scholarly journals Transcriptome Analysis Reveals Dynamic Cultivar-Dependent Patterns of Gene Expression in Potato Spindle Tuber Viroid-Infected Pepper

Plants ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 2687
Author(s):  
Nikol Hadjieva ◽  
Elena Apostolova ◽  
Vesselin Baev ◽  
Galina Yahubyan ◽  
Mariyana Gozmanova
Keyword(s):  
Gene Expression ◽  
Gene Ontology ◽  
Differentially Expressed Genes ◽  
Transcriptome Analysis ◽  
Quantitative Polymerase Chain Reaction ◽  
Potato Spindle Tuber Viroid ◽  
Fine Tuning ◽  
Differentially Expressed ◽  
Gene Ontology Term ◽  
Ontology Term

Potato spindle tuber viroid (PSTVd) infects various plants. PSTVd pathogenesis is associated with interference with the cellular metabolism and defense signaling pathways via direct interaction with host factors or via the transcriptional or post-transcriptional modulation of gene expression. To better understand host defense mechanisms to PSTVd infection, we analyzed the gene expression in two pepper cultivars, Capsicum annuum Kurtovska kapia (KK) and Djulunska shipka (DS), which exhibit mild symptoms of PSTVd infection. Deep sequencing-based transcriptome analysis revealed differential gene expression upon infection, with some genes displaying contrasting expression patterns in KK and DS plants. More genes were downregulated in DS plants upon infection than in KK plants, which could underlie the more severe symptoms seen in DS plants. Gene ontology enrichment analysis revealed that most of the downregulated differentially expressed genes in both cultivars were enriched in the gene ontology term photosynthesis. The genes upregulated in DS plants fell in the biological process of gene ontology term defense response. We validated the expression of six overlapping differentially expressed genes that are involved in photosynthesis, plant hormone signaling, and defense pathways by quantitative polymerase chain reaction. The observed differences in the responses of the two cultivars to PSTVd infection expand the understanding of the fine-tuning of plant gene expression that is needed to overcome the infection.

scholarly journals Digital Gene Expression Profiling Analysis of Aged Mice under Moxibustion Treatment

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Nan Liu ◽  
Yunyao Jiang ◽  
Min Xing ◽  
Baixiao Zhao ◽  
Jincai Hou ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Ontology ◽  
Gene Expression Profiling ◽  
Differentially Expressed Genes ◽  
Pathway Analysis ◽  
Expression Profiling ◽  
Digital Gene Expression ◽  
Differentially Expressed ◽  
Aged Mice ◽  
Digital Gene Expression Profiling

Aging is closely connected with death, progressive physiological decline, and increased risk of diseases, such as cancer, arteriosclerosis, heart disease, hypertension, and neurodegenerative diseases. It is reported that moxibustion can treat more than 300 kinds of diseases including aging related problems and can improve immune function and physiological functions. The digital gene expression profiling of aged mice with or without moxibustion treatment was investigated and the mechanisms of moxibustion in aged mice were speculated by gene ontology and pathway analysis in the study. Almost 145 million raw reads were obtained by digital gene expression analysis and about 140 million (96.55%) were clean reads. Five differentially expressed genes with an adjusted P value < 0.05 and |log⁡2(fold  change)| > 1 were identified between the control and moxibustion groups. They were Gm6563, Gm8116, Rps26-ps1, Nat8f4, and Igkv3-12. Gene ontology analysis was carried out by the GOseq R package and functional annotations of the differentially expressed genes related to translation, mRNA export from nucleus, mRNA transport, nuclear body, acetyltransferase activity, and so on. Kyoto Encyclopedia of Genes and Genomes database was used for pathway analysis and ribosome was the most significantly enriched pathway term.

scholarly journals Screening differentially expressed genes of pancreatic cancer between Mongolian and Han people using bioinformatics technology

2019 ◽  
Author(s):  
Jiasheng Xu ◽  
Kaili Liao ◽  
ZHONGHUA FU ◽  
ZHENFANG XIONG
Keyword(s):  
Gene Expression ◽  
Pancreatic Cancer ◽  
Gene Ontology ◽  
Differentially Expressed Genes ◽  
Microarray Analysis ◽  
Differentially Expressed ◽  
Cancer Tissue ◽  
Pancreatic Cancer Tissue ◽  
Tissue Samples ◽  
Multiple Difference

