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 childhood acute lymphoblastic leukaemia ALL93 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.
Genetic polymorphisms in genes of drug metabolic enzymes, drug transporters and drug targets can influence diabetes and endocrine center of florida 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.
The polymorphisms of thecytochrome p 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.
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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. The dysfunction of the MTHFR member of the folic acid metabolic pathwayand GST glutathione S-transferases, genes of redox system gene family can also contribute to the development of diabetes and endocrine center of florida disease.
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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 hasmenés és cukorbetegség 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 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 asthmatic, allergic and healthy children. In the future we aim to continuously enlarge our biobank by involving foreign samples also.
- DGCI - Medical Genomics Research Group
- Konszenzus kezelésére a 2 típusú diabetes mellitus
- Első hónapom Amerikában Már több, mint egy hónap telt el azóta, hogy Debrecenben, egy hajnali órában, körülbelül 40 kg csomaggal felszerelkezve, izgatottan vártam reptéri transzferemet, hogy megkezdhessem hosszú utazásomat.
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.
Euphyllinum diabetes A 2-es típusú cukorbetegséggel málna is fogyasztható The published literature suggests that low pretreatment glucose, no history of diabetes mellitus, female gender, abnormal renal function, and lower body weight increase the risk of hypoglycemia. Apr 30, · It affects the thighs, hips, buttocks and legs, causing pain and muscle wasting.
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.
A 2-es típusú diabeteshez társuló májkárosodások
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 diabetes and endocrine center of florida 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. 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 diabetes and endocrine center of florida countries.
It is well known that obesity is a risk factor diabetes and endocrine center of florida 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 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. The colleagues of the two departments work together on developing various bioinformatics methods that can accelerate the disease specific e.
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. 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.
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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 diabetes and endocrine center of florida 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 diabetes and endocrine center of florida 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.
The bioinformatics methods developed by our team are based on the Bayesian statistical framework.
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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.