Hegang Li

Dr. Hegang Li


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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22 August 2025 in Frontiers in Genetics
Verified
Journal Article
Open access
PDF file
ZHZhengang Huang
Zhengang Huang
YTYuanping Tang
Yuanping Tang
JZJianyu Zhou
Jianyu Zhou
DXDongliang Xu
Dongliang Xu
XLXiaokun Lin
Xiaokun Lin
HLHegang Li
Hegang Li
Introduction: Identifying genetic markers associated with economically important traits in dairy goats helps enhance breeding efficiency, thereby increasing industry value. However, the potential genetic structure of key economic traits in dairy goats is still largely unknown.Methods: This study used three genome-wide association study (GWAS) models (GLM, MLM, FarmCPU) to analyze dairy goat milk production traits (milk yield, fat percentage, protein percentage, lactose percentage, ash percentage, total dry matter, and somatic cell count). The goal was to identify SNPs and positional and functional candidate genes significantly associated with these traits.Results: The GWAS analysis results identified a total of 242 significant SNPs. Among these, 45 SNPs exhibited genome-wide significance, while 197 SNPs demonstrated suggestive associations, corresponding to 99 positional candidate genes within a 50 kb upstream and downstream range. 15 significant SNP loci were consistently identified across all three models, corresponding to 18 candidate genes.The integrated analysis of three models detected 2, 19, 17, 4, 115, 23, and 62 significant SNPs associated with milk yield, ash percentage, protein percentage, lactose percentage, somatic cell count, fat percentage, and total dry matter percentage, respectively. Correspondingly, 6, 24, 9, 12, 37, 14, and 30 candidate genes were identified for these traits. Additionally, several new candidate genes related to milk production traits were proposed (LCORL, TNFRSF1A, VWF, SPATA6, MAN1C1, MASP1, BRCA2).Discussion: In summary, the results of this study provide an important reference for further exploration of the genetic mechanisms underlying dairy goat milk production traits and the development of molecular breeding markers.

4 February 2024 in Veterinary Sciences
Verified
Journal Article
Open access
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WGWei Guo
Wei Guo
RCRenzeng Ciwang
Renzeng Ciwang
LWLei Wang
Lei Wang
SZShuer Zhang
Shuer Zhang
NLNan Liu
Nan Liu
JHJianning He
Jianning He
The content of intramuscular fat (IMF) from preadipocytes is proportional to meat quality in livestock. However, the roles of circRNAs in IMF deposition in sheep are not well known. In this study, we show that circRNA-5335/miR-125a-3p/STAT3 play a crucial adjective role in the proliferation and differentiation of sheep preadipocytes. In this study, we characterized the roles of differentially expressed circRNA-5335/miR-125a-3p/STAT3, which were screened from sheep of different months of age and based on sequencing data. Firstly, the expression profiles of circRNA-5335/miR-125a-3p/STAT3 were identified during the differentiation of preadipocytes in vitro by RT-qPCR and WB. Then, the targeting relationship of the circRNA-5335/miR-125a-3p/STAT3 was verified by dual-luciferase reporter assays. The results of RT-qPCR, CCK8, EdU and Oil Red O staining assay showed that miR-125a-3p suppressed the differentiation and raised the proliferation of preadipocytes by targeting STAT3. As a competing endogenous RNA, the downregulation of circRNA-5335 decreased the expression of STAT3 by increasing miR-125a-3p, which inhibited the differentiation of preadipocytes and promoted proliferation. Our present study demonstrates the functional significance of circRNA-5335/miR-125a-3p/STAT3 in the differentiation of sheep preadipocytes, and provides novel insights into exploring the mechanism of IMF.

