Juan Huang

Juan Huang


Research Focus & Metrics

Academic Career Length

5Years (2022-Present)

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

Representative Publications

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Verified
Journal Article
Open access
PDF file
JHJuan Huang
Juan Huang
BTBin Tang
Bin Tang
RRRongrong Ren
Rongrong Ren
MWMin Wu
Min Wu
FLFei Liu
Fei Liu
QCQing-Fu Chen
Qing-Fu Chen
Starch is a major component of crop grains, and its content affects food quality and taste. Tartary buckwheat is a traditional pseudo-cereal used in food as well as medicine. Starch content, granule morphology, and physicochemical properties have been extensively studied in Tartary buckwheat. However, the complex regulatory network related to its starch biosynthesis needs to be elucidated. Here, we performed RNA-seq analyses using seven Tartary buckwheat varieties differing in starch content and combined the RNA-seq data with starch content by weighted correlation network analysis (WGCNA). As a result, 10,873 differentially expressed genes (DEGs) were identified and were functionally clustered to six hierarchical clusters. Fifteen starch biosynthesis genes had higher expression level in seeds. Four trait-specific modules and 3131 hub genes were identified by WGCNA, with the lightcyan and brown modules positively correlated with starch-related traits. Furthermore, two potential gene regulatory networks were proposed, including the co-expression of FtNAC70, FtPUL, and FtGBSS1-3 in the lightcyan module and FtbHLH5, C3H, FtBE2, FtISA3, FtSS3-5, and FtSS1 in the brown. All the above genes were preferentially expressed in seeds, further suggesting their role in seed starch biosynthesis. These results provide crucial guidance for further research on starch biosynthesis and its regulatory network in Tartary buckwheat.

30 March 2022 in Agronomy
Verified
Journal Article
Open access
PDF file
JHJuan Huang
Juan Huang
RRRongrong Ren
Rongrong Ren
YRYuping Rong
Yuping Rong
BTBin Tang
Bin Tang
JDJiao Deng
Jiao Deng
TSTaoxiong Shi
Taoxiong Shi
Plant NAC transcription factors play vital roles in diverse biological processes, especially in response to stress. However, only a limited number of the FtNAC genes have been cloned and functionally analyzed in Tartary buckwheat (Fagopyrum tataricum (L.) Gaertn.), which is a coarse grain crop that is highly resistant to abiotic stress. Here, we identified seven Tartary buckwheat FtNAC genes, namely FtNAC11, FtNAC12, FtNAC26, FtNAC28, FtNAC65, FtNAC70, and FtNAC78. Multiple-sequence alignments revealed that all of them contained the conserved NAM domain, which consisted of the A, B, C, D, and E subdomains. Seven NAC proteins were classified into six subgroups based on phylogenetic analyses. Cis-acting regulatory elements analyses (CARE) indicated that there were numerous CAREs related to biotic and abiotic responses and hormone responses in the promoters of FtNAC genes. qRT-PCR showed that they were tissue-specifically expressed and could be induced by four out of six treatments, namely PEG, NaCl, cold, MeJA, ABA, and GA, implying that they were abiotic and hormone responsive. Among these, FtNAC70 was the most strongly induced gene and could be induced by all six treatments. Meanwhile, FtNAC70-overexpressed Arabidopsis showed more resistance to salt and drought. The identification, phylogenetics, expression, and functional study of seven stress-responsive FtNAC genes laid a foundation for further research on the molecular mechanism of abiotic stress regulated by NAC TFs in Tartary buckwheat.

Verified
Journal Article
Open access
PDF file
JHJuan Huang
Juan Huang
FLFei Liu
Fei Liu
RRRongrong Ren
Rongrong Ren
JDJiao Deng
Jiao Deng
ZLZhu liwei
Zhu liwei
TSTaoxiong Shi
Taoxiong Shi
Starch is the main component that determines the yield and quality of Tartary buckwheat. As a quantitative trait, using quantitative trait locus (QTL) mapping to excavate genes associated with starch-related traits is crucial for understanding the genetic mechanisms involved in starch synthesis and molecular breeding of Tartary buckwheat varieties with high-quality starch. Employing a recombinant inbred line population as research material, this study used QTL mapping to investigate the amylose, amylopectin, and total starch contents across four distinct environments. The results identified a total of 20 QTLs spanning six chromosomes, which explained 4.07% to 14.41% of the phenotypic variation. One major QTL cluster containing three stable QTLs governing both amylose and amylopectin content, qClu-4-1, was identified and located in the physical interval of 39.85–43.34 Mbp on chromosome Ft4. Within this cluster, we predicted 239 candidate genes and analyzed their SNP/InDel mutations, expression patterns, and enriched KEGG pathways. Ultimately, five key candidate genes, namely FtPinG0004897100.01, FtPinG0002636200.01, FtPinG0009329200.01, FtPinG0007371600.01, and FtPinG0005109900.01, were highlighted, which are potentially involved in starch synthesis and regulation, paving the way for further investigative studies. This study, for the first time, utilized QTL mapping to detect major QTLs controlling amylose, amylopectin, and total starch contents in Tartary buckwheat. The QTLs and candidate genes would provide valuable insights into the genetic mechanisms underlying starch synthesis and improving starch-related traits of Tartary buckwheat.

19 August 2024 in Agronomy
Verified
Journal Article
Open access
PDF file
ZLZhu liwei
Zhu liwei
QDQianxi Du
Qianxi Du
TSTaoxiong Shi
Taoxiong Shi
JHJuan Huang
Juan Huang
JDJiao Deng
Jiao Deng
QCQing-Fu Chen
Qing-Fu Chen
According to the requirements of Tartary buckwheat breeding, it is necessary to develop a method for the rapid detection of functional substances in seeds. To ensure a diverse sample pool, we utilized the stable recombinant inbred lines (RILs) of Tartary buckwheat. The coefficients of variation of the total flavonoid, vitamin E (VE), and GABA contents of the RIL population were 15.06, 16.53, and 36.93, respectively. Subsequently, we established prediction models for the functional substance contents in Tartary buckwheat using near-infrared spectroscopy (NIRS) combined with chemometrics. The Kennard–Stone algorithm divided the dataset into training and test sets, employing six different methods for preprocessing spectra. The Competitive Adaptive Reweighted Sampling algorithm extracted the characteristic spectra. The best models for total flavonoid and VE were normalized using the first derivative. The calibration correlation coefficient (Rc) and prediction correlation coefficient (Rp) of the total flavonoid and VE prediction models were greater than 0.94. The optimal GABA prediction model underwent preprocessing via normalization combined with the standard normal variate, and the Rc and Rp values were greater than 0.93. The results demonstrated that the NIRS-based prediction model could satisfy the requirements for the rapid determination of total flavonoids, VE, and GABA in Tartary buckwheat seeds.