Automated Author ProfileZeng, Yi-Xin
Zeng, Yi-Xin
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets for this author
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the author's datasets
Total Mentions
Total mentions of the author's datasets
S-Index Interpretation
The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.
What it means:
- A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
- The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
- It provides a single number to track your research data impact over time
Current S-Index: 8.8 (sum of 11 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
Replicating SARS-CoV-2 has been shown to degrade HLA class I on target cells to evade the cytotoxic T-cell (CTL) response. HLA-I downregulation can be sensed by NK cells to unleash killer cell immunoglobulin-like receptor (KIR)-mediated self-inhibition by the cognate HLA-I ligands. Here, we investigated the impact of HLA and KIR genotypes and HLA-KIR combinations on COVID-19 outcome. We found that the peptide affinities of HLA alleles were not correlated with COVID-19 severity. The predicted poor binders for SARS-CoV-2 peptides belong to HLA-B subtypes that encode KIR ligands, including Bw4 and C1 (introduced by B*46:01), which have a small F pocket and cannot accommodate SARS-CoV-2 CTL epitopes. However, HLA-Bw4 weak binders were beneficial for COVID-19 outcome, and individuals lacking the HLA-Bw4 motif were at higher risk for serious illness from COVID-19. The presence of the HLA-Bw4 and KIR3DL1 combination had a 58.8% lower risk of developing severe COVID-19 (OR = 0.412, 95% CI = 0.187-0.904, p = 0.02). This suggests that HLA-Bw4 alleles that impair their ability to load SARS-CoV-2 peptides will become targets for NK-mediated destruction. Thus, we proposed that the synergistic responsiveness of CTLs and NK cells can efficiently control SARS-CoV-2 infection and replication, and NK-cell-mediated anti-SARS-CoV-2 immune responses being mostly involved in severe infection when the level of ORF8 is high enough to degrade HLA-I. The HLA-Bw4/KIR3DL1 genotype may be particularly important for East Asians undergoing COVID-19 who are enriched in HLA-Bw4-inhibitory KIR interactions and carry a high frequency of HLA-Bw4 alleles that bind poorly to coronavirus peptides.
Authors
- Wang, Ruihua ;
- Sun, Ying ;
- Kuang, Bo-Hua ;
- Yan, Xiao ;
- Lei, Jinju ;
- Lin, Yu-Xin ;
- Tian, Jinxiu ;
- Li, Yating ;
- Xie, Xiaoduo ;
- Chen, Tao ;
- Zhang, Hui ;
- Zeng, Yi-Xin ;
- Zhao, Jincun ;
- Feng, Lin
Replicating SARS-CoV-2 has been shown to degrade HLA class I on target cells to evade the cytotoxic T-cell (CTL) response. HLA-I downregulation can be sensed by NK cells to unleash killer cell immunoglobulin-like receptor (KIR)-mediated self-inhibition by the cognate HLA-I ligands. Here, we investigated the impact of HLA and KIR genotypes and HLA-KIR combinations on COVID-19 outcome. We found that the peptide affinities of HLA alleles were not correlated with COVID-19 severity. The predicted poor binders for SARS-CoV-2 peptides belong to HLA-B subtypes that encode KIR ligands, including Bw4 and C1 (introduced by B*46:01), which have a small F pocket and cannot accommodate SARS-CoV-2 CTL epitopes. However, HLA-Bw4 weak binders were beneficial for COVID-19 outcome, and individuals lacking the HLA-Bw4 motif were at higher risk for serious illness from COVID-19. The presence of the HLA-Bw4 and KIR3DL1 combination had a 58.8% lower risk of developing severe COVID-19 (OR = 0.412, 95% CI = 0.187-0.904, p = 0.02). This suggests that HLA-Bw4 alleles that impair their ability to load SARS-CoV-2 peptides will become targets for NK-mediated destruction. Thus, we proposed that the synergistic responsiveness of CTLs and NK cells can efficiently control SARS-CoV-2 infection and replication, and NK-cell-mediated anti-SARS-CoV-2 immune responses being mostly involved in severe infection when the level of ORF8 is high enough to degrade HLA-I. The HLA-Bw4/KIR3DL1 genotype may be particularly important for East Asians undergoing COVID-19 who are enriched in HLA-Bw4-inhibitory KIR interactions and carry a high frequency of HLA-Bw4 alleles that bind poorly to coronavirus peptides.
Authors
- Wang, Ruihua ;
- Sun, Ying ;
- Kuang, Bo-Hua ;
- Yan, Xiao ;
- Lei, Jinju ;
- Lin, Yu-Xin ;
- Tian, Jinxiu ;
- Li, Yating ;
- Xie, Xiaoduo ;
- Chen, Tao ;
- Zhang, Hui ;
- Zeng, Yi-Xin ;
- Zhao, Jincun ;
- Feng, Lin
Summary statistics of all genotyped SNPs in discovery stage GWAS. Data includes SNP, CHR, BP.B37, TestAllele, TAF_Cases, TAF_Controls, MajorAlelle, OR, SE, P. TAF: Test allele frequency, BP: position (build 37), OR: Odds ratio, SE: standard error and P: pvalue.
