scholarly journals Functional Classification of Skinning Injury Responsive Genes in Storage Roots of Sweetpotato

2017 ◽  
Vol 45 (1) ◽  
pp. 36-42
Author(s):  
Jollanda Effendy ◽  
Darda Efendi ◽  
Nurul Khumaida ◽  
And Gustaaf Adolf Wattimena
Keyword(s):  
Gene Expression ◽  
Stress Responses ◽  
Functional Annotation ◽  
Ipomoea Batatas ◽  
Differentially Expressed Gene ◽  
Differentially Expressed ◽  
Functional Classification ◽  
Financial Loss ◽  
Storage Roots

Skinning injury in sweetpotato due to loss of skin or periderm which occurred during harvest is inevitable and account for financial loss due to dehydration, pests, and pathogens. Hence, studies on gene expression changed due to skinning injury can provide important information about this protective tissue and for improving the life of storage roots. New candidate genes involved in skinning injury were isolated with an Annealing Control Primer (ACP). Using 20 ACP primers, a total of 103 differentially expressed genes (DEGs) were retrieved. In this study, the functional annotation of these selected 15 up-regulated DEGs (10 contigs and 5 singletons) were characterized. The results showed that these 15 “DEG-unigenes” are mainly associated with defense and stress responses, regulation and signaling, protein synthesis and fate, and metabolism may play an important role in the primary responses to skinning injury in storage roots of sweetpotato. This study showed the importance of defense and stress responses genes to the formation of wound periderm. Furthermore, this results can be used for better understanding of the molecular mechanism of skinning/mechanical injury-related genes in the storage roots of sweetpotato as well as to all stems, fruits, and roots of all plants. Keywords: differentially expressed gene, gene function, Ipomoea batatas, wounding

Decoding Psoriasis: Integrated Bioinformatics Approach to Understand Hub Genes and Involved Pathways

2020 ◽  
Vol 26 (29) ◽  
pp. 3619-3630
Author(s):  
Saumya Choudhary ◽  
Dibyabhaba Pradhan ◽  
Noor S. Khan ◽  
Harpreet Singh ◽  
George Thomas ◽  
...  
Keyword(s):  
Gene Expression ◽  
Differentially Expressed Gene ◽  
Therapeutic Targets ◽  
Interaction Network ◽  
Gene Expression Omnibus ◽  
Differentially Expressed ◽  
Keratinocyte Differentiation ◽  
Hub Genes ◽  
Lesional Skin ◽  
Ncbi Gene Expression Omnibus

Background: Psoriasis is a chronic immune mediated skin disorder with global prevalence of 0.2- 11.4%. Despite rare mortality, the severity of the disease could be understood by the accompanying comorbidities, that has even led to psychological problems among several patients. The cause and the disease mechanism still remain elusive. Objective: To identify potential therapeutic targets and affecting pathways for better insight of the disease pathogenesis. Method: The gene expression profile GSE13355 and GSE14905 were retrieved from NCBI, Gene Expression Omnibus database. The GEO profiles were integrated and the DEGs of lesional and non-lesional psoriasis skin were identified using the affy package in R software. The Kyoto Encyclopaedia of Genes and Genomes pathways of the DEGs were analyzed using clusterProfiler. Cytoscape, V3.7.1 was utilized to construct protein interaction network and analyze the interactome map of candidate proteins encoded in DEGs. Functionally relevant clusters were detected through Cytohubba and MCODE. Results: A total of 1013 genes were differentially expressed in lesional skin of which 557 were upregulated and 456 were downregulated. Seven dysregulated genes were extracted in non-lesional skin. The disease gene network of these DEGs revealed 75 newly identified differentially expressed gene that might have a role in development and progression of the disease. GO analysis revealed keratinocyte differentiation and positive regulation of cytokine production to be the most enriched biological process and molecular function. Cytokines -cytokine receptor was the most enriched pathways. Among 1013 identified DEGs in lesional group, 36 DEGs were found to have altered genetic signature including IL1B and STAT3 which are also reported as hub genes. CCNB1, CCNA2, CDK1, IL1B, CXCL8, MKI 67, ESR1, UBE2C, STAT1 and STAT3 were top 10 hub gene. Conclusion: The hub genes, genomic altered DEGs and other newly identified differentially dysregulated genes would improve our understanding of psoriasis pathogenesis, moreover, the hub genes could be explored as potential therapeutic targets for psoriasis.

