Personal Profile
Wang Jinkai is a professor introduced by Sun Yat-sen University and a doctoral supervisor. He graduated from the School of Life Sciences, Wuhan University in 2005 with a Bachelor of Science degree. In 2010, he obtained his Ph.D. in Genetics from the Kunming Institute of Zoology, Chinese Academy of Sciences (under the supervision of Researcher Su Bing). From 2010 to 2016, he conducted postdoctoral research in the field of bioinformatics in the laboratory of Professor Xing Yi at the University of Iowa (2010 - 2012) and the University of California, Los Angeles (2013 - 2016). In 2016, he was hired as an introduced talent by Sun Yat-sen University. In 2018, he was selected as a "Young Outstanding Talent" in the Pearl River Talent Plan of Guangdong Province.

He has published 11 papers as a corresponding author or (co-)first author in journals such as Nature Communications (IF: 17.7), Nucleic Acids Research (IF: 19.2, two papers), Nature Methods (IF: 47.9), Cell Stem Cell (IF: 25.3), and Genome Biology (IF: 17.9, two papers).

His main research interests involve developing detection technologies and algorithms for m6A modification, studying the regulatory patterns and mechanisms of m6A RNA modification, and exploring its roles in major physiological and pathological processes such as stem cells and tumors, by combining second-generation and third-generation high-throughput sequencing technologies with bioinformatics, genomics, and molecular biology methods.

Academic Achievements
Important Academic Research Results and Contributions

m6A RNA methylation is a widely existing RNA modification in mRNA and lincRNA that can be removed by demethylases. In mammals, more than 7,000 protein-coding genes transcribe mRNAs with m6A methylation modifications. However, the functions of these m6A modifications are not fully understood. The author and his collaborators found through m6A genomics and functional studies of mammalian embryonic stem cells that m6A dynamically changes in embryonic stem cells and plays a key role in stem cell differentiation (Cell Stem Cell, 2014). To systematically identify m6A sites that play a crucial role in cell fate determination, they developed a transcriptome-level m6A site functional screening technology using ABE single-base editing and systematically identified m6A sites that play a crucial role in the differentiation of human embryonic stem cells into endoderm (Nature Communications, 2022).

Since the dynamic changes of m6A are mainly manifested as changes in methylation levels, and existing technologies cannot precisely and absolutely quantify m6A at the genome level, nor can they distinguish which specific transcript isoforms m6A occurs on, these technical deficiencies have severely hindered the further development of this field. The author and his collaborators improved the existing m6A-seq technology and invented the first method that can precisely and absolutely quantify m6A at the whole-genome level (m6A-LAIC-seq). Through this new technology, they discovered for the first time that m6A is widely present on mRNAs using proximal Poly(A) tailing signals. This new pattern of m6A enables it to work with miRNAs and RNA-binding proteins to regulate RNA post-transcriptionally in a more refined manner (Nature Methods, 2016).

To further reveal the mechanisms of m6A dynamic changes, they established a computational framework based on the m6A co-methylation network to systematically identify cell-specific m6A trans-regulatory factors. By systematically integrating 104 m6A-seq data from 25 different cell lines, hundreds of CLIP-seq data of RNA-binding proteins, and recognition sequences, they identified 32 RNA-binding proteins that specifically regulate m6A. They experimentally verified two of the three m6A regulatory factors specific to the HepG2 cell line, revealing that a large number of RNA-binding proteins are likely to participate in establishing cell-specific m6A methylation patterns by specifically regulating m6A (Nucleic Acids Research, 2020). They also conducted a detailed study on one of the RNA-binding proteins, SRSF7, and found that it promotes glioma progression by specifically regulating m6A (Genomics, Proteomics & Bioinformatics, 2022).

To answer how different genes in the same cell form specific m6A modifications, they used the developed m6A-LAIC-seq m6A quantification technology to compare the m6A levels of pseudogenes and their homologous protein-coding genes. They found that the m6A level of pseudogenes is much higher than that of their homologous protein-coding genes. Further research revealed that processed pseudogenes rapidly accumulate mutations in m6A motifs under the drive of Darwinian positive selection during evolution, resulting in a significantly higher m6A level in processed pseudogenes than in their homologous protein-coding genes. These evolved m6A sites play an important role in degrading processed pseudogenes to prevent them from interfering with the expression regulatory network of homologous protein-coding genes through the ceRNA mechanism (Genome Biology, 2021).

Academic Works and Textbooks

Co-first author; # Corresponding author. Members or visiting personnel of this research group are shown in bold.

