Our research group previously completed a whole genome gene expression microarray analysis (Agilent Whole Mouse Genome Oligo Microarray 44K chips were used) of OVA induced mouse model of asthma to follow the lung gene expression profile change in four state of the progression of mouse allergic airway inflammation. Microarray analysis revealed some new potential previously not investigated candidate genes which showed significantly changed gene expression profile during allergic progression. Out of these genes presently the study of paraoxonase 1 (PON1) is going on.
The examinations of human polymorphisms, selected genes from animal experiments and their role in asthma pathogenesis are carried out with the following protocol:
(I) Selection of genes according to the results of animal experiments. We favor previously not studied genes showing markedly changed gene expression during the disease progression.
(II) Identification of SNPs with bioinformatic methods and examination of their role with association studies on our biobank.
(III) Investigation of gene function on human samples. Experiments carry out according to known, revealed and supposed function of genes. Following studies are planning: gene and protein expression in lung (asthmatics vs. healthy subjects); enzyme activity in different samples and activity changes e.g. serum level during treatment; reveal the influencing factors (e.g. disease, smoking, age, drugs, treatment etc.) of gene and/or protein function
Our purpose with the above-mentioned examinations is to identify genes, genetic polymorphisms implicated in asthma pathomechanism or susceptibility. The realization of our aims could lead to the understanding of asthma development and further result in exploration of new therapy targets which could contribute to work out more effective and wide-ranging therapies for both patients and doctors.
Genetics of obesity
Obesity has become a major public health problem in most developed countries. According to some data 60% of the Hungarian population isoverweight or obese. It is well known that obesity is a risk factor for type 2 diabetes, dyslipidemia, hypertension, and atherosclerosis. The etiology of obesity is complex and consists of the interaction of genetics, diet and physical activity, additionally influenced by environmental, socioeconomic and behavioral factors. It is a metabolic disorder with energy balance disturbance, which is manifested in elevated nutrition or in decreased energy expenditure, and leads to fat accumulation. During the pathogenesis of obesity not only the fat storage increase but the size and number of adipose cells too.
Traditionally, adipose tissue was considered to passively store triacylglycerols and release free fatty acids, but it is now recognized as an active endocrine organ that produces a large number of bioactive mediators, which are interact with immune- and cardiovascular systems.
At the Department of Genetics, Cell- and Immunobiology we have examined the genetic background of obesity since 2007. The project is granted partly by ETT and partly by the NKTH, in which we participated as a member of the Obekon Consortium.
One of the most crucial aims of our research is to explain the pathogenesis of obesity by monitoring the DNA and transcriptional changing in human samples and animal models. In our research we study the genes influencing the pathomechanism of obesity, their polymorphisms, and the molecular disease pathways.
Developing Bioinformatics Tools
The Department of Genetics, Cell- and Immunobiology (DGCI) at Semmelweis University cooperates with Department of Measurement and Information Systems (DMIS) at Budapest University of Technology and Economics since 2005. The colleagues of the two departments work together on developing various bioinformatics methods that can accelerate the disease specific (e.g. asthma, acute lymphoblastic leukaemia, childhood tumors) genetic research in DGCI.
The bioinformatics tools and methods developed at DMIS can be utilized in two main areas: (i) they help in the economical designing of the investigations of the genomics laboratory so the investigations on the available samples should provide the most information with the least possible cost. (ii) On the other hand the exhaustive evaluation of the datasets provided by the genetic investigations and the design of further genetic investigations based on that information need complex bioinformatics apparatus.
The inter-individual difference in genetic background between two patients can considerably influence the effects of specific treatments. Identifyingbiomarkers connected with the expected effectiveness of treatments can be an important milestone on the road of personal genomics. Investigating the different responds to treatments can sometimes lead to the discovery of a combination of genetic variants which can be utilized to predict with great certainty the effectiveness of the specific treatment or drug on a new patient. Achievement of this goal can be influenced by numerous factors: the a prior knowledge of the effect of treatment, the nature of effect and the factors concerning it, the sampling protocol, the size of biobank and the type and quality of information assigned to the samples, examining the appropriate genomic regions, the type of instruments used for genomic measurements, the quality of information provided by these instruments and the type of statistical methods used for evaluating the data. While precise biological interpretation of the different responds to the specific treatment is very difficult especially in case of multifactorial diseases, to discover statistically efficient biomarkers can be a realistic goal in most cases. Our team's main aim is to find the factors influencing the discovery of biomarkers used for treatment selection and to find or develop the bioinformatics tools and medical biologic methodology that can be efficiently used for biomarker discovery. We aim to develop general methodology and protocols but we mainly focus on diseases already examined by DGCI and DMIS (e.g. asthma, acute lymphoblastic leukemia).
