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Faculty Detail    
Name JOHN L. HARTMAN, IV
Associate Professor
 
Campus Address KAUL 702 Zip 0024
Phone 205-996-4195
E-mail jhartman@uab.edu
Other websites Research Gate
     


Faculty Appointment(s)
Appointment Type Department Division Rank
Primary  Genetics   Genetics Research Div Associate Professor
Secondary  Biochemistry & Molecular Genetics  Biochemistry & Molecular Genetics Assistant Professor
Secondary  Medicine  Med - Hematology & Oncology Assistant Professor
Secondary  Pharmacology/Toxicology   Pharmacology/Toxicology Chair's Office Assistant Professor
Center  Arthritis & Musculoskeletal Diseases Center  Arthritis & Musculoskeletal Diseases Center Associate Professor
Center  Comprehensive Cancer Center  Comprehensive Cancer Center Associate Professor
Center  Comprehensive Ctr for Healthy Aging  Comprehensive Ctr for Healthy Aging Associate Professor
Center  Medicine  Comprehensive Diabetes Ctr (Org Ret) Associate Professor
Center  General Clinical Research Center  Ctr for Clinical & Translational Sci Associate Professor
Center  Neurology   Ctr Neurodegeneration & Exp Ther (CNET) Associate Professor
Center  Cystic Fibrosis Research Center  Cystic Fibrosis Research Center Associate Professor
Center  Nutrition Sciences   Nutrition Obesity Res Ctr (NORC) Associate Professor

Graduate Biomedical Sciences Affiliations
Biochemistry and Structural Biology 
Cancer Biology 
Cell, Molecular, & Developmental Biology 
Cellular and Molecular Biology Program 
Genetics and Genomic Sciences 
Medical Scientist Training Program 
Microbiology 
Neuroscience 
Pathobiology and Molecular Medicine 

Biographical Sketch 
John Hartman received a B.S. from Duke University in 1989, and an M.D. from UAB in 1995. He completed Internal Medicine Residency and Hematology Fellowship at the University of Washington and Fred Hutchinson Cancer Research Center in Seattle, WA from 1995-2001. Past research experience has been with Max Cooper (UAB, Immunology), George Philips (Duke, Hematology), Eric Sorshcer (UAB, Cystic Fibrosis), John Northup (NIH, G-protein Signaling), and Lee Hartwell (Fred Hutchinson Cancer Research Center, Yeast Genetics). Past awards include a Howard Hughes Medical Institute (HHMI) Research Fellowship for Medical Students, HHMI Research Fellowship for Physician-Scientists, NIH K08 Career Development Award, HHMI Early Career Award for Physician-Scientists, American Cancer Society Research Scholar Grant, and R01 from NIH/NIA for Demonstration Projects for Systems Biology of Aging in Saccharomyces cerevisiae.

Society Memberships
Organization Name Position Held Org Link
American Assoc. for the Advancement of Science (AAAS)  Member  http://www.aaas.org/ 
Cancer Molecular Therapeutics Research Association (CMTRA)  Member  http://www.cancermoleculartherapeutics.org/ 
Genetics Society of America (GSA)  Member  http://www.genetics-gsa.org/ 

Research/Clinical Interest
Title
Experimental models of gene interaction networks that buffer human disease using cell array phenotyping of yeast gene knockout libraries
Description
Biological systems are robust, having the capacity to maintain relatively stable phenotypic outputs over a range of perturbing genetic and environmental inputs. Genetic buffering refers to gene activities within a cell that confer phenotypic stability in a particular context. Genetic interactions, defined whenever the phenotype resulting from a chemical or genetic perturbation is dependent upon a particular gene, underlie buffering. Buffering networks are manifest, for example, by chemical sensitivity or synthetic lethality revealed through high throughput phenotyping of yeast gene knockout library . Research in our laboratory is focused on understanding genotype-phenotype complexity through global, quantitative analysis of genetic interactions. Using the powerful model system, S. cerevisiae, we aim to understand the various structures of gene interaction networks that influence different of human diseases. To measure gene interaction globally, we perturb an array of ~5000 isogenic yeast deletion strains, and use cell proliferation as a phenotypic readout to quantify the interacting effects between the perturbation and deletion at each locus. By varying the type and intensity of perturbation, the resulting selectivity and strengths of interaction are determined, revealing the relative buffering specificity of each gene. Using gene annotation and other bioinformatics resources to analyze the quantitative patterns of gene interaction, testable hypotheses are generated to further understand the molecular basis of the observed interaction networks. Genes that interact (exacerbate or compensate) with a known genetic or environmental disease-susceptibility factor can act as disease modifiers, contributing to complex disease traits. Systematic, comprehensive, quantitative understanding of how genetic buffering and cellular robustness are achieved in the highly tractable yeast model system is a strategy for understanding complex genotype-phenotype relationships that may exist generally for eukaryotic cells. The Hartman laboratory has developed novel methodologies for the type of global, quantitative analysis of genetic interactions described above, and these are being applied to understand genetic buffering networks that modulate disease expression.

