Dataset Information

Computational Prediction of Alzheimer's and Parkinson's Disease MicroRNAs in Domestic Animals.

ABSTRACT: As the most common neurodegenerative diseases, Alzheimer's disease (AD) and Parkinson's disease (PD) are two of the main health concerns for the elderly population. Recently, microRNAs (miRNAs) have been used as biomarkers of infectious, genetic, and metabolic diseases in humans but they have not been well studied in domestic animals. Here we describe a computational biology study in which human AD- and PD-associated miRNAs (ADM and PDM) were utilized to predict orthologous miRNAs in the following domestic animal species: dog, cow, pig, horse, and chicken. In this study, a total of 121 and 70 published human ADM and PDM were identified, respectively. Thirty-seven miRNAs were co-regulated in AD and PD. We identified a total of 105 unrepeated human ADM and PDM that had at least one 100% identical animal homolog, among which 81 and 54 showed 100% sequence identity with 241 and 161 domestic animal miRNAs, respectively. Over 20% of the total mature horse miRNAs (92) showed perfect matches to AD/PD-associated miRNAs. Pigs, dogs, and cows have similar numbers of AD/PD-associated miRNAs (63, 62, and 59). Chickens had the least number of perfect matches (34). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses suggested that humans and dogs are relatively similar in the functional pathways of the five selected highly conserved miRNAs. Taken together, our study provides the first evidence for better understanding the miRNA-AD/PD associations in domestic animals, and provides guidance to generate domestic animal models of AD/PD to replace the current rodent models.

SUBMITTER: Wang HY

PROVIDER: S-EPMC4852244 | biostudies-literature | 2016 Jun

REPOSITORIES: biostudies-literature

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Computational Prediction of Alzheimer's and Parkinson's Disease MicroRNAs in Domestic Animals.

Wang Hai Yang HY Lin Zi Li ZL Yu Xian Feng XF Bao Yuan Y Cui Xiang-Shun XS Kim Nam-Hyung NH

Asian-Australasian journal of animal sciences. 20150903 6

As the most common neurodegenerative diseases, Alzheimer's disease (AD) and Parkinson's disease (PD) are two of the main health concerns for the elderly population. Recently, microRNAs (miRNAs) have been used as biomarkers of infectious, genetic, and metabolic diseases in humans but they have not been well studied in domestic animals. Here we describe a computational biology study in which human AD- and PD-associated miRNAs (ADM and PDM) were utilized to predict orthologous miRNAs in the followi ...[more]

PMID: 26954182

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Project description:BackgroundThe most important means of identifying diseases before symptoms appear is through the discovery of disease-associated biomarkers. Recently, microRNAs (miRNAs) have become highly useful biomarkers of infectious, genetic and metabolic diseases in human but they have not been well studied in domestic animals. It is probable that many of the animal homologs of human disease-associated miRNAs may be involved in domestic animal diseases. Here we describe a computational biology study in which human disease miRNAs were utilized to predict orthologous miRNAs in cow, chicken, pig, horse, and dog.ResultsWe identified 287 human disease-associated miRNAs which had at least one 100% identical animal homolog. The 287 miRNAs were associated with 359 human diseases referenced in 2,863 Pubmed articles. Multiple sequence analysis indicated that over 60% of known horse mature miRNAs found perfect matches in human disease-associated miRNAs, followed by dog (50%). As expected, chicken had the least number of perfect matches (5%). Phylogenetic analysis of miRNA precursors indicated that 85% of human disease pre-miRNAs were highly conserved in animals, showing less than 5% nucleotide substitution rates over evolutionary time. As an example we demonstrated conservation of human hsa-miR-143-3p which is associated with type 2 diabetes and targets AKT1 gene which is highly conserved in pig, horse and dog. Functional analysis of AKT1 gene using Gene Ontology (GO) showed that it is involved in glucose homeostasis, positive regulation of glucose import, positive regulation of glycogen biosynthetic process, glucose transport and response to food.ConclusionsThis data provides the animal and veterinary research community with a resource to assist in generating hypothesis-driven research for discovering animal disease-related miRNA from their datasets and expedite development of prophylactic and disease-treatment strategies and also influence research efforts to identify novel disease models in large animals. Integrated data is available for download at http://agbase.hpc.msstate.edu/cgi-bin/animal_mirna.cgi.

