49 results on '"Slowikowski, K"'
Search Results
2. Luminosity measurement method for the LHC: The detector requirements studies
- Author
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Krasny, M. W., Chwastowski, J., Cyz, A., and Slowikowski, K.
- Subjects
Physics - Instrumentation and Detectors ,High Energy Physics - Experiment - Abstract
Absolute normalisation of the LHC measurements with a precision of O(1%) is desirable but beyond the reach of the present LHC detectors. This series of papers proposes and evaluates a measurement method capable to achieve such a precision target. In our earlier paper we have selected the phase-space region where the lepton pair production cross section in pp collisions at the LHC can be controlled with < 1 % precision and is large enough to reach a comparable statistical accuracy of the absolute luminosity measurement on the day-by-day basis. In the present one the performance requirements for a dedicated detector, indispensable to efficiently select events in the proposed phase-space region, are discussed., Comment: 26 pages, 13 figures
- Published
- 2010
3. Luminosity Measurement Method for LHC: The theoretical precision and the experimental challenges
- Author
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Krasny, M. W., Chwastowski, J., and Slowikowski, K.
- Subjects
High Energy Physics - Experiment - Abstract
This is the first of the series of papers which present a precision method of the day-by-day monitoring of the absolute LHC luminosity. The method is based on the measurement of the rate of coplanar lepton pairs produced in peripheral collisions of the beams' particles. In the present paper we evaluate the modeling precision of the lepton pair production processes in proton-proton collisions, optimize the measurement region to achieve better than 1% accuracy of the predicted rates, and discuss the experimental challenges to filter out the luminosity monitoring lepton pairs at LHC., Comment: 20 pages, 16 figures
- Published
- 2006
- Full Text
- View/download PDF
4. Hillock Cells Are Immunoresponsive Airway Epithelial Cells That Promote Type 2 Inflammation and Barrier Dysfunction in Asthmatics
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Alladina, J., primary, Smith, N.P., additional, Slowikowski, K., additional, Kooistra, T., additional, Rahimi, R.A., additional, Haring, A., additional, Nguyen, N.D., additional, Sheng, S.L., additional, Hariri, L.P., additional, Luster, A.D., additional, Villani, A.-C., additional, Cho, J.L., additional, Giacona, F.L., additional, and Medoff, B.D., additional
- Published
- 2023
- Full Text
- View/download PDF
5. A Human Asthma Exacerbation Model Identifies Pathogenic Cellular and Molecular Programs in the Lower Airway Mucosa Specific to Allergic Asthma
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Alladina, J., primary, Smith, N., additional, Manakongtreecheep, K., additional, Tantivit, J., additional, Slowikowski, K., additional, Keen, H., additional, Kooistra, T., additional, Rahimi, R.A., additional, Giacona, F., additional, Sheng, S., additional, Hariri, L.P., additional, Hamilos, D., additional, Luster, A., additional, Villani, A.-C., additional, Cho, J.L., additional, and Medoff, B.D., additional
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- 2022
- Full Text
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6. Single-cell meta-analysis of SARS-CoV-2 entry genes across tissues and demographics
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Muus, C, Luecken, MD, Eraslan, G, Sikkema, L, Waghray, A, Heimberg, G, Kobayashi, Y, Vaishnav, ED, Subramanian, A, Smillie, C, Jagadeesh, KA, Duong, ET, Fiskin, E, Triglia, ET, Ansari, M, Cai, P, Lin, B, Buchanan, J, Chen, S, Shu, J, Haber, AL, Chung, H, Montoro, DT, Adams, T, Aliee, H, Allon, SJ, Andrusivova, Z, Angelidis, I, Ashenberg, O, Bassler, K, Bécavin, C, Benhar, I, Bergenstråhle, J, Bergenstråhle, L, Bolt, L, Braun, E, Bui, LT, Callori, S, Chaffin, M, Chichelnitskiy, E, Chiou, J, Conlon, TM, Cuoco, MS, Cuomo, ASE, Deprez, M, Duclos, G, Fine, D, Fischer, DS, Ghazanfar, S, Gillich, A, Giotti, B, Gould, J, Guo, M, Gutierrez, AJ, Habermann, AC, Harvey, T, He, P, Hou, X, Hu, L, Hu, Y, Jaiswal, A, Ji, L, Jiang, P, Kapellos, TS, Kuo, CS, Larsson, L, Leney-Greene, MA, Lim, K, Litviňuková, M, Ludwig, LS, Lukassen, S, Luo, W, Maatz, H, Madissoon, E, Mamanova, L, Manakongtreecheep, K, Leroy, S, Mayr, CH, Mbano, IM, McAdams, AM, Nabhan, AN, Nyquist, SK, Penland, L, Poirion, OB, Poli, S, Qi, CC, Queen, R, Reichart, D, Rosas, I, Schupp, JC, Shea, CV, Shi, X, Sinha, R, Sit, RV, Slowikowski, K, Slyper, M, Smith, NP, Sountoulidis, A, Strunz, M, Sullivan, TB, Muus, C, Luecken, MD, Eraslan, G, Sikkema, L, Waghray, A, Heimberg, G, Kobayashi, Y, Vaishnav, ED, Subramanian, A, Smillie, C, Jagadeesh, KA, Duong, ET, Fiskin, E, Triglia, ET, Ansari, M, Cai, P, Lin, B, Buchanan, J, Chen, S, Shu, J, Haber, AL, Chung, H, Montoro, DT, Adams, T, Aliee, H, Allon, SJ, Andrusivova, Z, Angelidis, I, Ashenberg, O, Bassler, K, Bécavin, C, Benhar, I, Bergenstråhle, J, Bergenstråhle, L, Bolt, L, Braun, E, Bui, LT, Callori, S, Chaffin, M, Chichelnitskiy, E, Chiou, J, Conlon, TM, Cuoco, MS, Cuomo, ASE, Deprez, M, Duclos, G, Fine, D, Fischer, DS, Ghazanfar, S, Gillich, A, Giotti, B, Gould, J, Guo, M, Gutierrez, AJ, Habermann, AC, Harvey, T, He, P, Hou, X, Hu, L, Hu, Y, Jaiswal, A, Ji, L, Jiang, P, Kapellos, TS, Kuo, CS, Larsson, L, Leney-Greene, MA, Lim, K, Litviňuková, M, Ludwig, LS, Lukassen, S, Luo, W, Maatz, H, Madissoon, E, Mamanova, L, Manakongtreecheep, K, Leroy, S, Mayr, CH, Mbano, IM, McAdams, AM, Nabhan, AN, Nyquist, SK, Penland, L, Poirion, OB, Poli, S, Qi, CC, Queen, R, Reichart, D, Rosas, I, Schupp, JC, Shea, CV, Shi, X, Sinha, R, Sit, RV, Slowikowski, K, Slyper, M, Smith, NP, Sountoulidis, A, Strunz, M, and Sullivan, TB
- Abstract
Angiotensin-converting enzyme 2 (ACE2) and accessory proteases (TMPRSS2 and CTSL) are needed for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) cellular entry, and their expression may shed light on viral tropism and impact across the body. We assessed the cell-type-specific expression of ACE2, TMPRSS2 and CTSL across 107 single-cell RNA-sequencing studies from different tissues. ACE2, TMPRSS2 and CTSL are coexpressed in specific subsets of respiratory epithelial cells in the nasal passages, airways and alveoli, and in cells from other organs associated with coronavirus disease 2019 (COVID-19) transmission or pathology. We performed a meta-analysis of 31 lung single-cell RNA-sequencing studies with 1,320,896 cells from 377 nasal, airway and lung parenchyma samples from 228 individuals. This revealed cell-type-specific associations of age, sex and smoking with expression levels of ACE2, TMPRSS2 and CTSL. Expression of entry factors increased with age and in males, including in airway secretory cells and alveolar type 2 cells. Expression programs shared by ACE2+TMPRSS2+ cells in nasal, lung and gut tissues included genes that may mediate viral entry, key immune functions and epithelial–macrophage cross-talk, such as genes involved in the interleukin-6, interleukin-1, tumor necrosis factor and complement pathways. Cell-type-specific expression patterns may contribute to the pathogenesis of COVID-19, and our work highlights putative molecular pathways for therapeutic intervention.
- Published
- 2021
7. Single-cell meta-analysis of SARS-CoV-2 entry genes across tissues and demographics
- Author
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Muus, C, Luecken, MD, Eraslan, G, Sikkema, L, Waghray, A, Heimberg, G, Kobayashi, Y, Vaishnav, ED, Subramanian, A, Smillie, C, Jagadeesh, KA, Duong, ET, Fiskin, E, Triglia, ET, Ansari, M, Cai, P, Lin, B, Buchanan, J, Chen, S, Shu, J, Haber, AL, Chung, H, Montoro, DT, Adams, T, Aliee, H, Allon, SJ, Andrusivova, Z, Angelidis, I, Ashenberg, O, Bassler, K, Bécavin, C, Benhar, I, Bergenstråhle, J, Bergenstråhle, L, Bolt, L, Braun, E, Bui, LT, Callori, S, Chaffin, M, Chichelnitskiy, E, Chiou, J, Conlon, TM, Cuoco, MS, Cuomo, ASE, Deprez, M, Duclos, G, Fine, D, Fischer, DS, Ghazanfar, S, Gillich, A, Giotti, B, Gould, J, Guo, M, Gutierrez, AJ, Habermann, AC, Harvey, T, He, P, Hou, X, Hu, L, Hu, Y, Jaiswal, A, Ji, L, Jiang, P, Kapellos, TS, Kuo, CS, Larsson, L, Leney-Greene, MA, Lim, K, Litviňuková, M, Ludwig, LS, Lukassen, S, Luo, W, Maatz, H, Madissoon, E, Mamanova, L, Manakongtreecheep, K, Leroy, S, Mayr, CH, Mbano, IM, McAdams, AM, Nabhan, AN, Nyquist, SK, Penland, L, Poirion, OB, Poli, S, Qi, C, Queen, R, Reichart, D, Rosas, I, Schupp, JC, Shea, CV, Shi, X, Sinha, R, Sit, RV, Slowikowski, K, Slyper, M, Smith, NP, Sountoulidis, A, Strunz, M, Sullivan, TB, Sun, D, Talavera-López, C, Tan, P, Tantivit, J, Travaglini, KJ, Tucker, NR, Vernon, KA, Wadsworth, MH, Waldman, J, Wang, X, Xu, K, Yan, W, Zhao, W, Ziegler, CGK, NHLBI LungMap Consortium, and Human Cell Atlas Lung Biological Network
- Subjects
Adult ,Male ,Cathepsin L ,Immunology ,Respiratory System ,Datasets as Topic ,Humans ,Lung ,11 Medical and Health Sciences ,Aged ,Demography ,Aged, 80 and over ,SARS-CoV-2 ,Sequence Analysis, RNA ,Gene Expression Profiling ,Serine Endopeptidases ,COVID-19 ,respiratory system ,Middle Aged ,Virus Internalization ,Organ Specificity ,Alveolar Epithelial Cells ,Host-Pathogen Interactions ,Female ,Angiotensin-Converting Enzyme 2 ,Single-Cell Analysis - Abstract
Angiotensin-converting enzyme 2 (ACE2) and accessory proteases (TMPRSS2 and CTSL) are needed for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) cellular entry, and their expression may shed light on viral tropism and impact across the body. We assessed the cell-type-specific expression of ACE2, TMPRSS2 and CTSL across 107 single-cell RNA-sequencing studies from different tissues. ACE2, TMPRSS2 and CTSL are coexpressed in specific subsets of respiratory epithelial cells in the nasal passages, airways and alveoli, and in cells from other organs associated with coronavirus disease 2019 (COVID-19) transmission or pathology. We performed a meta-analysis of 31 lung single-cell RNA-sequencing studies with 1,320,896 cells from 377 nasal, airway and lung parenchyma samples from 228 individuals. This revealed cell-type-specific associations of age, sex and smoking with expression levels of ACE2, TMPRSS2 and CTSL. Expression of entry factors increased with age and in males, including in airway secretory cells and alveolar type 2 cells. Expression programs shared by ACE2+TMPRSS2+ cells in nasal, lung and gut tissues included genes that may mediate viral entry, key immune functions and epithelial-macrophage cross-talk, such as genes involved in the interleukin-6, interleukin-1, tumor necrosis factor and complement pathways. Cell-type-specific expression patterns may contribute to the pathogenesis of COVID-19, and our work highlights putative molecular pathways for therapeutic intervention.