Selected Publications 
Publication PUBMEDID
Rodgers J, Guo J, Hartman IV JL. Phenomic assessment of genetic buffering by kinetic analysis of cell arrays. Methods Mol Biol 2014;1205:187-208.  25213246 
Allison DB, Antoine LH, Ballinger SW, Bamman MM, Biga P, Darley-Usmar VM, Fisher G, Gohlke JM, Halade GV, Hartman IV JL, Hunter GR, Messina JL, Nagy TR, Plaisance EP, Powell ML, Roth KA, Sandel MW, Schwartz TS, Smith DL, Sweatt JD, Tollefsbol TO, Watts SA, Yang Y, Zhang J, Austad SN. Aging and energetics' 'Top 40' future research opportunities 2010-2013. F1000Research 2014;3:219.  25324965 
Wei S, Roessler BC, Chauvet S, Guo J, Hartman IV JL, Kirk KL. Conserved Allosteric Hot Spots in the Transmembrane Domains of Cystic Fibrosis Transmembrane Conductance Regulator (CFTR) Channels and Multidrug Resistance Protein (MRP) Pumps. J Biol Chem 2014;289:19942-57.  24876383 
Zhang Y, Anderson SJ, French SL, Sikes ML, Viktorovskaya OV, Huband J, Holcomb K, Hartman IV JL, Beyer AL, Schneider DA. The SWI/SNF Chromatin Remodeling Complex Influences Transcription by RNA Polymerase I in Saccharomyces cerevisiae. PloS one 2013;8:e56793.  23437238 
Louie RJ, Guo J, Rodgers JW, White R, Shah N, Pagant S, Kim P, Livstone M, Dolinski K, McKinney BA, Hong J, Sorscher EJ, Bryan J, Miller EA, Hartman IV JL. A yeast phenomic model for the gene interaction network modulating F508del-CFTR protein biogenesis. Genome Med 2012;4:103.  23270647 
Guo J, Tian D, McKinney BA, Hartman IV JL. Recursive expectation-maximization clustering: a method for identifying buffering mechanisms composed of phenomic modules. Chaos 2010;20:026103.  20590332 
Copic A, Dorrington M, Pagant S, Barry J, Lee MC, Singh I, Hartman IV JL, Miller EA. Genomewide analysis reveals novel pathways affecting endoplasmic reticulum homeostasis, protein modification and quality control. Genetics 2009;182:757-69.  19433630 
Singh I, Pass R, Togay SO, Rodgers JW, Hartman IV JL. Stringent Mating-Type-Regulated Auxotrophy Increases the Accuracy of Systematic Genetic Interaction Screens with Saccharomyces cerevisiae Mutant Arrays. Genetics 2009;181:289-300.  18957706 
Mani R, St Onge RP, Hartman IV JL, Giaever G, Roth FP. Defining genetic interaction. Proc Natl Acad Sci U S A 2008;105:3461-6.  18305163 
Shah NA, Laws RJ, Wardman B, Zhao LP, Hartman IV JL. Accurate, precise modeling of cell proliferation kinetics from time-lapse imaging and automated image analysis of agar yeast culture arrays. BMC Syst Biol 2007;1:3.  17408510 
Hartman IV JL. Buffering of deoxyribonucleotide pool homeostasis by threonine metabolism. Proc Natl Acad Sci U S A 2007;104:11700-5.  17606896 
Hartman IV JL, Tippery NP. Systematic quantification of gene interactions by phenotypic array analysis. Genome Biol 2004;5:R49.  15239834 
Hartman IV JL, Garvik B, Hartwell L. Principles for the buffering of genetic variation. Science 2001;291:1001-4.  11232561 

Keywords
yeast genetics, quantitative high throughput cell array phenotyping (Q-HTCP), gene interaction networks, aging, cystic fibrosis, dNTP metabolism, systems biology, drug discovery, lab automation