Project description:Background: Alzheimer's disease (AD) is the major cause of dementia in population aged over 65 years, accounting up to 70% dementia cases. However, validated peripheral biomarkers for AD diagnosis are not available up to present. In this study, we adopted a new strategy of combination of computational prediction and experimental validation to identify blood protein biomarkers for AD. Methods: First, we collected tissue-based gene expression data of AD patients and healthy controls from GEO database. Second, we analyzed these data and identified differentially expressed genes for AD. Third, we applied a blood-secretory protein prediction program on these genes and predicted AD-related proteins in blood. Finally, we collected blood samples of AD patients and healthy controls to validate the potential AD biomarkers by using ELISA experiments and Western blot analyses. Results: A total of 2754 genes were identified to express differentially in brain tissues of AD, among which 296 genes were predicted to encode AD-related blood-secretory proteins. After careful analysis and literature survey on these predicted blood-secretory proteins, ten proteins were considered as potential AD biomarkers, five of which were experimentally verified with significant change in blood samples of AD vs. controls by ELISA, including GSN, BDNF, TIMP1, VLDLR, and APLP2. ROC analyses showed that VLDLR and TIMP1 had excellent performance in distinguishing AD patients from controls (area under the curve, AUC = 0.932 and 0.903, respectively). Further validation of VLDLR and TIMP1 by Western blot analyses has confirmed the results obtained in ELISA experiments. Conclusion: VLDLR and TIMP1 had better discriminative abilities between ADs and controls, and might serve as potential blood biomarkers for AD. To our knowledge, this is the first time to identify blood protein biomarkers for AD through combination of computational prediction and experimental validation. In addition, VLDLR was first reported here as potential blood protein biomarker for AD. Thus, our findings might provide important information for AD diagnosis and therapies.

Project description:BACKGROUND:Parkinson's disease is a widespread neurodegenerative disorder which affects brain metabolism. Although changes in gene expression during disease are often measured, it is difficult to predict metabolic fluxes from gene expression data. Here we explore the hypothesis that changes in gene expression for enzymes tend to parallel flux changes in biochemical reaction pathways in the brain metabolic network. This hypothesis is the basis of a computational method to predict metabolic flux changes from post-mortem gene expression measurements in Parkinson's disease (PD) brain. RESULTS:We use a network model of central metabolism and optimize the correspondence between relative changes in fluxes and in gene expression. To this end we apply the Least-squares with Equalities and Inequalities algorithm integrated with Flux Balance Analysis (Lsei-FBA). We predict for PD (1) decreases in glycolytic rate and oxygen consumption and an increase in lactate production in brain cortex that correspond with measurements (2) relative flux decreases in ATP synthesis, in the malate-aspartate shuttle and midway in the TCA cycle that are substantially larger than relative changes in glucose uptake in the substantia nigra, dopaminergic neurons and most other brain regions (3) shifts in redox shuttles between cytosol and mitochondria (4) in contrast to Alzheimer's disease: little activation of the gamma-aminobutyric acid shunt pathway in compensation for decreased alpha-ketoglutarate dehydrogenase activity (5) in the globus pallidus internus, metabolic fluxes are increased, reflecting increased functional activity. CONCLUSION:Our method predicts metabolic changes from gene expression data that correspond in direction and order of magnitude with presently available experimental observations during Parkinson's disease, indicating that the hypothesis may be useful for some biochemical pathways. Lsei-FBA generates predictions of flux distributions in neurons and small brain regions for which accurate metabolic flux measurements are not yet possible.

Dataset Information

Computational Prediction of Alzheimer's and Parkinson's Disease MicroRNAs in Domestic Animals.

Publications

Computational Prediction of Alzheimer's and Parkinson's Disease MicroRNAs in Domestic Animals.

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