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- 2020
8. Selection of kinematic region for precision measurement of the LHC luminosity using coplanar lepton pairs
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Krasny, M.W., Chwastowski, J., and Słowikowski, K.
- Published
- 2008
- Full Text
- View/download PDF
9. Luminosity measurement method for LHC: The theoretical precision and the experimental challenges
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Krasny, M.W., Chwastowski, J., and Słowikowski, K.
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- 2008
- Full Text
- View/download PDF
10. Defining inflammatory cell states in rheumatoid arthritis joint synovial tissues by integrating single-cell transcriptomics and mass cytometry
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Zhang, F, Wei, K, Slowikowski, K, Fonseka, CY, Rao, DA, Kelly, S, Goodman, SM, Tabechian, D, Hughes, LB, Salomon-Escoto, K, Watts, GFM, Jonsson, AH, Rangel-Moreno, J, Meednu, N, Rozo, C, Apruzzese, W, Eisenhaure, TM, Lieb, DJ, Boyle, DL, Mandelin, AM, Albrecht, J, Bridges, SL, Buckley, CD, Buckner, JH, Dolan, J, Guthridge, JM, Gutierrez-Arcelus, M, Ivashkiv, LB, James, EA, James, JA, Keegan, J, Lee, YC, McGeachy, MJ, McNamara, MA, Mears, JR, Mizoguchi, F, Nguyen, JP, Noma, A, Orange, DE, Rohani-Pichavant, M, Ritchlin, C, Robinson, WH, Seshadri, A, Sutherby, D, Seifert, J, Turner, JD, Utz, PJ, Boyce, BF, Dicarlo, E, Gravallese, EM, Gregersen, PK, Moreland, L, Firestein, GS, Hacohen, N, Nusbaum, C, Lederer, JA, Perlman, H, Pitzalis, C, Filer, A, Holers, VM, Bykerk, VP, Donlin, LT, Anolik, JH, Brenner, MB, and Raychaudhuri, S
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0301 basic medicine ,Immunology ,Cell ,Arthritis ,Gene Expression ,Autoimmunity ,Monocytes ,Article ,Flow cytometry ,GZMB ,Workflow ,Arthritis, Rheumatoid ,03 medical and health sciences ,0302 clinical medicine ,GNLY ,T-Lymphocyte Subsets ,medicine ,Leukocytes ,Immunology and Allergy ,Humans ,CD90 ,Mass cytometry ,medicine.diagnostic_test ,Chemistry ,Gene Expression Profiling ,Synovial Membrane ,Histocompatibility Antigens Class II ,Computational Biology ,High-Throughput Nucleotide Sequencing ,Fibroblasts ,medicine.disease ,Flow Cytometry ,Molecular biology ,030104 developmental biology ,medicine.anatomical_structure ,Cross-Sectional Studies ,Cytokines ,Single-Cell Analysis ,Transcriptome ,CD8 ,Biomarkers ,030215 immunology ,Signal Transduction - Abstract
To define the cell populations that drive joint inflammation in rheumatoid arthritis (RA), we applied single-cell RNA sequencing (scRNA-seq), mass cytometry, bulk RNA sequencing (RNA-seq) and flow cytometry to T cells, B cells, monocytes, and fibroblasts from 51 samples of synovial tissue from patients with RA or osteoarthritis (OA). Utilizing an integrated strategy based on canonical correlation analysis of 5,265 scRNA-seq profiles, we identified 18 unique cell populations. Combining mass cytometry and transcriptomics revealed cell states expanded in RA synovia:THY1(CD90)+HLA-DRAhisublining fibroblasts,IL1B+pro-inflammatory monocytes,ITGAX+TBX21+autoimmune-associated B cells andPDCD1+peripheral helper T (TPH) cells and follicular helper T (TFH) cells. We defined distinct subsets of CD8+T cells characterized byGZMK+,GZMB+, andGNLY+phenotypes. We mapped inflammatory mediators to their source cell populations; for example, we attributedIL6expression toTHY1+HLA-DRAhifibroblasts andIL1Bproduction to pro-inflammatory monocytes. These populations are potentially key mediators of RA pathogenesis.
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- 2019
11. OP0363 Optimisingprecision medicine by using genetics to assign diagnostic prior probabilities to patients with synovitis – proof of principle
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Knevel, R., primary, Terao, C., additional, Cui, J., additional, Slowikowski, K., additional, Huizinga, T., additional, Karlson, B., additional, Liao, K., additional, le Cessie, S., additional, and Raychaudhuri, S., additional
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- 2018
- Full Text
- View/download PDF
12. A genome-wide innateness gradient defines the functional state of human innate T cells
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Gutierrez-Arcelus, M., primary, Teslovich, N., additional, Mola, A. R., additional, Kim, H., additional, Hannes, S., additional, Slowikowski, K., additional, Watts, G. F. M., additional, Brenner, M., additional, Raychaudhuri, S., additional, and Brennan, P. J., additional
- Published
- 2018
- Full Text
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13. Genetic association study of QT interval highlights role for calcium signaling pathways in myocardial repolarization
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Arking, D.E. (Dan), Pulit, S.L. (Sara), Crotti, L. (Lia), Harst, P. (Pim) van der, Munroe, P. (Patricia), Koopmann, T.T. (Tamara), Sotoodehnia, N. (Nona), Rossin, E. (Elizabeth), Morley, M. (Michael), Wang, X. (Xinchen), Johnson, A.D. (Andrew), Lundby, A. (Alicia), Gudbjartsson, D.F. (Daniel), Noseworthy, P.A. (Peter), Eijgelsheim, M. (Mark), Bradford, Y. (Yuki), Tarasov, K.V. (Kirill), Dörr, M. (Marcus), Müller-Nurasyid, M. (Martina), Lahtinen, A.M. (Annukka), Nolte, I.M. (Ilja), Smith, A.V. (Davey), Bis, J.C. (Joshua), Isaacs, A.J. (Aaron), Newhouse, S.J. (Stephen), Evans, D.S. (Daniel), Post, W.S. (Wendy S.), Waggott, D. (Daryl), Lyytikäinen, L.-P. (Leo-Pekka), Hicks, A.A. (Andrew), Eisele, L. (Lewin), Ellinghaus, D. (David), Hayward, C. (Caroline), Navarro, P. (Pau), Ulivi, S. (Shelia), Tanaka, T. (Toshiko), Tester, D.J. (David), Chatel, S. (Stéphanie), Gustafsson, S. (Stefan), Kumari, M. (Meena), Morris, R.W. (Richard), Naluai, A.T. (Asa), Padmanabhan, S. (Sandosh), Kluttig, A. (Alexander), Strohmer, B. (Bernhard), Panayiotou, A.G. (Andrie), Torres, M. (Maria), Knoflach, M. (Michael), Hubacek, J.A. (Jaroslav A.), Slowikowski, K. (Kamil), Raychaudhuri, S. (Soumya), Kumar, R.D. (Runjun), Harris, T.B. (Tamara), Launer, L.J. (Lenore), Shuldiner, A.R. (Alan), Alonso, A. (Alvaro), Bader, J.S. (Joel), Ehret, G.B. (Georg), Huang, H. (Hailiang), Kao, W.H.L. (Wen), Strait, J.B. (James), Macfarlane, P.W. (Peter), Brown, M.J. (Morris), Caulfield, M. (Mark), Samani, N.J. (Nilesh), Kronenberg, F. (Florian), Willeit, J. (Johann), Smith, J.G. (J. Gustav), Greiser, K.H. (Karin Halina), Zu Schwabedissen, H.M. (Henriette Meyer), Werdan, K. (Karl), Carella, C. (Cintia), Zelante, L. (Leopoldo), Heckbert, S.R. (Susan), Psaty, B.M. (Bruce), Rotter, J.I. (Jerome), Kolcic, I. (Ivana), Polasek, O. (Ozren), Wright, A.F. (Alan), Griffin, M. (Maura), Daly, M.J. (Mark), Arnar, D.O. (David), Hólm, H. (Hilma), Thorsteinsdottir, U. (Unnur), Denny, J.C. (Joshua), Roden, D.M. (Dan), Zuvich, R.L. (Rebecca), Emilsson, V. (Valur), Plump, A.S. (Andrew), Larson, M.G. (Martin), O'Donnell, C.J. (Christopher), Yin, X. (Xiaoyan), Bobbo, M. (Marco), Adamo, P. (Pio) d', Iorio, A. (Annamaria), Sinagra, G. (Gianfranco), Carracedo, A. (Angel), Cummings, S.R. (Steven), Nalls, M.A. (Michael), Jula, A. (Antti), Kontula, K.K. (Kimmo), Marjamaa, A. (Annukka), Oikarinen, L. (Lasse), Perola, M. (Markus), Porthan, K. (Kimmo), Erbel, R. (Raimund), Hoffmann, P. (Per), Jöckel, K.