教育经历:2002 - 2008，理学博士，进化生物学，芝加哥大学
1999 - 2002，理学硕士，遗传学，北京大学
工作经历:2013 - 至今，研究员，北京大学生命科学学院
2008 - 2013，博士后，康奈尔大学分子生物学和遗传学系
杂志任职:2018- Science Bulletin, Editorial Board
2017- Hereditas, Editorial Board
2016- non-coding RNA research, Editorial Board
A central tenet in evolutionary biology is to decipher the genotype-phenotype relationships and the underlying evolutionary driving force. Our long-term goal seeks to elucidate how gene expression architecture evolves to cause phenotypic changes and environmental adaptation at the systems level. The advent of high-throughput sequencing-based functional genomics provides us with an unprecedented opportunity to address many fundamental issues related to gene expression regulation and evolution. Our research team at Peking University take an integrative genomics approach to dissect the molecular mechanisms and evolutionary principles of translational control, with a focus on the evolution and functions of the cis- and trans-acting elements and their interactions in translational regulation.
During the past five years, we have made a series of significant discoveries related to translational regulation, which is summarized as follows.
I) Adaptive evolution of uORFs and their function in translational regulation
uORFs are cis-acting elements in 5`UTRs of metazoan mRNAs that potentially inhibit translation initiation of downstream CDSs by sequestering ribosomes. To decipher the evolutionary principles of uORFs, we performed extensive mRNA-Seq and ribosome profiling to generate genome-wide maps of ribosome occupancy at the codon level during the life cycle of D. melanogaster (Zhang et al. 2018a). We demonstrate for the first time that the majority of the newly fixed uORFs in D. melanogaster, especially the translated ones, are driven by positive Darwinian selection. We also show that during Drosophila development, changes in the translation efficiency of uORFs, as well as the inclusion/exclusion of uORFs by alternative splicing or altering transcriptional initiation, are frequently exploited to influence the translation of CDSs inversely. Our results provide novel insight into the evolutionary principles of uORFs and their biological importance in translational regulation.
II) A-to-I RNA editing increases protein diversity in Drosophila
A-to-I RNA editing is an evolutionarily conserved mechanism by which ADAR recognizes double-stranded RNAs and convert adenosine into inosine. Since inosine is recognized as guanosine (G) by the cellular machinery, A-to-I editing is hypothesized to facilitate adaptive evolution by expanding transcriptomic and proteomic diversities (Gommans et al. 2009). To test this hypothesis, we systematically identified A-to-I RNA editing sites in the brains of three Drosophila species. We demonstrate for the first time that positive selection has caused excessive editing events that change protein sequences at the editome level (Duan et al. 2017), suggesting A-to-I RNA editing has important implications in evolution. We are currently testing whether A-to-I editing affects translation dynamics. Furthermore, we show that many of the adaptive nonsynonymous editing events are significantly linked in the same RNA molecules in Drosophila in which nonsynonymous editing are overall adaptive (Duan et al. 2018). This finding therefore highlights the necessity to consider the possible combinatory effect of editing on multiple sites when elucidating the functional consequences of RNA editing. Collectively, we are among the first to prove the hypothesis that A-to-I editing provides a driving force for adaptive evolution from different aspects. Our results suggest A-to-I RNA editing adds a layer of complexity to the proteome and provide valuable insights into the molecular mechanisms of adaptation.
III) Evolutionary principles of small RNAs and their function in translational regulation
miRNAs are small RNAs that suppress targets by degrading mRNAs or inhibiting translation. One of my longstanding interests is to decipher the evolutionary principles of miRNAs and their function. Previously, we found most newly emerged miRNAs are evolutionarily transient (Lu et al. 2008b) and many adaptive mutations are required to drive a new surviving miRNA to develop function (Lu et al. 2008a). Nevertheless, it remains unclear what factors affect the survival and function development for a new miRNA or target site. Recently, my group finds the genomic clustering helps new miRNAs survive and develop function in animals (Wang et al. 2016). We also show natural selection has driven miRNAs in the same cluster that have different origins to evolve convergently towards similar function. Moreover, we find the majority of evolutionarily conserved miRNAs in animals are caused by duplication, and functional diversification following miRNA duplications accelerates the recruitment of functional new targets (Luo et al. 2018a).
