<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Obesity has become a serious global problem that still needs a solution. One of the factors that leads to obesity is genetic predisposition. The identity and characteristics of the genes involved have not yet been fully confirmed. Analyzing the genetic contribution to obesity is a major step towards the solution. In this <italic>in silico</italic> study, using online bioinformatics tools, we evaluate the role of four genes that are believed to contribute to obesity. Data were collected and analyzed for the sequences of four so-called obesity genes: <italic>FTO</italic> (fat mass and obesity-associated protein), <italic>PPARG</italic> (peroxisome proliferator activated receptor γ), <italic>ADRB3</italic> (adrenergic receptor β 3) and <italic>FABP2</italic> (fatty acid binding protein 2). In the first part of the research, information about the genes was collected and organized and data in FASTA, format are extracted from the National Center for Biotechnology Information (NCBI). In the second part, all genes were analyzed by comparing three species of organisms, <italic>Homo sapiens</italic> (human), <italic>Mus musculus</italic> (mouse) and <italic>Gallus</italic> (chicken). In the third part of this study, phylogenetic trees were constructed for each of the four genes, using blast local alignment search tool (BLAST) and molecular evolutionary genetics analysis (MEGA X) software. Our analysis reveals that the functions of all these genes are associated with overweight and obesity.</p></abstract>

Obesity has become a serious global problem that still needs a solution. One of the factors that leads to obesity is genetic predisposition. The identity and characteristics of the genes involved have not yet been fully confirmed. Analyzing the genetic contribution to obesity is a major step towards the solution. In this in silico study, using online bioinformatics tools, we evaluate the role of four genes that are believed to contribute to obesity. Data were collected and analyzed for the sequences of four so-called obesity genes: FTO (fat mass and obesity-associated protein), PPARG (peroxisome proliferator activated receptor γ), ADRB3 (adrenergic receptor β 3) and FABP2 (fatty acid binding protein 2). In the first part of the research, information about the genes was collected and organized and data in FASTA, format are extracted from the National Center for Biotechnology Information (NCBI). In the second part, all genes were analyzed by comparing three species of organisms, Homo sapiens (human), Mus musculus (mouse) and Gallus (chicken). In the third part of this study, phylogenetic trees were constructed for each of the four genes, using blast local alignment search tool (BLAST) and molecular evolutionary genetics analysis (MEGA X) software. Our analysis reveals that the functions of all these genes are associated with overweight and obesity.

Comparative analysis of genes associated with obesity in humans using bioinformatic data and tools

Balkan Journal of Medical Genetics

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Obesity has become a serious global problem that still needs a solution. One of the factors that leads to obesity is genetic predisposition. The identity and...

Comparative analysis of genes associated with obesity in humans using bioinformatic data and tools

Article Category: Original Article

Published Online: Jul 27, 2021

Page range: 35 - 40

DOI: https://doi.org/10.2478/bjmg-2021-0012

Keywords
Bioinformatics, Genes, Obesity, Phylogenetic trees

© 2021 Musliji ZS, Pollozhani AK, Lisichkov K, Deligios M, Popovski ZT, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Comparative analysis of genes associated with obesity in humans using bioinformatic data and tools

Article Category: Original Article

Published Online: Jul 27, 2021

Page range: 35 - 40

DOI: https://doi.org/10.2478/bjmg-2021-0012

KeywordsBioinformatics, Genes, Obesity, Phylogenetic trees

© 2021 Musliji ZS, Pollozhani AK, Lisichkov K, Deligios M, Popovski ZT, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Keywords
Bioinformatics, Genes, Obesity, Phylogenetic trees