-H. (Karl-Heinz), Kälsch, H. (Hagen), Nöthen, M.M. (Markus), Hoed, M. (Marcel) den, Loos, R.J.F. (Ruth), Thelle, D.S. (Dag), Gieger, C. (Christian), Meitinger, T. (Thomas), Perz, S. (Siegfried), Peters, A. (Annette), Prucha, H. (Hanna), Sinner, M.F. (Moritz), Waldenberger, M. (Melanie), Boer, R.A. (Rudolf) de, Franke, L. (Lude), Vleuten, P.A. (Pieter) van der, Beckmann, B.M. (Britt), Martens, E. (Eimo), Bardai, A. (Abdennasser), Hofman, N. (Nynke), Wilde, A.A.M. (Arthur), Behr, E.R. (Elijah), Dalageorgou, C. (Chrysoula), Giudicessi, J.R. (John), Medeiros-Domingo, A. (Argelia), Barc, J. (Julien), Kyndt, F. (Florence), Probst, V. (Vincent), Ghidoni, A. (Alice), Insolia, R. (Roberto), Hamilton, R.M. (Robert), Scherer, S.W. (Stephen), Brandimarto, J. (Jeffrey), Margulies, K. (Kenneth), Moravec, C.E. (Christine), Del Greco, F. (Fabiola), Fuchsberger, C. (Christian), O'Connell, J.R. (Jeffery), Lee, W.K. (Wai), Watt, G.C.M. (Graham), Campbell, H. (Harry), Wild, S.H. (Sarah), El Mokhtari, N.E. (Nour), Frey, N. (Norbert), Asselbergs, F.W. (Folkert), Leach, I.M. (Irene Mateo), Navis, G. (Gerjan), Berg, M.P. (Maarten) van den, Veldhuisen, D.J. (Dirk) van, Kellis, M. (Manolis), Krijthe, B.P. (Bouwe), Franco, O.H. (Oscar), Hofman, A. (Albert), Kors, J.A. (Jan), Uitterlinden, A.G. (André), Witteman, J.C.M. (Jacqueline), Kedenko, L. (Lyudmyla), Lamina, C. (Claudia), Oostra, B.A. (Ben), Abecasis, G.R. (Gonçalo), Lakatta, E. (Edward), Mulas, A. (Antonella), Orrù, M. (Marco), Schlessinger, D. (David), Uda, M. (Manuela), Markus, M.R.P. (Marcello R. P.), Völker, U. (Uwe), Snieder, H. (Harold), Spector, T.D. (Timothy), Ärnlöv, J. (Johan), Lind, L. (Lars), Sundstrom, J. (Johan), Syvanen, A.C., Kivimaki, M. (Mika), Kähönen, M. (Mika), Mononen, K. (Kari), Raitakari, O. (Olli), Viikari, J. (Jorma), Adamkova, V. (Vera), Kiechl, S. (Stefan), Brion, M.-J. (Maria), Nicolaides, A.N. (Andrew), Paulweber, B. (Bernhard), Haerting, J. (Johannes), Dominiczak, A. (Anna), Nyberg, F. (Fredrik), Whincup, P.H. (Peter), Hingorani, A. (Aroon), Schott, J.-J. (Jean-Jacques), Bezzina, C.R. (Connie), Ingelsson, E. (Erik), Ferrucci, L. (Luigi), Gasparini, P. (Paolo), Wilson, J.F. (James), Rudan, I. (Igor), Franke, A. (Andre), Mühleisen, T.W. (Thomas), Pramstaller, P.P. (Peter Paul), Lehtimäki, T. (Terho), Paterson, A.D. (Andrew), Parsa, A. (Afshin), Liu, Y. (YongMei), Duijn, C.M. (Cornelia) van, Siscovick, D.S. (David), Gudnason, V. (Vilmundur), Jamshidi, Y. (Yalda), Salomaa, V. (Veikko), Felix, S.B. (Stephan), Sanna, S. (Serena), Ritchie, M.D. (Marylyn), Stricker, B.H.Ch. (Bruno), Zwart, J-A. (John-Anker), Boyer, L.A. (Laurie), Cappola, T.P. (Thomas), Olsen, J.V. (Jesper), Lage, P. (Pedro), Schwartz, P.J. (Peter), Kääb, S. (Stefan), Chakravarti, A. (Aravinda), Ackerman, M. (Margaret), Pfeufer, A. (Arne), Bakker, P.I.W. (Paul) de, Newton-Cheh, C. (Christopher), Arking, D.E. (Dan), Pulit, S.L. (Sara), Crotti, L. (Lia), Harst, P. (Pim) van der, Munroe, P. (Patricia), Koopmann, T.T. (Tamara), Sotoodehnia, N. (Nona), Rossin, E. (Elizabeth), Morley, M. (Michael), Wang, X. (Xinchen), Johnson, A.D. (Andrew), Lundby, A. (Alicia), Gudbjartsson, D.F. (Daniel), Noseworthy, P.A. (Peter), Eijgelsheim, M. (Mark), Bradford, Y. (Yuki), Tarasov, K.V. (Kirill), Dörr, M. (Marcus), Müller-Nurasyid, M. (Martina), Lahtinen, A.M. (Annukka), Nolte, I.M. (Ilja), Smith, A.V. (Davey), Bis, J.C. (Joshua), Isaacs, A.J. (Aaron), Newhouse, S.J. (Stephen), Evans, D.S. (Daniel), Post, W.S. (Wendy S.), Waggott, D. (Daryl), Lyytikäinen, L.-P. (Leo-Pekka), Hicks, A.A. (Andrew), Eisele, L. (Lewin), Ellinghaus, D. (David), Hayward, C. (Caroline), Navarro, P. (Pau), Ulivi, S. (Shelia), Tanaka, T. (Toshiko), Tester, D.J. (David), Chatel, S. (Stéphanie), Gustafsson, S. (Stefan), Kumari, M. (Meena), Morris, R.W. (Richard), Naluai, A.T. (Asa), Padmanabhan, S. (Sandosh), Kluttig, A. (Alexander), Strohmer, B. (Bernhard), Panayiotou, A.G. (Andrie), Torres, M. (Maria), Knoflach, M. (Michael), Hubacek, J.A. (Jaroslav A.), Slowikowski, K. (Kamil), Raychaudhuri, S. (Soumya), Kumar, R.D. (Runjun), Harris, T.B. (Tamara), Launer, L.J. (Lenore), Shuldiner, A.R. (Alan), Alonso, A. (Alvaro), Bader, J.S. (Joel), Ehret, G.B. (Georg), Huang, H. (Hailiang), Kao, W.H.L. (Wen), Strait, J.B. (James), Macfarlane, P.W. (Peter), Brown, M.J. (Morris), Caulfield, M. (Mark), Samani, N.J. (Nilesh), Kronenberg, F. (Florian), Willeit, J. (Johann), Smith, J.G. (J. Gustav), Greiser, K.H. (Karin Halina), Zu Schwabedissen, H.M. (Henriette Meyer), Werdan, K. (Karl), Carella, C. (Cintia), Zelante, L. (Leopoldo), Heckbert, S.R. (Susan), Psaty, B.M. (Bruce), Rotter, J.I. (Jerome), Kolcic, I. (Ivana), Polasek, O. (Ozren), Wright, A.F. (Alan), Griffin, M. (Maura), Daly, M.J. (Mark), Arnar, D.O. (David), Hólm, H. (Hilma), Thorsteinsdottir, U. (Unnur), Denny, J.C. (Joshua), Roden, D.M. (Dan), Zuvich, R.L. (Rebecca), Emilsson, V. (Valur), Plump, A.S. (Andrew), Larson, M.G. (Martin), O'Donnell, C.J. (Christopher), Yin, X. (Xiaoyan), Bobbo, M. (Marco), Adamo, P. (Pio) d', Iorio, A. (Annamaria), Sinagra, G. (Gianfranco), Carracedo, A. (Angel), Cummings, S.R. (Steven), Nalls, M.A. (Michael), Jula, A. (Antti), Kontula, K.K. (Kimmo), Marjamaa, A. (Annukka), Oikarinen, L. (Lasse), Perola, M. (Markus), Porthan, K. (Kimmo), Erbel, R. (Raimund), Hoffmann, P. (Per), Jöckel, K.-H. (Karl-Heinz), Kälsch, H. (Hagen), Nöthen, M.M. (Markus), Hoed, M. (Marcel) den, Loos, R.J.F. (Ruth), Thelle, D.S. (Dag), Gieger, C. (Christian), Meitinger, T. (Thomas), Perz, S. (Siegfried), Peters, A. (Annette), Prucha, H. (Hanna), Sinner, M.F. (Moritz), Waldenberger, M. (Melanie), Boer, R.A. (Rudolf) de, Franke, L. (Lude), Vleuten, P.A. (Pieter) van der, Beckmann, B.M. (Britt), Martens, E. (Eimo), Bardai, A. (Abdennasser), Hofman, N. (Nynke), Wilde, A.A.M. (Arthur), Behr, E.R. (Elijah), Dalageorgou, C. (Chrysoula), Giudicessi, J.R. (John), Medeiros-Domingo, A. (Argelia), Barc, J. (Julien), Kyndt, F. (Florence), Probst, V. (Vincent), Ghidoni, A. (Alice), Insolia, R. (Roberto), Hamilton, R.M. (Robert), Scherer, S.W. (Stephen), Brandimarto, J. (Jeffrey), Margulies, K. (Kenneth), Moravec, C.E. (Christine), Del Greco, F. (Fabiola), Fuchsberger, C. (Christian), O'Connell, J.R. (Jeffery), Lee, W.K. (Wai), Watt, G.C.M. (Graham), Campbell, H. (Harry), Wild, S.H. (Sarah), El Mokhtari, N.E. (Nour), Frey, N. (Norbert), Asselbergs, F.W. (Folkert), Leach, I.M. (Irene Mateo), Navis, G. (Gerjan), Berg, M.P. (Maarten) van den, Veldhuisen, D.J. (Dirk) van, Kellis, M. (Manolis), Krijthe, B.P. (Bouwe), Franco, O.H. (Oscar), Hofman, A. (Albert), Kors, J.A. (Jan), Uitterlinden, A.G. (André), Witteman, J.C.M. (Jacqueline), Kedenko, L. (Lyudmyla), Lamina, C. (Claudia), Oostra, B.A. (Ben), Abecasis, G.R. (Gonçalo), Lakatta, E. (Edward), Mulas, A. (Antonella), Orrù, M. (Marco), Schlessinger, D. (David), Uda, M. (Manuela), Markus, M.R.P. (Marcello R. P.), Völker, U. (Uwe), Snieder, H. (Harold), Spector, T.D. (Timothy), Ärnlöv, J. (Johan), Lind, L. (Lars), Sundstrom, J. (Johan), Syvanen, A.C., Kivimaki, M. (Mika), Kähönen, M. (Mika), Mononen, K. (Kari), Raitakari, O. (Olli), Viikari, J. (Jorma), Adamkova, V. (Vera), Kiechl, S. (Stefan), Brion, M.-J. (Maria), Nicolaides, A.N. (Andrew), Paulweber, B. (Bernhard), Haerting, J. (Johannes), Dominiczak, A. (Anna), Nyberg, F. (Fredrik), Whincup, P.H. (Peter), Hingorani, A. (Aroon), Schott, J.-J. (Jean-Jacques), Bezzina, C.R. (Connie), Ingelsson, E. (Erik), Ferrucci, L. (Luigi), Gasparini, P. (Paolo), Wilson, J.F. (James), Rudan, I. (Igor), Franke, A. (Andre), Mühleisen, T.W. (Thomas), Pramstaller, P.P. (Peter Paul), Lehtimäki, T. (Terho), Paterson, A.D. (Andrew), Parsa, A. (Afshin), Liu, Y. (YongMei), Duijn, C.M. (Cornelia) van, Siscovick, D.S. (David), Gudnason, V. (Vilmundur), Jamshidi, Y. (Yalda), Salomaa, V. (Veikko), Felix, S.B. (Stephan), Sanna, S. (Serena), Ritchie, M.D. (Marylyn), Stricker, B.H.Ch. (Bruno), Zwart, J-A. (John-Anker), Boyer, L.A. (Laurie), Cappola, T.P. (Thomas), Olsen, J.V. (Jesper), Lage, P. (Pedro), Schwartz, P.J. (Peter), Kääb, S. (Stefan), Chakravarti, A. (Aravinda), Ackerman, M. (Margaret), Pfeufer, A. (Arne), Bakker, P.I.W. (Paul) de, and Newton-Cheh, C. (Christopher)
