scholarly journals Differential gene expression in scrub typhus compared to other acute febrile infections by bioinformatic approaches

Author(s):  
Manisha Mandal ◽  
Shyamapada Mandal

AbstractScrub typhus (ST), caused with the infection of Orientia tsutsugamushi, without eschar, is a febrile illness that mimics malaria (ML), dengue (DG), and other rickettsioses such as murine typhus (MT). Comparative analysis of microarray gene expression profiles of GSE16463 dataset, from O. tsutsugamushi infected monocytes, was performed to identify transcriptional signatures in ST discriminated from other acute febrile infections, accompanied by functional pathways and enrichment analysis in disease pathogenesis. A unique 31 ST-associated signature genes obtained in this study could help distinguish ST from other febrile illnesses DG, ML and MT. The functional pathways significantly enriched in ST disease group included translocation of ZAP-70 to immunological synapse, and phosphorylation of CD3 and TCR zeta chains, involving PTPN22 and CD3G genes, which could further help in the understanding of molecular pathophysiology of ST and discovering novel drug targets as well as vaccine developments.

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Baojie Wu ◽  
Shuyi Xi

Abstract Background This study aimed to explore and identify key genes and signaling pathways that contribute to the progression of cervical cancer to improve prognosis. Methods Three gene expression profiles (GSE63514, GSE64217 and GSE138080) were screened and downloaded from the Gene Expression Omnibus database (GEO). Differentially expressed genes (DEGs) were screened using the GEO2R and Venn diagram tools. Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed. Gene set enrichment analysis (GSEA) was performed to analyze the three gene expression profiles. Moreover, a protein–protein interaction (PPI) network of the DEGs was constructed, and functional enrichment analysis was performed. On this basis, hub genes from critical PPI subnetworks were explored with Cytoscape software. The expression of these genes in tumors was verified, and survival analysis of potential prognostic genes from critical subnetworks was conducted. Functional annotation, multiple gene comparison and dimensionality reduction in candidate genes indicated the clinical significance of potential targets. Results A total of 476 DEGs were screened: 253 upregulated genes and 223 downregulated genes. DEGs were enriched in 22 biological processes, 16 cellular components and 9 molecular functions in precancerous lesions and cervical cancer. DEGs were mainly enriched in 10 KEGG pathways. Through intersection analysis and data mining, 3 key KEGG pathways and related core genes were revealed by GSEA. Moreover, a PPI network of 476 DEGs was constructed, hub genes from 12 critical subnetworks were explored, and a total of 14 potential molecular targets were obtained. Conclusions These findings promote the understanding of the molecular mechanism of and clinically related molecular targets for cervical cancer.


2016 ◽  
Vol 32 (1) ◽  
pp. 70-79 ◽  
Author(s):  
S. A. Babichev ◽  
A. I. Kornelyuk ◽  
V. I. Lytvynenko ◽  
V. V. Osypenko

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Chenlei Zheng ◽  
Cheng Wang ◽  
Tan Zhang ◽  
Ding Li ◽  
Xiao-feng Ni ◽  
...  

Objective. Posttransplantation diabetes mellitus (PTDM) is a known complication of transplantation that affects the prognosis. Tacrolimus (Tac or FK506) is a widely used immunosuppressant that has been reported to be a risk factor for PTDM and to further induce complications in heart and skeletal muscles, but the mechanism is still largely unknown. In our preliminary experiments, we found that after Tac treatment, blood glucose increased, and the weight of skeletal muscle declined. Here, we hypothesize that tacrolimus can induce PTDM and influence the atrophy of skeletal muscle. Methods. We designed preliminary experiments to establish a tacrolimus-induced PTDM model. Gene expression profiles in quadriceps muscle from this rat model were characterized by oligonucleotide microarrays. Then, differences in gene expression profiles in muscle from PTDM rats that received tacrolimus and control subjects were analyzed by using GeneSpring GX 11.0 software (Agilent). Functional annotation and enrichment analysis of differentially expressed genes (DEGs) helped us identify clues for the side effects of tacrolimus. Results. Our experiments found that the quadriceps in tacrolimus-induced PTDM group were smaller than those in the control group. The study identified 275 DEGs that may be responsible for insulin resistance and the progression of PTDM, including 86 upregulated genes and 199 downregulated genes. GO and KEGG functional analysis of the DEGs showed a significant correlation between PTDM and muscle development. PPI network analysis screened eight hub genes and found that they were related to troponin and tropomyosin. Conclusions. This study explored the molecular mechanism of muscle atrophy in a tacrolimus-induced PTDM model by bioinformatics analyses. We identified 275 DEGs and identified significant biomarkers for predicting the development and progression of tacrolimus-induced PTDM.