Gene Set Correlation Analysis and Visualization Using Gene Expression Data

2020 ◽  
Vol 15 ◽  
Author(s):  
Chen-An Tsai ◽  
James J. Chen
Keyword(s):  
Gene Expression ◽  
Correlation Analysis ◽  
Gene Expression Data ◽  
Differentially Expressed Gene ◽  
Differentially Expressed ◽  
Superior Performance ◽  
Expression Data ◽  
Gene Set ◽  
Gene Sets ◽  
Set Correlation

Background: Gene set enrichment analyses (GSEA) provide a useful and powerful approach to identify differentially expressed gene sets with prior biological knowledge. Several GSEA algorithms have been proposed to perform enrichment analyses on groups of genes. However, many of these algorithms have focused on identification of differentially expressed gene sets in a given phenotype. Objective: In this paper, we propose a gene set analytic framework, Gene Set Correlation Analysis (GSCoA), that simultaneously measures within and between gene sets variation to identify sets of genes enriched for differential expression and highly co-related pathways. Methods: We apply co-inertia analysis to the comparisons of cross-gene sets in gene expression data to measure the costructure of expression profiles in pairs of gene sets. Co-inertia analysis (CIA) is one multivariate method to identify trends or co-relationships in multiple datasets, which contain the same samples. The objective of CIA is to seek ordinations (dimension reduction diagrams) of two gene sets such that the square covariance between the projections of the gene sets on successive axes is maximized. Simulation studies illustrate that CIA offers superior performance in identifying corelationships between gene sets in all simulation settings when compared to correlation-based gene set methods. Result and Conclusion: We also combine between-gene set CIA and GSEA to discover the relationships between gene sets significantly associated with phenotypes. In addition, we provide a graphical technique for visualizing and simultaneously exploring the associations of between and within gene sets and their interaction and network. We then demonstrate integration of within and between gene sets variation using CIA and GSEA, applied to the p53 gene expression data using the c2 curated gene sets. Ultimately, the GSCoA approach provides an attractive tool for identification and visualization of novel associations between pairs of gene sets by integrating co-relationships between gene sets into gene set analysis.

scholarly journals Discriminative feature of cells characterizes cell populations of interest by a small subset of genes

2021 ◽  
Author(s):  
Takeru Fujii ◽  
Kazumitsu Maehara ◽  
Masatoshi Fujita ◽  
Yasuyuki Ohkawa
Keyword(s):  
Gene Expression ◽  
Statistical Methods ◽  
Cell Population ◽  
Differentially Expressed Gene ◽  
Adaptive Lasso ◽  
Differentially Expressed ◽  
Small Subset ◽  
Specific Gene ◽  
Large Sample Size ◽  
Discriminative Feature

ABSTRACTStatistical methods for detecting differences in individual gene expression are indispensable for understanding cell types. However, conventional statistical methods have faced difficulties associated with the inflation of P-values because of both the large sample size and selection bias introduced by exploratory data analysis such as single-cell transcriptomics. Here, we propose the concept of discriminative feature of cells (DFC), an alternative to using differentially expressed gene-based approaches. We implemented DFC using logistic regression with an adaptive LASSO penalty to perform binary classification for the discrimination of a population of interest and variable selection to obtain a small subset of defining genes. We demonstrated that DFC prioritized gene pairs with non-independent expression using artificial data, and that DFC enabled to characterize the muscle satellite cell population. The results revealed that DFC well captured cell-type-specific markers, specific gene expression patterns, and subcategories of this cell population. DFC may complement differentially expressed gene-based methods for interpreting large data sets.

scholarly journals Mitochondrial DNA Affects the Expression of Nuclear Genes Involved in Immune and Stress Responses in a Breast Cancer Model

2020 ◽  
Vol 11 ◽  
Author(s):  
Carole Grasso ◽  
David A. Eccles ◽  
Stepana Boukalova ◽  
Marie-Sophie Fabre ◽  
Rebecca H. Dawson ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Tumor Cells ◽  
Stress Responses ◽  
Communication Systems ◽  
Nuclear Genes ◽  
Host Cells ◽  
Differentially Expressed ◽  
Mitochondrial Transfer ◽  
Metabolic Remodeling

