David Geffen School of Medicine at UCLA
Department of Human Genetics

Speaker Series - Winter Quarter 2010

Mondays, 11am - 12pm, Gonda Building First Floor Conference Room, 1357

Mon, Jan 11
Neuroscience Research Building Auditorium
Incremental solutions to the problem of unclassified variants in breast cancer susceptibility genes
Sean V. Tavtigian, Ph.D., Associate Professor, Department of Oncological Sciences, Huntsman Cancer Institute, University of Utah
Contact & Intro: Paivi Pajukanta, ext. 72011
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ABSTRACT: n/a

LITERATURE:

1) Goldgar, D. E., Easton, D. F., Deffenbaugh, A. M., Monteiro, A. N., Tavtigian, S. V., & Couch, F. J. (2004). Integrated evaluation of DNA sequence variants of unknown clinical significance: application to BRCA1 and BRCA2. Am J Hum Genet, 75(4): 535-44. 2) Tavtigian, S. V., Deffenbaugh, A. M., Yin, L., Judkins, T., Scholl, T., Samollow, P. B., de Silva, D., Zharkikh, A., & Thomas, A. (2006). Comprehensive statistical study of 452 BRCA1 missense substitutions with classification of eight recurrent substitutions as neutral. J Med Genet, 43(4): 295-305. 3) Tavtigian, S. V., Byrnes, G. B., Goldgar, D. E., & Thomas, A. (2008). Classification of rare missense substitutions, using risk surfaces, with genetic- and molecular-epidemiology applications. Hum Mutat, 29(11): 1342-54. 4) Tavtigian, S. V., Oefner, P. J., Babikyan, D., Hartmann, A., Healey, S., Le Calvez-Kelm, F., Lesueur, F., Byrnes, G. B., Chuang, S. C., Forey, N., Feuchtinger, C., Gioia, L., Hall, J., Hashibe, M., Herte, B., McKay-Chopin, S., Thomas, A., Vallee, M. P., Voegele, C., Webb, P. M., Whiteman, D. C., Sangrajrang, S., Hopper, J. L., Southey, M. C., Andrulis, I. L., John, E. M., & Chenevix-Trench, G. (2009). Rare, evolutionarily unlikely missense substitutions in ATM confer increased risk of breast cancer. Am J Hum Genet, 85(4): 427-46.

Mon, Jan 25
Cancelled
Mon, Feb 01
Transgenerational genetics effects on cancer, metabolic disease and behavior
Joseph H. Nadeau, Ph.D., Chairman & James H. Jewel Professor, Department of Genetics Case Western Reserve University
Contact & Intro: Paivi Pajukanta, ext. 72011 and Jake Lusis, ext. 51359
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ABSTRACT: n/a

LITERATURE:

1) Man-Yee J. Lam, Jason D. Heaney, Kirsten K. Youngren, Jean H. Kawasoe and Joseph H. Nadeau (2007). Trans-generational epistasis between Dnd1Ter and other modifier genes controls susceptibility to testicular germ cell tumors. Hum. Mol. Genet. 16:2233-2240, 2007. 2) Joseph H. Nadeau (2009) Transgenerational genetic effects on phenotypic variation and disease risk. Hum. Mol. Genet., Vol. 18, Review Issue 2, R202-210. 3) Haifeng Shaoa, Lindsay C. Burragea, David S. Sinasaca, Annie E. Hilla, Sheila R. Ernesta, William O’Brienc, Hayden-William Courtlandd, Karl J. Jepsend, Andrew Kirbye, E. J. Kulbokase, Mark J. Dalye,f, Karl W. Bromang, Eric S. Landerf, Joseph H. Nadeaua. Genetic architecture of complex traits: Large phenotypic effects and pervasive epistasis. PNAS 2008 105:19910-19914.

Mon, Feb 08
Genetic calcium signaling abnormalities in the CNS: seizures, migraine & autism
J. Jay Gargus, M.D., Ph.D, Professor, Pediatrics, Section of Human Genetics and Physiology & Biophysics, University of California, Irvine
Contact & Intro: Paivi Pajukanta, ext. 72011
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ABSTRACT: The calcium ion is one of the most versatile, ancient, and universal of biological signaling molecules, known to regulate physiological systems at every level from membrane potential and ion transporters to kinases and transcription factors. Disruptions of intracellular calcium homeostasis underlie a host of emerging diseases, the calciumopathies. Cytosolic calcium signals originate either as extracellular calcium enters through plasma membrane ion channels or from the release of an intracellular store in the endoplasmic reticulum (ER) via inositol triphosphate receptor and ryanodine receptor channels. Therefore, to a large extent, calciumopathies represent a subset of the channelopathies, but include regulatory pathways and the mitochondria, the major intracellular calcium repository that dynamically participateswith the ER stores in calcium signaling, thereby integrating cellular energy metabolism into these pathways, a process of emerging importance in the analysis of the neurodegenerative and neuropsychiatric diseases.Many of the calciumopathies arecommoncomplex polygenic diseases, but leads to their understanding come most prominently from rare monogenic channelopathy paradigms. Monogenic forms of common neuronal disease phenotypes— such as seizures, ataxia, and migraine—produce a constitutionally hyperexcitable tissue that is susceptible to periodic decompensations. The gene families and genetic lesions underlying familial hemiplegic migraine, FHM1/CACNA1A, FHM2/ATP1A2, and FHM3/SCN1A, and monogenic mitochondrial migraine syndromes, provide a robust platform from which genes, such as CACNA1C, which encodes the calcium channel mutated in Timothy syndrome, can be evaluated for their role in autism and bipolar disease.

