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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
     

Education
Undergraduate  Duke University    1989  B.S. 
Medical School  University of Alabama at Birmingham    1995  M.D. 
Residency  University of Washington    1997  Internal Medicine 
Fellowship  University of Washington    2001  Hematology 
Fellowship  Fred Hutchinson Cancer Research Center    2004  Yeast Genetics 

Certifications
Internal Medicine  2001 


Faculty Appointment(s)
Appointment Type Department Division Rank
Primary  Genetics Research Div  Genetics Research Div Associate Professor
Secondary  Biochemistry & Molecular Genetics  Biochemistry & Molecular Genetics Associate Professor
Secondary  Med - Hematology & Oncology  Med - Hematology & Oncology Associate Professor
Secondary  Pharmacology/Toxicology Chair's Office  Pharmacology/Toxicology Chair's Office Associate Professor
Center  Comp Arthritis, MSK, Bone & Autoimmunity Ctr  Comp Arthritis, MSK, Bone & Autoimmunity Ctr Associate Professor
Center  Comprehensive Cancer Center  Comprehensive Cancer Center Associate Professor
Center  Comprehensive Diabetes Center  Comprehensive Diabetes Center Associate Professor
Center  Ctr for Clinical & Translational Sci  Ctr for Clinical & Translational Sci Associate Professor
Center  Ctr Neurodegeneration & Exp Ther (CNET)  Ctr Neurodegeneration & Exp Ther (CNET) Associate Professor
Center  Cystic Fibrosis Research Center  Cystic Fibrosis Research Center Associate Professor
Center  GL Ctr for Craniofacial, Oral, & Dental Disorders  GL Ctr for Craniofacial, Oral, & Dental Disorders Associate Professor
Center  Integrative Center for Aging Research  Integrative Center for Aging Research Associate Professor
Center  Nutrition Sciences Research  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, Genomics and Bioinformatics 
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, where he also conducted postdoctoral research in yeast genetics with Lee Hartwell at the Fred Hutchinson Cancer Research Center. Hartman became a faculty member in the UAB Department of Genetics in 2004, where he has developed yeast genetic approaches for phenomic modeling of human disease and disease complexity. Other prior research experiences as an undergraduate and medical student included working with Max Cooper (UAB, Immunology), George Philips (Duke, Hematology), Eric Sorshcer (UAB, Cystic Fibrosis), and John Northup (NIH, G-protein Signaling). Past awards include Howard Hughes Medical Institute (HHMI) Research Fellowship for Medical Students, HHMI Postdoctoral Fellowship, and HHMI Early Career Award for Physician-Scientists. Hartman has also received support through a K08 Career Development Award from NCI, American Cancer Society Research Scholar Grant, Cystic Fibrosis Foundation Research Grants, and R01 funding from NIA and NHLBI.

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 methodology to globally and quantitatively analyze genetic interactions and is applying them to model genetic buffering networks that modulate disease expression. The yeast homolog of CFTR, YOR1, is used as a model protein to understand different gene modifier networks relevant to cystic fibrosis. The chronological lifespan model is used to characterize factors regulating cell cycle exit and quiescence. The approach is also employed to gain a deeper understanding of the genetic factors that potentially influence the response of cancer to cytotoxic chemotherapy.

Selected Publications 
Publication PUBMEDID
Enriquez-Hesles E, Smith, DL, Maqani N, Wierman MB, Sutcliffe MD, Fine RD, Kalita A, Santos SM, Muehlbauer MJ, Bain JR, Janes KA, Hartman 4th JL, Hirschey MD, Smith JS. A cell non-autonomous mechanism of yeast chronological aging regulated by caloric restriction and one-carbon metabolism. J Biol Chem 2021;Jan-Jun;296:100125. doi: 10.1074/jbc.RA120.015402.  33243834 
Santos SM, Laflin S, Broadway A, Burnet C, Hartheimer J, Rodgers J, Smith DL Jr, Hartman JL 4th. High-resolution yeast quiescence profiling in human-like media reveals complex influences of auxotrophy and nutrient availability. Geroscience 2021 Apr;43(2):941-964. doi: 10.1007/s11357-020-00265-2.  33015753 
Spurlock B, Tullet J, Hartman JL 4th, Mitra K. Interplay of mitochondrial fission-fusion with cell cycle regulation: Possible impacts on stem cell and organismal aging. Exp Gerontol. 2020 Jul 1;135:110919. doi: 10.1016/j.exger.2020.110919.  32220593 
Slowing ribosome velocity restores folding and function of mutant CFTR. Oliver KE, Rauscher R, Mijnders M, Wang W, Wolpert MJ, Maya J, Sabusap CM, Kesterson RA, Kirk KL, Rab A, Braakman I, Hong JS, Hartman JL 4th, Ignatova Z, Sorscher EJ. J Clin Invest. 2019 Dec 2;129(12):5236-5253. doi: 10.1172/JCI124282.  31657788 
Santos SM, Icyuz M, Pound I, William D, Guo J, McKinney BA, Niederweis M, Rodgers J, Hartman JL IV. A Humanized Yeast Phenomic Model of Deoxycytidine Kinase to Predict Genetic Buffering of Nucleoside Analog Cytotoxicity. Genes (Basel). 2019 Sep 30;10(10):770. doi: 10.3390/genes10100770  31575041 
A yeast phenomic model for the influence of Warburg metabolism on genetic buffering of doxorubicin. Santos SM, Hartman JL 4th. Cancer Metab. 2019 Oct 23;7:9. doi: 10.1186/s40170-019-0201-3. eCollection 2019.  31660150 
Smith DL Jr, Maharrey CH, Carey CR, White RA, Hartman JL 4th. Gene-nutrient interaction markedly influences yeast chronological lifespan. Exp Gerontol. 2016 Dec 15;86:113-123.  27125759 
Veit G, Oliver K, Apaja PM, Perdomo D, Bidaud-Meynard A, Lin ST, Guo J, Icyuz M, Sorscher EJ, Hartman IV JL, Lukacs GL. Ribosomal Stalk Protein Silencing Partially Corrects the DeltaF508-CFTR Functional Expression Defect. PLoS Biol 2016, 14:e1002462. doi: 10.1371/journal.pbio.1002462.  27168400 
Wei S, Roessler BC, Icyuz M, Chauvet S, Tao B, Hartman JL 4th, Kirk KL. Long-range coupling between the extracellular gates and the intracellular ATP binding domains of multidrug resistance protein pumps and cystic fibrosis transmembrane conductance regulator channels. FASEB J. 2016 Mar;30(3):1247-62.  26606940 
Hartman JL 4th, Stisher C, Outlaw DA, Guo J, Shah NA, Tian D, Santos SM, Rodgers JW, White RA. Yeast Phenomics: An Experimental Approach for Modeling Gene Interaction Networks that Buffer Disease. Genes (Basel). 2015 Feb 6;6(1):24-45.  25668739 
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, cellular quiescence, aging, cystic fibrosis, chemotherapy response, systems biology, drug discovery, lab automation