Tumor cells without mitochondrial (mt) DNA (ρ0 cells) are auxotrophic for uridine, and their growth is supported by pyruvate. While ATP synthesis in ρ0 cells relies on glycolysis, they fail to form tumors unless they acquire mitochondria from stromal cells. Mitochondrial acquisition restores respiration that is essential for de novo pyrimidine biosynthesis and for mitochondrial ATP production. The physiological processes that underpin intercellular mitochondrial transfer to tumor cells lacking mtDNA and the metabolic remodeling and restored tumorigenic properties of cells that acquire mitochondria are not well understood. Here, we investigated the changes in mitochondrial and nuclear gene expression that accompany mtDNA deletion and acquisition in metastatic murine 4T1 breast cancer cells. Loss of mitochondrial gene expression in 4T1ρ0 cells was restored in cells recovered from subcutaneous tumors that grew from 4T1ρ0 cells following acquisition of mtDNA from host cells. In contrast, the expression of most nuclear genes that encode respiratory complex subunits and mitochondrial ribosomal subunits was not greatly affected by loss of mtDNA, indicating ineffective mitochondria-to-nucleus communication systems for these nuclear genes. Further, analysis of nuclear genes whose expression was compromised in 4T1ρ0 cells showed that immune- and stress-related genes were the most highly differentially expressed, representing over 70% of those with greater than 16-fold higher expression in 4T1 compared with 4T1ρ0 cells. The monocyte recruiting chemokine, Ccl2, and Psmb8, a subunit of the immunoproteasome that generates MHCI-binding peptides, were the most highly differentially expressed. Early monocyte/macrophage recruitment into the tumor mass was compromised in 4T1ρ0 cells but recovered before mtDNA could be detected. Taken together, our results show that mitochondrial acquisition by tumor cells without mtDNA results in bioenergetic remodeling and re-expression of genes involved in immune function and stress adaptation.

A New Molecular Classification of Multiple Myeloma Using Gene Expression Profiling and Fluorescence In Situ Hybridisation as Predictor for Event Free Survival.

Blood ◽  
2004 ◽  
Vol 104 (11) ◽  
pp. 73-73 ◽  
Author(s):  
Dirk Hose ◽  
Jean-Francois Rossi ◽  
Carina Ittrich ◽  
John deVos ◽  
Axel Benner ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Molecular Classification ◽  
Differentially Expressed ◽  
Event Free Survival ◽  
Free Survival ◽  
Cytogenetic Aberrations

Abstract AIM was to establish a new molecular classification of Multiple Myeloma (MM) based on changes in global gene expression attributable to cytogenetic aberrations detected by interphase FISH (iFISH) in order to (i) predict event free survival (EFS) and (ii) investigate differentially expressed genes as basis for a group specific and risk adapted therapy. PATIENTS AND METHODS. Bone marrow aspirates of 105 newly diagnosed MM-patients (65 trial (TG) / 40 independent validation group (VG)) and 7 normal donors (ND) were CD138-purified by magnetic activated cell sorting. RNA was in-vitro transcribed and hybridised to Affymetrix HG U133 A+B GeneChip (TG) and HG U133 2.0 plus arrays (VG). CCND1 and CCND2 expression was verified by real time RT-PCR. iFISH was performed on purified MM-cells using probes for chromosomes 11q23, 11q13, 13q14, 17p13 and the IgH-translocations t(4;14) and t(11;14). Expression data were normalised (Bioconductor package gcrma) and nearest shrunken centroids (NSC) applied to calculate and cross validate a predictor on 40 patients of the TG with a comprehensive iFISH panel available combined with CCND overexpression. Differentially expressed genes were identified using empirical Bayes statistics for pairwise comparison. RESULTS. Overexpression of a D-type cyclin (D1 or D2) was found in 61/65 patients with MM compared to ND. CCND3 overexpression only appeared concomitantly with CCND2 overexpression. Four groups could be distinguished: (1.1) CCND1 (11q13) overexpression and trisomy 11q13, (1.2) CCND1 overexpression and translocations involving 11q13 i.e. t(11;14), (2.1) CCND2 overexpression without 11q13+, t(11;14), t(4;14), (2.2) CCND2 overexpression with t(4;14) and FGFR3 upregulation. A predictor of 6 to 566 genes correctly classifies all 40 patients of the TG (estimated cross validated error rate 0%). An independent VG of 40 patients was used. Genes with highest scores in NSC are: (1.1) CCND1, ribosomal proteins (e.g. RPL 28, 29), GPX1, CCRL2, (1.2) CCND1, TGIF, and NCAM (non-overexpression), (2.1) CCND2, (2.2) FGFR3, WHSC1, CCND2, IRTA2, SELL, and MAGED4. Distribution of clinical parameters (i.e. β2M, Durie Salmon stages, ISS) was not significantly different between the groups. The distribution of del(13)(q14q14) was (1.1) 31.5%, (1.2) 37.5%, (2.1) 37.5% and (2.2) 100%. (p<0.01). I.e. HGF, DKK1, VCAM, CD163 are differentially expressed between all 4 groups and ND (adjusted p<0.001). The groups defined by the predictor show a significantly different EFS after autologous stem cell transplantation according to the GMMG-HD3 protocol (median: (1.1) 18 / (1.2) not reached (no event) / (2.1) 22 / (2.2) 6 months; log-rank-test: p=0.004). CONCLUSION. CCND1 or CCND2 overexpression is nearly ubiquitous in MM and attributable to defined cytogenetic aberrations. Gene expression and iFISH allow a molecular classification of MM which can be predicted by gene expression profiling alone. Groups in the classification show a distinctive pattern in gene expression as well as a different EFS interpretable as risk stratification and indicator of therapeutic targets.

