Peltz Lab In the Department of Anesthesia

Peltz Lab

main_topic

The Peltz laboratory utilizes computational genetics and integrative ‘biomic’ analysis for translational biomedical discovery. The two (distinct but related) types of research projects in the lab are described below.

I. Computational Genetics and Integrative Biomic Analysis

better_drugs gen

To reduce the cost and the time frame for genetic research, we have developed a novel computational method that rapidly identifies the genetic basis for biomedical trait differences among inbred mouse strains [1-3] [4]. Conventional (QTL) mouse genetic analysis methods require 5 to 10 years to produce results; while a computational analysis can be completed in less than one day, which significantly accelerates the rate of genetic discovery. In a mapping experiment, a property of interest is measured in >10 inbred mouse strains; genetic factors are then computationally predicted by identifying genomic regions where the pattern of genetic variation correlates with the distribution of trait values among the inbred strains [1]. This innovative approach enables genetic analyses to be completed in far less time (days vs. years), with far fewer personnel, and with a higher overall success rate than could be achieved using conventional mouse genetic analysis methods [3]. Since its inception in 2004, there have been multiple successful demonstrations of its ability to rapidly identify causative genetic factors for biomedical traits in mice, including: gene expression [1]; pharmacogenetic factors [5-8]; susceptibility to invasive Aspergillosis [9] and Respiratory Syncitial Virus infections [10]; analgesic medication [11] and inflammatory [12, 13] and chronic [14] pain; incisional wound biology [15, 16]; and 4 genes affecting narcotic drug responses [11, 17-19]. The latter genetic discovery generated a novel treatment strategy for narcotic drug withdrawal, which was effective in human subjects [19].

science_new
gen_res

To attack 21st Century biomedical problems, the lab is applying integrative genomic (genetic, metabolomic, transcriptional and knowledge-based) approaches that will accelerate the rate of translational biomedical discovery. These integrated multi-dimensional studies of biomedical systems will be referred to as 'Biomic Analysis.' The level of thousands of mRNAs, metabolites or proteins within a tissue can now be simultaneously measured; which provide orthogonal information to help eliminate false positive results. Our recent analysis of a murine genetic model of acetaminophen (Tylenol)-induced liver toxicity provides an exciting example of how Biomic analysis can lead to exciting discoveries that can provide potential solutions to a major public health problem. Acetaminophen is a safe and effective drug when administered appropriately, but an acute overdose causes sever liver damage. Because of its widespread use, acetaminophen toxicity has become the most frequent cause of acute liver failure in the United States [20]. To uncover the genetic basis for inter-strain differences in susceptibility to this toxicity; we performed an integrative genetic, transcriptional, and metabolomic analysis of the drug-induced response in livers obtained from resistant and sensitive strains. Changes in endogenous metabolites (1H-13C 2-dimensional-NMR) [21] and gene expression (microarrays) in liver were simultaneously examined at 0, 3 and 6 hr after acetaminophen exposure [22]. This integrative analysis identified homocysteine S-methyl transferase 2 (Bhmt2) as a diet-dependent genetic factor that affected susceptibility to acetaminophen-induced liver toxicity in mice. Through an effect on methionine and glutathione biosynthesis, Bhmt2 could utilize its substrate [S-methylmethionine (SMM)] to confer protection against acetaminophen-induced injury in vivo. This work demonstrates how an integrative genomic analysis in mice can provide a unique and clinically applicable approach to a major public health problem.

II. Mice with ‘Humanized’ Livers

mice liver 

This project was selected by the NIH director for a Transformative R01 Award in October of 2010.

A novel experimental in vivo platform that replaces mouse liver with functioning human liver tissue was developed in collaboration with investigators at the Central Institute for Experimental Animals (Japan). To do this, a thymidine kinase (TK) transgene was expressed within the liver of highly immunodeficient mice (TK-NOG). Mouse liver cells expressing this transgene were ablated after a brief exposure to a non-toxic dose of gancyclovir, and transplanted human liver cells were stably maintained within the liver (humanized TK-NOG) without exogenous drug or immunosuppressive treatments. The reconstituted liver was shown to be a mature and functioning "human organ." It had zonal position-specific enzyme expression and bile duct function representative of mature human liver, and could generate a human-specific profile of drug metabolism. These features make the TK-NOG mouse the preferred experimental platform for in vivo analysis of drug metabolism or liver regeneration. The mice are maintained in a specialized barrier facility that was designed for housing these mice.


post

The humanized mice are being used to: develop a novel platform for predicting human drug metabolism and human drug responses; for understanding stem cell development; and to develop a new method for liver transplantation that uses autologous cells without immunosuppression.