- Abstract
The QT interval, an electrocardiographic measure reflecting myocardial repolarization, is a heritable trait. QT prolongation is a risk factor for ventricular arrhythmias and sudden cardiac death (SCD) and could indicate the presence of the potentially lethal mendelian long-QT syndrome (LQTS). Using a genome-wide association and replication study in up to 100,000 individuals, we identified 35 common variant loci associated with QT interval that collectively explain ∼ 8-10% of QT-interval variation and highlight the importance of calcium regulation in myocardial repolarization. Rare variant analysis of 6 new QT interval-associated loci in 298 unrelated probands with LQTS identified coding variants not found in controls but of uncertain causality and therefore requiring validation. Several newly identified loci encode proteins that physically interact with other recognized repolarization proteins. Our integration of common variant association, expression and orthogonal protein-protein interaction screens provides new insights into cardiac electrophysiology and identifies new candidate genes for ventricular arrhythmias, LQTS and SCD.
- Published
- 2014
- Full Text
- View/download PDF
14. Genetic association study of QT interval highlights role for calcium signaling pathways in myocardial repolarization
- Author
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Arking, D, Pulit, S, Crotti, L, van der Harst, P, Munroe, P, Koopmann, T, Sotoodehnia, N, Rossin, E, Morley, M, Wang, X, Johnson, A, Lundby, A, Gudbjartsson, D, Noseworthy, P, Eijgelsheim, M, Bradford, Y, Tarasov, K, Dörr, M, Müller-Nurasyid, M, Lahtinen, A, Nolte, I, Smith, A, Bis, J, Isaacs, A, Newhouse, S, Evans, D, Post, W, Waggott, D, Lyytikäinen, L, Hicks, A, Eisele, L, Ellinghaus, D, Hayward, C, Navarro, P, Ulivi, S, Tanaka, T, Tester, D, Chatel, S, Gustafsson, S, Kumari, M, Morris, R, Naluai, A, Padmanabhan, S, Kluttig, A, Strohmer, B, Panayiotou, A, Torres, M, Knoflach, M, Hubacek, J, Slowikowski, K, Raychaudhuri, S, Kumar, R, Harris, T, Launer, L, Shuldiner, A, Alonso, A, Bader, J, Ehret, G, Huang, H, Kao, W, Strait, J, Macfarlane, P, Brown, M, Caulfield, M, Samani, N, Kronenberg, F, Willeit, J, Smith, J, Greiser, K, Meyer Zu Schwabedissen, H, Werdan, K, Carella, M, Zelante, L, Heckbert, S, Psaty, B, Rotter, J, Kolcic, I, Polašek, O, Wright, A, Griffin, M, Daly, M, Arnar, D, Hólm, H, Thorsteinsdottir, U, Denny, J, Roden, D, Zuvich, R, Emilsson, V, Plump, A, Larson, M, O'Donnell, C, Yin, X, Bobbo, M, D'Adamo, A, Iorio, A, Sinagra, G, Carracedo, A, Cummings, S, Nalls, M, Jula, A, Kontula, K, Marjamaa, A, Oikarinen, L, Perola, M, Porthan, K, Erbel, R, Hoffmann, P, Jöckel, K, Kälsch, H, Nöthen, M, den Hoed, M, Loos, R, Thelle, D, Gieger, C, Meitinger, T, Perz, S, Peters, A, Prucha, H, Sinner, M, Waldenberger, M, de Boer, R, Franke, L, van der Vleuten, P, Beckmann, B, Martens, E, Bardai, A, Hofman, N, Wilde, A, Behr, E, Dalageorgou, C, Giudicessi, J, Medeiros-Domingo, A, Kyndt, F, Probst, V, Ghidoni, A, Insolia, R, Hamilton, R, Scherer, S, Brandimarto, J, Margulies, K, Moravec, C, Greco, M, Fuchsberger, C, O'Connell, J, Lee, W, Watt, G, Campbell, H, Wild, S, El Mokhtari, N, Frey, N, Asselbergs, F, Mateo Leach, I, Navis, G, van den Berg, M, van Veldhuisen, D, Kellis, M, Krijthe, B, Franco, O, Hofman, A, Kors, J, Uitterlinden, A, Witteman, J, Kedenko, L, Lamina, C, Oostra, B, Abecasis, G, Lakatta, E, Mulas, A, Orrú, M, Schlessinger, D, Uda, M, Markus, M, Völker, U, Snieder, H, Spector, T, Arnlöv, J, Lind, L, Sundström, J, Syvänen, A, Kivimaki, M, Kähönen, M, Mononen, N, Raitakari, O, Viikari, J, Adamkova, V, Kiechl, S, Brion, M, Nicolaides, A, Paulweber, B, Haerting, J, Dominiczak, A, Nyberg, F, Whincup, P, Hingorani, A, Schott, J, Bezzina, C, Ingelsson, E, Ferrucci, L, Gasparini, P, Wilson, J, Rudan, I, Franke, A, Mühleisen, T, Pramstaller, P, Lehtimäki, T, Paterson, A, Parsa, A, Liu, Y, van Duijn, C, Siscovick, D, Gudnason, V, Jamshidi, Y, Salomaa, V, Felix, S, Sanna, S, Ritchie, M, Stricker, B, Stefansson, K, Boyer, L, Cappola, T, Olsen, J, Lage, K, Schwartz, P, Kääb, S, Chakravarti, A, Ackerman, M, Pfeufer, A, de Bakker, P, Newton-Cheh, C, Arking, DE, Pulit, SL, Munroe, PB, Rossin, EJ, Johnson, AD, Gudbjartsson, DF, Noseworthy, PA, Tarasov, KV, Lahtinen, AM, Nolte, IM, Smith, AV, Bis, JC, Newhouse, SJ, Evans, DS, Post, WS, Lyytikäinen, LP, Hicks, AA, Tester, DJ, Morris, RW, Naluai, AT, Panayiotou, AG, Hubacek, JA, Kumar, RD, Harris, TB, Launer, LJ, Shuldiner, AR, Bader, JS, Kao, WH, Strait, JB, Macfarlane, PW, Caulfield, MJ, Samani, NJ, Smith, JG, Greiser, KH, Heckbert, SR, Psaty, BM, Rotter, JI, Wright, AF, Daly, MJ, Arnar, DO, Denny, JC, Roden, DM, Zuvich, RL, Plump, AS, Larson, MG, O'Donnell, CJ, D'Adamo, AP, Cummings, SR, Nalls, MA, Kontula, KK, Jöckel, KH, Nöthen, MM, Loos, RJ, Thelle, DS, Sinner, MF, de Boer, RA, van der Vleuten, PA, Beckmann, BM, Wilde, AA, Behr, ER, Giudicessi, JR, Hamilton, RM, Scherer, SW, Moravec, CE, Greco, MFD, O'Connell, JR, Lee, WK, Watt, GC, Wild, SH, El Mokhtari, NE, Asselbergs, FW, van den Berg, MP, van Veldhuisen, DJ, Krijthe, BP, Franco, OH, Kors, JA, Uitterlinden, AG, Witteman, JC, Oostra, BA, Abecasis, GR, Lakatta, EG, Markus, MR, Spector, TD, Syvänen, AC, Raitakari, OT, Viikari, JS, Nicolaides, AN, Dominiczak, AF, Whincup, PH, Hingorani, AD, Schott, JJ, Bezzina, CR, Wilson, JF, Mühleisen, TW, Pramstaller, PP, Lehtimäki, TJ, Paterson, AD, van Duijn, CM, Siscovick, DS, Felix, SB, Ritchie, MD, Stricker, BH, Boyer, LA, Cappola, TP, Olsen, JV, Schwartz, PJ, Ackerman, MJ, de Bakker, PI, Arking, D, Pulit, S, Crotti, L, van der Harst, P, Munroe, P, Koopmann, T, Sotoodehnia, N, Rossin, E, Morley, M, Wang, X, Johnson, A, Lundby, A, Gudbjartsson, D, Noseworthy, P, Eijgelsheim, M, Bradford, Y, Tarasov, K, Dörr, M, Müller-Nurasyid, M, Lahtinen, A, Nolte, I, Smith, A, Bis, J, Isaacs, A, Newhouse, S, Evans, D, Post, W, Waggott, D, Lyytikäinen, L, Hicks, A, Eisele, L, Ellinghaus, D, Hayward, C, Navarro, P, Ulivi, S, Tanaka, T, Tester, D, Chatel, S, Gustafsson, S, Kumari, M, Morris, R, Naluai, A, Padmanabhan, S, Kluttig, A, Strohmer, B, Panayiotou, A, Torres, M, Knoflach, M, Hubacek, J, Slowikowski, K, Raychaudhuri, S, Kumar, R, Harris, T, Launer, L, Shuldiner, A, Alonso, A, Bader, J, Ehret, G, Huang, H, Kao, W, Strait, J, Macfarlane, P, Brown, M, Caulfield, M, Samani, N, Kronenberg, F, Willeit, J, Smith, J, Greiser, K, Meyer Zu Schwabedissen, H, Werdan, K, Carella, M, Zelante, L, Heckbert, S, Psaty, B, Rotter, J, Kolcic, I, Polašek, O, Wright, A, Griffin, M, Daly, M, Arnar, D, Hólm, H, Thorsteinsdottir, U, Denny, J, Roden, D, Zuvich, R, Emilsson, V, Plump, A, Larson, M, O'Donnell, C, Yin, X, Bobbo, M, D'Adamo, A, Iorio, A, Sinagra, G, Carracedo, A, Cummings, S, Nalls, M, Jula, A, Kontula, K, Marjamaa, A, Oikarinen, L, Perola, M, Porthan, K, Erbel, R, Hoffmann, P, Jöckel, K, Kälsch, H, Nöthen, M, den