scholarly journals Selenium Regulates Gene Expression for Glucosinolate and Carotenoid Biosynthesis in Arabidopsis

2011 ◽  
Vol 136 (1) ◽  
pp. 23-34 ◽  
Author(s):  
Carl E. Sams ◽  
Dilip R. Panthee ◽  
Craig S. Charron ◽  
Dean A. Kopsell ◽  
Joshua S. Yuan
Keyword(s):  
Gene Expression ◽  
Differentially Expressed Genes ◽  
Plant Species ◽  
Chlorophyll Biosynthesis ◽  
Plant Secondary Metabolites ◽  
Carotenoid Biosynthesis ◽  
Differentially Expressed ◽  
Biosynthesis Pathway ◽  
Tissue Samples ◽  
Expression Of Genes

Glucosinolates (GSs) and carotenoids are important plant secondary metabolites present in several plant species, including arabidopsis (Arabidopsis thaliana). Although genotypic and environmental regulation of GSs and carotenoid compounds has been reported, few studies present data on their regulation at the molecular level. Therefore, the objective of this study was to explore differential expression of genes associated with GSs and carotenoids in arabidopsis in response to selenium fertilization, shown previously to impact accumulations of both classes of metabolites in Brassica species. Arabidopsis was grown under 0.0 or 10.0 μM Na2SeO4 in hydroponic culture. Shoot and root tissue samples were collected before anthesis to measure GSs and carotenoid compounds and conduct gene expression analysis. Gene expression was determined using arabidopsis oligonucleotide chips containing more than 31,000 genes. There were 1274 differentially expressed genes in response to selenium (Se), of which 516 genes were upregulated. Ontology analysis partitioned differentially expressed genes into 20 classes. Biosynthesis pathway analysis using AraCyc revealed that four GSs, one carotenoid, and one chlorophyll biosynthesis pathways were invoked by the differentially expressed genes. Involvement of the same gene in more than one biosynthesis pathway indicated that the same enzyme may be involved in multiple GS biosynthesis pathways. The decrease in carotenoid biosynthesis under Se treatment occurred through the downregulation of phytoene synthase at the beginning of the carotenoid biosynthesis pathway. These findings may be useful to modify the GS and carotenoid levels in arabidopsis and may lead to modification in agriculturally important plant species.

Gene Expression Profiling of Hyperdiploid Multiple Reveal Complex Gene Dosage Effects and an mRNA Translation/Protein Synthesis Signature.

Blood ◽  
2005 ◽  
Vol 106 (11) ◽  
pp. 1538-1538
Author(s):  
Wee-Joo Chng ◽  
Scott Van Wier ◽  
Gregory Ahmann ◽  
Tammy Price-Troska ◽  
Kim Henderson ◽  
...  
Keyword(s):  
Gene Expression ◽  
Protein Synthesis ◽  
Differentially Expressed Genes ◽  
Gene Dosage ◽  
Gene Copy Number ◽  
Mrna Translation ◽  
Differentially Expressed ◽  
Gene Copy ◽  
Gene Dosage Effect ◽  
Expression Of Genes

Abstract Hyperdiploid MM (H-MM), characterized by recurrent trisomies constitute about 50% of MM, yet very little is known about its pathogenesis and oncogenic mechanisms. Studies in leukemia and solid tumors have shown gene dosage effect of aneuploidy on gene expression. To determine the possible gene dosage effect and deregulated cellular program in H-MM we undertook a gene expression study of CD138-enriched plasma-cell RNA from 53 hyperdiploid and 37 non-hyperdiploid MM (NH-MM) patients using the Affymetrix U133A chip (Affymetrix, Santa Clara, CA). Gene expression data was analyzed using GeneSpring 7 (Agilent Technologies, Palo Alto, CA). Genes differentially expressed between H-MM and NH-MM were obtained by t-test (p<0.01). The majority of the differentially expressed genes (57%) were under-expressed in H-MM. Genes located on the commonly trisomic chromosomes were mostly (but not always) over-expressed in H-MM and constitute 76% of over-expressed genes. Chromosome 1 contained the most differentially expressed genes (17%) followed by chromosome 12 (9%), and 19 (8%). To examine the relationship of gene copy number to gene expression, we examined the expression of genes on chromosomes 9 and 15 in subjects with 2 copies (15 normal control and 20 NH-MM) and 3 copies (12 H-MM) of each chromosome as detected by interphase FISH. We then derived a ratio of the mean expression of each gene on these chromosomes between patients with 3 copies and 2 copies of the chromosome. If a simple relationship exists between gene expression and gene copy number, one would expect the ratio of expression of most genes on these two chromosomes to be about 1.5 in H-MM compared to NH-MM. However, many genes have ratios either higher than 2 or lower than 0.5. Furthermore, when the heterogeneity of cells with underlying trisomies is taken into consideration by correcting the ratio for the number of cells with trisomies, the actual ratio is always lower than the expected ratio. When the expression of genes on a chromosome was compressed to a median value, this value was always higher in the trisomic chromosomes for H-MM compared to NH-MM. The data suggests that although gene dosage influence gene expression, the relationship is complex and some genes are more gene dosage dependent than others. Amongst the differentially expressed genes with known function, 33% are involved in mRNA translation/protein synthesis. Of note, 37 of the top 100 differentially expressed genes are involved in these processes. In particular, 60 ribosomal protein (RP) genes are significantly (p<0.05) upregulated in H-MM. This signature in H-MM is not associated with increase proliferation as measured by PCLI. This predominant signature suggests that deregulated protein synthesis may be important for the biology of H-MM. Many of these RPs are involved in the synthesis of product of oncogenic pathways (e.g. MYC, NF-KB pathways) and may mediate the growth and survival of tumor cells. It is therefore possible that these tumor cells may be sensitive to the disruption of mRNA translation/protein synthesis. Targeting the mTOR pathway with rapamycin may therefore be useful for treatment of H-MM.