Immune-related gene expression deficit of leukemia stem cells (LSC) in AML.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. 7011-7011
Author(s):  
Kamal Chamoun ◽  
Christopher Brent Benton ◽  
Ahmed AlRawi ◽  
Rodrigo Jacamo ◽  
Patrick Williams ◽  
...  
Keyword(s):  
Gene Expression ◽  
Antigen Presentation ◽  
Mhc Class Ii ◽  
Antigen Processing ◽  
Differentially Expressed Gene ◽  
Enrichment Analysis ◽  
Class Ii ◽  
Differentially Expressed ◽  
Pathway Gene ◽  
Presentation Pathway

7011 Background: AML LSC are believed to be responsible for residual and resistant leukemic disease leading to relapse. Understanding differences between bulk AML and the LSC subpopulation may allow the identification of novel LSC targets, especially for the most adverse risk AML where few patients are cured. Targeting LSC may be needed to eradicate AML, and immune-based therapies provide an approach for eliminating LSC. The transcriptional landscape of immune-related genes in LSC is not well understood. Methods: Samples were collected at diagnosis from 12 patients with high-risk AML prior to therapy. Bulk (CD45-dim blasts) and LSC (Lin-CD34+CD38-CD123+) AML marrow cells were FACS-sorted and analyzed using whole genome RNA-sequencing. Transcriptomes were analyzed using AltAnalyze software to identify differentially expressed genes in bulk AML cells and in AML LSC populations. These genes were further assessed by gene enrichment analysis using data from Gene Ontology (GO) and the Cancer Genome Atlas Project (CGAP). Results: Sixty-eight genes were identified with greater than 3-fold differential expression between bulk AML and LSC. GO enrichment analysis demonstrated more than 10-fold enrichment of genes involved in the molecular functions, biologic processes, and cell components related to the antigen presentation pathway, with the comparative down-regulation occurring in LSC. Among the top differentially expressed gene clusters, both the MHC class II and interferon-gamma signaling/response pathway gene expression was blunted in LSC. Additional expression analysis revealed that 42% of a CGAP-curated list of 201 antigen-processing and -presentation genes had significantly decreased expression in the LSC subpopulation compared to bulk AML. Conclusions: LSC from primary AML patient samples are characterized by reduction in expression of MHC class II receptor and antigen presentation genes compared to bulk AML. These results suggest that impairment in the presentation and/or processing of tumor associated antigens by MHC class II on LSC, along with tonic sponging of immune response cells and diversion away from LSC by bulk AML, may contribute to LSC evasion of immune surveillance and response.

scholarly journals Genomic Profile of Fatigued Men Receiving Localized Radiation Therapy

2015 ◽  
Vol 18 (3) ◽  
pp. 281-289 ◽  
Author(s):  
Chao-Pin Hsiao ◽  
Swarnalatha Y. Reddy ◽  
Mei-Kuang Chen ◽  
Leorey N. Saligan
Keyword(s):  
Gene Expression ◽  
Prostate Cancer ◽  
Radiation Therapy ◽  
Differentially Expressed Gene ◽  
Differentially Expressed ◽  
External Beam Radiation ◽  
Cell Immune Response ◽  
Enzyme Linked Immunosorbent Assay ◽  
Beam Radiation ◽  
End Point