Abstract Objective: To screen and analyze differentially expressed genes in pancreatic carcinoma tissues taken from Mongolian and Han patients by Affymetrix Genechip. Methods: Pancreatic ductal cell carcinoma tissues were collected from the Mongolian and Han patients undergoing resection in the Second Affiliated Hospital of Nanchang University from March 2015 to May 2018 and the total RNA was extracted. Differentially expressed genes were selected from the total RNA qualified by Nanodrop 2000 and Agilent 2100 using Affymetrix and a cartogram was drawn; The gene ontology (GO) analysis and Pathway analysis were used for the collection and analysis of biological information of these differentially expressed genes. Finally, some differentially expressed genes were verified by real-time PCR. Results: Through the microarray analysis of gene expression, 970 differentially expressed genes were detected by comparing pancreatic cancer tissue samples between Mongolian and Han patients. A total of 257 genes were significantly up-regulated in pancreatic cancer tissue samples in Mongolian patients; while a total of 713 genes were down-regulated. In the Gene Ontology database, 815 differentially expressed genes were identified with clear GO classification, and CPB1 gene showed the highest increase in expression level (multiple difference: 31.76). The pathway analysis detected 28 signaling pathways that included these differentially expressed genes, involving a total of 178 genes. Among these pathways, the enrichment of differentially expressed genes in the FAK signaling pathway was the strongest and COL11A1 gene showed the highest multiple difference (multiple difference: 5.02). The expression of differentially expressed genes CPB1, COL11A1、ITGA4、BIRC3、PAK4、CPA1、CLPS、PIK3CG and HLA-DPA1 determined by real-time PCR were consistent with the results of gene microarray analysis. Conclusions: The results of microarray analysis of gene expression profiles showed that there are a large number of differentially expressed genes in pancreatic cancer tissue samples comparing Mongolian and Han population. These genes are closely related to the cell proliferation, differentiation, invasion, metastasis and multi-drug resistance in pancreatic cancer. They are also involved in the regulation of multiple important signaling pathways in organisms.

scholarly journals Screening differentially expressed genes of pancreatic cancer between Mongolian and Han people using bioinformatics technology

2019 ◽  
Author(s):  
Jiasheng Xu ◽  
Kaili Liao ◽  
ZHONGHUA FU ◽  
ZHENFANG XIONG
Keyword(s):  
Gene Expression ◽  
Pancreatic Cancer ◽  
Gene Ontology ◽  
Differentially Expressed Genes ◽  
Pathway Analysis ◽  
Differentially Expressed ◽  
Cancer Tissue ◽  
Pancreatic Cancer Tissue ◽  
Tissue Samples ◽  
Multiple Difference

Abstract Objective To screen and analyze differentially expressed genes in pancreatic carcinoma tissues taken from Mongolian and Han patients by Affymetrix Genechip. Methods: Pancreatic ductal cell carcinoma tissues were collected from the Mongolian and Han patients undergoing resection in the Second Affiliated Hospital of Nanchang University during March 2015 to May 2018 and the total RNA was extracted. Differentially expressed genes were selected from the total RNA qualified by Nanodrop 2000 and Agilent 2100 using Affymetrix and a cartogram was drawn; The gene ontology (GO) analysis and Pathway analysis were used for the collection and analysis of biological information of these differentially expressed genes. Finally, some differentially expressed genes were verified by real-time PCR. Results Through the microarray analysis of gene expression, 970 differentially expressed genes were detected by comparing pancreatic cancer tissue samples between Mongolian and Han patients. A total of 257 genes were significantly up-regulated in pancreatic cancer tissue samples in Mongolian patients;while a total of 713 genes were down-regulated. In the Gene Ontology database, 815 differentially expressed genes were identified with clear GO classification, and CPB1 gene had the highest multiple of differential expression (difference multiple: 31.76). The Pathway analysis detected 28 signaling pathways that included these differentially expressed genes, involving a total of 178 genes. Among these pathways, the enrichment of differentially expressed genes in the FAK signaling pathway was the highest and COL11A1 gene had the highest multiple difference (multiple difference: 5.02). The expressions of differentially expressed genes CPB1, COL11A1、ITGA4、BIRC3、PAK4、CPA1、CLPS、PIK3CG and HLA-DPA1 determined by real-time PCR were consistent with the results of gene chip analysis. Conclusions The results of microarray analysis of gene expression profiles showed that there are a large number of differentially expressed genes in pancreatic cancer tissue samples compared between Mongolian and Han populations. These genes are closely related to the proliferation, differentiation, invasion and metastasis and multi-drug resistance of pancreatic cancer and are involved in the regulation of multiple important signaling pathways in organisms.

scholarly journals Screening differentially expressed genes of pancreatic cancer between Mongolian and Han people using bioinformatics technology