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.

Differentially expressed genes reveal adaptations between free-living and symbiotic niches of Vibrio fischeri in a fully established mutualism

2006 ◽  
Vol 52 (12) ◽  
pp. 1218-1227 ◽  
Author(s):  
B W Jones ◽  
M K Nishiguchi
Keyword(s):  
Gene Expression ◽  
Host Specificity ◽  
Differentially Expressed Genes ◽  
Vibrio Fischeri ◽  
Differentially Expressed ◽  
Light Organ ◽  
Free Living ◽  
Expression Of Genes ◽  
Unique System ◽  
Insight Into

A major force driving in the innovation of mutualistic symbioses is the number of adaptations that both organisms must acquire to provide overall increased fitness for a successful partnership. Many of these symbioses are relatively dependent on the ability of the symbiont to locate a host (specificity), as well as provide some novel capability upon colonization. The mutualism between sepiolid squids and members of the Vibrionaceae is a unique system in which development of the symbiotic partnership has been studied in detail, but much remains unknown about the genetics of symbiont colonization and persistence within the host. Using a method that captures exclusively expressed transcripts in either free-living or host-associated strains of Vibrio fischeri, we identified and verified expression of genes differentially expressed in both states from two symbiotic strains of V. fischeri. These genes provide a glimpse into the microhabitat V. fischeri encounters in both free-living seawater and symbiotic host light organ-associated habitats, providing insight into the elements necessary for local adaptation and the evolution of host specificity in this unique mutualism.Key words: Vibrionaceae, gene expression, Sepiolidae, Euprymna, SCOTS.

scholarly journals Transcriptome analysis of the oriental melon (Cucumis meloL. var.makuwa) during fruit development

PeerJ ◽  
2017 ◽  
Vol 5 ◽  
pp. e2834 ◽  
Author(s):  
Ah-Young Shin ◽  
Yong-Min Kim ◽  
Namjin Koo ◽  
Su Min Lee ◽  
Seokhyeon Nahm ◽  
...  
Keyword(s):  
Differentially Expressed Genes ◽  
Fruit Development ◽  
De Novo ◽  
Expression Patterns ◽  
Soluble Sugars ◽  
Carotenoid Biosynthesis ◽  
Northern China ◽  
Differentially Expressed ◽  
Oriental Melon ◽  
Expression Of Genes

BackgroundThe oriental melon (Cucumis meloL. var.makuwa) is one of the most important cultivated cucurbits grown widely in Korea, Japan, and northern China. It is cultivated because its fruit has a sweet aromatic flavor and is rich in soluble sugars, organic acids, minerals, and vitamins. In order to elucidate the genetic and molecular basis of the developmental changes that determine size, color, and sugar contents of the fruit, we performedde novotranscriptome sequencing to analyze the genes expressed during fruit development.ResultsWe identified a total of 47,666 of representative loci from 100,875 transcripts and functionally annotated 33,963 of the loci based on orthologs inArabidopsis thaliana. Among those loci, we identified 5,173 differentially expressed genes, which were classified into 14 clusters base on the modulation of their expression patterns. The expression patterns suggested that the differentially expressed genes were related to fruit development and maturation through diverse metabolic pathways. Analyses based on gene set enrichment and the pathways described in the Kyoto Encyclopedia of Genes and Genomes suggested that the expression of genes involved in starch and sucrose metabolism and carotenoid biosynthesis were regulated dynamically during fruit development and subsequent maturation.ConclusionOur results provide the gene expression patterns related to different stages of fruit development and maturation in the oriental melon. The expression patterns give clues about important regulatory mechanisms, especially those involving starch, sugar, and carotenoid biosynthesis, in the development of the oriental melon fruit.