Purpose: The purpose of this study was to explore gene expression changes in fatigued men with nonmetastatic prostate cancer receiving localized external beam radiation therapy (EBRT). Methods: Fatigue was measured in 40 men with prostate cancer (20 receiving EBRT and 20 controls on active surveillance) using the Functional Assessment of Cancer Therapy–Fatigue (FACT-F). EBRT subjects were followed from baseline to midpoint and end point of EBRT, while controls were seen at one time point. EBRT subjects were categorized into high- and low-fatigue groups based on change in FACT-F scores from baseline to EBRT completion. Full genome microarray was performed from peripheral leukocyte RNA to determine gene expression changes related to fatigue phenotypes. Real-time polymerase chain reaction and enzyme-linked immunosorbent assay confirmed the most differentially expressed gene in the microarray experiment. Results: At baseline, mean FACT-F scores were not different between EBRT subjects (44.3 ± 7.16) and controls (46.7 ± 4.32, p = .24). Fatigue scores of EBRT subjects decreased at treatment midpoint (38.6 ± 9.17, p = .01) and completion (37.6 ± 9.9, p = .06), indicating worsening fatigue. Differential expression of 42 genes was observed between fatigue groups when EBRT time points were controlled. Membrane-spanning four domains, subfamily A, member ( MS4A1) was the most differentially expressed gene and was associated with fatigue at treatment end point ( r = −.46, p = .04). Conclusion: Fatigue intensification was associated with MS4A1 downregulation, suggesting that fatigue during EBRT may be related to impairment in B-cell immune response. The 42 differentially expressed fatigue-related genes are associated with glutathione biosynthesis, γ-glutamyl cycle, and antigen presentation pathways.

scholarly journals Characterization of cold stress responses in different rapeseed ecotypes based on metabolomics and transcriptomics analyses

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e8704 ◽  
Author(s):  
Hongju Jian ◽  
Ling Xie ◽  
Yanhua Wang ◽  
Yanru Cao ◽  
Mengyuan Wan ◽  
...  
Keyword(s):  
Gene Expression ◽  
Amino Acids ◽  
Cold Stress ◽  
Stress Responses ◽  
Differentially Expressed ◽  
Regulatory Mechanisms ◽  
Primary Metabolites ◽  
Stress Treatment ◽  
Organic Acids And Sugars

The winter oilseed ecotype is more tolerant to low temperature than the spring ecotype. Transcriptome and metabolome analyses of leaf samples of five spring Brassica napus L. (B. napus) ecotype lines and five winter B. napus ecotype lines treated at 4 °C and 28 °C were performed. A total of 25,460 differentially expressed genes (DEGs) of the spring oilseed ecotype and 28,512 DEGs of the winter oilseed ecotype were identified after cold stress; there were 41 differentially expressed metabolites (DEMs) in the spring and 47 in the winter oilseed ecotypes. Moreover, more than 46.2% DEGs were commonly detected in both ecotypes, and the extent of the changes were much more pronounced in the winter than spring ecotype. By contrast, only six DEMs were detected in both the spring and winter oilseed ecotypes. Eighty-one DEMs mainly belonged to primary metabolites, including amino acids, organic acids and sugars. The large number of specific genes and metabolites emphasizes the complex regulatory mechanisms involved in the cold stress response in oilseed rape. Furthermore, these data suggest that lipid, ABA, secondary metabolism, signal transduction and transcription factors may play distinct roles in the spring and winter ecotypes in response to cold stress. Differences in gene expression and metabolite levels after cold stress treatment may have contributed to the cold tolerance of the different oilseed ecotypes.

177 GENE EXPRESSION PROFILES OF IN VITRO- AND IN VIVO-DERIVED BOVINE EMBRYOS

2011 ◽  
Vol 23 (1) ◽  
pp. 190
Author(s):  
D. Aktoprakligil Aksu ◽  
C. Agca ◽  
S. Aksu ◽  
T. Akkoc ◽  
A. Tas Caputcu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Functional Annotation ◽  
Differentially Expressed ◽  
Annotation Tool ◽  
Bovine Embryos ◽  
University Of Missouri ◽  
Total Rna ◽  
David Functional Annotation Tool