2020 ◽  
Author(s):  
Jiasheng Xu ◽  
Kaili Liao ◽  
Zhonghua Fu ◽  
Zhenfang Xiong
Keyword(s):  
Gene Expression ◽  
Pancreatic Cancer ◽  
Gene Ontology ◽  
Differentially Expressed Genes ◽  
Microarray Analysis ◽  
Differentially Expressed ◽  
Cancer Tissue ◽  
Pancreatic Cancer Tissue ◽  
Tissue Samples ◽  
Multiple Difference

Abstract Objective: To screen and analyze differentially expressed genes in pancreatic carcinoma tissues taken from Mongolian and Han patients by Affymetrix Genechip. Methods: Pancreatic ductal cell carcinoma tissues were collected from the Mongolian and Han patients undergoing resection in the Second Affiliated Hospital of Nanchang University from March 2015 to May 2018 and the total RNA was extracted. Differentially expressed genes were selected from the total RNA qualified by Nanodrop 2000 and Agilent 2100 using Affymetrix and a cartogram was drawn; The gene ontology (GO) analysis and Pathway analysis were used for the collection and analysis of biological information of these differentially expressed genes. Finally, some differentially expressed genes were verified by real-time PCR. Results: Through the microarray analysis of gene expression, 970 differentially expressed genes were detected by comparing pancreatic cancer tissue samples between Mongolian and Han patients. A total of 257 genes were significantly up-regulated in pancreatic cancer tissue samples in Mongolian patients; while a total of 713 genes were down-regulated. In the Gene Ontology database, 815 differentially expressed genes were identified with clear GO classification, and CPB1 gene showed the highest increase in expression level (multiple difference: 31.76). The pathway analysis detected 28 signaling pathways that included these differentially expressed genes, involving a total of 178 genes. Among these pathways, the enrichment of differentially expressed genes in the FAK signaling pathway was the strongest and COL11A1 gene showed the highest multiple difference (multiple difference: 5.02). The expression of differentially expressed genes CPB1, COL11A1、ITGA4、BIRC3、PAK4、CPA1、CLPS、PIK3CG and HLA-DPA1 determined by real-time PCR were consistent with the results of gene microarray analysis. Conclusions: The results of microarray analysis of gene expression profiles showed that there are a large number of differentially expressed genes in pancreatic cancer tissue samples comparing Mongolian and Han population. These genes are closely related to the cell proliferation, differentiation, invasion, metastasis and multi-drug resistance in pancreatic cancer. They are also involved in the regulation of multiple important signaling pathways in organisms.

A three-gene expression signature model to predict neo-chemoradiotherapy response of esophageal squamous cell carcinomas.

2013 ◽  
Vol 31 (15_suppl) ◽  
pp. e15135-e15135
Author(s):  
Jing Wen ◽  
Hong Yang ◽  
Kongjia Luo ◽  
Yi Hu ◽  
Xu Zhang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Prediction Model ◽  
Differentially Expressed Genes ◽  
Predictive Power ◽  
Predictive Accuracy ◽  
Quantitative Polymerase Chain Reaction ◽  
Gene Expression Signature ◽  
Differentially Expressed ◽  
Linear Discriminant ◽  
Crt Response

e15135 Background: Preoperative chemoradiotherapy (CRT) followed by surgery has been proved to improve survival in comparison with surgery alone. However, the outcomes of CRT are heterogeneous, and no clinical or pathological method could prediction CRT response. In this study, we aim to identify mRNA markers for ESCC CRT-response prediction. Methods: Gene expression analyses were performed on pretreatment cancer biopsies from 28 ESCCs who received neoadjuvant CRT and surgery. Surgical specimens were assessed for the pathological response to CRT. The identified differentially expressed genes were validated by real-time quantitative polymerase chain reaction (qPCR), based on which a classifying model was built up by Fisher’s linear discriminant analysis. The predictive power of this model was further assessed in another set of 32 ESCCs. Results: The profiling of the 28 ESCCs identified 10 differentially expressed genes with more than 2-fold changes between pathological complete responsers (pCRs) and less than pCRs (<pCRs), among which 6 genes (LIMCH1, SDPR, Clorf226, SLC9A9, GSTM3, and IGSF10) were down-regulated and 4 genes (MMP9, MMP1, MMP12 and OASL) up-regulated in pCRs versus <pCRs. A prediction model based on qPCR values of 3 genes was built up, Y=-10.388 - 0.343 × MMP1 + 0.642 × LIMCH1 + 0.921 × Clorf226 with a cut-off value of 0.607. It provided a predictive accuracy of 85.7% with leave-one-out cross-validation. Further, the predictive power of this model was validated in another set of 32 ESCCs, among which a predictive accuracy of 81.3% was achieved. Conclusions: The combination of three genes by qPCR identified by microarrays in our study provides possibility for ESCC CRT prediction, which will facilitate individualization of ESCC treatment. Further perspective validation in larger independent cohorts is warranted to fully assess the predictive power of this prediction model.

