Qiu-ling Li

Dr. Qiu-ling Li


Research Focus & Metrics

Academic Career Length

19Years (2008-Present)

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

Representative Publications

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28 December 2018 in BMC Genomics
Verified
Journal Article
Open access
PDF file
QLQiu-ling Li
Qiu-ling Li
CYChunhong Yang
Chunhong Yang
JDJuan Du
Juan Du
BZBaogui Zhang
Baogui Zhang
YHYing He
Ying He
JZJifeng Zhong
Jifeng Zhong
MicroRNAs (miRNAs) are a class of small noncoding RNAs that play important roles in the regulation of gene expression. However, the role of miRNAs in bovine mammary gland responses to heat stress is not well understood. In the present study, we performed a deep RNA sequencing analysis to identify miRNAs associated with the heat stress potential of the bovine mammary gland. We identified 27 miRNAs that were differentially expressed significantly between the mammary tissue of Holstein cattle heat stress and normal conditions. Twenty miRNAs had higher expression in the mammary tissue of heat-stressed Holstein cattle. The seven highest differentially expressed candidate miRNAs (bta-miR-21-5p, bta-miR-99a-5p, bta-miR-146b, bta-miR-145, bta-miR-2285 t, bta-miR-133a, and bta-miR-29c) identified by deep RNA sequencing were additionally evaluated by stem-loop qPCR. Enrichment analyses for targeted genes revealed that the major differences between miRNAs expression in the mammary gland of heat-stressed versus control were associated with the regulation of Wnt, TGF-β, MAPK, Notch, and JAK-STAT. These data indicated that the differentially expressed miRNAs identified in this study may act as dominant regulators during heat stress. We might reduce heat stress damage of Holstein cows by up-regulating or down-regulating these differentially expressed miRNAs.

14 December 2022 in BMC Genomics
Verified
Journal Article
Open access
PDF file
YQYing Qi
Ying Qi
YZYiming Zhang
Yiming Zhang
JZJing Zhang
Jing Zhang
JWJing Wang
Jing Wang
QLQiu-ling Li
Qiu-ling Li
Background: Heat stress has a substantial negative economic impact on the dairy industry. N6-methyladenosine (m6A) is the most common internal RNA modification in eukaryotes and plays a key role in regulating heat stress response in animals. In dairy cows, however, this modification remains largely unexplored. Therefore, we examined the effects of heat stress on the m6A modification and gene expression in bovine mammary epithelial cells to elucidate the mechanism of heat stress response. In this study, Mammary alveolar cells-large T antigen (MAC-T) cells were incubated at 37 °C (non-heat stress group, NH) and 40 °C (heat stress group, H) for 2 hours, respectively. HSP70, HSF1, BAX and CASP3 were up regulated in H group compared with those in the NH group. Results: Methylated RNA immunoprecipitation sequencing (MeRIP-seq) and RNA sequencing (RNA-seq) were conducted to identify m6A peaks and to produce gene expression data of MAC-T cells in the H and NH groups. In total, we identified 17,927 m6A peaks within 9355 genes in the H group, and 18,974 peaks within 9660 genes in the NH groups using MeRIP-seq. Compared with the NH group, 3005 significantly differentially enriched m6A peaks were identified, among which 1131 were up-regulated and 1874 were down-regulated. In addition, 1502 significantly differentially expressed genes were identified using RNA-seq, among which 796 were up-regulated and 706 were down-regulated in the H group compared to the NH group. Furthermore, 199 differentially expressed and synchronously differentially methylated genes were identified by conjoint analysis of the MeRIP-seq and RNA-seq data, which were subsequently divided into four groups: 47 hyper-up, 53 hyper-down, 59 hypo-up and 40 hypo-down genes. In addition, GO enrichment and KEGG analyses were used to analyzed the potential functions of the genes in each section. Conclusion: The comparisons of m6A modification patterns and conjoint analyses of m6A modification and gene expression profiles suggest that m6A modification plays a critical role in the heat stress response by regulating gene expression.