Microarray technology is one of the most powerful tools for gene expression profiling in animal sciences. The objectives of this study were to determine the effect of vitrification on gene expression in in vitro- and in vivo-derived bovine embryos, and to identify differential mRNA expression patterns between embryos produced by in vivo v. in vitro conditions. Three pools of in vivo- and in vitro-derived blastocyst-stage embryos were used for microarray analysis. Total RNA was isolated using the PicoPure RNA Isolation Kit (Arcturus Bioscience, Mountain View, CA). Bovine ovarian tissue total RNA was used as the reference. Total RNA samples were amplified using an Ovation® Pico WTA System (NuGEN Technologies, San Carlos, CA). The bovine 16 846-member microarrays spotted with 70-mer oligonucleotides were purchased from the Bovine Genomics Laboratory, University of Missouri. Amplified cDNA samples were labeled with Alexa Fluor 647 and 546 dyes (Molecular Probes, Eugene, OR), respectively. Combined, labeled samples were dried and resuspended in hybridization buffer containing 50% formamide (vol/vol), 5× SSC, and 0.1% sodium dodecyl sulfate (wt/vol). After denaturation and cooling, cDNA was applied onto a microarray slide. Microarrays were hybridized overnight at 42°C. Following hybridization, the slides were washed with different stringency buffers and water. After drying by centrifugation, the arrays were scanned on a GenePix 4000B scanner (Axon Instruments, Union City, CA). GenePix Pro4.1 software was used for griding and analysis of spot intensities. Good-quality spots were analyzed using the GeneSpring 7.3 software (Agilent Technologies, Inc., CA, Santa Clara, CA). The data were normalized per spot and per array by Lowess normalization. When comparing two treatments, the Welch t-test with Benjamini and Hochberg multiple testing correction was performed to determine the differentially expressed genes between embryo groups. Microarray experiments were performed in 3 biological and 2 technical replicates for all embryo samples. Differentially expressed genes between all embryo groups were identified. The DAVID Functional Annotation Tool was used to analyze the genes that were differentially expressed. The DAVID Functional Annotation Tool determined the co-occurrence probability and provided gene-GO term enrichment analysis to highlight the most relevant GO terms associated with a given gene list. Differentially expressed Kyoto Encyclopedia of Genes and Genomes pathways are as follows: Ribosome, oxidative phosphorylation, spliceosome, and oocyte meiosis were significantly upregulated in the fresh embryos, whereas sphingolipid and purine metabolism was the upregulated in the vitrified in vitro-derived embryos. Gene expression was very similar between fresh and vitrified in vivo-derived, as opposed to in vitro-derived, embryos. This study was funded by the TUBITAK (Project no. KAMAG107G027) and startup funds to Yuksel Agca at the University of Missouri.

scholarly journals Data MiningMycobacterium tuberculosisPathogenic Gene Transcription Factors and Their Regulatory Network Nodes

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Guangxin Yuan ◽  
Yu Bai ◽  
Yuhang Zhang ◽  
Guangyu Xu
Keyword(s):  
Gene Expression ◽  
Transcription Factors ◽  
Expression Profile ◽  
Regulatory Network ◽  
Regulatory Element ◽  
Single Gene ◽  
Differentially Expressed Gene ◽  
Differentially Expressed ◽  
Profile Data ◽  
Extracellular Region

Tuberculosis (TB) is one of the deadliest infectious diseases worldwide. InMycobacterium tuberculosis, changes in gene expression are highly variable and involve many genes, so traditional single-gene screening ofM. tuberculosistargets has been unable to meet the needs of clinical diagnosis. In this study, using the National Center for Biotechnology Information (NCBI) GEO Datasets, whole blood gene expression profile data were obtained in patients with active pulmonary tuberculosis. Linear model-experience Bayesian statistics using the Limma package in R combined witht-tests were applied for nonspecific filtration of the expression profile data, and the differentially expressed human genes were determined. Using DAVID and KEGG, the functional analysis of differentially expressed genes (GO analysis) and the analysis of signaling pathways were performed. Based on the differentially expressed gene, the transcriptional regulatory element databases (TRED) were integrated to construct theM. tuberculosispathogenic gene regulatory network, and the correlation of the network genes with disease was analyzed with the DAVID online annotation tool. It was predicted that IL-6, JUN, and TP53, along with transcription factors SRC, TNF, and MAPK14, could regulate the immune response, with their function being extracellular region activity and protein binding during infection withM. tuberculosis.

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