2019 ◽  
Author(s):  
Kulwadee Thanamit ◽  
Franziska Hoerhold ◽  
Marcus Oswald ◽  
Rainer Koenig

ABSTRACTFinding drug targets for antimicrobial treatment is a central focus in biomedical research. To discover new drug targets, we developed a method to identify which nutrients are essential for microorganisms. Using 13C labeled metabolites to infer metabolic fluxes is the most informative way to infer metabolic fluxes to date. However, the data can get difficult to acquire in complicated environments, for example, if the pathogen homes in host cells. Although data from gene expression profiling is less informative compared to metabolic tracer derived data, its generation is less laborious, and may still provide the relevant information. Besides this, metabolic fluxes have been successfully predicted by flux balance analysis (FBA). We developed an FBA based approach using the stoichiometric knowledge of the metabolic reactions of a cell combining them with expression profiles of the coding genes. We aimed to identify essential drug targets for specific nutritional uptakes of microorganisms. As a case study, we predicted each single carbon source out of a pool of eight different carbon sources for B. subtilis based on gene expression profiles. The models were in good agreement to models basing on 13C metabolic flux data of the same conditions. We could well predict every carbon source. Later, we applied successfully the model to unseen data from a study in which the carbon source was shifted from glucose to malate and vice versa. Technically, we present a new and fast method to reduce thermodynamically infeasible loops, which is a necessary preprocessing step for such model-building algorithms.SIGNIFICANCEIdentifying metabolic fluxes using 13C labeled tracers is the most informative way to gain insight into metabolic fluxes. However, obtaining the data can be laborious and challenging in a complex environment. Though transcriptional data is an indirect mean to estimate the fluxes, it can help to identify this. Here, we developed a new method employing constraint-based modeling to predict metabolic fluxes embedding gene expression profiles in a linear regression model. As a case study, we used the data from Bacillus subtilis grown under different carbon sources. We could well predict the correct carbon source. Additionally, we established a novel and fast method to remove thermodynamically infeasible loops.


BioTechniques ◽  
2003 ◽  
Vol 35 (4) ◽  
pp. 812-814 ◽  
Author(s):  
Crispin J. Miller ◽  
Heba S. Kassem ◽  
Stuart D. Pepper ◽  
Yvonne Hey ◽  
Timothy H. Ward ◽  
...  

Viruses ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 404 ◽  
Author(s):  
Claudia Cava ◽  
Gloria Bertoli ◽  
Isabella Castiglioni

Previous studies reported that Angiotensin converting enzyme 2 (ACE2) is the main cell receptor of SARS-CoV and SARS-CoV-2. It plays a key role in the access of the virus into the cell to produce the final infection. In the present study we investigated in silico the basic mechanism of ACE2 in the lung and provided evidences for new potentially effective drugs for Covid-19. Specifically, we used the gene expression profiles from public datasets including The Cancer Genome Atlas, Gene Expression Omnibus and Genotype-Tissue Expression, Gene Ontology and pathway enrichment analysis to investigate the main functions of ACE2-correlated genes. We constructed a protein-protein interaction network containing the genes co-expressed with ACE2. Finally, we focused on the genes in the network that are already associated with known drugs and evaluated their role for a potential treatment of Covid-19. Our results demonstrate that the genes correlated with ACE2 are mainly enriched in the sterol biosynthetic process, Aryldialkylphosphatase activity, adenosylhomocysteinase activity, trialkylsulfonium hydrolase activity, acetate-CoA and CoA ligase activity. We identified a network of 193 genes, 222 interactions and 36 potential drugs that could have a crucial role. Among possible interesting drugs for Covid-19 treatment, we found Nimesulide, Fluticasone Propionate, Thiabendazole, Photofrin, Didanosine and Flutamide.


