Clustering and evolutionary analysis of small RNAs identify regulatory siRNA clusters induced under drought stress in rice.
BACKGROUND
Drought tolerance is an important trait related to growth and yield in crop. Until now, drought related research has focused on coding genes. However, non-coding RNAs also respond significantly to environmental stimuli such as drought stress. Unfortunately, characterizing the role of siRNAs under drought stress is difficult since a large number of heterogenous siRNA species are expressed under drought stress and non-coding RNAs have very weak evolutionary conservation. Thus, to characterize the role of siRNAs, we need a well designed biological and bioinformatics strategy. In this paper, to characterize the function of siRNAs we developed and used a bioinformatics pipeline that includes a genomic-location based clustering technique and an evolutionary conservation tool.


RESULTS
By comparing the wild type Nipponbare and two drought resistant rice varities, we found that 21 nt and 24 nt siRNAs are significantly expressed in the three rice plants but at different time points under a short-term (0, 1, and 6 hrs) drought treatment. siRNAs were up-regulated in the wild type at an early stage while the up-regulation was delayed in the two drought tolerant plants. Genes targeted by up-regulated siRNAs were related to oxidation reduction and proteolysis, which are well known to be associated with water deficit phenotypes. More interestingly, we found that siRNAs were located in intronic regions as clusters and were of high evolutionary conservation among monocot grass plants. In summary, we show that siRNAs are important respondents to drought stress and regulate genes related to the drought tolerance in water deficit conditions.
Interdisciplinary Program in Bioinformatics, Seoul National University, Gwanak-Gu, Seoul, 151-747, Republic of Korea.
Inuk
I
Jung
Department of Computer Science and Engineering, Seoul National University, Gwanak-Gu, Seoul, 151-747, Republic of Korea.
Hongryul
H
Ahn
Department of Biomedical Sciences, Sunmoon University, Asan, 336-708, Republic of Korea.
Seon-Ju
SJ
Shin
Crop Biotechnology Institute, Green Bio Science & Technology, Seoul National University, Gangwon, Republic of Korea.
Jukon
J
Kim
Department of Biomedical Sciences, Sunmoon University, Asan, 336-708, Republic of Korea.
Hawk-Bin
HB
Kwon
Department of Applied Bioscience, Konkuk University, Gwangjin-Gu, Seoul, Republic of Korea.
Woosuk
W
Jung
Interdisciplinary Program in Bioinformatics, Seoul National University, Gwanak-Gu, Seoul, 151-747, Republic of Korea. sunkim.bioinfo@snu.ac.kr.|Department of Computer Science and Engineering, Seoul National University, Gwanak-Gu, Seoul, 151-747, Republic of Korea. sunkim.bioinfo@snu.ac.kr.|Bioinformatics Institute, Seoul National University, Gwanak-Gu, Seoul, 151-747, Republic of Korea. sunkim.bioinfo@snu.ac.kr.
Sun
S
Kim
D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D017124:Conserved Sequence; D055864:Droughts; D019143:Evolution, Molecular; D059372:Nucleotide Motifs; D012275:Oryza / Q000235:genetics* / Q000502:physiology*; D012333:RNA, Messenger / Q000235:genetics / Q000378:metabolism; D034741:RNA, Small Interfering / Q000235:genetics*; D013312:Stress, Physiological / Q000235:genetics*
D012333:RNA, Messenger; D034741:RNA, Small Interfering
Cluster; Dehydration; Rice; SiRNA