دورية أكاديمية
أعرض في EDS

Novel candidate genes for environmental stresses response in Synechocystis sp. PCC 6803 revealed by machine learning algorithms.

التفاصيل البيبلوغرافية
العنوان: Novel candidate genes for environmental stresses response in Synechocystis sp. PCC 6803 revealed by machine learning algorithms.
المؤلفون: Karimi-Fard A; Department of Cell and Molecular Biology, Faculty of Life Sciences and Biotechnology, Shahid Beheshti University, Tehran, Iran., Saidi A; Department of Cell and Molecular Biology, Faculty of Life Sciences and Biotechnology, Shahid Beheshti University, Tehran, Iran. abbas.saidi@gmail.com., TohidFar M; Department of Cell and Molecular Biology, Faculty of Life Sciences and Biotechnology, Shahid Beheshti University, Tehran, Iran. gtohidfar@yahoo.com., Emami SN; Department of Molecular Biosciences, Wenner-Gren Institute, Stockholm University, Stockholm, Sweden.
المصدر: Brazilian journal of microbiology : [publication of the Brazilian Society for Microbiology] [Braz J Microbiol] 2024 Jun; Vol. 55 (2), pp. 1219-1229. Date of Electronic Publication: 2024 May 06.
نوع المنشور: Journal Article
اللغة: English
بيانات الدورية: Publisher: Springer International Publishing Country of Publication: Brazil NLM ID: 101095924 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1678-4405 (Electronic) Linking ISSN: 15178382 NLM ISO Abbreviation: Braz J Microbiol Subsets: MEDLINE
أسماء مطبوعة: Publication: 2019- : Switzerland, AG : Springer International Publishing
Original Publication: Rio de Janeiro, RJ, Brasil : Sociedade Brasileira de Microbiologia
مواضيع طبية MeSH: Synechocystis*/genetics , Synechocystis*/physiology , Stress, Physiological*/genetics , Machine Learning* , Algorithms*, Gene Expression Regulation, Bacterial ; Bacterial Proteins/genetics ; Bacterial Proteins/metabolism ; Transcriptome ; Computational Biology/methods ; Support Vector Machine ; Gene Expression Profiling ; Light ; Genes, Bacterial
مستخلص: Cyanobacteria have developed acclimation strategies to adapt to harsh environments, making them a model organism. Understanding the molecular mechanisms of tolerance to abiotic stresses can help elucidate how cells change their gene expression patterns in response to stress. Recent advances in sequencing techniques and bioinformatics analysis methods have led to the discovery of many genes involved in stress response in organisms. The Synechocystis sp. PCC 6803 is a suitable microorganism for studying transcriptome response under environmental stress. Therefore, for the first time, we employed two effective feature selection techniques namely and support vector machine recursive feature elimination (SVM-RFE) and LASSO (Least Absolute Shrinkage Selector Operator) to pinpoint the crucial genes responsive to environmental stresses in Synechocystis sp. PCC 6803. We applied these algorithms of machine learning to analyze the transcriptomic data of Synechocystis sp. PCC 6803 under distinct conditions, encompassing light, salt and iron stress conditions. Seven candidate genes namely sll1862, slr0650, sll0760, slr0091, ssl3044, slr1285, and slr1687 were selected by both LASSO and SVM-RFE algorithms. RNA-seq analysis was performed to validate the efficiency of our feature selection approach in selecting the most important genes. The RNA-seq analysis revealed significantly high expression for five genes namely sll1862, slr1687, ssl3044, slr1285, and slr0650 under ion stress condition. Among these five genes, ssl3044 and slr0650 could be introduced as new potential candidate genes for further confirmatory genetic studies, to determine their roles in their response to abiotic stresses.
(© 2024. The Author(s) under exclusive licence to Sociedade Brasileira de Microbiologia.)