References

1.       Liao G, Wang J, Guo J, Allard J, Cheng J, Ng A, Shafer S, Puech A, McPherson JD, Foernzler D et al: In Silico Genetics: Identification of a Functional Element Regulating H2-Ea Gene Expression. Science 2004, 306(5696):690-695.
2.       Wang J, Peltz G: Haplotype-Based Computational Genetic Analysis in Mice. In: Computational Genetics and Genomics: New Tools for Understanding Disease. Totowa, New Jersey: Humana Press Inc.; 2005: 51-70.
3.       Wang J, Liao G, Usuka J, Peltz G: Computational Genetics: From Mouse to Man? Trends in Genetics 2005, 21(9):526-532.
4.       Zheng M, Dill D, Peltz G: A better prognosis for genetic association studies in mice. Trends in Genetics 2012, In Press.
5.       Guo YY, Weller PF, Farrell E, Cheung P, Fitch B, Clark D, Wu SY, Wang J, Liao G, Zhang Z et al: In Silico Pharmacogenetics: Warfarin Metabolism. Nature Biotechnology 2006, 24:531-536.
6.       Guo YY, Liu P, Zhang X, Weller PMM, Wang J, Liao G, Zhang Z, Hu J, Allard J, Shafer S et al: In vitro and In silico Pharmacogenetic Analysis in Mice. Proceedings of the National Academy of Sciences 2007, 104:17735-17740.
7.       Liao G, Zhang X, Clark DJ, Peltz G: A genomic "roadmap" to "better" drugs. Drug Metabolism Reviews 2008, 40(2):225-239.
8.       Zhang X, Liu H-H, Weller P, Tao W, Wang J, Liao G, Zheng M, Monshouwer M, Peltz G: In Silico and In Vitro Pharmacogenetics: Aldehyde Oxidase Rapidly Metabolizes a p38 Kinase Inhibitor. The Pharmacogenomics Journal 2011, 11(1):15-24.
9.       Zaas AK, Liao G, Chein J, Usuka J, Weinberg C, Shore D, Giles D, Marr K, Burch L, Perara et al: Plasminogen Alleles Influence Susceptibility to Invasive Aspergillosis. PLoS genetics 2008, 4(6):e1000101.
10.     Tregoning JS, Yamaguchi Y, Wang B, Mihm D, Harker JA, Bushell ESC, Zheng M, Liao G, Peltz G, Openshaw PJM: Genetic Susceptibility to the Delayed Sequelae of RSV Infection is MHC-Dependent, but Modified by Other Genetic Loci. J Immunology 2010, 185(6):5384-5391.
11.     Smith SB, Marker CL, Perry C, Liao G, Sotocinal SG, Austin JS, Melmed K, David Clark J, Peltz G, Wickman K et al: Quantitative trait locus and computational mapping identifies Kcnj9 (GIRK3) as a candidate gene affecting analgesia from multiple drug classes. Pharmacogenetics and Genomics 2008, 18(3):231-241.
12.     LaCroix-Fralish ML, Mo G, Smith SB, Sotocinal SG, Ritchie JG, Austin JS, Melmed K, Schorscher-Petcu A, Laferriere AC, Lee TH et al: The β3 Subunit of the Na+,K+-ATPase Affects Pain Sensitivity. Pain 2009, 144:294-302.
13.     Li X, Sahbaie P, Zheng M, Ritchie J, Peltz G, Mogil JS, Clark JD: Expression genetics identifies spinal mechanisms supporting formalin late phase behaviors. Molecular Pain 2010, 6:11.
14.     Sorge RE, Trang T, Dorfman R, Smith SB, Beggs S, Ritchie J, Austin J-S, Zaykin DV, Meulen HV, Costigan M et al: Genetically determined P2X7 receptor pore formation regulates variability in chronic pain sensitivity. Nature Medicine 2012, In Press.
15.     Hu Y, Liang D, Li X, Liu H-H, Zhang X, Zheng M, Dill D, Shi X, Qiao Y, Yeomans D et al: The Role of IL-1 in Wound Biology Part I: Murine in Silico and In vitro Experimental Analysis. Anesthesia & Analgesia 2010, 111(6):1525-1533.
16.     Hu Y, Liang D, Li X, Liu H-H, Zhang X, Zheng M, Dill D, Shi X, Qiao Y, Yeomans D et al: The Role of IL-1 in Wound Biology Part II: In vivo and Human Translational Studies. Anesthesia & Analgesia 2010, 111(6):1534-1542.
17.     Liang D, Liao G, Wang J, Usuka J, Guo YY, Peltz G, Clark JD: A Genetic Analysis of Opioid-Induced Hyperalgesia in Mice  Anesthesiology 2006, 104:1054-1062.
18.     Liang DY, Liao G, Lighthall G, Peltz G, Clark JD: Genetic Variants of the P-Glycoprotein Gene Abcb1b Modulate Opioid-Induced Hyperalgesia, Tolerance and Dependence. Pharmacogenetics and Genomics 2006, 16:825-835.
19.     Chu LF, Liang D-Y, Li X, Sahbaie P, D'Arcy N, Liao G, Peltz G, Clark JD: From Mouse to Man: The 5-HT3 Receptor Modulates Physical Dependence on Opioid Narcotics. Pharmacogenetics and Genomics 2009, 19:193-205.
20.     Larson AM, Polson J, Fontana RJ, Davern TJ, Lalani E, Hynan LS, Reisch JS, Schiodt FV, Ostapowicz G, Shakil AO et al: Acetaminophen sets records in the United States: number 1 analgesic and number 1 cause of acute liver failure. Liver Transplantation 2006, 12(4):682-683.
21.     Zheng M, Lu P, Pease J, Liao G, Peltz G: An Automated Method for Analysis of 2-Dimensional 1H 13C NMR Spectra. Bioinformatics 2007, 23:2926-2933.
22.     Liu H-H, Lu P, Guo Y, Farrell E, Zhang X, Zheng M, Bosano B, Zhang Z, Allard J, Liao G et al: An Integrative Genomic Analysis Identifies Bhmt2 As A Diet-Dependent Genetic Factor Protecting Against Acetaminophen-Induced Liver Toxicity Genome Research 2010, 20:28-35.

Stanford Medicine Resources:

Footer Links: