Motivation: Recent abundance of data from studies employing high-throughput technologies to reveal alterations in human disease on genomic, transcriptomic, proteomic and other levels, offer the possibility to integrate this information into a comprehensive picture of molecular events occurring in human disease. Diversity of data originating from these studies presents a methodological obstacle in the integration process, also due to difficulties in choosing the optimal unified denominator that would allow inclusion of variables from various types of studies. We present a novel approach for integration of such multi-origin data based on positions of genetic alterations occurring in human diseases. Parkinson's disease (PD) was chosen as a model for evaluation of our methodology. Methods: Datasets from various types of studies in PD (linkage, genome-wide association, transcriptomic and proteomic studies) were obtained from online repositories or were extracted from available research papers. Subsequently, human genome assembly was subdivided into 10 kb regions, and significant signals from aforementioned studies were arranged into their corresponding regions according to their genomic position. For each region, rank product values were calculated and significance values were estimated by permuting the original dataset. Results: Altogether, 179 regions (representing 33 contiguous genomic regions) had significant accumulation of signals when P-value cut-off was set at 0.0001. Identified regions with significant accumulation of signals contained 29 plausible candidate genes for PD. In conclusion, we present a novel approach for identification of candidate regions and genes for various human disorders, based on the positional integration of data across various types of omic studies.
COBISS.SI-ID: 687532
Histamine is a central neurotransmitter degraded by histamine-N-methyltransferase (HNMT). Several abnormalities in the histaminergic system were found in patients with Parkinson's disease (PD), thus we tested the possible association of a Thr105Ile functional polymorphism in HNMT with PD. A total of 913 patients with PD and 958 controls were genotyped using a TaqMan RT-PCR Genotyping Assay (Foster City, California, USA). Lower frequency of HNMT Ile105 allele that is associated with decreased enzymatic activity was found in patients compared with controls (χ2 = 11.65; p = 0.0006). We performed meta-analysis to confirm the association of Thr105Ile functional polymorphism with PD. Our results indicate that lower HNMT activity plays a role in the pathogenesis of PD.
COBISS.SI-ID: 685996
Microarray searches have revealed potential genetic biomarkers in a wide variety of human diseases. Identification of biomarkers for disease status is particularly important in chronic neurodegenerative diseases where brain tissue cannot be sampled. A previous study identified 12 genes from microarray analysis as associated with Huntington's disease, although the relationships had not been validated. We used new machine learning approaches to reanalyse those microarray data and to rank the identified potential genetic biomarkers. We then performed quantitative reverse transcription-polymerase chain reaction analysis on a subset of the candidate genes in blood samples from an independent cohort of 23 Huntington's disease patients and 23 healthy controls. Our highest ranked genes did not overlap with the 12 previously identified, but two were significantly up-regulated in the Huntington's disease group: ARFGEF2 and GOLGA8G. Little is known about the latter, but the former warrants further analysis as it is known to be associated with intracellular vesicular trafficking, disturbances of which characterize Huntington's disease.
COBISS.SI-ID: 24222247
The interleukin 7 receptor alpha single nucleotide polymorphism rs6897932 was identified as a multiple sclerosis susceptibility-modifying polymorphism in genome-wide and gene scan studies, mainly in populations in western countries.The aim of this study was to investigate the association of interleukin 7 receptor alpha rs6897932 with multiple sclerosis in populations from the Western Balkans: Serbia, Croatia, and Slovenia.A total of 678 unrelated white patients and 597 unrelated, ethnically matched healthy controls were included in the study. Genotyping was performed by real-time polymerase chain reaction.We found no significant difference in genotype or allele frequencies between controls and patients with multiple sclerosis either separately in Serbian, Croatian, and Slovenian populations or in the whole sample from the Western Balkans. The odds ratio for multiple sclerosis in this study was 1.04 (0.86-1.25) for the C allele.It is known that demographic as well as environmental factors have a substantial role in multiple sclerosis development, as well as population genetic background. The results of this study indicate that other types of genome variants should be required for the development and/or progression of multiple sclerosis, which may vary among populations.
COBISS.SI-ID: 26885593
Analysis of whole genome transcriptome in brain might give us insights into the disturbed pathways and processes involved in disease onset and progression. Many different mechanisms have been proposed to be dysregulated in NDG diseases. We collected all reported studies to date on brain transcriptome in Parkinson's disease, Alzheimer disease, Huntington disease and Down syndrome and performed an integrated meta-analysis. Alltogether, our data collection comprised of data from 9 whole-genome expression studies, performed on samples from 4 neurodegenerative conditions (AD, DS, HD and PD). Collectively, 200, 33, 201, and 186 microarray analysed samples were included in the investigations of AD, DS, HD and PD, respectively, which accounted for 620 separate experiments included overall. Separate analyses of datasets for each NDG disorder have revealed significant perturbances in expression profiles of several genes. When arbitrary permutation p-value cut-off was set at 0.05 for upregulated genes, 5701 probesets attained significance in the AD dataset, 3291 in DS dataset, 4174 in the HD dataset and 3043 in the PD dataset. In the downregulated gene group the p(0.05 significance was reached for 5496 probesets in the AD dataset, 2983 probesets in the DS dataset, 4079 in the HD dataset and 3410 in the PD dataset. We have shown that whole-genome transcription analysis might be useful for identification and clarification of pathophysiological mechanisms in neurodegenerative diseases. We have used innovative approach of comparing and integrating experiment results from different NDG diseases and provided new important insights into the common NDG processes. Elucidation of these mechanisms holds important potential for future prediction and development of new useful treatments as well as for identification of biomarkers of neurodegeneration.