Investigation of candidate genes and mechanisms underlying obesity associated type
      2 diabetes mellitus using bioinformatics analysis and screening of small drug molecules

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Abstract

Background

Obesity associated type 2 diabetes mellitus is a metabolic disorder ; however, the etiology of obesity associated type 2 diabetes mellitus remains largely unknown. There is an urgent need to further broaden the understanding of the molecular mechanism associated in obesity associated type 2 diabetes mellitus.

Methods

To screen the differentially expressed genes (DEGs) that might play essential roles in obesity associated type 2 diabetes mellitus, the publicly available expression profiling by high throughput sequencing data (GSE143319) was downloaded and screened for DEGs. Then, Gene Ontology (GO) and REACTOME pathway enrichment analysis were performed. The protein - protein interaction network, miRNA - target genes regulatory network and TF-target gene regulatory network were constructed and analyzed for identification of hub and target genes. The hub genes were validated by receiver operating characteristic (ROC) curve analysis and RT- PCR analysis. Finally, a molecular docking study was performed on over expressed proteins to predict the target small drug molecules.

Results

A total of 820 DEGs were identified between healthy obese and metabolically unhealthy obese, among 409 up regulated and 411 down regulated genes. The GO enrichment analysis results showed that these DEGs were significantly enriched in ion transmembrane transport, intrinsic component of plasma membrane, transferase activity, transferring phosphorus-containing groups, cell adhesion, integral component of plasma membrane and signaling receptor binding, whereas, the REACTOME pathway enrichment analysis results showed that these DEGs were significantly enriched in integration of energy metabolism and extracellular matrix organization. The hub genes CEBPD, TP73, ESR2, TAB1, MAP 3K5, FN1, UBD, RUNX1, PIK3R2 and TNF, which might play an essential role in obesity associated type 2 diabetes mellitus was further screened.

Conclusions

The present study could deepen the understanding of the molecular mechanism of obesity associated type 2 diabetes mellitus, which could be useful in developing therapeutic targets for obesity associated type 2 diabetes mellitus.

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Most cited references 166

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Cytoscape: a software environment for integrated models of biomolecular interaction networks.

Paul Shannon, Andrew Markiel, Owen Ozier … (2003)

Cytoscape is an open source software project for integrating biomolecular interaction networks with high-throughput expression data and other molecular states into a unified conceptual framework. Although applicable to any system of molecular components and interactions, Cytoscape is most powerful when used in conjunction with large databases of protein-protein, protein-DNA, and genetic interactions that are increasingly available for humans and model organisms. Cytoscape's software Core provides basic functionality to layout and query the network; to visually integrate the network with expression profiles, phenotypes, and other molecular states; and to link the network to databases of functional annotations. The Core is extensible through a straightforward plug-in architecture, allowing rapid development of additional computational analyses and features. Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.

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limma powers differential expression analyses for RNA-sequencing and microarray studies

Matthew E. Ritchie, Belinda Phipson, Di Wu … (2015)

limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.

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pROC: an open-source package for R and S+ to analyze and compare ROC curves

Xavier Robin, Natacha Turck, Alexandre Hainard … (2013)

Background Receiver operating characteristic (ROC) curves are useful tools to evaluate classifiers in biomedical and bioinformatics applications. However, conclusions are often reached through inconsistent use or insufficient statistical analysis. To support researchers in their ROC curves analysis we developed pROC, a package for R and S+ that contains a set of tools displaying, analyzing, smoothing and comparing ROC curves in a user-friendly, object-oriented and flexible interface. Results With data previously imported into the R or S+ environment, the pROC package builds ROC curves and includes functions for computing confidence intervals, statistical tests for comparing total or partial area under the curve or the operating points of different classifiers, and methods for smoothing ROC curves. Intermediary and final results are visualised in user-friendly interfaces. A case study based on published clinical and biomarker data shows how to perform a typical ROC analysis with pROC. Conclusions pROC is a package for R and S+ specifically dedicated to ROC analysis. It proposes multiple statistical tests to compare ROC curves, and in particular partial areas under the curve, allowing proper ROC interpretation. pROC is available in two versions: in the R programming language or with a graphical user interface in the S+ statistical software. It is accessible at http://expasy.org/tools/pROC/ under the GNU General Public License. It is also distributed through the CRAN and CSAN public repositories, facilitating its installation.

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Author and article information

Contributors

Chanabasayya Vastrad:

ORCID: https://orcid.org/0000-0003-3615-4450

channu.vastrad@gmail.com

Journal

Journal ID (nlm-ta): BMC Endocr Disord

Journal ID (iso-abbrev): BMC Endocr Disord

Title: BMC Endocrine Disorders

Publisher: BioMed Central (London )

ISSN (Electronic): 1472-6823

Publication date (Electronic): 26 April 2021

Publication date PMC-release: 26 April 2021

Publication date Collection: 2021

Volume: 21

Electronic Location Identifier: 80

Affiliations

[1 ]Department of General Medicine, Basaveshwara Medical College, Chitradurga, Karnataka 577501 India

[2 ]Department of Biochemistry, Basaveshwar College of Pharmacy, Gadag, Karnataka 582103 India

[3 ]GRID grid.411962.9, ISNI 0000 0004 1761 157X, Department of Pharmaceutical Chemistry, JSS College of Pharmacy, , Mysuru and JSS Academy of Higher Education & Research, ; Mysuru, Karnataka 570015 India

[4 ]Biostatistics and Bioinformatics, Chanabasava Nilaya, Bharthinagar, Dharwad, Karnataka 580001 India

[5 ]Department of Ayurveda, Rajiv Gandhi Education Society`s Ayurvedic Medical College, Ron, Karnataka 582209 India

Author information

G. Prashanth https://orcid.org/0000-0003-2571-5734

Basavaraj Vastrad https://orcid.org/0000-0003-2202-7637

Anandkumar Tengli https://orcid.org/0000-0001-8076-928X

Chanabasayya Vastrad https://orcid.org/0000-0003-3615-4450

Iranna Kotturshetti https://orcid.org/0000-0003-1988-7345

Article

Publisher ID: 718

DOI: 10.1186/s12902-021-00718-5

PMC ID: 8074411

PubMed ID: 33902539

SO-VID: 7458d426-657e-4840-89d2-ce906fb9f11e

License:

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

History

Date received : 4 December 2020

Date accepted : 2 March 2021

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ScienceOpen disciplines: Endocrinology & Diabetes

Keywords: obesity associated type 2 diabetes mellitus,differentially expressed gene,pathway,protein-protein interaction network,mirna-target genes regulatory network

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ScienceOpen disciplines: Endocrinology & Diabetes

Keywords: obesity associated type 2 diabetes mellitus, differentially expressed gene, pathway, protein-protein interaction network, mirna-target genes regulatory network

Investigation of candidate genes and mechanisms underlying obesity associated type 2 diabetes mellitus using bioinformatics analysis and screening of small drug molecules

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Journal of the ASEAN Federation of Endocrine Societies

Most cited references 166

Cytoscape: a software environment for integrated models of biomolecular interaction networks.

limma powers differential expression analyses for RNA-sequencing and microarray studies

pROC: an open-source package for R and S+ to analyze and compare ROC curves

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