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Your search keyword '"Susan E. Koester"' showing total 3 results
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3 results on '"Susan E. Koester"'

1. Population-scale tissue transcriptomics maps long non-coding RNAs to complex disease

Author

Olivia M. de Goede, Daniel C. Nachun, Nicole M. Ferraro, Michael J. Gloudemans, Abhiram S. Rao, Craig Smail, Tiffany Y. Eulalio, François Aguet, Bernard Ng, Jishu Xu, Alvaro N. Barbeira, Stephane E. Castel, Sarah Kim-Hellmuth, YoSon Park, Alexandra J. Scott, Benjamin J. Strober, Christopher D. Brown, Xiaoquan Wen, Ira M. Hall, Alexis Battle, Tuuli Lappalainen, Hae Kyung Im, Kristin G. Ardlie, Sara Mostafavi, Thomas Quertermous, Karla Kirkegaard, Stephen B. Montgomery, Shankara Anand, Stacey Gabriel, Gad A. Getz, Aaron Graubert, Kane Hadley, Robert E. Handsaker, Katherine H. Huang, Xiao Li, Daniel G. MacArthur, Samuel R. Meier, Jared L. Nedzel, Duyen T. Nguyen, Ayellet V. Segrè, Ellen Todres, Brunilda Balliu, Rodrigo Bonazzola, Andrew Brown, Donald F. Conrad, Daniel J. Cotter, Nancy Cox, Sayantan Das, Emmanouil T. Dermitzakis, Jonah Einson, Barbara E. Engelhardt, Eleazar Eskin, Elise D. Flynn, Laure Fresard, Eric R. Gamazon, Diego Garrido-Martín, Nicole R. Gay, Roderic Guigó, Andrew R. Hamel, Yuan He, Paul J. Hoffman, Farhad Hormozdiari, Lei Hou, Brian Jo, Silva Kasela, Seva Kashin, Manolis Kellis, Alan Kwong, Xin Li, Yanyu Liang, Serghei Mangul, Pejman Mohammadi, Manuel Muñoz-Aguirre, Andrew B. Nobel, Meritxell Oliva, Yongjin Park, Princy Parsana, Ferran Reverter, John M. Rouhana, Chiara Sabatti, Ashis Saha, Matthew Stephens, Barbara E. Stranger, Nicole A. Teran, Ana Viñuela, Gao Wang, Fred Wright, Valentin Wucher, Yuxin Zou, Pedro G. Ferreira, Gen Li, Marta Melé, Esti Yeger-Lotem, Debra Bradbury, Tanya Krubit, Jeffrey A. McLean, Liqun Qi, Karna Robinson, Nancy V. Roche, Anna M. Smith, David E. Tabor, Anita Undale, Jason Bridge, Lori E. Brigham, Barbara A. Foster, Bryan M. Gillard, Richard Hasz, Marcus Hunter, Christopher Johns, Mark Johnson, Ellen Karasik, Gene Kopen, William F. Leinweber, Alisa McDonald, Michael T. Moser, Kevin Myer, Kimberley D. Ramsey, Brian Roe, Saboor Shad, Jeffrey A. Thomas, Gary Walters, Michael Washington, Joseph Wheeler, Scott D. Jewell, Daniel C. Rohrer, Dana R. Valley, David A. Davis, Deborah C. Mash, Mary E. Barcus, Philip A. Branton, Leslie Sobin, Laura K. Barker, Heather M. Gardiner, Maghboeba Mosavel, Laura A. Siminoff, Paul Flicek, Maximilian Haeussler, Thomas Juettemann, W. James Kent, Christopher M. Lee, Conner C. Powell, Kate R. Rosenbloom, Magali Ruffier, Dan Sheppard, Kieron Taylor, Stephen J. Trevanion, Daniel R. Zerbino, Nathan S. Abell, Joshua Akey, Lin Chen, Kathryn Demanelis, Jennifer A. Doherty, Andrew P. Feinberg, Kasper D. Hansen, Peter F. Hickey, Farzana Jasmine, Lihua Jiang, Rajinder Kaul, Muhammad G. Kibriya, Jin Billy Li, Qin Li, Shin Lin, Sandra E. Linder, Brandon L. Pierce, Lindsay F. Rizzardi, Andrew D. Skol, Kevin S. Smith, Michael Snyder, John Stamatoyannopoulos, Hua Tang, Meng Wang, Latarsha J. Carithers, Ping Guan, Susan E. Koester, A. Roger Little, Helen M. Moore, Concepcion R. Nierras, Abhi K. Rao, Jimmie B. Vaught, and Simona Volpi

Subjects
Multifactorial Inheritance, Population, Quantitative Trait Loci, Coronary Artery Disease, Disease, Computational biology, Quantitative trait locus, Biology, Article, General Biochemistry, Genetics and Molecular Biology, Transcriptome, 03 medical and health sciences, 0302 clinical medicine, Humans, education, Gene, 030304 developmental biology, 0303 health sciences, education.field_of_study, Gene Expression Profiling, Genetic Variation, Inflammatory Bowel Diseases, Long non-coding RNA, Diabetes Mellitus, Type 1, Diabetes Mellitus, Type 2, Organ Specificity, Expression quantitative trait loci, Trait, RNA, Long Noncoding, 030217 neurology & neurosurgery
Abstract