In line with the view that mammalian miRNAs inhibit their targets primarily by destabilizing mRNAs (Guo et al. 2010), we previously found that variation in miRNA target sites is associated with increased mRNA expression variation in human populations (Lu and Clark 2012). However, it is not well understood how miRNAs repress their targets at the genomic scale in Drosophila. To address this question, we recently performed extensive RNA-Seq and ribosome profiling experiments and find many miRNAs inhibit translation of the target genes in the major developmental stages of Drosophila (Zhang and Lu). Remarkably, we find extensive cross-talks between miRNAs and uORFs in suppressing translation of the targets, suggesting miRNAs and uORFs interact to regulate the translational programs in a fail-safe manner (Zhang and Lu). Meanwhile, we also found another class of small RNAs in Drosophila, tsRNAs (tRNA-derived small RNAs), preferentially suppress translation of key components of the general translational machinery by antisense pairing to their mRNAs, which further inhibits the global translational activities under cellular stress (Luo et al. 2018b). However, whether (and how) tsRNAs and miRNAs cross-talk to regulate cellular energy homeostasis and metabolic adaptation deserves further investigations.
IV) The biosynthetic cost of amino acids influences global mRNA translation and provides insights into cancer progression
Besides translational regulation at the RNA level, we further ask whether the global mRNA translation is affected by the biosynthetic costs of amino acids (AAs) which vary wildly. Consistent with previous observations that the biosynthetic costs of AAs constrain their usage in the protein sequences (Akashi and Gojobori 2002; Raiford et al. 2008), we found the mRNA expression levels are also inversely correlated with the average cost of AAs in the protein products in various human tissues, suggesting human gene expressions are optimized due to metabolic efficiency (Zhang et al. 2018b). We calculated the ECPAcell (Energy Cost Per Amino Acid) which incorporates the global mRNA expression levels to quantitatively characterize the use of 20 AAs during protein synthesis in human cells. To test whether changes in the usage of AAs in protein synthesis by altering global mRNA expression profiles affect the fitness of a cell, we compared the ECPAcell values in normal versus tumor tissues because 1) tumor cells demand more AAs for biomass synthesis, and 2) a tumor sample is a multi-step fast-evolving system where selective pressure on the tumor cells is pervasive. By analyzing gene expression data from The Cancer Genome Atlas, we find that cancer cells evolve to utilize amino acids more economically by optimizing the global mRNA expression profile in multiple cancer types. In ten cancer types, patients with lower ECPAcell showed significantly worse survival probability compared with those with higher ECPAcell. We further validate this pattern in experimental evolution of xenograft tumors. Our results suggest cancer cells that reprogram their genome-wide expression profiles to utilizing AAs more economically in protein synthesis gain advantage during proliferation, and that might be a common principle during cancer evolution.
Please see http://evolution-pku.org/ for details.
1. Zhang H #, Wang YR #, Li J, Chen H, He XL, Zhang HW, Liang H*, Lu J* (2018). Biosynthetic Energy Cost for Amino Acids Decreases in Cancer Evolution. Nature Communications. 9(1):4124. doi: 10.1038/s41467-018-06461-1.
2. Zhang H#, Dou SQ#, He F, Luo JJ, Wei LP, and Lu J* (2018). Genome-wide maps of ribosomal occupancy provide insights into adaptive evolution and regulatory roles of uORFs during Drosophila development. PLoS Biology. 16(7):e2003903. doi: 10.1371/journal.pbio.2003903.
3. Luo S#, He F#, Luo J#, Dou S#, Wang Y#, Guo A, Lu J* (2018). Drosophila tsRNAs preferentially suppress general translation machinery via antisense pairing and participate in cellular starvation response. Nucleic Acids Research. 46(10):5250-5268.
4. Luo J#, Wang Y#, Yuan J#, Zhao Z, Lu J* (2018). MicroRNA duplication accelerates the recruitment of new targets during vertebrate evolution. RNA. 24(6):787-802.
5. Duan Y#, Dou S#, Zhang H#, Wu C, Wu M, Lu J* (2018). Linkage of A-to-I RNA editing in metazoans and the impact on genome evolution. Mol Biol Evol. 35(1):132-148.
6. Luo S, Lu J* (2017). Silencing of Transposable Elements by piRNAs in Drosophila: An Evolutionary Perspective. Genomics Proteomics & Bioinformatics. 15(3):164-176.
7. Duan Y#, Dou S#, Luo S#, Zhang H, Lu J* (2017). Adaptation of A-to-I RNA editing in Drosophila. PLoS Genetics. 13(3):e1006648.
8. Wang YR, Luo JJ, Zhang H, and Lu J* (2016) microRNAs in the same clusters evolve to coordinately regulate functionally related genes. Molecular Biology and Evolution 33(9):2232-47.
9. Zhang XY, Zhu Y, Liu XD, Hong XY, Xu Y, Zhu P, Shen Y, Ji YS, Wen X, Zhang C, Zhao Q, Wang YC, Lu J, Guo HW*. (2015) Suppression of endogenous gene silencing by degradation of normal cytoplasmic RNA in Arabidopsis. Science 348(6230): 120-123.
