Fazhan Qiu

Prof. Fazhan Qiu

College of Plant Science and Technology, National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China
orcid

Research Focus & Metrics

Academic Career Length

13Years (2014-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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29 April 2019 in Plant Biotechnology Journal
Verified
Journal Article
Open access
PDF file
FYFeng Yu
Feng Yu
KLKun Liang
Kun Liang
TFTian Fang
Tian Fang
HZHailiang Zhao
Hailiang Zhao
XHXuesong Han
Xuesong Han
FQFazhan Qiu
Fazhan Qiu
Summary: Group VII ethylene response factors (ERFVIIs) play important roles in ethylene signalling and plant responses to flooding. However, natural ERFVII variations in maize (ZmERFVIIs) that are directly associated with waterlogging tolerance have not been reported. Here, a candidate gene association analysis of the ZmERFVII gene family showed that a waterlogging‐responsive gene, ZmEREB180, was tightly associated with waterlogging tolerance. ZmEREB180 expression specifically responded to waterlogging and was up‐regulated by ethylene; in addition, its gene product localized to the nucleus. Variations in the 5ʹ‐untranslated region (5ʹ‐UTR) and mRNA abundance of this gene under waterlogging conditions were significantly associated with survival rate (SR). Ectopic expression of ZmEREB180 in Arabidopsis increased the SR after submergence stress, and overexpression of ZmEREB180 in maize also enhanced the SR after long‐term waterlogging stress, apparently through enhanced formation of adventitious roots (ARs) and regulation of antioxidant levels. Transcriptomic assays of the transgenic maize line under normal and waterlogged conditions further provided evidence that ZmEREB180 regulated AR development and reactive oxygen species homeostasis. Our study provides direct evidence that a ZmERFVII gene is involved in waterlogging tolerance. These findings could be applied directly to breed waterlogging‐tolerant maize cultivars and improve our understanding of waterlogging stress.

19 July 2015 in Theoretical and Applied Genetics
Verified
Journal Article
Open access
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LLLei Liu
Lei Liu
YDYanfang Du
Yanfang Du
DHDongao Huo
Dongao Huo
MWMan Wang
Man Wang
XSXiaomeng Shen
Xiaomeng Shen
ZZzuxin Zhang
zuxin Zhang
Key message Maize kernel row number might be dominated by a set of large additive or partially dominant loci and several small dominant loci and can be accurately predicted by fewer than 300 top KRN-associated SNPs. Abstract Kernel row number (KRN) is an important yield component in maize and directly affects grain yield. In this study, we combined linkage and association mapping to uncover the genetic architecture of maize KRN and to evaluate the phenotypic predictability using these detected loci. A genome-wide association study revealed 31 associated single nucleotide polymorphisms (SNPs) representing 17 genomic loci with an effect in at least one of five individual environments and the best linear unbiased prediction (BLUP) over all environments. Linkage mapping in three F2:3 populations identified 33 KRN quantitative trait loci (QTLs) representing 21 QTLs common to several population/environments. The majority of these common QTLs that displayed a large effect were additive or partially dominant. We found 70 % KRN-associated genomic loci were mapped in KRN QTLs identified in this study, KRN-associated SNP hotspots detected in NAM population and/or previous identified KRN QTL hotspots. Furthermore, the KRN of inbred lines and hybrids could be predicted by the additive effect of the SNPs, which was estimated using inbred lines as a training set. The prediction accuracy using the top KRN-associated tag SNPs was obviously higher than that of the randomly selected SNPs, and approximately 300 top KRN-associated tag SNPs were sufficient for predicting the KRN of the inbred lines and hybrids. The results suggest that the KRN-associated loci and QTLs that were detected in this study show great potential for improving the KRN with genomic selection in maize breeding.

