scholarly journals Investigation of candidate genes for osteoarthritis based on gene expression profiles

2016 ◽  
Vol 50 (6) ◽  
pp. 686-690 ◽  
Author(s):  
Shuanghai Dong ◽  
Tian Xia ◽  
Lei Wang ◽  
Qinghua Zhao ◽  
Jiwei Tian
Keyword(s):  
Gene Expression ◽  
Candidate Genes ◽  
Expression Profiles ◽  
Gene Expression Profiles

263 USE OF PORCINE PARTHENOTES AND GENE EXPRESSION PROFILING USING MICROARRAYS FOR IDENTIFICATION OF IMPRINTED GENES

2006 ◽  
Vol 18 (2) ◽  
pp. 239
Author(s):  
J. Piedrahita ◽  
S. Bischoff ◽  
J. Estrada ◽  
B. Freking ◽  
D. Nonneman ◽  
...  
Keyword(s):  
Gene Expression ◽  
Candidate Genes ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Expression Profiles ◽  
Mammalian Species ◽  
Polar Body ◽  
Gene Expression Profiles ◽  
Tissue Type ◽  
Imprinted Genes

Genomic imprinting arises from differential epigenetic markings including DNA methylation and histone modifications and results in one allele being expressed in a parent-of-origin specific manner. For further insight into the porcine epigenome, gene expression profiles of parthenogenetic (PRT; two maternally derived chromosome sets) and biparental embryos (BP; one maternal and one paternal set of chromosomes) were compared using microarrays. Comparison of the expression profiles of the two tissue types permits identification of both maternally and paternally imprinted genes and thus the degree of conservation of imprinted genes between swine and other mammalian species. Diploid porcine parthenogenetic fetuses were generated using follicular oocytes (BOMED, Madison, WI, USA). Oocytes with a visible polar body were activated using a single square pulse of direct current of 50 V/mm for 100 �s and diploidized by culture in 10 �g/mL cycloheximide for 6 h to limit extrusion of the second polar body. Following culture, BP embryos obtained by natural matings, and PRT embryos, were surgically transferred to oviducts on the first day of estrus. Fetuses recovered at 28-30 days of gestation were dissected to separate viscera including brain, liver, and placenta; the visceral tissues were then flash-frozen in liquid nitrogen. Porcine fibroblast tissue was obtained from the remaining carcass by mincing, trypsinization, and plating cells in �-MEM. Total RNA was extracted from frozen tissue or cell culture using RNA Aqueous kit (Ambion, Austin, TX, USA) according to the manufacturer's protocol. Gene expression differences between BP and PRT tissues were determined using the GeneChip� Porcine Genome Array (Affymetrix, Santa Clara, CA) containing 23 256 transcripts from Sus scrofa and representing 42 genes known to be imprinted in human and/or mice. Triplicate arrays were utilized for each tissue type, and for PRT versus BP combination. Significant differential gene expression was identified by a linear mixed model analysis using SAS 5.0 (SAS Institute, Cary, NC, USA). Storey's q-value method was used to correct for multiple testing at q d 0.05. The following genes were classified as imprinted on the basis of their expression profiles: In fibroblasts, ARHI, HTR2A, MEST, NDN, NNAT, PEG3, PLAGL1, PEG10, SGCE, SNRPN, and UBE3A; in liver, IGF2, PEG3, PLAGL1, PEG10, and SNRPN; in placenta, HTR2A, IGF2, MEST, NDN, NNAT, PEG3, PLAGL1, PEG10, and SNRPN; and in brain, none. Additionally, several genes not known to be imprinted in humans/mice were highly differentially expressed between the two tissue types. Overall, utilizing the PRT models and gene expression profiles, we have identified thirteen genes where imprinting is conserved between swine and humans/mice, and several candidate genes that represent potentially imprinted genes. Presently, our efforts are focused in the identification of single nucleotide polymorphisms (SNPs) to more carefully evaluate the behavior of these genes in normal and abnormal gestations and to test whether the candidate genes are indeed imprinted. This research was supported by USDA-CSREES grant 524383 to J. P. and B. F.