Blood ◽  
2014 ◽  
Vol 124 (21) ◽  
pp. 3360-3360
Author(s):  
Erik Wendlandt ◽  
Guido J. Tricot ◽  
Benjamin Darbro ◽  
Fenghuang Zhan

Abstract Background: Multiple myeloma is the second most common blood borne neoplasia, accounting for nearly 10% of all diagnosed hematologic malignancies and has a disproportionately high incidence in elderly populations. Here we explored copy number variations using the high fidelity CytoScan HD arrays to develop a detailed map of copy number variations and identify novel mediators of disease progression. The results from CytoScan HD microarrays provide a detailed view of the entire genome with a resolution up to 25kb. Furthermore, 750,000 single-nucleotide polymorphisms are included and the array provides information about loss of heterozygosity and uniparental disomy. Materials and methods: CytoScan HD arrays were performed on 97 myeloma patient samples to identify cytogenetic regions important to the development and progression of the disease. Gene expression profiles from 351 patients were analyzed to identify genes with a change in gene expression of 1.5 fold or more. Data from CytoScan and gene expression arrays was combined to perform chromosomal positional enrichment analysis to identify cytogenetic driver lesions, or lesions that provide a small, but significant growth and survival advantage to the cell. Furthermore, Kaplan-Meier, log-rank test and Hazard ratio analyses were performed to identify gene within the driver lesions that have a significant impact on survival when dysregulated. Results: The results from the CytoScan HD analysis closely mirrored what has been shown by FISH and SNP arrays, with gains to the odd numbered chromosomes, specifically 3, 5, 7, 9, 11, 15 and 17 as well as losses to chromosomes 1p and 13. Interestingly, we identified gains to a small region within chromosome 8p, contrary to published reports demonstrating a large scale loss of this region. We identified numerous genes within this region that are important for survival and their overexpression resulted in a decreased progression free survival. For example, Cathepsin B (CTSB) is encoded for in chromosome 8p22-p21 with an increased gene expression of at least 1.5 fold over normal controls, among others. Furthermore, Cathepsin B, a cysteine protease, has been linked to cancer of the ileum, suggesting that a similar role may be present within myeloma. We then integrated the 97 copy number profiles results with 351 myeloma gene expression profiles to identify cytogenetic driver lesions in myeloma important for disease development, progression and poor clinical outcome. Chromosomal positional enrichment analysis was employed to identify global myeloma cytogenetic driver aneuploidies as well as develop unique cytogenetic copy number profiles. Our results identified portions of chromosomes 1q, 3, 8p, 9, 13q and 16q, among others, as important driver lesions with changes to these regions providing growth advantages to the cell. Furthermore, our analysis identified five unique cytogenetic classifications based on common cytogenetic lesions. We continue to explore these driver regions to identify lesions important for the oncogenic properties of the larger regions. Conclusion: The data presented here represents a novel and highly sensitive approach for the identification of novel copy number variations and driver lesions. Furthermore, correlations between copy number variations and gene expression arrays identified novel targets important for disease progression and patient survival. CytoScan HD arrays in conjunction with gene expression analysis provided a high resolution image of important cytogenetic lesions in myeloma and identified potentially important therapeutic targets for drug development. Further work is needed to validate our findings and determine the therapeutic efficacy of the identified targets. Disclosures No relevant conflicts of interest to declare.


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