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FlyDEGdb knowledge base on differentially expressed genes of Drosophila melanogaster, a model object in biomedicine

https://doi.org/10.18699/vjgb-25-101

Abstract

   Since the work of Nobel Prize winner Thomas Morgan in 1909, the fruit fly Drosophila melanogaster has been one of the most popular model animals in genetics. Research using this fly was honored with the Nobel Prize many times: in 1946 (Muller, X-ray mutagenesis), in 1995 (Lewis, Nüsslein-Volhard, Wieschaus, genetic control of embryogenesis), in 2004 (Axel and Buck, the olfactory system), in 2011 (Steinman, dendritic cells in adaptive immunity; Beutler and Hoffman, activation of innate immunity), and in 2017 (Hall, Rosbash and Young, the molecular mechanism of the circadian rhythm). The prominent role of Drosophila in genetics is due to its key features: short life cycle, frequent generational turnover, ease of maintenance, high fertility, small size, transparent embryos, simple larval structure, the possibility to observe visually chromosomal rearrangements due to the presence of polytene chromosomes, and accessibility to molecular genetic manipulation. Furthermore, the highly conserved nature of several signaling pathways and gene networks in Drosophila and their similarity to those of mammals and humans, taken together with the development of high-throughput genomic sequencing, motivated the use of D. melanogaster as a model organism in biomedical fields of inquiry: pharmacology, toxicology, cardiology, oncology, immunology, gerontology, and radiobiology. These studies add to the understanding of the genetic and epigenetic basis of the pathogenesis of human diseases. This paper describes our curated knowledge base, FlyDEGdb (https://www.sysbio.ru/FlyDEGdb), which stores information on differentially expressed genes (DEGs) in Drosophila. This information was extracted from 50 scientific articles containing experimental data on changes in the expression of 20,058 genes (80 %) out of the 25,079 Drosophila genes stored in the NCBI Gene database. The changes were induced by 52 stress factors, including heat and cold exposure, dehydration, heavy metals, radiation, starvation, household chemicals, drugs, fertilizers, insecticides, pesticides, herbicides, and other toxicants. The FlyDEGdb knowledge base is illustrated using the example of the dysf (dysfusion) Drosophila gene, which had been identified as a DEG under cold shock and in toxicity tests of the herbicide paraquat, the solvent toluene, the drug menadione, and the food additive E923. FlyDEGdb stores information on changes in the expression of the dysf gene and its homologues: (a) the Clk, cyc, and per genes in Drosophila, and (b) the NPAS4, CLOCK, BMAL1, PER1, and PER2 genes in humans. These data are supplemented with information on the biological processes in which these genes are involved: oocyte maturation (oogenesis), regulation of stress response and circadian rhythm, carcinogenesis, aging, etc. Therefore, FlyDEGdb, containing information on the widely used model organism, Drosophila, can be helpful for researchers working in the molecular biology and genetics of humans and animals, physiology, translational medicine, pharmacology, dietetics, agricultural chemistry, radiobiology, toxicology, and bioinformatics.

About the Authors

O. A. Podkolodnaya
Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences
Russian Federation

Novosibirsk



M. A. Deryuzhenko
Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences
Russian Federation

Novosibirsk



N. N. Tverdokhleb
Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences
Russian Federation

Novosibirsk



K. A. Zolotareva
Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences
Russian Federation

Novosibirsk



Yu. V. Makovka
Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences
Russian Federation

Novosibirsk



N. L. Podkolodny
Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences; Institute of Computational Mathematics and Mathematical Geophysics of the Siberian Branch of the Russian Academy of Sciences
Russian Federation

Novosibirsk



V. V. Suslov
Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences
Russian Federation

Novosibirsk



I. V. Chadaeva
Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences
Russian Federation

Novosibirsk



L. A. Fedoseeva
Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences
Russian Federation

Novosibirsk



A. A. Seryapina
Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences
Russian Federation

Novosibirsk



D. Yu. Oshchepkov
Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences
Russian Federation

Novosibirsk



A. G. Bogomolov
Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences
Russian Federation

Novosibirsk



E. Yu. Kondratyuk
Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences; Siberian Federal Scientific Centre of Agro-BioTechnologies of the Russian Academy of Sciences
Russian Federation

Novosibirsk; Novosibirsk region; Krasnoobsk



O. E. Redina
Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences
Russian Federation

Novosibirsk



A. L. Markel
Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences; Novosibirsk State University
Russian Federation

Novosibirsk



N. E. Gruntenko
Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences
Russian Federation

Novosibirsk



M. P. Ponomarenko
Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences
Russian Federation

Novosibirsk



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