1. Yu P*, Zhou S*, Gao Y, Liang Y, Guo W, Wang DO, Ding S, Lin S#, Wang J#, Cun Y# (2022), Dynamic Landscapes of tRNA Transcriptomes and Translatomes in Diverse Mouse Tissues. Genomics, Proteomics & Bioinformatics S1672-0229(22)00092-4 (IF=9.5)
2. Sun X#, Wang DO, Wang J# (2022). Targeted manipulation of m6A RNA modification through CRISPR-Cas-based strategies. Methods 203:56-61（Invited review).
3. Cheng W*, Liu F*, Ren Z, Chen W, Chen Y, Liu T, Ma Y, Cao N #, Wang J # (2022), Parallel functional assessment of m6A sites in human endodermal differentiation with base editor screens, Nature Communications 13:478 (IF=17.7) (BioArt 报道)
4. Cun Y*, An S*, Zheng H*, Lan J, Chen W, Luo W, Yao C, Li X, Huang X, Sun X, Wu Z, Hu Y, Li Z, Zhang S, Wu G, Yang M, Tang M, Yu R, Liao X, Gao G, Zhao W, Wang J#, Li J# (2021), Specific Regulation of m6A by SRSF7 Promotes the Progression of Glioblastoma. Genomics, Proteomics & Bioinformatics S1672-0229(21)00252-7. (IF=9.5) 
5. Tan L*, Cheng W *, Liu F, Wang DO, Wu L, Cao N, Wang J # (2021) Positive natural selection of N6-methyladenosine on the RNAs of processed pseudogenes. Genome Biology, 22:180. (IF=17.9) （BioArt 报道）
6. Wang J (2021). Integrative analyses of transcriptome data reveal the mechanisms of post-transcriptional regulation. Briefings in Functional Genomics elab004 (Invited review)
7. Xia TL, Li X, Wang X, Zhu YJ, Zhang H, Cheng W, Chen ML, Ye Y, Li Y, Zhang A, Dai DL, Zhu QY, Yuan L, Zheng J, Huang H, Chen SQ, Xiao ZW, Wang HB, Roy G, Zhong Q, Lin D, Zeng YX, Wang J, Zhao B, Gewurz BE, Chen J, Zuo Z, and Zeng MS (2021). N(6)-methyladenosine-binding protein YTHDF1 suppresses EBV replication and promotes EBV RNA decay. EMBO Reports e50128.
8. Sun X*, Ren Z*, Cun Y, Zhao C, Huang X, Zhou J, Hu R, Su X, Ji L, Li P, Mak KLK, Gao F, Yang Y, Xu H, Ding J, Cao N, Li S, Zhang W, Lan P, Sun H, Wang J #, Yuan P # (2020). Hippo-YAP signaling controls lineage differentiation of mouse embryonic stem cells through modulating the formation of super-enhancers. Nucleic Acids Research 48(13):7182-7196. (IF=19.2)
9. An S*, Huang W*, Huang X*, Cun Y, Cheng W, Sun X, Ren Z, Chen Y, Chen W, Wang J#. (2020). Integrative network analysis identifies cell-specific trans regulators of m6A. Nucleic Acids Research  48(4):1715-1729. (IF=19.2) (BioArtMED 报道）
10. Shi J, Deng Y, Huang S, Huang C, Wang J, Xiang AP, and Yao C (2019). Suboptimal RNA-RNA interaction limits U1 snRNP inhibition of canonical mRNA 3' processing. RNA Biology 16(10): 1448-1460.
11. Li F, Yi Y, Miao Y, Long W, Long T, Chen S, Cheng W, Zou C, Zheng Y, Wu X, Ding J, Zhu K, Chen D, Xu Q, Wang J, Liu Q, Zhi F, Ren J, Cao Q, and Zhao W (2019). N(6)-Methyladenosine Modulates Nonsense-Mediated mRNA Decay in Human Glioblastoma. Cancer Research 79(22): 5785-5798.
12. Zhou C*, Molinie B*, Daneshvar K, Pondick JV, Wang J, Wittenberghe NV, Xing Y, Giallourakis CC#, Mullen AC#. (2017). Genome-Wide Maps of m6A circRNAs Identify Widespread and Cell-Type-Specific Methylation Patterns that Are Distinct from mRNAs. Cell Reports, 20(9), 2262–2276.  (IF=9.4)
13. Wang J, Pan Y, Shen S, Lin L, Xing Y#. (2017). rMATS-DVR: rMATS discovery of Differential Variants in RNA. Bioinformatics 33(14), 2216-2217.  (IF=6.9)