Changes in Differential Gene and Protein Expression Patterns during Development of Cord Blood (CB) Versus Adult Peripheral Blood (APB) Monocyte (Mo) into Mature Dendritic Cells (mDC): Insight into Immaturity of CB DC Immunity.

Blood ◽  
2006 ◽  
Vol 108 (11) ◽  
pp. 5204-5204
Author(s):  
Hong Jiang ◽  
Cheryl Wade-Harris ◽  
Megan Lim ◽  
Laxmi Baxi ◽  
Mitchell S. Cairo
Keyword(s):  
Gene Expression ◽  
Differentially Expressed Genes ◽  
Developmental Stages ◽  
Expression Patterns ◽  
Oligonucleotide Microarray ◽  
Differentially Expressed ◽  
Expression Of Genes ◽  
Differential Gene ◽  
Hla Dqa1 ◽  
Antigen Presenting

Abstract It has been recognized that dysfunction of CB immune system is in part due to the immaturity of CB cellular immunity (Cairo, Blood,1997). The molecular mechanisms associated with the immaturity of CB cellular immunity including DC subset remain to be defined. The maturation status of DC greatly influences its antigen presentation capacity. Recently, we have utilized oligonucleotide microarray to demonstrate differential gene expression profiles of CB vs APB Mo (Jiang/Cairo, JI, 2004). In the current study, differential expressed genes and proteins were examined in Mo-derived CB vs. APB DC during DC developmental stages: Mo, immature DC (iDC) and mDC, by utilizing oligonucleotide microarray and proteomics. Briefly, Mo isolated from CB or APB and cultured for 8 days with GM-CSF/IL-4 (iDC) and further stimulated with LPS (mDC). Oligonucleotide microarray was carried out using U133A gene chip (Affymetrix). The representative differentially expressed genes resulted from microarray analysis were selected and analyzed by quantitative RT-PCR (Roche). The proteomic technique was conducted by liquid chromatography (LC) and mass spectrometry (MS) (Lim, Mol Cell Proteomics, 2006). The differentially expressed proteins were compared in CB vs. APB for iDC and mDC. We identified different gene expression patterns that were significantly lower in CB vs. APB in different stages during DC differentiation: Mo, iDC and mDC. These differentially expressed genes included RELA (5F), JUNB (6F), IRF-1 (3F) in Mo; CREB5 (3F), MAP7 (5F), IL1R2 (6F) in iDC; and HLA-DQA1 (4F), CD80 (3F), IRF-5 (3F) in mDC. The proteomic studies demonstrated Tyrosine Kinase Fer (12.5F), Actin regulator 3 (2.5F), Rap guanine nucleotide exchange factor 1 (2.4F) and Myeloid cell nuclear differentiation antigen (1.5F) were expressed higher in APB vs.CB iDC, while MAX binding protein MNT (5.5F), IRS2 (2.2F) and Zinc-Finger Proteins (514, 212, 462) (3–14F) were expressed higher in CB vs. APB iDC. Further, the proteomic results also indicated other Zinc-Finger Proteins (292, 221, 474) (2–5F), Fibrillin 1 precursor (2.5F) and interleukin-4 (7.7F) were expressed higher in APB vs. CB mDC. In contrast, cyclin I (3F), Rb-like protein 2 (4.35 F) and PKC theta (2F) were significantly lower in APB vs. CB DC. Moreover, the comparison of CB vs. APB DC antigen presenting activity by ELISPOT was performed and the influenza-peptide loaded CB-mDC demonstrated weaker ability to induce T cells to produce IFNg compared with APB-mDC. In summary, these differentially expressed genes in Mo (RELA, JUN) may play key roles in initiating Mo differentiation toward DC. The increased expression of genes in APB vs. CB iDC, like CREB5, IL1R2, may be involved in mediating maturation process of iDC to mDC. Lastly, the elevated expression of genes in APB vs. CB mDC, such as HLA-DQA1, CD80, IRF5 among others, may be likely to control mDC functionality as demonstrated by weaker antigen presenting activity of CB vs. APB mDC. We postulate that decreased expression of specific genes in CB vs. APB DC during DC developmental stages may in part be responsible for the lack of maturity of CB, and ultimately may partially be responsible for differential CB vs. APB innate and adaptive immunity.