27 December 2018 in Animal Biotechnology
Verified
Journal Article
Closed access
QLQiu-ling Li
Qiu-ling Li
JQJie Qiao
Jie Qiao
ZZZhengfeng Zhang
Zhengfeng Zhang
XSXiaolan Shang
Xiaolan Shang
ZCZhuodong Chu
Zhuodong Chu
Mingxing ChuMingxing Chu
Mingxing Chu
The mechanisms that dairy cattle respond to environmental stresses are very complicated. Previous research into the molecular mechanisms of mammalian heat stress has largely focused on the role of protein-coding genes and small non–coding RNAs. Recently, it has become apparent that large numbers of long non-coding RNAs transcribed from mammalian genomes play extensive roles in transcriptional regulation. However, the expression of lncRNAs and their biological functions in heat stress in dairy cattle remain unknown. In this study, we employed a deep RNA sequencing to examine lncRNA expression profiles of heat stressed and non-heat stressed Chinese Holstein cattle. We found that 24,795 novel and 3763 known lncRNAs were expressed in the bovine mammary gland, of which 174 were differentially expressed in heat stress condition, among them, 156 lncRNAs were up-regulated and 18 were down-regulated. Through Cis role analysis, 16,474 lncRNAs were transcribed close to protein-coding neighbors. In addition, 11 and 2024 lncRNAs harbored precursors of known and predicted microRNAs, respectively, were annotated in the precursor analysis of miRNAs. Taken together, our findings represent the first systematic investigation of lncRNA expression in heat stressed Chinese Holstein and provide a resource for further research into the molecular mechanisms of lncRNAs function in dairy cattle.

13 April 2017 in Journal of Biological Research-Thessaloniki
Verified
Journal Article
Open access
PDF file
XGXiaofei Guo
Xiaofei Guo
WHWenping Hu
Wenping Hu
XLXian-Zheng Lang
Xian-Zheng Lang
QLQiu-ling Li
Qiu-ling Li
XWXiangyu Wang
Xiangyu Wang
Mingxing ChuMingxing Chu
Mingxing Chu
Alpha 1-antitrypsin (α1-AT) may affect the susceptibility of mastitis in dairy cattle for its possible role in the protection of lactoferrin from proteolytic degradation in the mammary. Milk somatic cell score (SCS) is a logarithmic transformation of the milk somatic cell count widely used as an index to evaluate mastitis. To study the relationships of α1-AT gene and SCS in Chinese Holstein cows, methods of PCR-SSCP, DNA sequencing, PCR-RFLP, and CRS-PCR technologies were used to detect single nucleotide polymorphisms sites in α1-AT gene. Two polymorphic sites at G5503A and G5746C of α1-AT gene were found. AA (0.3633), AB (0.4644) and BB (0.1723) genotypes were detected at G5503A site, CC (0.3483), CD (0.4906) and DD (0.1611) genotypes were found at G5746C in Chinese Holstein cows. Least squares mean of SCS for individuals with BB genotype was significantly lower than that with AA and AB genotype (p < 0.01), and that with AB genotype was significantly lower than that with AA (p < 0.05). There was no significant difference among individuals with CC, CD and DD genotypes (p > 0.05). Least squares mean of SCS for individuals with BBDD genotype combination were significantly lower than those with AACC and AACD (p < 0.05). Statistical analysis indicated that B allele and BBDD genotype combination of α1-AT can improve mastitis resistance in dairy cattle.

1 November 2020 in Food Science of Animal Resources
Verified
Journal Article
Open access
PDF file
XSXiaolan Shang
Xiaolan Shang
XYXunyou Yan
Xunyou Yan
QLQiu-ling Li
Qiu-ling Li
ZLZizheng Liu
Zizheng Liu
ATAnguo Teng
Anguo Teng
Food Science of Animal Resources (Food Sci. Anim. Resour.) is an international, peer-reviewed bimonthly journal publishing original research and reviews on basic and applied aspects of the use of animal resources including the meat, egg, dairy, and edible insect/worm products. Food Sci. Anim. Resour. is published and distributed by the Korean Society for Food Science of Animal Resources.

9 March 2010 in Molecular Biology Reports
Verified
Journal Article
Closed access
PDF file
WLWenyan Liu
Wenyan Liu
JWJi Wang
Ji Wang
QLQiu-ling Li
Qiu-ling Li
ZJZhihua Ju
Zhihua Ju
JHjinming huang
jinming huang
CWChangfa Wang
Changfa Wang