Long non-coding RNA (lncRNA) genes have well-established and important impacts on molecular and cellular functions. However, among the thousands of lncRNA genes, it is still a major challenge to identify the subset with disease or trait relevance. To systematically characterize these lncRNA genes, we used Genotype Tissue Expression (GTEx) project v8 genetic and multi-tissue transcriptomic data to profile the expression, genetic regulation, cellular contexts, and trait associations of 14,100 lncRNA genes across 49 tissues for 101 distinct complex genetic traits. Using these approaches, we identified 1,432 lncRNA gene-trait associations, 800 of which were not explained by stronger effects of neighboring protein-coding genes. This included associations between lncRNA quantitative trait loci and inflammatory bowel disease, type 1 and type 2 diabetes, and coronary artery disease, as well as rare variant associations to body mass index.

Published
2021

2. A Quantitative Proteome Map of the Human Body

Author

Lihua Jiang, Meng Wang, Shin Lin, Ruiqi Jian, Xiao Li, Joanne Chan, Guanlan Dong, Huaying Fang, Aaron E. Robinson, Michael P. Snyder, François Aguet, Shankara Anand, Kristin G. Ardlie, Stacey Gabriel, Gad Getz, Aaron Graubert, Kane Hadley, Robert E. Handsaker, Katherine H. Huang, Seva Kashin, Daniel G. MacArthur, Samuel R. Meier, Jared L. Nedzel, Duyen Y. Nguyen, Ayellet V. Segrè, Ellen Todres, Brunilda Balliu, Alvaro N. Barbeira, Alexis Battle, Rodrigo Bonazzola, Andrew Brown, Christopher D. Brown, Stephane E. Castel, Don Conrad, Daniel J. Cotter, Nancy Cox, Sayantan Das, Olivia M. de Goede, Emmanouil T. Dermitzakis, Barbara E. Engelhardt, Eleazar Eskin, Tiffany Y. Eulalio, Nicole M. Ferraro, Elise Flynn, Laure Fresard, Eric R. Gamazon, Diego Garrido-Martín, Nicole R. Gay, Roderic Guigó, Andrew R. Hamel, Yuan He, Paul J. Hoffman, Farhad Hormozdiari, Lei Hou, Hae Kyung Im, Brian Jo, Silva Kasela, Manolis Kellis, Sarah Kim-Hellmuth, Alan Kwong, Tuuli Lappalainen, Xin Li, Yanyu Liang, Serghei Mangul, Pejman Mohammadi, Stephen B. Montgomery, Manuel Muñoz-Aguirre, Daniel C. Nachun, Andrew B. Nobel, Meritxell Oliva, YoSon Park, Yongjin Park, Princy Parsana, Ferran Reverter, John M. Rouhana, Chiara Sabatti, Ashis Saha, Andrew D. Skol, Matthew Stephens, Barbara E. Stranger, Benjamin J. Strober, Nicole A. Teran, Ana Viñuela, Gao Wang, Xiaoquan Wen, Fred Wright, Valentin Wucher, Yuxin Zou, Pedro G. Ferreira, Gen Li, Marta Melé, Esti Yeger-Lotem, Mary E. Barcus, Debra Bradbury, Tanya Krubit, Jeffrey A. McLean, Liqun Qi, Karna Robinson, Nancy V. Roche, Anna M. Smith, Leslie Sobin, David E. Tabor, Anita Undale, Jason Bridge, Lori E. Brigham, Barbara A. Foster, Bryan M. Gillard, Richard Hasz, Marcus Hunter, Christopher Johns, Mark Johnson, Ellen Karasik, Gene Kopen, William F. Leinweber, Alisa McDonald, Michael T. Moser, Kevin Myer, Kimberley D. Ramsey, Brian Roe, Saboor Shad, Jeffrey A. Thomas, Gary Walters, Michael Washington, Joseph Wheeler, Scott D. Jewell, Daniel C. Rohrer, Dana R. Valley, David A. Davis, Deborah C. Mash, Philip A. Branton, Laura K. Barker, Heather M. Gardiner, Maghboeba Mosavel, Laura A. Siminoff, Paul Flicek, Maximilian Haeussler, Thomas Juettemann, W. James Kent, Christopher M. Lee, Conner C. Powell, Kate R. Rosenbloom, Magali Ruffier, Dan Sheppard, Kieron Taylor, Stephen J. Trevanion, Daniel R. Zerbino, Nathan S. Abell, Joshua Akey, Lin Chen, Kathryn Demanelis, Jennifer A. Doherty, Andrew P. Feinberg, Kasper D. Hansen, Peter F. Hickey, Farzana Jasmine, Rajinder Kaul, Muhammad G. Kibriya, Jin Billy Li, Qin Li, Sandra E. Linder, Brandon L. Pierce, Lindsay F. Rizzardi, Kevin S. Smith, John Stamatoyannopoulos, Hua Tang, Latarsha J. Carithers, Ping Guan, Susan E. Koester, A. Roger Little, Helen M. Moore, Concepcion R. Nierras, Abhi K. Rao, Jimmie B. Vaught, and Simona Volpi