Hoed, M, Loos, R, Thelle, D, Gieger, C, Meitinger, T, Perz, S, Peters, A, Prucha, H, Sinner, M, Waldenberger, M, de Boer, R, Franke, L, van der Vleuten, P, Beckmann, B, Martens, E, Bardai, A, Hofman, N, Wilde, A, Behr, E, Dalageorgou, C, Giudicessi, J, Medeiros-Domingo, A, Kyndt, F, Probst, V, Ghidoni, A, Insolia, R, Hamilton, R, Scherer, S, Brandimarto, J, Margulies, K, Moravec, C, Greco, M, Fuchsberger, C, O'Connell, J, Lee, W, Watt, G, Campbell, H, Wild, S, El Mokhtari, N, Frey, N, Asselbergs, F, Mateo Leach, I, Navis, G, van den Berg, M, van Veldhuisen, D, Kellis, M, Krijthe, B, Franco, O, Hofman, A, Kors, J, Uitterlinden, A, Witteman, J, Kedenko, L, Lamina, C, Oostra, B, Abecasis, G, Lakatta, E, Mulas, A, Orrú, M, Schlessinger, D, Uda, M, Markus, M, Völker, U, Snieder, H, Spector, T, Arnlöv, J, Lind, L, Sundström, J, Syvänen, A, Kivimaki, M, Kähönen, M, Mononen, N, Raitakari, O, Viikari, J, Adamkova, V, Kiechl, S, Brion, M, Nicolaides, A, Paulweber, B, Haerting, J, Dominiczak, A, Nyberg, F, Whincup, P, Hingorani, A, Schott, J, Bezzina, C, Ingelsson, E, Ferrucci, L, Gasparini, P, Wilson, J, Rudan, I, Franke, A, Mühleisen, T, Pramstaller, P, Lehtimäki, T, Paterson, A, Parsa, A, Liu, Y, van Duijn, C, Siscovick, D, Gudnason, V, Jamshidi, Y, Salomaa, V, Felix, S, Sanna, S, Ritchie, M, Stricker, B, Stefansson, K, Boyer, L, Cappola, T, Olsen, J, Lage, K, Schwartz, P, Kääb, S, Chakravarti, A, Ackerman, M, Pfeufer, A, de Bakker, P, Newton-Cheh, C, Arking, DE, Pulit, SL, Munroe, PB, Rossin, EJ, Johnson, AD, Gudbjartsson, DF, Noseworthy, PA, Tarasov, KV, Lahtinen, AM, Nolte, IM, Smith, AV, Bis, JC, Newhouse, SJ, Evans, DS, Post, WS, Lyytikäinen, LP, Hicks, AA, Tester, DJ, Morris, RW, Naluai, AT, Panayiotou, AG, Hubacek, JA, Kumar, RD, Harris, TB, Launer, LJ, Shuldiner, AR, Bader, JS, Kao, WH, Strait, JB, Macfarlane, PW, Caulfield, MJ, Samani, NJ, Smith, JG, Greiser, KH, Heckbert, SR, Psaty, BM, Rotter, JI, Wright, AF, Daly, MJ, Arnar, DO, Denny, JC, Roden, DM, Zuvich, RL, Plump, AS, Larson, MG, O'Donnell, CJ, D'Adamo, AP, Cummings, SR, Nalls, MA, Kontula, KK, Jöckel, KH, Nöthen, MM, Loos, RJ, Thelle, DS, Sinner, MF, de Boer, RA, van der Vleuten, PA, Beckmann, BM, Wilde, AA, Behr, ER, Giudicessi, JR, Hamilton, RM, Scherer, SW, Moravec, CE, Greco, MFD, O'Connell, JR, Lee, WK, Watt, GC, Wild, SH, El Mokhtari, NE, Asselbergs, FW, van den Berg, MP, van Veldhuisen, DJ, Krijthe, BP, Franco, OH, Kors, JA, Uitterlinden, AG, Witteman, JC, Oostra, BA, Abecasis, GR, Lakatta, EG, Markus, MR, Spector, TD, Syvänen, AC, Raitakari, OT, Viikari, JS, Nicolaides, AN, Dominiczak, AF, Whincup, PH, Hingorani, AD, Schott, JJ, Bezzina, CR, Wilson, JF, Mühleisen, TW, Pramstaller, PP, Lehtimäki, TJ, Paterson, AD, van Duijn, CM, Siscovick, DS, Felix, SB, Ritchie, MD, Stricker, BH, Boyer, LA, Cappola, TP, Olsen, JV, Schwartz, PJ, Ackerman, MJ, and de Bakker, PI
- Abstract
The QT interval, an electrocardiographic measure reflecting myocardial repolarization, is a heritable trait. QT prolongation is a risk factor for ventricular arrhythmias and sudden cardiac death (SCD) and could indicate the presence of the potentially lethal mendelian long-QT syndrome (LQTS). Using a genome-wide association and replication study in up to 100,000 individuals, we identified 35 common variant loci associated with QT interval that collectively explain ∼8-10% of QT-interval variation and highlight the importance of calcium regulation in myocardial repolarization. Rare variant analysis of 6 new QT interval-associated loci in 298 unrelated probands with LQTS identified coding variants not found in controls but of uncertain causality and therefore requiring validation. Several newly identified loci encode proteins that physically interact with other recognized repolarization proteins. Our integration of common variant association, expression and orthogonal protein-protein interaction screens provides new insights into cardiac electrophysiology and identifies new candidate genes for ventricular arrhythmias, LQTS and SCD
- Published
- 2014
15. Optimizing Precision Medicine By Using Genetics to Assign Diagnostic Prior Probabilities to Patients with Synovitis
- Author
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Knevel, R., Terao, C., Cui, J., Slowikowski, K., Huizinga, T.W.J., Karlson, E., and Raychaudhuri, S.
16. Genetic association study of QT interval highlights role for calcium signaling pathways in myocardial repolarization
- Author
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Yuki Bradford, Toshiko Tanaka, Jeffrey R. O'Connell, Florence Kyndt, Unnur Thorsteinsdottir, Ivana Kolcic, Xiaoyan Yin, Vincent Probst, Manolis Kellis, Christopher Newton-Cheh, Stefan Kääb, Argelia Medeiros-Domingo, Markus M. Nöthen, Paolo Gasparini, Jean-Jacques Schott, Ruth J. F. Loos, Thomas W. Mühleisen, Annukka Marjamaa, Morris Brown, Igor Rudan, Runjun D. Kumar, Peter J. Schwartz, Lars Lind, Martina Müller-Nurasyid, Xinchen Wang, Joshua C. Denny, Roberto Insolia, Soumya Raychaudhuri, Stephen W. Scherer, Bruno H. Stricker, Alexander Kluttig, Adamo Pio D'Adamo, Laurie A. Boyer, Moritz F. Sinner, Norbert Frey, Nour Eddine El Mokhtari, Thomas Meitinger, Jesper V. Olsen, Gerjan Navis, Steven R. Cummings, Richard W Morris, Nynke Hofman, Marcel den Hoed, Rudolf A. de Boer, Gonçalo R. Abecasis, Mark J. Daly, Dan M. Roden, Christian Gieger, Lyudmyla Kedenko, Marcus Dörr, Thomas P. Cappola, Afshin Parsa, Kari Stefansson, Markus Perola, Mark Eijgelsheim, Fredrik Nyberg, Robert M. Hamilton, Yalda Jamshidi, W. H. Linda Kao, Terho Lehtimäki, Annette Peters, David Schlessinger, Peter P. Pramstaller, James F. Wilson, Vilmundur Gudnason, Florian Kronenberg, Aroon D. Hingorani, Connie R. Bezzina, Abdennasser Bardai, Marylyn D. Ritchie, Andrew S. Plump, Johan Sundström, Daryl Waggott, Chrysoula Dalageorgou, Paul I.W. de Bakker, Uwe Völker, Aaron Isaacs, Oscar H. Franco, Yongmei Liu, Andrew N. Nicolaides, Lia Crotti, Cornelia M. van Duijn, Ben A. Oostra, Arne Pfeufer, Karl Werdan, Michael Morley, Jan A. Kors, Julien Barc, Lewin Eisele, Siegfried Perz, Stéphanie Chatel, Pieter A. van der Vleuten, Sara L. Pulit, Anna F. Dominiczak, Harry Campbell, Alice Ghidoni, Irene Mateo Leach, Nona Sotoodehnia, Nina Mononen, Henriette E. Meyer zu Schwabedissen, Alvaro Alonso, Fabiola Del Greco M, Dan E. Arking, Vera Adamkova, Mike A. Nalls, Valur Emilsson, Edward G. Lakatta, Kirill Tarasov, Alan F. Wright, Lenore J. Launer, Erik Ingelsson, Karin Halina Greiser, Ozren Polasek, Massimo Carella, Daniel F. Gudbjartsson, Bouwe P. Krijthe, Hanna Prucha, Per Hoffmann, Maura Griffin, Stefan Kiechl, Angel Carracedo, Ilja M. Nolte, Christine E. Moravec, Johann Willeit, Joshua C. Bis, Patricia B. Munroe, Marcello Ricardo Paulista Markus, Hailiang Huang, Mika Kähönen, Albert Hofman, Peter H. Whincup, Dirk J. van Veldhuisen, Michael Knoflach, Alicia Lundby, Serena Sanna, Hagen Kälsch, Bernhard Paulweber, Kamil Slowikowski, Luigi Ferrucci, Melanie Waldenberger, Marco Bobbo, Annukka M. Lahtinen, Ann-Christine Syvänen, J. Gustav Smith, Åsa Torinsson Naluai, Jaroslav A. Hubacek, Jeffrey Brandimarto, Wendy S. Post, Lude Franke, Mark J. Caulfield, Folkert W. Asselbergs, André G. Uitterlinden, Stefan Gustafsson, Pim van der Harst, David J. Tester, David S. Siscovick, David