9 December 2022 in Microorganisms
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Journal Article
Open access
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ZWZixuan Wang
Zixuan Wang
HWHailong Wang
Hailong Wang
ASAstrid Lissette Barreto Sánchez
Astrid Lissette Barreto Sánchez
MTMamadou Thiam
Mamadou Thiam
ZJzhang jin
zhang jin
QWQiao Wang
Qiao Wang
Salmonella enterica serovar Typhimurium (ST) is a food-borne pathogen that can infect animals and humans. It is currently the most common bacterial pathogen that negatively affects the poultry industry. Although different chicken breeds have been observed to exhibit diverse resistance to ST infection, the underlying genetic mechanisms remain unclear and the genes involved in this differential disease resistance need to be identified. To overcome this knowledge gap, we used a liver transcriptome analysis to screen differentially expressed genes (DEGs) in two different chicken breeds (local Beijing You (BY) and commercial Guang Ming No. 2 broiler line B (GM)) before and after ST infection. We also performed weighted gene co-expression network analysis (WGCNA) to detect hub genes, and employed selection signal analysis of candidate genes. Three promising genes (EGR1, JUN and FOS) were eventually identified, and were significantly and differentially expressed in the same breed under different conditions, and in the two breeds after ST infection. Hub genes, such as PPFIA4 and ZNF395, were identified using WGCNA, and were associated with the ratio of heterophils to lymphocytes (H/L), an indicator of disease resistance. the present study identified several genes and pathways associated with resistance to ST infection, and found that BY had greater resistance to ST infection than GM. The results obtained provide valuable resources for investigating the mechanisms of resistance to ST infection in different chicken breeds.

Verified
Journal Article
Open access
PDF file
XTXinxin Tang
Xinxin Tang
PLPeihao Liu
Peihao Liu
NLNa Luo
Na Luo
JWJie Wen
Jie Wen
HLHegang Li
Hegang Li
BABingxing An
Bingxing An
Clinical investigations have highlighted disruptions in bone metabolic processes and abnormal fluctuations in serum indicator levels during the onset of leg disease (LD) in broilers. However, the presence of a genetic causal relationship for this association remains undetermined. Therefore, the aim of this study is to discern the risk factors underlying LD development using 1235 sequenced white-feathered broilers. We employed Mendelian randomization (MR) analysis to assess the associations of bone strength (BS), bone mineral density (BMD), tibial bone weight (TBW), tibial bone length (TBL), tibial bone diameter (TBD), bone ash (BA), ash calcium (Ash Ca), ash phosphorus (Ash P), serum calcium (Ca), serum phosphorus (P), serum alkaline phosphatase (ALP), and serum osteoprotegerin (OPG) with the incidence of LD. Compelling evidence underscores a causal link between the risk of developing LD and decreased BMD (odds ratio (OR) = 0.998; 95% CI: 0.983, 0.993; P < 0.001) and narrower TBD (OR = 0.985, 95% CI: 0.975, 0.994, P = 0.002). Additionally, serum OPG concentrations (OR: 0.995, 95% CI: 0.992, 0.999, P = 0.008) were associated with BMD (OR = 0.0078, 95% CI = 0.0043 to 0.0140, P < 0.001), indicating a robust genetic relationship between ALP concentrations (OR: 0.988, 95% CI: 0.984, 0.993, P < 0.001) and TBD (OR = 0.0046, 95% CI = 0.0026, 0.0083, P < 0.001). Moreover, elevated serum Ca (OR: 0.564, 95% CI: 0.487, 0.655, P < 0.001) and P (OR: 0.614, 95% CI: 0.539, 0.699, P < 0.001) levels were associated with a narrower TBD. Elevated serum levels of Ca, P, ALP, and OPG contribute to disturbances in bone metabolism, while decreased BMD and narrower TBD are associated with a greater risk of developing LD in broilers. This discovery elucidates the metabolic risk factors for LD in broilers and could provide information on LDs, such as osteoporosis, in humans.

18 July 2025 in Research in Veterinary Science
Verified
Journal Article
Closed access
DXDongliang Xu
Dongliang Xu
DWDi Wang
Di Wang
LZLisheng Zhou
Lisheng Zhou
ZHZhengang Huang
Zhengang Huang
MSMingzhi Sun
Mingzhi Sun
HLHegang Li
Hegang Li

25 October 2025 in Molecular Genetics and Genomics
Verified
Journal Article
Closed access
PDF file
FYFengjiao Yu
Fengjiao Yu
KLKaidong Liu
Kaidong Liu
ZWZipeng Wang
Zipeng Wang
SWShasha Wang
Shasha Wang
HLHegang Li
Hegang Li
JHJianning He
Jianning He