Meta Analysis of Sugar Beet (Beta vulgaris L.) Transcriptome Profiles Under Different Biotic and Abiotic Stress Conditions

Burak Bulut, Songül Gürel, Ömer Can Ünüvar, Ekrem Gürel, Yunus Şahin, Uğur Çabuk, Ercan Selçuk Ünlü*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Sugar beet (Beta vulgaris L.) meets the 21% of world sugar production. Soil pollution, biotic and abiotic factors in production areas greatly reduce product quantity and quality. Sugar beet responds to biotic and abiotic stresses such as drought, salt, heat, light, and infections of nematode, bacteria and fungi at the molecular level. Understanding molecular mechanisms require comprehensive genomics studies in order to control these mechanisms to increase the yield and quality. Transcriptome studies performed under stress conditions can shed light on the responses of plants at the molecular level. In addition, meta-analysis can help to find common responses under different stress conditions. In this study four different stress-related transcriptome data were used: two of them are related with biotic stress (nematode and fungi infection) and two of them are related with abiotic stress (ABA treatment and salt stress). In this study, we performed meta-analysis of studies conducted under biotic and abiotic stress conditions. Our results revealed 460 commonly regulated genes from biotic stress related data and 1031 commonly regulated genes from abiotic stress related data. Our data also showed that expression of ten genes is controlled regardless of the type of stress condition. The data can be useful for understanding the molecular aspect of adaptive stress response in sugar beet.

Original languageEnglish
Pages (from-to)199-207
Number of pages9
JournalTropical Plant Biology
Volume16
Issue number3
DOIs
Publication statusPublished - Sept 2023
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

Keywords

  • Beta vulgaris
  • Meta analysis
  • RNA-Seq
  • Sugar beet
  • Transcriptome

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