O. Arnar, Sarah H Wild, Elizabeth J. Rossin, Albert V. Smith, Bruce M. Psaty, Georg Ehret, Alan R. Shuldiner, Stephen Newhouse, Kimmo Kontula, Maria Brion, Andre Franke, Peter W. Macfarlane, Mika Kivimäki, Tamara B. Harris, Lasse Oikarinen, Tamara T. Koopmann, Kenneth B. Margulies, Aravinda Chakravarti, Gianfranco Sinagra, Maarten P. van den Berg, Veikko Salomaa, Karl-Heinz Jöckel, Daniel S. Evans, Caroline Hayward, Kimmo Porthan, Michael J. Ackerman, Jacqueline C.M. Witteman, Arthur A.M. Wilde, Martin G. Larson, Kasper Lage, Manuela Uda, Susan R. Heckbert, Joel S. Bader, Graham Watt, María Dolores Torres, Stephan B. Felix, Jerome I. Rotter, Pau Navarro, Meena Kumari, Johan Ärnlöv, Andrew D. Paterson, Antti Jula, Olli T. Raitakari, Raimund Erbel, Christopher J. O'Donnell, Britt M. Beckmann, Peter A. Noseworthy, Tim D. Spector, Wai K. Lee, Leopoldo Zelante, Nilesh J. Samani, John R. Giudicessi, Harold Snieder, Dag S. Thelle, David Ellinghaus, Eimo Martens, James B. Strait, Jorma S. A. Viikari, Andrew D. Johnson, Antonella Mulas, Hilma Holm, Johannes Haerting, Annamaria Iorio, Rebecca L. Zuvich, Sheila Ulivi, Andrew A. Hicks, Elijah R. Behr, Leo-Pekka Lyytikäinen, Bernhard Strohmer, Marco Orru, Claudia Lamina, Sandosh Padmanabhan, Christian Fuchsberger, Andrie G. Panayiotou, Ehret, Georg Benedikt, Internal Medicine, Public Health, Epidemiology, Rehabilitation Medicine, Medical Informatics, Clinical Genetics, Cardiovascular Centre (CVC), Life Course Epidemiology (LCE), Groningen Institute for Gastro Intestinal Genetics and Immunology (3GI), Lifestyle Medicine (LM), Groningen Kidney Center (GKC), Vascular Ageing Programme (VAP), Ethical, Legal, Social Issues in Genetics (ELSI), Stem Cell Aging Leukemia and Lymphoma (SALL), Arking, D, Pulit, S, Crotti, L, van der Harst, P, Munroe, P, Koopmann, T, Sotoodehnia, N, Rossin, E, Morley, M, Wang, X, Johnson, A, Lundby, A, Gudbjartsson, D, Noseworthy, P, Eijgelsheim, M, Bradford, Y, Tarasov, K, Dörr, M, Müller-Nurasyid, M, Lahtinen, A, Nolte, I, Smith, A, Bis, J, Isaacs, A, Newhouse, S, Evans, D, Post, W, Waggott, D, Lyytikäinen, L, Hicks, A, Eisele, L, Ellinghaus, D, Hayward, C, Navarro, P, Ulivi, S, Tanaka, T, Tester, D, Chatel, S, Gustafsson, S, Kumari, M, Morris, R, Naluai, A, Padmanabhan, S, Kluttig, A, Strohmer, B, Panayiotou, A, Torres, M, Knoflach, M, Hubacek, J, Slowikowski, K, Raychaudhuri, S, Kumar, R, Harris, T, Launer, L, Shuldiner, A, Alonso, A, Bader, J, Ehret, G, Huang, H, Kao, W, Strait, J, Macfarlane, P, Brown, M, Caulfield, M, Samani, N, Kronenberg, F, Willeit, J, Smith, J, Greiser, K, Meyer Zu Schwabedissen, H, Werdan, K, Carella, M, Zelante, L, Heckbert, S, Psaty, B, Rotter, J, Kolcic, I, Polašek, O, Wright, A, Griffin, M, Daly, M, Arnar, D, Hólm, H, Thorsteinsdottir, U, Denny, J, Roden, D, Zuvich, R, Emilsson, V, Plump, A, Larson, M, O'Donnell, C, Yin, X, Bobbo, M, D'Adamo, A, Iorio, A, Sinagra, G, Carracedo, A, Cummings, S, Nalls, M, Jula, A, Kontula, K, Marjamaa, A, Oikarinen, L, Perola, M, Porthan, K, Erbel, R, Hoffmann, P, Jöckel, K, Kälsch, H, Nöthen, M, den Hoed, M, Loos, R, Thelle, D, Gieger, C, Meitinger, T, Perz, S, Peters, A, Prucha, H, Sinner, M, Waldenberger, M, de Boer, R, Franke, L, van der Vleuten, P, Beckmann, B, Martens, E, Bardai, A, Hofman, N, Wilde, A, Behr, E, Dalageorgou, C, Giudicessi, J, Medeiros-Domingo, A, Kyndt, F, Probst, V, Ghidoni, A, Insolia, R, Hamilton, R, Scherer, S, Brandimarto, J, Margulies, K, Moravec, C, Greco, M, Fuchsberger, C, O'Connell, J, Lee, W, Watt, G, Campbell, H, Wild, S, El Mokhtari, N, Frey, N, Asselbergs, F, Mateo Leach, I, Navis, G, van den Berg, M, van Veldhuisen, D, Kellis, M, Krijthe, B, Franco, O, Hofman, A, Kors, J, Uitterlinden, A, Witteman, J, Kedenko, L, Lamina, C, Oostra, B, Abecasis, G, Lakatta, E, Mulas, A, Orrú, M, Schlessinger, D, Uda, M, Markus, M, Völker, U, Snieder, H, Spector, T, Arnlöv, J, Lind, L, Sundström, J, Syvänen, A, Kivimaki, M, Kähönen, M, Mononen, N, Raitakari, O, Viikari, J, Adamkova, V, Kiechl, S, Brion, M, Nicolaides, A, Paulweber, B, Haerting, J, Dominiczak, A, Nyberg, F, Whincup, P, Hingorani, A, Schott, J, Bezzina, C, Ingelsson, E, Ferrucci, L, Gasparini, P, Wilson, J, Rudan, I, Franke, A, Mühleisen, T, Pramstaller, P, Lehtimäki, T, Paterson, A, Parsa, A, Liu, Y, van Duijn, C, Siscovick, D, Gudnason, V, Jamshidi, Y, Salomaa, V, Felix, S, Sanna, S, Ritchie, M, Stricker, B, Stefansson, K, Boyer, L, Cappola, T, Olsen, J, Lage, K, Schwartz, P, Kääb, S, Chakravarti, A, Ackerman, M, Pfeufer, A, de Bakker, P, Newton-Cheh, C, Arking, Dan E., Pulit, Sara L., Crotti, Lia, Van Der Harst, Pim, Munroe, Patricia B., Koopmann, Tamara T., Sotoodehnia, Nona, Rossin, Elizabeth J., Morley, Michael, Wang, Xinchen, Johnson, Andrew D., Lundby, Alicia, Gudbjartsson, Daníel F., Noseworthy, Peter A., Eijgelsheim, Mark, Bradford, Yuki, Tarasov, Kirill V., Dörr, Marcu, Müller Nurasyid, Martina, Lahtinen, Annukka M., Nolte, Ilja M., Smith, Albert Vernon, Bis, Joshua C., Isaacs, Aaron, Newhouse, Stephen J., Evans, Daniel S., Post, Wendy S., Waggott, Daryl, Lyytikäinen, Leo Pekka, Hicks, Andrew A., Eisele, Lewin, Ellinghaus, David, Hayward, Caroline, Navarro, Pau, Ulivi, Sheila, Tanaka, Toshiko, Tester, David J., Chatel, Stéphanie, Gustafsson, Stefan, Kumari, Meena, Morris, Richard W., Naluai, Asa T., Padmanabhan, Sandosh, Kluttig, Alexander, Strohmer, Bernhard, Panayiotou, Andrie G., Torres, Maria, Knoflach, Michael, Hubacek, Jaroslav A., Slowikowski, Kamil, Raychaudhuri, Soumya, Kumar, Runjun D., Harris, Tamara B., Launer, Lenore J., Shuldiner, Alan R., Alonso, Alvaro, Bader, Joel S., Ehret, Georg, Huang, Hailiang, Kao, W. H. Linda, Strait, James B., Macfarlane, Peter W., Brown, Morri, Caulfield, Mark J., Samani, Nilesh J., Kronenberg, Florian, Willeit, Johann, Smith, J. Gustav, Greiser, Karin H., Zu Schwabedissen, Henriette Meyer, Werdan, Karl, Carella, Massimo, Zelante, Leopoldo, Heckbert, Susan R., Psaty, Bruce M., Rotter, Jerome I., Kolcic, Ivana, Polašek, Ozren, Wright, Alan F., Griffin, Maura, Daly, Mark J., Arnar, David O., Hólm, Hilma, Thorsteinsdottir, Unnur, Denny, Joshua C., Roden, Dan M., Zuvich, Rebecca L., Emilsson, Valur, Plump, Andrew S., Larson, Martin G., O'Donnell, Christopher J., Yin, Xiaoyan, Bobbo, Marco, D'Adamo, ADAMO PIO, Iorio, Annamaria, Sinagra, Gianfranco, Carracedo, Angel, Cummings, Steven R., Nalls, Michael A., Jula, Antti, Kontula, Kimmo K., Marjamaa, Annukka, Oikarinen, Lasse, Perola, Marku, Porthan, Kimmo, Erbel, Raimund, Hoffmann, Per, Jöckel, Karl Heinz, Kälsch, Hagen, Nöthen, Markus M., Den Hoed, Marcel, Loos, Ruth J. F., Thelle, Dag S., Gieger, Christian, Meitinger, Thoma, Perz, Siegfried, Peters, Annette, Prucha, Hanna, Sinner, Moritz F., Waldenberger, Melanie, De Boer, Rudolf A., Franke, Lude, Van Der Vleuten, Pieter A., Beckmann, Britt Maria, Martens, Eimo, Bardai, Abdennasser, Hofman, Nynke, Wilde, Arthur A. M., Behr, Elijah R., Dalageorgou, Chrysoula, Giudicessi, John R., Medeiros Domingo, Argelia, Barc, Julien, Kyndt, Florence, Probst, Vincent, Ghidoni, Alice, Insolia, Roberto, Hamilton, Robert M., Scherer, Stephen W., Brandimarto, Jeffrey, Margulies, Kenneth, Moravec, Christine E., Del Greco M, Fabiola, Fuchsberger, Christian, O'Connell, Jeffrey R., Lee, Wai K., Watt, Graham C. M., Campbell, Harry, Wild, Sarah H., El Mokhtari, Nour E., Frey, Norbert, Asselbergs, Folkert W., Leach, Irene Mateo, Navis, Gerjan, Van Den Berg, Maarten