The proposal intends to advance both novel statistical methods for genomic research and discoveringcomplex molecular mechanisms on the teratoma initiation and progression after transplantation ofembryonic stem cells (ESCs). The biological aims are to investigate the mechanisms and biologic pathwaysof ESC-induced macrophage activation and teratoma development;and to unveil how the human MIFpolymorphisms associated with the serum MIF concentration in order to select patients for safe celltransplantation. To meet these challenges, high-throughput genomic tools and novel statistical methods willbe combined to enhance our understanding on the macrophage function on initiation and progression ofteratomas. The research will not only focus on identifying individual gene change on macrophages, but alsoanalyze underlying biologic pathways. In addition, time-course analysis in animal models will lead to identifygenes in different transition periods from early- to late-stage of teratoma development. These biologicalstudies prompt many new challenges in statistics. In particular, novel statistical techniques are proposed toanswer the following important questions: how to improve the power of existing tests for large-scale multipletesting problems with sparsity;how to detect whether genes have expressed over time course;how to usequantitative tools for discoveries of molecular mechanisms;how to control false discovery rate undergeneral dependence;what optimal detecting boundaries of signals are under arbitrary dependence;how toestimate residual variance in ultrahigh-dimensional regression. These topics have also importantimplications on compress sensing and computer security. In addition, several robust and effective methodsare proposed for ultrahigh-dimensional sparse regression, classification, and genetic networking. Thesenewly proposed methods will be applied to genome-wide association studies to identify single-nucleotidepolymorphisms associated with MIF expression in order to select patients for safe stem cell transplantation.An important feature of the proposal is the synthesis of PIs'extensive knowledge in statistics and cellbiology to gain better understanding of complicated biological problems.
The teratoma formation caused by ESCs can hamper clinical applications of stem cell based therapies. Theproposed research will advance our knowledge on the initiation and progression of teratomas after ESCtransplantation and lead to effective reduction of teratoma formation by inhibition of angiogenesis viamanipulating MIF expression and targeting M2 macrophages, making stem cell transplantation safe.
|Fan, Jianqing; Feng, Yang; Jiang, Jiancheng et al. (2016) Feature Augmentation via Nonparametrics and Selection (FANS) in High-Dimensional Classification. J Am Stat Assoc 111:275-287|
|Rolfe, Alyssa J; Bosco, Dale B; Wang, Jingying et al. (2016) Bioinformatic analysis reveals the expression of unique transcriptomic signatures in Zika virus infected human neural stem cells. Cell Biosci 6:42|
|Sun, Xin; Jones, Zachary B; Chen, Xiao-Ming et al. (2016) Multiple organ dysfunction and systemic inflammation after spinal cord injury: a complex relationship. J Neuroinflammation 13:260|
|Jones, Zachary B; Ren, Yi (2016) Sphingolipids in spinal cord injury. Int J Physiol Pathophysiol Pharmacol 8:52-69|
|Dobriban, Edgar; Fan, Jianqing (2016) Regularity Properties for Sparse Regression. Commun Math Stat 4:1-19|
|Guo, Lei; Rolfe, Alyssa J; Wang, Xi et al. (2016) Rescuing macrophage normal function in spinal cord injury with embryonic stem cell conditioned media. Mol Brain 9:48|
|Fan, Jianqing; Liao, Yuan; Shi, Xiaofeng (2015) Risks of Large Portfolios. J Econom 186:367-387|
|Wang, Xi; Cao, Kai; Sun, Xin et al. (2015) Macrophages in spinal cord injury: phenotypic and functional change from exposure to myelin debris. Glia 63:635-51|
|Fan, Jianqing; Ke, Zheng Tracy; Liu, Han et al. (2015) QUADRO: A SUPERVISED DIMENSION REDUCTION METHOD VIA RAYLEIGH QUOTIENT OPTIMIZATION. Ann Stat 43:1498-1534|
|Fan, Jianqing; Liao, Yuan; Yao, Jiawei (2015) Power Enhancement in High Dimensional Cross-Sectional Tests. Econometrica 83:1497-1541|
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