Integration of expression profiles and genetic mapping data to identify candidate genes in intracranial aneurysm

2007 ◽  
Vol 32 (1) ◽  
pp. 45-57 ◽  
Author(s):  
Shantel Weinsheimer ◽  
Guy M. Lenk ◽  
Monique van der Voet ◽  
Susan Land ◽  
Antti Ronkainen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Intracranial Aneurysm ◽  
Genetic Mapping ◽  
Candidate Genes ◽  
Expression Profiles ◽  
Adherens Junction ◽  
Gene Expression Profiles ◽  
Mapping Data ◽  
Positional Candidate ◽  
Genetic Mapping Data

Intracranial aneurysm (IA) is a complex genetic disease for which, to date, 10 loci have been identified by linkage. Identification of the risk-conferring genes in the loci has proven difficult, since the regions often contain several hundreds of genes. An approach to prioritize positional candidate genes for further studies is to use gene expression data from diseased and nondiseased tissue. Genes that are not expressed, either in diseased or nondiseased tissue, are ranked as unlikely to contribute to the disease. We demonstrate an approach for integrating expression and genetic mapping data to identify likely pathways involved in the pathogenesis of a disease. We used expression profiles for IAs and nonaneurysmal intracranial arteries (IVs) together with the 10 reported linkage intervals for IA. Expressed genes were analyzed for membership in Kyoto Encyclopedia of Genes and Genomes (KEGG) biological pathways. The 10 IA loci harbor 1,858 candidate genes, of which 1,561 (84%) were represented on the microarrays. We identified 810 positional candidate genes for IA that were expressed in IVs or IAs. Pathway information was available for 294 of these genes and involved 32 KEGG biological function pathways represented on at least 2 loci. A likelihood-based score was calculated to rank pathways for involvement in the pathogenesis of IA. Adherens junction, MAPK, and Notch signaling pathways ranked high. Integration of gene expression profiles with genetic mapping data for IA provides an approach to identify candidate genes that are more likely to function in the pathology of IA.

scholarly journals Bioinformatics Analysis Reveals Most Prominent Gene Candidates to Distinguish Colorectal Adenoma from Adenocarcinoma

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Nina Hauptman ◽  
Emanuela Boštjančič ◽  
Margareta Žlajpah ◽  
Branislava Ranković ◽  
Nina Zidar
Keyword(s):  
Gene Expression ◽  
Candidate Genes ◽  
Colorectal Adenoma ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Gene Expression Profiles ◽  
Normal Mucosa ◽  
Gene Expression Omnibus ◽  
Colorectal Adenomas ◽  
Screening Programs

Colorectal cancer (CRC) is one of the leading causes of death by cancer worldwide. Bowel cancer screening programs enable us to detect early lesions and improve the prognosis of patients with CRC. However, they also generate a significant number of problematic polyps, e.g., adenomas with epithelial misplacement (pseudoinvasion) which can mimic early adenocarcinoma. Therefore, biomarkers that would enable us to distinguish between adenoma with epithelial misplacement (pseudoinvasion) and adenoma with early adenocarcinomas (true invasion) are needed. We hypothesized that the former are genetically similar to adenoma and the latter to adenocarcinoma and we used bioinformatics approach to search for candidate genes that might be potentially used to distinguish between the two lesions. We used publicly available data from Gene Expression Omnibus database and we analyzed gene expression profiles of 252 samples of normal mucosa, colorectal adenoma, and carcinoma. In total, we analyzed 122 colorectal adenomas, 59 colorectal carcinomas, and 62 normal mucosa samples. We have identified 16 genes with differential expression in carcinoma compared to adenoma:COL12A1,COL1A2,COL3A1, DCN, PLAU, SPARC, SPON2, SPP1,SULF1,FADS1, G0S2, EPHA4, KIAA1324,L1TD1, PCKS1, andC11orf96. In conclusion, ourin silicoanalysis revealed 16 candidate genes with different expression patterns in adenoma compared to carcinoma, which might be used to discriminate between these two lesions.

scholarly journals Transcriptomic analysis of temporal shifts in berry development between two grapevine cultivars of the Pinot family reveals potential ripening-regulative genes

2021 ◽  
Author(s):  
Jens Theine ◽  
Daniela Holtgräwe ◽  
Katja Herzog ◽  
Florian Schwander ◽  
Anna Kicherer ◽  
...  
Keyword(s):  
Gene Expression ◽  
Candidate Genes ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Regulatory Genes ◽  
Pinot Noir ◽  
Differentially Expressed ◽  
Berry Development ◽  
Ripening Time ◽  
Berry Ripening