Identification of key biomarkers for thyroid cancer by integrative gene expression profiles

2021 ◽  
pp. 153537022110088
Author(s):  
Jinyi Tian ◽  
Yizhou Bai ◽  
Anyang Liu ◽  
Bin Luo
Keyword(s):  
Gene Expression ◽  
Thyroid Cancer ◽  
Differentially Expressed Genes ◽  
Molecular Mechanisms ◽  
Expression Profiles ◽  
Thyroid Tissue ◽  
Gene Signature ◽  
Gene Expression Omnibus ◽  
Differentially Expressed ◽  
Tissue Samples

Thyroid cancer is a frequently diagnosed malignancy and the incidence has been increased rapidly in recent years. Despite the favorable prognosis of most thyroid cancer patients, advanced patients with metastasis and recurrence still have poor prognosis. Therefore, the molecular mechanisms of progression and targeted biomarkers were investigated for developing effective targets for treating thyroid cancer. Eight chip datasets from the gene expression omnibus database were selected and the inSilicoDb and inSilicoMerging R/Bioconductor packages were used to integrate and normalize them across platforms. After merging the eight gene expression omnibus datasets, we obtained one dataset that contained the expression profiles of 319 samples (188 tumor samples plus 131 normal thyroid tissue samples). After screening, we identified 594 significantly differentially expressed genes (277 up-regulated genes plus 317 down-regulated genes) between the tumor and normal tissue samples. The differentially expressed genes exhibited enrichment in multiple signaling pathways, such as p53 signaling. By building a protein–protein interaction network and module analysis, we confirmed seven hub genes, and they were all differentially expressed at all the clinical stages of thyroid cancer. A diagnostic seven-gene signature was established using a logistic regression model with the area under the receiver operating characteristic curve (AUC) of 0.967. Seven robust candidate biomarkers predictive of thyroid cancer were identified, and the obtained seven-gene signature may serve as a useful marker for thyroid cancer diagnosis and prognosis.

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.

scholarly journals Differentially expressed genes reflect disease-induced rather than disease-causing changes in the transcriptome

2020 ◽  
Author(s):  
Eleonora Porcu ◽  
Marie C. Sadler ◽  
Kaido Lepik ◽  
Chiara Auwerx ◽  
Andrew R. Wood ◽  
...  
Keyword(s):  
Gene Expression ◽  
Differentially Expressed Genes ◽  
Whole Blood ◽  
Complex Traits ◽  
Causal Effects ◽  
Differentially Expressed ◽  
Polygenic Risk Score ◽  
Transcript Levels ◽  
Expression Of Genes ◽  
Shed Light

AbstractComparing transcript levels between healthy and diseased individuals allows the identification of differentially expressed genes, which may be causes, consequences or mere correlates of the disease under scrutiny. Here, we propose a bi-directional Transcriptome-Wide Mendelian Randomization (TWMR) approach that integrates summary-level data from GWAS and whole-blood eQTLs in a MR framework to investigate the causal effects between gene expression and complex traits.Whereas we have previously developed a TWMR approach to elucidate gene expression to trait causal effects, here we are adapting the method to shed light on the causal imprint of complex traits on transcript levels. We termed this new approach reverse TWMR (revTWMR). Integrating bi-directional causal effects between gene expression and complex traits enables to evaluate their respective contributions to the correlation between gene expression and traits. We uncovered that whole blood gene expression-trait correlation is mainly driven by causal effect from the phenotype on the expression rather than the reverse. For example, BMI- and triglycerides-gene expression correlation coefficients robustly correlate with trait-to-expression causal effects (r=0.09, P=1.54×10−39 and r=0.09, P=1.19×10−34, respectively), but not detectably with expression-to-trait effects.Genes implicated by revTWMR confirmed known associations, such as rheumathoid arthritis and Crohn’s disease induced changes in expression of TRBV and GBP2, respectively. They also shed light on how clinical biomarkers can influence their own levels. For instance, we observed that high levels of high-density lipoprotein (HDL) cholesterol lowers the expression of genes involved in cholesterol biosynthesis (SQLE, FDFT1) and increases the expression of genes responsible for cholesterol efflux (ABCA1, ABCG1), two key molecular pathways in determining HDL levels. Importantly, revTWMR is more robust to pleiotropy than polygenic risk score (PRS) approaches which can be misled by pleiotropic outliers. As one example, revTWMR revealed that the previously reported association between educational attainment PRS and STX1B is exclusively driven by a highly pleiotropic SNP (rs2456973), which is strongly associated with several hematological and anthropometric traits.In conclusion, our method disentangles the relationship between gene expression and phenotypes and reveals that complex traits have more pronounced impact on gene expression than the reverse. We demonstrated that studies comparing the transcriptome of diseased and healthy subjects are more prone to reveal disease-induced gene expression changes rather than disease causing ones.

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