References: Biology (Basel). 2023 Jul 13;12(7):. (PMID: 37508427)
BMC Syst Biol. 2010 Aug 02;4:105. (PMID: 20678200)
Free Radic Biol Med. 2019 Aug 20;140:206-223. (PMID: 31078731)
Plant Physiol Biochem. 2020 Oct;155:136-146. (PMID: 32750653)
J Biol Chem. 2020 Jul 10;295(28):9445-9454. (PMID: 32409585)
PLoS One. 2011;6(7):e21750. (PMID: 21789178)
Biochim Biophys Acta Bioenerg. 2021 Jan 1;1862(1):148327. (PMID: 33069682)
Mol Cells. 2012 Jul;34(1):43-52. (PMID: 22699755)
DNA Res. 2014 Oct;21(5):527-39. (PMID: 24935866)
Microb Ecol. 2016 May;71(4):887-900. (PMID: 26841797)
BMC Bioinformatics. 2018 Oct 29;19(1):396. (PMID: 30373514)
Science. 1999 Oct 15;286(5439):531-7. (PMID: 10521349)
Plants (Basel). 2022 Nov 24;11(23):. (PMID: 36501264)
Bioinformatics. 2012 Mar 15;28(6):882-3. (PMID: 22257669)
Plant Cell Physiol. 2019 Feb 1;60(2):436-447. (PMID: 30407607)
Biol Direct. 2021 Dec 14;16(1):26. (PMID: 34906211)
J Biosci Bioeng. 2018 Nov;126(5):596-602. (PMID: 29907527)
Plant Physiol. 2008 Sep;148(1):467-78. (PMID: 18599646)
Commun Biol. 2021 Mar 16;4(1):343. (PMID: 33727624)
Plant Cell. 2019 Dec;31(12):2912-2928. (PMID: 31615847)
Water Air Soil Pollut. 2023;234(5):313. (PMID: 37192997)
Life (Basel). 2019 Aug 20;9(3):. (PMID: 31434306)
Genome Biol. 2014;15(12):550. (PMID: 25516281)
Bot Stud. 2017 Dec;58(1):22. (PMID: 28510204)
Cancer Genomics Proteomics. 2018 Jan-Feb;15(1):41-51. (PMID: 29275361)
Mol Cell Proteomics. 2014 Dec;13(12):3519-32. (PMID: 25239498)
J Biol Chem. 2005 Jun 3;280(22):21531-8. (PMID: 15805106)
Proc Natl Acad Sci U S A. 2003 Jul 22;100(15):9061-6. (PMID: 12853569)
J Biomed Inform. 2011 Aug;44(4):529-35. (PMID: 21241823)
BMC Bioinformatics. 2013 Dec 21;14:368. (PMID: 24359104)
Life (Basel). 2020 Nov 26;10(12):. (PMID: 33256109)
J Biol Chem. 2004 Dec 17;279(51):53078-86. (PMID: 15471853)
Biochem J. 1995 May 15;308 ( Pt 1):1-8. (PMID: 7755550)
Sensors (Basel). 2010;10(3):2386-415. (PMID: 22294932)
Biochim Biophys Acta. 2014 Dec;1837(12):1990-1997. (PMID: 25261790)
BMJ Open Respir Res. 2018 Jan 30;5(1):e000240. (PMID: 29468073)
Nucleic Acids Res. 2019 Jan 8;47(D1):D607-D613. (PMID: 30476243)
Bioinformatics. 2001 Jun;17(6):509-19. (PMID: 11395427)
Plant Physiol. 2005 Jul;138(3):1409-21. (PMID: 15965020)
Arch Microbiol. 2002 Oct;178(4):256-66. (PMID: 12209258)
Int J Biol Sci. 2022 Jan 1;18(1):360-373. (PMID: 34975338)
فهرسة مساهمة: Keywords: Cyanobacteria; Environmental stresses; Gene expression; LASSO; Machine learning; SVM- RFE
المشرفين على المادة: 0 (Bacterial Proteins)
تواريخ الأحداث: Date Created: 20240505 Date Completed: 20240605 Latest Revision: 20240608
رمز التحديث: 20240608
مُعرف محوري في PubMed: PMC11153407
DOI: 10.1007/s42770-024-01338-6
PMID: 38705959
قاعدة البيانات: MEDLINE
الوصف
تدمد:1678-4405
DOI:10.1007/s42770-024-01338-6