10. Yin S, Fan Y, Zhang H, Zhao Z, Hao Y, Li J, Sun C, Yang J, Yang Z, Yang X, Lu J, Xi JJ*. (2016) Differential TGFβ pathway targeting by miR-122 in humans and mice affects liver cancer metastasis. Nature Communications. 7:11012. doi: 10.1038/ncomms11012.
11. Ye KX, Lu J, Ma F, Keinan A, Gu ZL*. (2014) Extensive Pathogenicity of Mitochondrial Heteroplasmy in Healthy Human Individuals. Proceedings of the National Academy of Sciences 111(29): 10654–10659.
12. Lu J* & Clark AG* (2012). Impact of microRNA regulation on variation in human gene expression. Genome Research. 22(7): 1243–1254.
13. Zhou RC, Ling SP, Zhao WM, Osada N, Chen SF, Zhang M, He ZW, Bao H, Zhong CR, Zhang B, Lu XM, Turissini D, Duke NC, Lu J*, Shi SH*, Wu CI* (2011) Population genetics in non-model organisms: II. Natural selection in marginal habitats revealed by deep sequencing on dual platforms. Molecular Biology and Evolution 28(10):2833-42.
14. Tang T, Kumar S, Shen Y, Lu J, Wu ML, Shi S, Li WH, Wu CI* (2010) Adverse interactions between micro-RNAs and target genes from different species. Proceedings of the National Academy of Sciences 107: 12935-12940.
15. Lu J, Clark AG* (2010). Population dynamics of PIWI-interacting RNAs (piRNAs) and their targets in Drosophila. Genome Research 20: 212-227.
16. Lu J, Shen Y,Wu QF, Kumar S, He B, Carthew RW, Wang SM*, Wu CI* (2008). The birth and death of microRNA genes in Drosophila. Nature Genetics 40: 351-355; author reply in 42: 9-10.
17. Lu J, Fu Y, Kumar S, Shen Y, Zeng K, Xu A, Carthew RW, Wu CI* (2008). Adaptive evolution of newly emerged micro-RNA genes in Drosophila. Molecular Biology and Evolution 25: 929-938.
18. Wang HY, Fu Y, McPeek MS, Lu X, Nuzhdin S, Xu A, Lu J, Wu ML, Wu CI* (2008). Complex genetic interactions underlying expression differences between Drosophila races: analysis of chromosome substitutions. Proceedings of the National Academy of Sciences 105: 6362-6367.
19. Wu QF, Kim YC, Lu J, Xuan ZY, Chen J, Zheng YL, Zhou T, Zhang MQ, Wu CI, Wang SM* (2008). Poly A- Transcripts Expressed in HeLa Cells. PLoS ONE 3(7): e2803.
20. Clark AG, Eisen MB, Smith DR, Bergman CM, Oliver B, Markow TA et al (2007) Evolution of genes and genomes on the Drosophila phylogeny. Nature 450: 203-218 (Lu J is a coauthor of this paper).
21. Shapiro JA, Huang W, Zhang C, Hubisz MJ, Lu J, Turissini DA, Fang S, Wang HY, Hudson RR, Nielsen R, Chen Z, Wu CI* (2007) Adaptive Genic Evolution in the Drosophila Genomes. Proceedings of the National Academy of Sciences 104: 2271-2276.
22. Lu J#, Tang T#, Tang H, Huang JZ, Shi SH*, Wu CI* (2006) The accumulation of deleterious mutations in rice genomes: a hypothesis on the cost of domestication. Trends in Genetics 22: 126-131.
23. Tang T#, Lu J#, Huang J, He J, McCouch SR, Purugganan MD, Shi SH*, Wu CI* (2006). Genomic variation in rice - Genesis of highly polymorphic linkage blocks during domestication. PLoS Genetics 2(11):e199.
24. Lu J, Wu CI* (2005). Weak selection revealed by the whole-genome comparison of the X chromosome and autosomes of human and chimpanzee. Proceedings of the National Academy of Sciences 102: 4063-4067.
25. Tang H, Wyckoff GJ, Lu J, Wu CI* (2004) A universal evolutionary index for amino acid changes. Molecular Biology and Evolution 21: 1548-1556.
26. Lu J, Li WH, Wu CI* (2003) Comment on `Chromosomal speciation and molecular divergence-accelerated evolution in rearranged chromosomes`. Science 302: 988.
27. Lu J, Lü J, Chen HX, Zhang WX, Dai ZH* (2002) Molecular phylogeny of Drosophila auraria species complex (in Chinese). Acta Genetica Sinica 29: 39-49.
28. Zhao Z, Lu J, Dai ZH* (2001). Genetic differentiation within Drosophila auraria species complex revealed by Random Amplified Polymorphic DNA (RAPD) (in Chinese). Acta Zool. Sinica 47: 625-631.