Epigenomics and longevity/health
What determines lifespan in humans? Recent findings in animal models show that lifespan is not exclusively encoded in genes, and that epigenetic factors such as DNA methylation and histone modification of the genome have important roles. Longevity is therefore regulated by both genetic and environmental factors. To decipher the relationship between longevity and factors such as diet, life style, faith and other variables, a large and unique human study, the Adventist Health Study (AHS2), was initiated at LLU in Loma Linda, CA, one of the five blue zones in the world where many people enjoy a long and healthy life span. We will use state-of-the-art next-generation sequencing (NGS) technologies and cutting-edge genomic and bioinformatic tools to map out the epigenomic and transcriptomic fingerprints that are characteristic of longevity. Using systems biology approaches and through a 3-D study design, our objectives are: 1) decipher genome-wide DNA methylation and its relationship with aging/longevity; 2) determine long-term dietary (vegan) modulations and reprogramming of the epigenome and their relationship with aging; 3) define the molecular underpinning of health and disease by interrogating the epigenome and transcriptome of the AHS2 population. Our long-term goal is to gain a better understanding (from a systems biology perspective) of epigenomic reprogramming due to diet and life style in relation to longevity. This study is in collaboration with Dr. Penelope Duerksen-Hughes and Dr. Gary Fraser.
Alternative splicing and ncRNAs in developmental rat heart and brain
The rat has been used extensively as a model for evaluating chemical toxicities and for studying human disease. However, the rat genome is still incomplete and our RNA-seq mapping data suggests ~3% of the sequence is missing. Furthermore, the rat transcriptome is not well annotated. Through the US Food and Drug Administration’s Sequencing Quality Control Consortium, we constructed a comprehensive rat transcriptomic BodyMap by RNA-seq across 11 organs of both sexes of 4 developmental stages (juvenile, adolescent, adult and aged) Fischer 344 rats. We catalogued the expression profiles of 40,064 genes, 65,167 transcripts, 31,909 alternatively spliced transcript variants and 2,367 non-coding genes/non-coding RNAs (ncRNAs) annotated in AceView (http://www.nature.com/ncomms/2014/140210/ncomms4230/full/ncomms4230.html). This represents the first usage of large amounts of next-generation deep sequence data in rat cross-validated against AceView annotation. However, the sequencing was carried out at 50 bp, single read only, which made it difficult to detect all alternative splicing isoforms and ncRNAs. Our lab is further carrying out a much deeper sequencing on rat heart and brain with 150 bp, paired-end reads. Our goals are: 1) identify the alternative splicing and isoforms that may dictate the rat heart and brain development across 4 different ages; 2) identify the ncRNAs that may regulate the rat heart and brain development across 4 different ages.
Hypoxia and epigenomic reprogramming in rat heart and brain
Animal models of gestational hypoxia have demonstrated heightened vulnerability of heart and brain to ischemic injury in offspring. We hypothesize that the hypoxia-induced epigenomic reprogramming is one of the major mechanisms affecting heart and brain development. Epigenomics is the genome-wide study of epigenetic elements and it deals with genomic maps of stable, yet reprogrammable nuclear changes that control gene expression and influence our health. DNA methylation is a chief mechanism for epigenetic modification of gene expression patterns and occurs at cytosines in the CpG dinucleotide areas enriched in small regions of DNA. It had been shown that prenatal hypoxia increases CpG methylation in the promoter of glucocorticoid receptor (GR) in rat fetal hearts whereas the methylation of the GR promoter has been reported to occur as function of physiological regulation of the hypothalamic-pituitary-adrenal axis. Nevertheless, it is unknown if prenatal hypoxia will cause a global epigenomic reprogramming (e.g., DNA methylation) which leads to a global transcriptomic alteration affecting the development of heart and brain. Using next-gen sequencing technologies, our objectives are: 1) map the genome-wide DNA methylation fingerprints of rat fetal heart and brain exposed to prenatal hypoxia; 2) determine the prenatal hypoxia-induced transcriptomic changes and it relationship to the genomic DNA methylation in the promoters of genes in rat fetal heart and brain. Our overall long-term goal is to interrogate epigenomic reprogramming and alteration of gene expression of prenatal hypoxia-induced developmental programming of ischemic-sensitive phenotype in the heart and brain. This study is in collaboration with Dr. Lubo Zhang, Director of Center for Perinatal Biology at LLU.