P., Van Veldhuisen, Dirk J., Kellis, Manoli, Krijthe, Bouwe P., Franco, Oscar H., Hofman, Albert, Kors, Jan A., Uitterlinden, André G., Witteman, Jacqueline C. M., Kedenko, Lyudmyla, Lamina, Claudia, Oostra, Ben A., Abecasis, Gonçalo R., Lakatta, Edward G., Mulas, Antonella, Orrú, Marco, Schlessinger, David, Uda, Manuela, Markus, Marcello R. P., Völker, Uwe, Snieder, Harold, Spector, Timothy D., Ärnlöv, Johan, Lind, Lar, Sundström, Johan, Syvänen, Ann Christine, Kivimaki, Mika, Kähönen, Mika, Mononen, Nina, Raitakari, Olli T., Viikari, Jorma S., Adamkova, Vera, Kiechl, Stefan, Brion, Maria, Nicolaides, Andrew N., Paulweber, Bernhard, Haerting, Johanne, Dominiczak, Anna F., Nyberg, Fredrik, Whincup, Peter H., Hingorani, Aroon D., Schott, Jean Jacque, Bezzina, Connie R., Ingelsson, Erik, Ferrucci, Luigi, Gasparini, Paolo, Wilson, James F., Rudan, Igor, Franke, Andre, Mühleisen, Thomas W., Pramstaller, Peter P., Lehtimäki, Terho J., Paterson, Andrew D., Parsa, Afshin, Liu, Yongmei, Van Duijn, Cornelia M., Siscovick, David S., Gudnason, Vilmundur, Jamshidi, Yalda, Salomaa, Veikko, Felix, Stephan B., Sanna, Serena, Ritchie, Marylyn D., Stricker, Bruno H., Stefansson, Kari, Boyer, Laurie A., Cappola, Thomas P., Olsen, Jesper V., Lage, Kasper, Schwartz, Peter J., Kääb, Stefan, Chakravarti, Aravinda, Ackerman, Michael J., Pfeufer, Arne, De Bakker, Paul I. W., Newton Cheh, Christopher, Cardiology, ACS - Amsterdam Cardiovascular Sciences, and Human Genetics
- Subjects
Male ,Candidate gene ,Myocardium/metabolism ,LOCI ,Medizin ,Heart electrophysiology ,Genome-wide association study ,Arrhythmias ,Bioinformatics ,Medical and Health Sciences ,Heart Ventricle ,Sudden cardiac death ,Electrocardiography ,PR INTERVAL ,Arrhythmias, Cardiac/genetics ,Death, Sudden, Cardiac/etiology ,Genetics ,ddc:616 ,Cardiac electrophysiology ,Adult ,Aged ,Arrhythmias, Cardiac ,Calcium Signaling ,Death, Sudden, Cardiac ,Female ,Genetic Predisposition to Disease ,Genome-Wide Association Study ,Genotype ,Heart Ventricles ,Humans ,Long QT Syndrome ,Middle Aged ,Myocardium ,Polymorphism, Single Nucleotide ,COMMON VARIANTS ,Heart Ventricles/metabolism ,Single Nucleotide ,Long QT Syndrome/genetics ,CHRONIC HEART-FAILURE ,Death ,Heart ventricle arrhythmia ,genetic association study ,gene ,SNP ,heart ,Genome-Wide Association Study/methods ,Long QT syndrome ,QRS DURATION ,Cardiac ,Cardiac/etiology ,Human ,QT interval ,congenital, hereditary, and neonatal diseases and abnormalities ,Electrocardiography/methods ,TRPM7 ,BIO/18 - GENETICA ,Cardiac/genetics ,Biology ,Article ,sudden cardiac death ,QRS complex ,CARDIAC REPOLARIZATION ,medicine ,Repolarization ,cardiovascular diseases ,GENOME-WIDE ASSOCIATION ,Polymorphism ,MED/01 - STATISTICA MEDICA ,calcium ,ta1184 ,Calcium signaling ,Calcium Signaling/genetics ,MED/11 - MALATTIE DELL'APPARATO CARDIOVASCOLARE ,ta3121 ,Cardiovascular risk ,medicine.disease ,SARCOPLASMIC-RETICULUM ,Sudden ,MODEL ,Genetic association ,myocardial repolarization ,Genetic variability ,Gene expression ,Clinical Medicine ,genetic ,Controlled study - Abstract
The QT interval, an electrocardiographic measure reflecting myocardial repolarization, is a heritable trait. QT prolongation is a risk factor for ventricular arrhythmias and sudden cardiac death (SCD) and could indicate the presence of the potentially lethal mendelian long-QT syndrome (LQTS). Using a genome-wide association and replication study in up to 100,000 individuals, we identified 35 common variant loci associated with QT interval that collectively explain similar to 8-10% of QT-interval variation and highlight the importance of calcium regulation in myocardial repolarization. Rare variant analysis of 6 new QT interval-associated loci in 298 unrelated probands with LQTS identified coding variants not found in controls but of uncertain causality and therefore requiring validation. Several newly identified loci encode proteins that physically interact with other recognized repolarization proteins. Our integration of common variant association, expression and orthogonal protein-protein interaction screens provides new insights into cardiac electrophysiology and identifies new candidate genes for ventricular arrhythmias, LQTS and SCD.
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- 2014
17. Single-cell transcriptomic analyses reveal distinct immune cell contributions to epithelial barrier dysfunction in checkpoint inhibitor colitis.
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Thomas MF, Slowikowski K, Manakongtreecheep K, Sen P, Samanta N, Tantivit J, Nasrallah M, Zubiri L, Smith NP, Tirard A, Ramesh S, Arnold BY, Nieman LT, Chen JH, Eisenhaure T, Pelka K, Song Y, Xu KH, Jorgji V, Pinto CJ, Sharova T, Glasser R, Chan P, Sullivan RJ, Khalili H, Juric D, Boland GM, Dougan M, Hacohen N, Li B, Reynolds KL, and Villani AC
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- Humans, Female, Male, Gene Expression Profiling, CD8-Positive T-Lymphocytes immunology, CD8-Positive T-Lymphocytes drug effects, Middle Aged, Programmed Cell Death 1 Receptor antagonists & inhibitors, Aged, Transcriptome, CTLA-4 Antigen antagonists & inhibitors, CTLA-4 Antigen genetics, CTLA-4 Antigen immunology, T-Lymphocytes, Regulatory immunology, T-Lymphocytes, Regulatory drug effects, Colon pathology, Colon immunology, Colon drug effects, Epithelial Cells immunology, Epithelial Cells drug effects, Epithelial Cells pathology, Immune Checkpoint Inhibitors adverse effects, Colitis chemically induced, Colitis immunology, Colitis genetics, Colitis pathology, Single-Cell Analysis, Intestinal Mucosa immunology, Intestinal Mucosa pathology, Intestinal Mucosa drug effects
- Abstract
Immune checkpoint inhibitor (ICI) therapy has revolutionized oncology, but treatments are limited by immune-related adverse events, including checkpoint inhibitor colitis (irColitis). Little is understood about the pathogenic mechanisms driving irColitis, which does not readily occur in model organisms, such as mice. To define molecular drivers of irColitis, we used single-cell multi-omics to profile approximately 300,000 cells from the colon mucosa and blood of 13 patients with cancer who developed irColitis (nine on anti-PD-1 or anti-CTLA-4 monotherapy and four on dual ICI therapy; most patients had skin or lung cancer), eight controls on ICI therapy and eight healthy controls. Patients with irColitis showed expanded mucosal Tregs, ITGAE
Hi CD8 tissue-resident memory T cells expressing CXCL13 and Th17 gene programs and recirculating ITGB2Hi CD8 T cells. Cytotoxic GNLYHi CD4 T cells, recirculating ITGB2Hi CD8 T cells and endothelial cells expressing hypoxia gene programs were further expanded in colitis associated with anti-PD-1/CTLA-4 therapy compared to anti-PD-1 therapy. Luminal epithelial cells in patients with irColitis expressed PCSK9, PD-L1 and interferon-induced signatures associated with apoptosis, increased cell turnover and malabsorption. Together, these data suggest roles for circulating T cells and epithelial-immune crosstalk critical to PD-1/CTLA-4-dependent tolerance and barrier function and identify potential therapeutic targets for irColitis., (© 2024. The Author(s), under exclusive licence to Springer Nature America, Inc.)- Published
- 2024
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18. SARS-CoV-2 infection elucidates unique features of pregnancy-specific immunity.