Background Grapevine cultivars of the Pinot family represent in the broader sense clonally propagated mutants with clear-cut phenotypes, such as different color or shifted ripening time, that result in major phenotypic and physiological differences as well as changes in important viticultural traits. Specifically, the cultivars 'Pinot Noir' (PN) and 'Pinot Noir Precoce' (PNP, early ripening) flower at the same time, but vary for the beginning of berry ripening (véraison) and consequently for the harvest time. Apart from the genotype, seasonal climatic conditions (i.e. high temperatures) also affect ripening times. To reveal possible ripening-regulatory genes affecting the timing of the start of ripening, we investigated differences in gene expression profiles between PN and PNP throughout berry development with a closely meshed time series and in two years. Results The difference in the duration of berry formation between PN and PNP was quantified to be about two weeks under the growth conditions applied, using plant material with a proven clonal relationship of PN and PNP. Clusters of co-expressed genes and differentially expressed genes (DEGs) were detected which reflect the shift in the beginning of ripening at the level of gene expression profiles. Functional annotation of these DEGs fits to phenotypic and physiological changes during berry development. In total, we observed between PN and PNP 3,342 DEGs in 2014 and 2,745 DEGs in 2017. The intersection of both years comprises 1,923 DEGs. Among these, 388 DEGs were identified as véraison-specific and 12 were considered as candidates for a regulatory effect on berry ripening time. The expression profiles revealed two candidate genes for Ripening Time Control, designated VviRTIC1 and VviRTIC2 (VIT_210s0071g01145 and VIT_200s0366g00020, respectively) that may contribute to controlling the phenotypic difference between PN and PNP. Conclusions Many of the 1,923 DEGs identified show highly similar expression profiles in both cultivars as far as accelerated berry formation of PNP is concerned. Putative ripening-regulatory genes differentially expressed between PNP and PN as well as véraison-specific genes were identified. We point out potential connections of these genes to molecular events during berry development and discuss potential ripening time controlling candidate genes, two of which are already differentially expressed in the early berry development phase. Several down-regulated genes are annotated to encode auxin response factors / ARFs. Conceivably, changes in auxin signaling may realize the earlier ripening phenotype of PNP.

scholarly journals Identifying Reliable Diagnostic/Predictive Biomarkers for Rheumatoid Arthritis

2018 ◽  
Vol 13 ◽  
pp. 117727191880100 ◽  
Author(s):  
Leroy Shervington ◽  
Ashish Darekar ◽  
Murassa Shaikh ◽  
Roshini Mathews ◽  
Amal Shervington
Keyword(s):  
Rheumatoid Arthritis ◽  
Gene Expression ◽  
Candidate Genes ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Predictive Biomarkers ◽  
Diagnostic Biomarkers ◽  
Reactive Protein ◽  
Transcription Profiles ◽  
Significant Expression

Introduction and objective: Elevated C-reactive protein is usually a good indicator of rheumatoid arthritis (RA); however, there are limitations that compromise its specificity and therefore there is an urgent need to identify more reliable diagnostic biomarkers to detect early stages of RA. In addition, identifying the correct therapeutic biomarker for the treatment of RA using methotrexate (MTX) would greatly increase the benefits experienced by the patients. Materials and methods: Primary normal synoviocytes human fibroblast-like synoviocytes (HFLS) and its phenotype rheumatic HFLS-RA cells were chosen for this study. The HFLS-RA–untreated and MTX-treated cells were subjected to microarray analysis. Results: Microarray data identified 74 differentially expressed genes. These genes were mapped against an RA inflammatory pathway, shortlisting 10 candidate genes. Gene expression profiling of the 10 genes were studied. Fold change (FC) was calculated to determine the differential expression of the samples. Discussion: The transcription profiles of the 10 candidate genes were highly induced in HFLS-RA cells compared with HFLS cells. However, on treating the HFLS-RA cells with MTX, the transcription profiles of these genes were highly downregulated. The most significant expression FC difference between HFLS and HFLS-RA (treated and untreated) was observed with HSPA6, MMP1, MMP13, and TNFSF10 genes. Conclusions: The data from this study suggest the use of HSPA6, MMP1, MMP13, and TNFSF10 gene expression profiles as potential diagnostic biomarkers. In addition, these gene profiles can help in predicting the therapeutic efficacy of MTX.

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