Authors
- Tan, Dennis E K ;
- Foo, Jia Nee ;
- Bei, Jin-Xin ;
- Chang, Jiang ;
- Peng, Roujun ;
- Zheng, Xiaohui ;
- Wei, Lixuan ;
- Huang, Ying ;
- Lim, Wei Yen ;
- Li, Juan ;
- Cui, Qian ;
- Chew, Soo Hong ;
- Ebstein, Richard P ;
- Kuperan, Ponnudurai ;
- Lim, Soon Thye ;
- Tao, Miriam ;
- Tan, Suat Hoon ;
- Wong, Alvin ;
- Wong, Gee Chuan ;
- Tan, Soo Yong ;
- Ng, Siok Bian ;
- Zeng, Yi-Xin ;
- Khor, Chiea Chuen ;
- Lin, Dongxin ;
- Seow, Adeline L H ;
- Jia, Wei-Hua ;
- Liu, Jianjun
Summary statistics of all genotyped SNPs in discovery stage GWAS. Data includes SNP, CHR, BP.B37, TestAllele, TAF_Cases, TAF_Controls, MajorAlelle, OR, SE, P. TAF: Test allele frequency, BP: position (build 37), OR: Odds ratio, SE: standard error and P: pvalue.
Authors
- Tan, Dennis E K ;
- Foo, Jia Nee ;
- Bei, Jin-Xin ;
- Chang, Jiang ;
- Roujun Peng ;
- Xiaohui Zheng ;
- Lixuan Wei ;
- Huang, Ying ;
- Lim, Wei Yen ;
- Li, Juan ;
- Cui, Qian ;
- Chew, Soo Hong ;
- Ebstein, Richard P ;
- Kuperan, Ponnudurai ;
- Lim, Soon Thye ;
- Tao, Miriam ;
- Tan, Suat Hoon ;
- Wong, Alvin ;
- Wong, Gee Chuan ;
- Tan, Soo Yong ;
- Siok Bian Ng ;
- Zeng, Yi-Xin ;
- Chiea Chuen Khor ;
- Dongxin Lin ;
- Seow, Adeline L H ;
- Jia, Wei-Hua ;
- Jianjun Liu
Supplementary table S4-S12
Authors
- Mengqi Chang ;
- Hongyi Lv ;
- Weilong Zhang ;
- Chunhui Ma ;
- He, Xue ;
- Shunli Zhao ;
- Zhang, Zhi-Wei ;
- Zeng, Yi-Xin ;
- Shuhui Song ;
- Yamei Niu ;
- Tong, Wei-Min
Supplementary table S4-S12
Authors
- Mengqi Chang ;
- Hongyi Lv ;
- Weilong Zhang ;
- Chunhui Ma ;
- He, Xue ;
- Shunli Zhao ;
- Zhang, Zhi-Wei ;
- Zeng, Yi-Xin ;
- Shuhui Song ;
- Yamei Niu ;
- Tong, Wei-Min
Supplementary table S4-S12
Authors
- Mengqi Chang ;
- Hongyi Lv ;
- Weilong Zhang ;
- Chunhui Ma ;
- He, Xue ;
- Shunli Zhao ;
- Zhang, Zhi-Wei ;
- Zeng, Yi-Xin ;
- Shuhui Song ;
- Yamei Niu ;
- Tong, Wei-Min
Genes with significantly altered expression following AGO2 knockdown in CNE2Z cells. (XLS 208Â kb)
Authors
- Peiyao Li ;
- Jinfeng Meng ;
- Zhai, Yun ;
- Hongxing Zhang ;
- Lixia Yu ;
- Zhifu Wang ;
- Xiaoai Zhang ;
- Pengbo Cao ;
- Chen, Xi ;
- Yuqing Han ;
- Zhang, Yang ;
- Huipeng Chen ;
- Ling, Yan ;
- Yuxia Li ;
- Cui, Ying ;
- Bei, Jin-Xin ;
- Zeng, Yi-Xin ;
- Fuchu He ;
- Gangqiao Zhou
Biological network analysis of genes with significantly altered expression following AGO2 knockdown in CNE2Z cells. (XLS 29Â kb)
Authors
- Peiyao Li ;
- Jinfeng Meng ;
- Zhai, Yun ;
- Hongxing Zhang ;
- Lixia Yu ;
- Zhifu Wang ;
- Xiaoai Zhang ;
- Pengbo Cao ;
- Chen, Xi ;
- Yuqing Han ;
- Zhang, Yang ;
- Huipeng Chen ;
- Ling, Yan ;
- Yuxia Li ;
- Cui, Ying ;
- Bei, Jin-Xin ;
- Zeng, Yi-Xin ;
- Fuchu He ;
- Gangqiao Zhou
Biological network analysis of genes with significantly altered expression following AGO2 knockdown in CNE2Z cells. (XLS 29Â kb)
Authors
- Peiyao Li ;
- Jinfeng Meng ;
- Zhai, Yun ;
- Hongxing Zhang ;
- Lixia Yu ;
- Zhifu Wang ;
- Xiaoai Zhang ;
- Pengbo Cao ;
- Chen, Xi ;
- Yuqing Han ;
- Zhang, Yang ;
- Huipeng Chen ;
- Ling, Yan ;
- Yuxia Li ;
- Cui, Ying ;
- Bei, Jin-Xin ;
- Zeng, Yi-Xin ;
- Fuchu He ;
- Gangqiao Zhou