LITERATURE:
  1. Segall L., A. Mezzetti, R. Scanzano, J.J. Gargus, E. Purisima, R. Blostein. (2005) Alterations in the α2 isoform of the Na,K-ATPase associated with Familial Hemiplegic Migraine Type 2. Proc. Nat. Acad. Sci. USA 102:11106-11111.
  2. Gargus J.J. (2009a) Genetic calcium signaling abnormalities in the CNS: seizures, migraine and autism. The Year in Human & Medical Genetics. Annals of the New York Academy of Sciences. 1151: 133 – 156.
  3. Gargus, J.J., A. Tournay (2007) Novel mutation confirms seizure locus SCN1A is also FHM3 migraine locus. Ped Neurol 37: 407-410.
Mon, Feb 22
Cancelled
Mon, Mar 01
Sorting of the Amyloid Precursor Protein Mediated by the AP-4 Complex
Juan S. Bonifacino, Head, Cell Biology and Metabolism Program (CBMP), Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), NIH
Contact & Intro: Esteban Dell’Angelica, ext. 63749
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ABSTRACT: The interior of eukaryotic cells is organized into an array of membrane-bound compartments. Each of these compartments has a characteristic protein composition that is responsible for its specific function. My laboratory has been interested in the molecular mechanisms that determine protein localization to different intracellular compartments, with a particular focus on organelles that make up the endosomal-lysosomal system. This system comprises various types of endosome (e.g., early, late, recycling), lysosomes and a family of “lysosome-related organelles” (LROs) (e.g., melanosomes, platelet dense bodies, cytotoxic granules, etc.), as well as the trans-Golgi network (TGN) and domains of the plasma membrane, which feed into this system. We have found that delivery of transmembrane proteins to these compartments is mediated by an elaborate system of sorting signals and adaptor proteins. Endosomal-lysosomal sorting signals generally occur within the cytosolic domains of transmembrane proteins and consist of linear arrays of amino acids that fit one of several consensus motifs. These include different types of “tyrosine-based” and “dileucine-based” sorting signals, so named on the basis of the most conserved and critical amino acids in the signal. These signals function as “bar codes” that are decoded by adaptors such as the AP-1, AP-2, AP-3 and AP-4 heterotetrameric complexes, the monomeric GGA1, GGA2, and GGA3 proteins, and several others, all of which are components of protein coats associated with the cytosolic face of membranes (e.g., clathrin coats). Signal-adaptor interactions result in incorporation of transmembrane cargoes into coated transport carriers destined to different organelles of the endosomal-lysosomal system. Understanding the mechanisms of protein sorting in this system is key to the elucidation of the pathogenesis of various genetic disorders caused by mutations in the signals (e.g., some forms of familial hypercholesterolemia) or the adaptors (e.g., Hermansky-Pudlak syndrome type 2), or by their exploitation by intracellular pathogens such as viruses and bacteria.

Over the years we have discovered and characterized various sorting signals and adaptors, and examined their involvement in several pathologies. We have recently found that the heterotetrameric AP-4 complex plays a role in the intracellular trafficking and processing of the Alzheimer’s Disease (AD) amyloid precursor protein (APP). An interaction screen showed that a previously unknown signal in the cytosolic tail of APP binds to the mu4 subunit of AP-4. The biochemical and structural details of this interaction are distinct from others that have been characterized to date. Mutation of the signal or depletion of cellular mu4 shift the steady-state localization of APP from endosomes to the TGN, and enhance amyloidogenic processing of APP to the pathogenic amyloid-beta (Abeta) peptide. These findings indicate that AP-4 sorts APP from the TGN to endosomes, and that amyloidogenic processing of APP is favored by localization to the TGN or the late secretory pathway. Thus, AP-4 exerts a protective effect that guards against excessive Abeta production. Defective expression of AP-4 should therefore be considered a potential risk factor for AD.

LITERATURE:

N/A

Mon, Mar 08
TALK IS FROM 12-1: Neuroscience Research Building Auditorium
Statistical methods for genome-wide analysis of quantitative traits in family samples
Josée Dupuis, Department of Biostatistics, Boston University School of Public Health, Boston, MA
Contact & Intro: Paivi Pajukanta, ext. 72011
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ABSTRACT: Multiple genome wide association scans using hundreds of thousands of single nucleotide polymorphisms have been performed recently, and have enabled researchers to identify genetic variants with small effect on quantitative traits. While most of these genome scans use unrelated samples, a small number of studies have pursued genome-wide association approaches in related subjects. Association analysis in family based samples presents certain additional statistical challenges because of the correlated nature of the observations; however, the advantages of family designs in genetic studies greatly outweigh the added analysis complexity. We present statistical approaches to exploit family attributes when searching for genetic variants influencing quantitative traits of interest. We illustrate the methods using examples from a high density scan in the Framingham Heart Study cohorts.

LITERATURE:
  1. Dupuis J, Shi J, Manning AK, Benjamin EJ, Meigs JB, Cupples LA, Siegmund D. Mapping quantitative traits in unselected families: algorithms and examples. Genet Epidemiol. 2009 33:617-27.
  2. Dupuis J, Siegmund DO, Yakir B. A unified framework for linkage and association analysis of quantitative traits. Proc Natl Acad Sci U S A. 2007 104:20210-5.

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