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2. Li, S., Zumbo, P., Labaj, P., Skyacek, P., Shi, W., Shi, L., Phan, J., Wu, L., Wang, M., Wang, C., Thierry-Mieg, J., Thierry-Mieg, D., Kreil, D., Mason, C. Detecting and Ameliorating Systematic Variation from Large-scale RNA Sequencing. Nature Biotechnology, 2014 (In Press).
3. Yu, Y., Zhao, C., Su, Z., Wang, C., Fuscoe, J., Tong, W., Shi, L. Comprehensive RNA-Seq transcriptomic profiling across 11 organs, 4 ages, and 2 sexes of Fischer 344 rats. Nature Scientific Data, 2014 (In Press).
4. Gong, B., Wang, C., Su, Z., Hong, H., Shi, L., Auerbach, S., Tong, W., and Xu, J. Transcriptomic profiling of rat liver samples in a comprehensive study design by RNA-Seq. Nature Scientific Data, 2014 (In Press).
5. SEQC consortium-Su, Z., Labaj, P. P., Li, S., Thierry-Mieg, J., Thierry, D., Shi, W., C. Wang, Schroth, G….. and Shi et al. (SEQC main study). The power and limitation of RNA-Seq: Findings from SEQC (MAQC_III) consortium. Nature Biotechnology 2014 (In Press).
6. Wu, S., Li, X., Gunawaqrdana, M., Maquire, K., Guerrero-Given, D., Schaudinn, C., Wang, C., Baum, M., M. and Webster, P. Beta- Lactam Antibiotics Stimulate Biofilm Formation in Non-Typeable Haemophilus influenzae by Up-Regulating Carbohydrate Metabolism, PLoS ONE, July 09, 2014 (DOI: 10.1371/journal.pone.0099204)
7. Yu Y, Fuscoe JC, Zhao C, Guo C, Jia M, Qing T, Bannon DI, Lancashire L, Bao W, Du T, Luo H, Su Z, Jones WD, Moland CL, Branham WS, Qian F, Ning B, Li Y, Hong H, Guo L, Mei N, Shi T, Wang KY, Wolfinger RD, Nikolsky Y, Walker SJ, Duerksen-Hughes P, Mason CE, Tong W, Thierry-Mieg J, Thierry-Mieg D, Shi L, Wang C. A rat RNA-seq transcriptomic bodymap across 11 organs and 4 developmental stages. Nat Commun. 2014;5:3230. PMCID: PMC3926002
8. Warden, C., Lee, H., Tompkins, J. Li, X., Wang, C., Riggs, A., Yu, H., Jove, R., Yuan, Y-C. An integrative genomic pipeline for single-nucleotide resolution DNA methylation analysis. Nucleic Acid Res., 2013 Jun;41(11):e117. doi: 10.1093/nar/gkt242. PMCID: PMC3675470
9. Chen G, Wang C., Shi L, Tong W, Qu X, Chen J, Yang J, Shi C, Chen L, Zhou P, Lu B, Shi T. Comprehensively identifying and characterizing the missing gene sequences in human reference genome with integrated analytic approaches. Hum Genet. 2013;132:899-911. PMID: 23572138
10. Chen, G., Wang, C., Shi, L., Qu, X. Chen, J. Yang, J. Shi. C., Chen, L., Zhou, P. Y., Ning, B., Tong, W. and Shi, T. Incorporating the human gene annotations in different databases significantly improved transcriptomic and genetic analyses. RNA, 19(4): 479-489, 2013.
11. Niesen, C. E., Xu, J., Fan, S., Li, X., Wheeler, C. J., Mamelak, A., C., Wang, C. Transcriptomic profiling of human peritumoral neocortex tissue revealed genes possibly involved in tumor-induced epilepsy. PLoS ONE, 8(2): e56077, 2013.
12. Li, W., Tian, E., Chen, Z., Sun, G. Q., Ye, P., Yang, S., Lu, D., Xie, J., Ho, T.V., Tsark, W. M., Wang, C., Horne, D. A., Riggs, A. D., Yi, M. L. R. and Shi, Y. Identification of Oct4-activating compounds that enhance reprogramming efficiency. PNAS, 109 (51): 20853-20858, 2012.
