In:
Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, Vol. 116, No. 8 ( 2019-02-19), p. 3091-3099
Abstract:
Time-series transcriptomes of a biological process obtained under different conditions are useful for identifying the regulators of the process and their regulatory networks. However, such data are 3D (gene expression, time, and condition), and there is currently no method that can deal with their full complexity. Here, we developed a method that avoids time-point alignment and normalization between conditions. We applied it to analyze time-series transcriptomes of developing maize leaves under light–dark cycles and under total darkness and obtained eight time-ordered gene coexpression networks (TO-GCNs), which can be used to predict upstream regulators of any genes in the GCNs. One of the eight TO-GCNs is light-independent and likely includes all genes involved in the development of Kranz anatomy, which is a structure crucial for the high efficiency of photosynthesis in C 4 plants. Using this TO-GCN, we predicted and experimentally validated a regulatory cascade upstream of SHORTROOT1 , a key Kranz anatomy regulator. Moreover, we applied the method to compare transcriptomes from maize and rice leaf segments and identified regulators of maize C 4 enzyme genes and RUBISCO SMALL SUBUNIT2 . Our study provides not only a powerful method but also novel insights into the regulatory networks underlying Kranz anatomy development and C 4 photosynthesis.
Type of Medium:
Online Resource
ISSN:
0027-8424
,
1091-6490
DOI:
10.1073/pnas.1817621116
Language:
English
Publisher:
Proceedings of the National Academy of Sciences
Publication Date:
2019
detail.hit.zdb_id:
209104-5
detail.hit.zdb_id:
1461794-8
SSG:
11
SSG:
12
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