14. Molinie B*, Wang J*, Lim KS, Hillebrand R, Lu ZX, Wittenberghe NV, Howard BD, Daneshvar K, Mullen AC, Dedon P, Xing Y#, Giallourakis CC#. (2016) m6A-LAIC-seq reveals the census and complexity of the m6A epitranscriptome. Nature Methods, 13(8):692-698. (IF=28.5)  
15. Batista PJ*, Molinie B*, Wang J*, Qu K, Zhang J, Li L, Bouley DM, Lujan E, Haddad B, Daneshvar K, Carter AC, Flynn RA, Zhou C, Lim KS, Dedon P, Wernig M, Mullen AC, Xing Y#, Giallourakis CC#, Chang HY# (2014) m6A RNA modification controls cell fate transition in mammalian embryonic stem cells. Cell Stem Cell 15(6): 707–719. (IF=24.6)
16. Wang J*, Lu ZX*, Tokheim C, Miller SE, Xing Y#. (2015) Species-specific exon loss in human transcriptomes. Molecular Biology and Evolution 32(2): 481-94. (IF=16.2)
17. Lin L*#, Jiang P*, Park JW*, Wang J*, Lu ZX, Lam MPY, Ping P, Xing Y#. (2016) The contribution of Alu exons to the human proteome.Genome Biology 28;17(1):15  (IF=13.6) 
18. Wang J*, Ma MCJ*, Mennie AK*, Pettus JM, Xu Y, Lin L, Traxler MG, Jakoubek J, Atanur SS, Aitman TJ, Xing Y#, Kwitek AE#. (2015) Systems biology with high-throughput sequencing reveals genetic mechanisms underlying the metabolic syndrome in the Lyon hypertensive rat. Circulation: Cardiovascular Genetics 8(2):316-326.
19. Zhao Y*, Ji S*, Wang J*, Huang J#, Zheng P# (2014) mRNA-Seq and microRNA-Seq whole-transcriptome analyses of rhesus monkey embryonic stem cell neural differentiation revealed the potential regulators of rosette neural stem cells. DNA Research 21(5): 541–554. 
20. Li M*, Huang L*, Wang J*, Su B#, Luo X-J# (2016) No association between schizophrenia susceptibility variants and macroscopic structural brain volume variation in healthy subjects. Am J Med Genet B Neuropsychiatr Genet.  171B(2):160-8. 
21. Wang J*, Cao X*, Zhang Y, Su B#. (2012) Genome-wide DNA methylation analyses in the brain reveal four differentially methylated regions between humans and non-human primates. BMC evolutionary biology 12(1): 144.
22. Wang J, Li Y, Su B#. (2008) A common SNP of MCPH1 is associated with cranial volume variation in Chinese population. Human Molecular Genetics 17(9): 1329-1335. 
23. Li M, Huang L, Li K, Huo Y, Chen C, Wang J, Liu J, Luo Z, Chen C, Dong Q, Yao YG, Su B, Luo XJ#. (2016) Adaptive evolution of interleukin-3 (IL3), a gene associated with brain volume variation in general human populations.  Human Genetics. 135(4):377-92. 
24. Luo X-J, Li M, Huang L, Nho K, Deng M, ... Wang J, … Su B#. (2012) The Interleukin 3 Gene (IL3) Contributes to Human Brain Volume Variation by Regulating Proliferation and Survival of Neural Progenitors. PLoS ONE 7: e50375.
25. Kim J, Zhao K, Jiang P, Lu ZX, Wang J, Murray JC, Xing Y#. (2012) Transcriptome landscape of the human placenta. BMC genomics 13: 115. 
26. Niu AL, Wang YQ, Zhang H, Liao CH, Wang J, Zhang R, Che J, Su B#. (2011) Rapid evolution and copy number variation of primate RHOXF2, an X-linked homeobox gene involved in male reproduction and possibly brain function. BMC evolutionary biology 11: 298. 
27. Luo XJ, Diao HB, Wang J, Zhang H, Zhao ZM and Su B#. (2008) Association of haplotypes spanning PDZ-GEF2, LOC728637 and ACSL6 with schizophrenia in Han Chinese. J Med Genet, 45(12), 818-826.
28. Zhou L, Wang J, Yi Q, Wang YZ, Zhu YG and Zhang ZH#. (2007) Quantitative trait loci for seedling vigor in rice under field conditions. Field Crop Res 100: 294-301.