13. Zheng, L., Dai, H., Zhou, M., Li, X. J., Liu, C., Guo, Z., Wu, X., Wu, J., Wang, C., Zhong, J., Huang, Q., Garcia-Aguila, J., Pfeifer, G. P., Shen, B. Polyploid cells rewire DNA damage response and repair networks to overcome DNA replication stress-induced senescence barriers for tumor progression. Nature Commun., 3(815), 2012.
14. Jin, W., Reddy, M. A., Chen, Z., Putta, S., Lanting, L., Kato, M., Park, J. T., Chandra, M., Wang, C., Tangirala, R., and Natarajan, R. Small RNA sequencing reveals micrornas that modulate angiotensin II effects in vascular smooth muscle cells. J. Biological Chem., 287(19): 15672-83, 2012.
15. Hines, H. M., Papa, R., Ruiz, M., Papanicolaou, A., Wang, C., Nijhout, F., McMillan, W. O., and Reed, R. D. Transcriptome analysis reveals novel patterning and pigmentation genes underlying Heliconius butterfly wing pattern variation. BMC Genomics, 13(288):1-16, 2012.
16. Zhang, Y., Li, T. S., Lee, S-T., Wawrowsky, K., A., Cheng, K., Galang, G., Abraham, R., Wang, C. & Marban, E. Dedifferentiation and Proliferation of Mammalian Cardiomyocytes. PLoS ONE, 5(9):e12559, 2010.
17. Shi, L., Campbell, G., Jones, W. D., Campagne, F., Walker, S. J., Su, Z., Goodsaid, F. M., Pusztai, L., Shaughnessy, J. D., Oberthuer, A., …, Wang, C., Tong, W., Wolfinger, R. D. The MAQC-II Project: A comprehensive study of common practices for the development and validation of microarray-based predictive models. Nature Biotechnology, 28: 827-838, 2010.
18. Shi, L., Jones, W…..Wang, C., Warrington, J. Tong, W. et al. The balance of reproducibility, sensitivity, and specificity of lists of differentially expressed genes in microarray studies. BMC Bioinformatics, 9(Suppl 9), S10, 2008. See also Nature Proceedings, http://precedings.nature.com/documents/306/version/2.
19. Xu, J., Deng, X., Demetriou, A. A., Farkas, D. L., Hui, T., and Wang, C. Factors released from cholestatic rat livers possibly involved in inducing bone marrow hepatic stem cell priming. Stem Cell & Development. 2008, 17(1):143-155.
20. Xu, J., Deng, X., Chan, V., Kelley-Loughnane, N., Harker, B. W., Shi, L., Hussain, S. M., Frazier, J. M. and Wang, C. Variability of DNA microarray gene expression profiles in cultured rat primary hepatocytes. Gene Regulation and System Biology, 2007, 1:235-250.
21. Guo, L., Lobenhofer, E., K., Wang, C., Shippy, R., Harris, S. C., Zhang, L., Mei, N., Chen, T., Herman, D., Goodsaid, F. M., Hurban, P., Phillips, K. L., Xu, J., Deng, X., Sun, Y. A., Tong, T., Dragan Y. P., & Shi, L. Rat toxicogenomic study reveals analytical consistency across microarray platforms. Nature Biotech, 24: 1162-1169, 2006.
22. Shi, L., Reid, L., Jones, W…..Wang, C., Wilson, M. et al. The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements. Nature Biotech, 24: 1151-1161, 2006.
23. Tan, Y. X., Shi, L., Hussain, S. M., Tong, W., Frazier, J. M., Wang, C. Integrating time-course microarray gene expression profiles with cytotoxicity for identification of biomarkers in primary rat hepatocytes exposed to cadmium. Bioinformatics, 22(1): 77-87, 2006.
24. Tan, Y. X., Shi, L., Tong, W., Wang, C. Multi-class cancer classification by Total Principal Component Regression using microarray gene expression data. Nucleic Acid Res. 33(1): 56-65, 2005
25. Wang, C., Chelly, M. R., Chai, N., Tan, Y. X., Hui, T., Li, H., Farkas, D. L., and Demetriou, A. A. Transcriptomic fingerprinting of bone marrow-derived hepatic b2m-/Thy-1+ stem cells. Biochem. Biophys. Res. Commun., 327: 252-260, 2005.
26. Tan, Y.X., Shi, L. M., Hwang, G. T., Tong, W. and Wang, C. Multi-class tumor classification by discriminant partial least squares using microarray gene expression data and the assessment of model quality. Computational Biology & Chemistry, 28(3): 235-243, 2004.