scholarly journals Effects of Monochromatic Lighting During Incubation and Vaccination on the Splenic Transcriptome Profiles of Chicken

2021 ◽  
Vol 12 ◽  
Author(s):  
Mohamed M. A. Ibrahim ◽  
Jill R. Nelson ◽  
Gregory S. Archer ◽  
Giridhar Athrey
Keyword(s):  
Gene Expression ◽  
Gene Ontology ◽  
Differentially Expressed Genes ◽  
Developmental Process ◽  
White Layer ◽  
Expression Patterns ◽  
Monochromatic Light ◽  
Light Treatment ◽  
Differentially Expressed ◽  
In Ovo

Lighting is a crucial environmental variable in poultry operations, but illumination during incubation is relatively understudied. The ability to stimulate development or immune performance using in ovo lighting is a promising approach for improving poultry health and welfare. This study investigated how monochromatic green light during incubation and vaccination method and timing affected chicken splenic gene expression patterns. We performed this study with 1,728 Hy-Line white layer eggs incubated under two light treatments during incubation: continuous dark and continuous green monochromatic light, over the entire incubation period. Half the eggs in each light treatment received in ovo vaccination, applied on embryonic day 18 (ED18). The remaining half were vaccinated by spraying on hatch day. After hatching, the light treatments followed the industry-standard lighting regimens. The study had six treatment groups with light–dark pairs for non-vaccinated, in ovo vaccinated, and post-hatch vaccinated. We assessed splenic gene expression at ED18 and at 7 days post-hatch (PH) in all the treatments. We isolated and sequenced 24 mRNA libraries on the Illumina platform, followed by bioinformatics and differential gene expression analyses. RNAseq analysis showed between 62 and 6,755 differentially expressed genes (DEGs) between comparisons, with the most prominent differences observed between ED and PH samples, followed by comparisons between vaccination methods. In contrast, light vs. dark treatments at ED showed limited effects on transcriptomic profiles. However, we observed a synergistic effect of lighting during incubation on post-hatch vaccination responses, with differentially expressed genes (DEGs) unique to the light treatment showing stimulation of cell proliferation with significance for immune activity (inferred from gene ontology terms). Gene ontology and pathway analysis indicated biological processes like cellular component organization or biogenesis, rhythmic process, developmental process, response to stimulus, and immune system processes were explained by the DEGs. While lighting is an important source of circadian stimulation, other controlled studies are required to clarify whether in ovo circadian entrainment plays a role in modulating immune responses.

scholarly journals Aberrantly hydroxymethylated differentially expressed genes and the associated protein pathways in osteoarthritis

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e6425 ◽  
Author(s):  
Yang Fang ◽  
Pingping Wang ◽  
Lin Xia ◽  
Suwen Bai ◽  
Yonggang Shen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Ontology ◽  
Differentially Expressed Genes ◽  
Kegg Pathway ◽  
Interaction Network ◽  
Differentially Expressed ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Highly Expressed Genes ◽  
Protein Protein Interaction Network

Background The elderly population is at risk of osteoarthritis (OA), a common, multifactorial, degenerative joint disease. Environmental, genetic, and epigenetic (such as DNA hydroxymethylation) factors may be involved in the etiology, development, and pathogenesis of OA. Here, comprehensive bioinformatic analyses were used to identify aberrantly hydroxymethylated differentially expressed genes and pathways in osteoarthritis to determine the underlying molecular mechanisms of osteoarthritis and susceptibility-related genes for osteoarthritis inheritance. Methods Gene expression microarray data, mRNA expression profile data, and a whole genome 5hmC dataset were obtained from the Gene Expression Omnibus repository. Differentially expressed genes with abnormal hydroxymethylation were identified by MATCH function. Gene ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of the genes differentially expressed in OA were performed using Metascape and the KOBAS online tool, respectively. The protein–protein interaction network was built using STRING and visualized in Cytoscape, and the modular analysis of the network was performed using the Molecular Complex Detection app. Results In total, 104 hyperhydroxymethylated highly expressed genes and 14 hypohydroxymethylated genes with low expression were identified. Gene ontology analyses indicated that the biological functions of hyperhydroxymethylated highly expressed genes included skeletal system development, ossification, and bone development; KEGG pathway analysis showed enrichment in protein digestion and absorption, extracellular matrix–receptor interaction, and focal adhesion. The top 10 hub genes in the protein–protein interaction network were COL1A1, COL1A2, COL2A1, COL3A1, COL5A1, COL5A2, COL6A1, COL8A1, COL11A1, and COL24A1. All the aforementioned results are consistent with changes observed in OA. Conclusion After comprehensive bioinformatics analysis, we found aberrantly hydroxymethylated differentially expressed genes and pathways in OA. The top 10 hub genes may be useful hydroxymethylation analysis biomarkers to provide more accurate OA diagnoses and target genes for treatment of OA.