4 May 2017 in The Plant Journal
Verified
Journal Article
Closed access
PDF file
Manjun CaiManjun Cai
Manjun Cai
SLShuzhen Li
Shuzhen Li
FSFeng Sun
Feng Sun
QSQin Sun
Qin Sun
HZHailiang Zhao
Hailiang Zhao
FQFazhan Qiu
Fazhan Qiu

20 July 2017 in Journal of Experimental Botany
Verified
Journal Article
Open access
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XRXuemei Ren
Xuemei Ren
ZPZhenyuan Pan
Zhenyuan Pan
HZHailiang Zhao
Hailiang Zhao
JZJunli Zhao
Junli Zhao
Manjun CaiManjun Cai
Manjun Cai
FQFazhan Qiu
Fazhan Qiu
Group II introns are common in the mitochondrial genome of higher plant species. The splicing of these introns is a complex process involving the synergistic action of multiple factors. However, few of these factors have been characterized in maize. In this study, we found that the Empty pericarp11 (Emp11) gene, which encodes a P-type pentatricopeptide repeat (PPR) protein, is required for the development of maize seeds. The loss of Emp11 function seriously impairs embryo and endosperm development, resulting in empty pericarp seeds in maize, and alteration in Emp11 expression leads to quantitative variation in kernel size and weight. We found that the emp11 mutants showed a failure in nad1 intron splicing, exhibited a severe reduction in complex I assembly and activity, mitochondrial structure disturbances, and an increase in alternative oxidase AOX2 and AOX3 levels. Interestingly, the emp11 phenotype was very severe in the W22 inbred line but could be partially recovered in B73 BC2F2 segregating ears. These results suggest that EMP11 serves as a factor for the splicing of mitochondrial nad1 introns and is required for mitochondrial function to ensure proper seed development in maize.

29 February 2020 in Genes
Verified
Journal Article
Open access
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FYFeng Yu
Feng Yu
ZTZengdong Tan
Zengdong Tan
TFTian Fang
Tian Fang
KTKaiyuan Tang
Kaiyuan Tang
KLKun Liang
Kun Liang
FQFazhan Qiu
Fazhan Qiu
Waterlogging stress (WS) in a dynamic environment seriously limits plant growth, development, and yield. The regulatory mechanism underlying WS conditions at an early stage in maize seedlings is largely unknown. In the present study, the primary root tips of B73 seedlings were sampled before (0 h) and after (2 h, 4 h, 6 h, 8 h, 10 h, and 12 h) WS and then subjected to transcriptome sequencing, resulting in the identification of differentially expressed protein-coding genes (DEpcGs) and long non-coding RNAs (DElncRs) in response to WS. These DEpcGs were classified into nine clusters, which were significantly enriched in several metabolic pathways, such as glycolysis and methionine metabolism. Several transcription factor families, including AP2-EREBP, bZIP, NAC, bHLH, and MYB, were also significantly enriched. In total, 6099 lncRNAs were identified, of which 3190 were DElncRs. A co-expression analysis revealed lncRNAs to be involved in 11 transcription modules, 10 of which were significantly associated with WS. The DEpcGs in the four modules were enriched in the hypoxia response pathways, including phenylpropanoid biosynthesis, MAPK signaling, and carotenoid biosynthesis, in which 137 DElncRs were also co-expressed. Most of the co-expressed DElncRs were co-localized with previously identified quantitative trait loci associated with waterlogging tolerance. A quantitative reverse transcription-polymerase chain reaction analysis of DEpcG and DElncR expression among the 32 maize genotypes after 4 h of WS verified significant expression correlations between them as well as significant correlation with the phenotype of waterlogging tolerance. Moreover, the high proportion of hypoxia response elements in the promoter region increased the reliability of the DElncRs identified in this study. These results provide a comprehensive transcriptome in response to WS at an early stage of maize seedlings and expand our understanding of the regulatory network involved in hypoxia in plants.

17 June 2020 in Journal of Integrative Plant Biology
Verified
Journal Article
Closed access
PDF file
ZPZhenyuan Pan
Zhenyuan Pan
MLMin Liu
Min Liu
HZHailiang Zhao
Hailiang Zhao
ZTZengdong Tan
Zengdong Tan
KLKun Liang
Kun Liang
FQFazhan Qiu
Fazhan Qiu