The bioinformatics methods developed by our team are based on the Bayesian statistical framework. It offers a normative way for knowledge representation and for learning from observations. Concerning utility theory the Bayesian framework can be used to make optimal decisions. It establishes an important connection between statistics and knowledge engineering because it is capable to incorporate heterogeneous a prior knowledge into the statistical learning process. Besides these, Bayesian framework provides a computational framework for the learning and using of complex probabilistic models because it enables the using of various stochastic simulation methods. This finally results in the appearance of computation-intensive statistics.
Bayesian Multilevel Analysis (levels: bivariate, multivariate, interaction, causal domain model) is a new, promising general method, which can be very useful in case of datasets with small sample sizes and multiple testing problems. Bayesian methods are effectively used in the field of genetic association studies (GAS). In the Bayesian framework, Bayesian belief networks (which are computationally intensive) have many advantages: (i) discovery of direct associations, (ii) multivariate management of missing variables, (iii) the ability to incorporate various types of a prior knowledge and (iv) discovery of causal effects and relationships.
The bioinformatics project is granted by the NKTH, in which we participate as a member of the Genagrid Consortium (http://www.genagrid.com/).
Dr. Csaba Szalai, University Professor
Nóra B. Kutszegi, PhD Student
Dr. Judit Cs. Sági, PhD Student
Dr. Ágnes F. Semsei, Assistant lecturer
|Zsófia Gál, PhD Student|
Dr. András Gézsi, Research Fellow
|Kinga Tibori, PhD Student|
Mónika S. Vángor, Assistant
Semmelweis Egyetem PhD Tudományos Napok 2009
2009. március 30-31. Budapest SE NET
MTA 2009. évi közgyűléséhez kapcsolódó tudományos előadás
MTA Székház, 2009. május 6.
Human Genome Variation Society, Human Variome Project, Towards Establishing Standards
22nd May 2009, Vienna, Austria
Allergy & Asthma Symposium: Bridging Innate and Adaptive Immunity
May 28-29, 2009 Bruges, Belgium
Genetikai Műhelyek Magyarországon
MTA Szegedi Biológiai Központ; Szeged, Temesvári krt. 62. 2009. szeptember 11.
Machine Learning in System Biology 2009 (MLSB09)
Sept 5-6, Ljubljana, Slovenia
2009 szeptember 20-24 Berlin, Németország
PHARMACOGENOMICS & PERSONALIZED MEDICINE
September 12 - 15, 2009, Wellcome Trust Conference Centre, Hinxton, UK
2nd Central European Congress on Obesity
1st-3rd October 2009, Budapest
Sarasota splash: Update in Biomedical and Clinical Sciences The Helmsley Sandcastle Hotel Sarasota, Lido Beach Florida 41st Annual Scientific Meeting of HMAA
October 25 - October 30, 2009
Late Effects in Cancer Survivors
2010 március 3-4. Cutler's Hall, Sheffield, UK
3rd HUNGARIAN-SINGAPOREAN WORKSHOP on SYSTEMS BIOLOGY and COMMUNICATION SYSTEMS
Budapest, March 29-30, 2010
12th International Paediatric Haematology and Oncology Update Meeting
Edinborough, UK, 2010 április 29-30.
MLDT 55. Nagygyűlése
Pécs 2010. augusztus 26-28.
Magyar Biokémiai Egyesület 2010. évi Vándorgyűlése
Semmelweis Egyetem Elméleti Orvostudományi Központja; 2010. augusztus 25-28.
Leuven (Belgium), 25-27 August 2010
Magyar Humángenetikai Társaság VIII. kongresszusa
2010, szept. 2-4, Debrecen