scholarly journals Screening differentially expressed genes of pancreatic cancer between Mongolian and Han people using bioinformatics technology

2020 ◽  
Author(s):  
Jiasheng Xu ◽  
Kaili Liao ◽  
ZHONGHUA FU ◽  
ZHENFANG XIONG
Keyword(s):  
Gene Expression ◽  
Pancreatic Cancer ◽  
Gene Ontology ◽  
Differentially Expressed Genes ◽  
Microarray Analysis ◽  
Differentially Expressed ◽  
Cancer Tissue ◽  
Pancreatic Cancer Tissue ◽  
Tissue Samples ◽  
Multiple Difference

Abstract Objective: To screen and analyze differentially expressed genes in pancreatic carcinoma tissues taken from Mongolian and Han patients by Affymetrix Genechip. Methods: Pancreatic ductal cell carcinoma tissues were collected from the Mongolian and Han patients undergoing resection in the Second Affiliated Hospital of Nanchang University from March 2015 to May 2018 and the total RNA was extracted. Differentially expressed genes were selected from the total RNA qualified by Nanodrop 2000 and Agilent 2100 using Affymetrix and a cartogram was drawn; The gene ontology (GO) analysis and Pathway analysis were used for the collection and analysis of biological information of these differentially expressed genes. Finally, some differentially expressed genes were verified by real-time PCR. Results: Through the microarray analysis of gene expression, 970 differentially expressed genes were detected by comparing pancreatic cancer tissue samples between Mongolian and Han patients. A total of 257 genes were significantly up-regulated in pancreatic cancer tissue samples in Mongolian patients; while a total of 713 genes were down-regulated. In the Gene Ontology database, 815 differentially expressed genes were identified with clear GO classification, and CPB1 gene showed the highest increase in expression level (multiple difference: 31.76). The pathway analysis detected 28 signaling pathways that included these differentially expressed genes, involving a total of 178 genes. Among these pathways, the enrichment of differentially expressed genes in the FAK signaling pathway was the strongest and COL11A1 gene showed the highest multiple difference (multiple difference: 5.02). The expression of differentially expressed genes CPB1, COL11A1、ITGA4、BIRC3、PAK4、CPA1、CLPS、PIK3CG and HLA-DPA1 determined by real-time PCR were consistent with the results of gene microarray analysis. Conclusions: The results of microarray analysis of gene expression profiles showed that there are a large number of differentially expressed genes in pancreatic cancer tissue samples comparing Mongolian and Han population. These genes are closely related to the cell proliferation, differentiation, invasion, metastasis and multi-drug resistance in pancreatic cancer. They are also involved in the regulation of multiple important signaling pathways in organisms.

Transcriptome Analysis of Cold Syndrome Using Microarray

2007 ◽  
Vol 35 (04) ◽  
pp. 609-620 ◽  
Author(s):  
Liping Yang ◽  
Miqu Wang ◽  
Wei Wu ◽  
Louxin Zhang
Keyword(s):  
Gene Expression ◽  
Chinese Medicine ◽  
Differentially Expressed Genes ◽  
Transcriptome Analysis ◽  
Expression Patterns ◽  
Differentially Expressed ◽  
Gene Expression Patterns ◽  
Molecular Pathways ◽  
Gene Modules

Microarrays are widely used to study changes in gene expression in diseases. In this paper, we use this technology to discover gene expression patterns in the cold syndrome in Chinese medicine. We identify differentially expressed genes and extracted gene modules that are enriched with differentially expressed genes in the cold syndrome by analyzing cDNA samples, which are purified from blood taken from a pedigree. Our results suggest that the cold syndrome might be caused by the physiological imbalance and/or the disorder of metabolite processes. The study confirms the hypotheses about molecular pathways responsible to human metabolic-related diseases.

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