Subjects
Proteomics, Cell signaling, Proteome, Quantitative proteomics, Gene Expression, Disease, Computational biology, Biology, ENCODE, Article, General Biochemistry, Genetics and Molecular Biology, Transcriptome, 03 medical and health sciences, 0302 clinical medicine, Humans, Secretion, RNA, Messenger, Gene, 030304 developmental biology, 0303 health sciences, Gene Expression Profiling, RNA, Metabolism, Phenotype, Secretory protein, 030217 neurology & neurosurgery
Abstract

Determining protein levels in each tissue and how they compare with RNA levels is important for understanding human biology and disease as well as regulatory processes that control protein levels. We quantified the relative protein levels from 12,627 genes across 32 normal human tissue types prepared by the GTEx project. Known and new tissue specific or enriched proteins (5,499) were identified and compared to transcriptome data. Many ubiquitous transcripts are found to encode highly tissue specific proteins. Discordance in the sites of RNA expression and protein detection also revealed potential sites of synthesis and action of protein signaling molecules. Overall, these results provide an extraordinary resource, and demonstrate that understanding protein levels can provide insights into metabolism, regulation, secretome, and human diseases.SummaryQuantitative proteome study of 32 human tissues and integrated analysis with transcriptome data revealed that understanding protein levels could provide in-depth knowledge to post transcriptional or translational regulations, human metabolism, secretome, and diseases.

Published
2020

3. Ectopic Pax-3 Activates MyoD and Myf-5 Expression in Embryonic Mesoderm and Neural Tissue

Author

Martyn Goulding, Susan E. Koester, Andrew B. Lassar, Miguel Maroto, Andrea Münsterberg, and Ram Reshef

Subjects
medicine.medical_specialty, animal structures, Notochord, Muscle Proteins, MyoD, Nervous System, General Biochemistry, Genetics and Molecular Biology, Mesoderm, Mice, Internal medicine, Proto-Oncogene Proteins, Ectoderm, medicine, Animals, Paired Box Transcription Factors, Hedgehog Proteins, Sonic hedgehog, Muscle, Skeletal, PAX3 Transcription Factor, Myogenin, Floor plate, MyoD Protein, Embryonic Induction, biology, PITX2, Myogenesis, Biochemistry, Genetics and Molecular Biology(all), Gene Expression Regulation, Developmental, Proteins, Cell Differentiation, Fibroblasts, Zebrafish Proteins, Cell biology, DNA-Binding Proteins, Wnt Proteins, Endocrinology, medicine.anatomical_structure, Spinal Cord, Myogenic regulatory factors, embryonic structures, biology.protein, Trans-Activators, Myogenic Regulatory Factor 5, Mitogens, Biomarkers, Signal Transduction, Transcription Factors
Abstract

Summary induce myogenic precursors or to maintain and expand a population of already specified cells (see Munsterberg To understand how the skeletal muscle lineage is in- and Lassar, 1995, and references therein). We have duced during vertebrate embryogenesis, we have shown that myogenesis of explanted presegmental sought to identify the regulatory molecules that medi- plate mesoderm (isolated from a stage 10 chick embryo) ate induction of the myogenic regulatory factors MyoD requires two signals from axial tissues, one emanating and Myf-5. In this work, we demonstrate that either from the floor plate-notochord complex and the other signals from the overlying ectoderm or Wnt and Sonic from more dorsal regions of the neural tube (Mun- hedgehog signals can induce somitic expression of sterberg and Lassar, 1995). Consistent with these find- the paired box transcription factors, Pax-3 and Pax-7, ings, Emerson and colleagues recently reported that concomitant with expression of Myf-5 and prior to that signals from the notochord are required to initiate quail of MyoD. Moreover, infection of embryonic tissues in MyoD expression and that signals from the neural tube vitro with a retrovirus encoding Pax-3 is sufficient to are required to maintain MyoD expression in ovo (Pow- induce expression of MyoD, Myf-5, and myogenin in nall et al., 1996). both paraxial and lateral plate mesoderm in the ab- The muscle-promoting signal from the floor plate- sence of inducing tissues as well as in the neural tube. notochord complex can be mimicked by the signaling Together, these findings imply that Pax-3 may mediate molecule Sonic hedgehog (Shh), and the muscle-pro- activation of MyoD and Myf-5 in response to muscle- moting signal from the neural tube can be mimicked by inducing signals from either the axial tissues or overly- cell lines programmed to express either Wnt-1 or Wnt-3 ing ectoderm and identify Pax-3 as a key regulator of (Munsterberg et al., 1995; Stern et al., 1995). Because somitic myogenesis.

Published
1997
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