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Oh DS, Kim E, Lu G, Normand R, Shook LL, Lyall A, Jasset O, Demidkin S, Gilbert E, Kim J, Akinwunmi B, Tantivit J, Tirard A, Arnold BY, Slowikowski K, Goldberg MB, Filbin MR, Hacohen N, Nguyen LH, Chan AT, Yu XG, Li JZ, Yonker L, Fasano A, Perlis RH, Pasternak O, Gray KJ, Choi GB, Drew DA, Sen P, Villani AC, Edlow AG, and Huh JR
- Abstract
Pregnancy is a risk factor for increased severity of SARS-CoV-2 and other respiratory infections. The mechanisms underlying this risk have not been well-established, partly due to a limited understanding of how pregnancy shapes immune responses. To gain insight into the role of pregnancy in modulating immune responses at steady state and upon perturbation, we collected peripheral blood mononuclear cells (PBMC), plasma, and stool from 226 women, including 152 pregnant individuals (n = 96 with SARS-CoV-2 infection and n = 56 healthy controls) and 74 non-pregnant women (n = 55 with SARS-CoV-2 and n = 19 healthy controls). We found that SARS-CoV-2 infection was associated with altered T cell responses in pregnant compared to non-pregnant women. Differences included a lower percentage of memory T cells, a distinct clonal expansion of CD4-expressing CD8
+ T cells, and the enhanced expression of T cell exhaustion markers, such as programmed cell death-1 (PD-1) and T cell immunoglobulin and mucin domain-3 (Tim-3), in pregnant women. We identified additional evidence of immune dysfunction in severely and critically ill pregnant women, including a lack of expected elevation in regulatory T cell (Treg) levels, diminished interferon responses, and profound suppression of monocyte function. Consistent with earlier data, we found maternal obesity was also associated with altered immune responses to SARS-CoV-2 infection, including enhanced production of inflammatory cytokines by T cells. Certain gut bacterial species were altered in pregnancy and upon SARS-CoV-2 infection in pregnant individuals compared to non-pregnant women. Shifts in cytokine and chemokine levels were also identified in the sera of pregnant individuals, most notably a robust increase of interleukin-27 (IL-27), a cytokine known to drive T cell exhaustion, in the pregnant uninfected control group compared to all non-pregnant groups. IL-27 levels were also significantly higher in uninfected pregnant controls compared to pregnant SARS-CoV-2-infected individuals. Using two different preclinical mouse models of inflammation-induced fetal demise and respiratory influenza viral infection, we found that enhanced IL-27 protects developing fetuses from maternal inflammation but renders adult female mice vulnerable to viral infection. These combined findings from human and murine studies reveal nuanced pregnancy-associated immune responses, suggesting mechanisms underlying the increased susceptibility of pregnant individuals to viral respiratory infections.- Published
- 2024
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19. Immune Responses in Checkpoint Myocarditis Across Heart, Blood, and Tumor.
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Blum SM, Zlotoff DA, Smith NP, Kernin IJ, Ramesh S, Zubiri L, Caplin J, Samanta N, Martin SC, Tirard A, Sen P, Song Y, Barth J, Slowikowski K, Nasrallah M, Tantivit J, Manakongtreecheep K, Arnold BY, McGuire J, Pinto CJ, McLoughlin D, Jackson M, Chan P, Lawless A, Sharova T, Nieman LT, Gainor JF, Juric D, Mino-Kenudsen M, Sullivan RJ, Boland GM, Stone JR, Thomas MF, Neilan TG, Reynolds KL, and Villani AC
- Abstract
Immune checkpoint inhibitors (ICIs) are widely used anti-cancer therapies that can cause morbid and potentially fatal immune-related adverse events (irAEs). ICI-related myocarditis (irMyocarditis) is uncommon but has the highest mortality of any irAE. The pathogenesis of irMyocarditis and its relationship to anti-tumor immunity remain poorly understood. We sought to define immune responses in heart, tumor, and blood during irMyocarditis and identify biomarkers of clinical severity by leveraging single-cell (sc)RNA-seq coupled with T cell receptor (TCR) sequencing, microscopy, and proteomics analysis of 28 irMyocarditis patients and 23 controls. Our analysis of 284,360 cells from heart and blood specimens identified cytotoxic T cells, inflammatory macrophages, conventional dendritic cells (cDCs), and fibroblasts enriched in irMyocarditis heart tissue. Additionally, potentially targetable, pro-inflammatory transcriptional programs were upregulated across multiple cell types. TCR clones enriched in heart and paired tumor tissue were largely non-overlapping, suggesting distinct T cell responses within these tissues. We also identify the presence of cardiac-expanded TCRs in a circulating, cycling CD8 T cell population as a novel peripheral biomarker of fatality. Collectively, these findings highlight critical biology driving irMyocarditis and putative biomarkers for therapeutic intervention., Competing Interests: Conflict of Interest S.M.B has been a paid consultant to Two River Consulting and Third Rock Ventures. He has equity positions in Kronos Bio, 76Bio, and Allogene Therapeutics. D.A.Z. has been a paid consultant to Bristol Myers Squibb, Freeline Therapeutics, and Intrinsic Imaging. L.Z. has received consulting fees from Bristol Myers Squibb and Merck. R.J.S has been a paid consultant to Bristol Myers Squibb, Merck, Pfizer, Marengo Therapeutics, Novartis, Eisai, Iovance, OncoSec, and AstraZeneca and has received research funding from Merck. T.G.N has been a paid consultant to Bristol Myers Squibb, Genentech, CRC Oncology, Roche, Sanofi and Parexel Imaging Pharmaceuticals and has received grant funding from Astra Zeneca and Bristol Myers Squibb related to the cardiac effects of immune checkpoint inhibitors. K.L.R has served as an advisory board to SAGA Diagnostics and received speaker’s fees from CMEOutfitters and Medscape as well as research funding from Bristol Myers Squibb. A.C.V. has been a paid consultant to Bristol Myers Squibb.
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- 2023
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20. Deconstruction of rheumatoid arthritis synovium defines inflammatory subtypes.
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Zhang F, Jonsson AH, Nathan A, Millard N, Curtis M, Xiao Q, Gutierrez-Arcelus M, Apruzzese W, Watts GFM, Weisenfeld D, Nayar S, Rangel-Moreno J, Meednu N, Marks KE, Mantel I, Kang JB, Rumker L, Mears J, Slowikowski K, Weinand K, Orange DE, Geraldino-Pardilla L, Deane KD, Tabechian D, Ceponis A, Firestein GS, Maybury M, Sahbudin I, Ben-Artzi A, Mandelin AM 2nd, Nerviani A, Lewis MJ, Rivellese F, Pitzalis C, Hughes LB, Horowitz D, DiCarlo E, Gravallese EM, Boyce BF, Moreland LW, Goodman SM, Perlman H, Holers VM, Liao KP, Filer A, Bykerk VP, Wei K, Rao DA, Donlin LT, Anolik JH, Brenner MB, and Raychaudhuri S
- Subjects
- Humans, Cytokines metabolism, Inflammation complications, Inflammation genetics, Inflammation immunology, Inflammation pathology, Synovial Membrane pathology, T-Lymphocytes immunology, B-Lymphocytes immunology, Genetic Predisposition to Disease genetics, Phenotype, Single-Cell Gene Expression Analysis, Arthritis, Rheumatoid complications, Arthritis, Rheumatoid genetics, Arthritis, Rheumatoid immunology, Arthritis, Rheumatoid pathology
- Abstract
Rheumatoid arthritis is a prototypical autoimmune disease that causes joint inflammation and destruction
1 . There is currently no cure for rheumatoid arthritis, and the effectiveness of treatments varies across patients, suggesting an undefined pathogenic diversity1,2 . Here, to deconstruct the cell states and pathways that characterize this pathogenic heterogeneity, we profiled the full spectrum of cells in inflamed synovium from patients with rheumatoid arthritis. We used multi-modal single-cell RNA-sequencing and surface protein data coupled with histology of synovial tissue from 79 donors to build single-cell atlas of rheumatoid arthritis synovial tissue that includes more than 314,000 cells. We stratified tissues into six groups, referred to as cell-type abundance phenotypes (CTAPs), each characterized by selectively enriched cell states. These CTAPs demonstrate the diversity of synovial inflammation in rheumatoid arthritis, ranging from samples enriched for T and B cells to those largely lacking lymphocytes. Disease-relevant cell states, cytokines, risk genes, histology and serology metrics are associated with particular CTAPs. CTAPs are dynamic and can predict treatment response, highlighting the clinical utility of classifying rheumatoid arthritis synovial phenotypes. This comprehensive atlas and molecular, tissue-based stratification of rheumatoid arthritis synovial tissue reveal new insights into rheumatoid arthritis pathology and heterogeneity that could inform novel targeted treatments., (© 2023. The Author(s).)- Published
- 2023
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21. A human model of asthma exacerbation reveals transcriptional programs and cell circuits specific to allergic asthma.
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Alladina J, Smith NP, Kooistra T, Slowikowski K, Kernin IJ, Deguine J, Keen HL, Manakongtreecheep K, Tantivit J, Rahimi RA, Sheng SL, Nguyen ND, Haring AM, Giacona FL, Hariri LP, Xavier RJ, Luster AD, Villani AC, Cho JL, and Medoff BD
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- Humans, Antioxidants, Allergens, Inflammation, Asthma genetics, Hypersensitivity
- Abstract
Asthma is a chronic disease most commonly associated with allergy and type 2 inflammation. However, the mechanisms that link airway inflammation to the structural changes that define asthma are incompletely understood. Using a human model of allergen-induced asthma exacerbation, we compared the lower airway mucosa in allergic asthmatics and allergic non-asthmatic controls using single-cell RNA sequencing. In response to allergen, the asthmatic airway epithelium was highly dynamic and up-regulated genes involved in matrix degradation, mucus metaplasia, and glycolysis while failing to induce injury-repair and antioxidant pathways observed in controls. IL9 -expressing pathogenic T
H 2 cells were specific to asthmatic airways and were only observed after allergen challenge. Additionally, conventional type 2 dendritic cells (DC2 that express CD1C ) and CCR2 -expressing monocyte-derived cells (MCs) were uniquely enriched in asthmatics after allergen, with up-regulation of genes that sustain type 2 inflammation and promote pathologic airway remodeling. In contrast, allergic controls were enriched for macrophage-like MCs that up-regulated tissue repair programs after allergen challenge, suggesting that these populations may protect against asthmatic airway remodeling. Cellular interaction analyses revealed a TH 2-mononuclear phagocyte-basal cell interactome unique to asthmatics. These pathogenic cellular circuits were characterized by type 2 programming of immune and structural cells and additional pathways that may sustain and amplify type 2 signals, including TNF family signaling, altered cellular metabolism, failure to engage antioxidant responses, and loss of growth factor signaling. Our findings therefore suggest that pathogenic effector circuits and the absence of proresolution programs drive structural airway disease in response to type 2 inflammation.- Published
- 2023
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22. Single-cell meta-analysis of SARS-CoV-2 entry genes across tissues and demographics.
- Author
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Muus C, Luecken MD, Eraslan G, Sikkema L, Waghray A, Heimberg G, Kobayashi Y, Vaishnav ED, Subramanian A, Smillie C, Jagadeesh KA, Duong ET, Fiskin E, Torlai Triglia E, Ansari M, Cai P, Lin B, Buchanan J, Chen S, Shu J, Haber AL, Chung H, Montoro DT, Adams T, Aliee H, Allon SJ, Andrusivova Z, Angelidis I, Ashenberg O, Bassler K, Bécavin C, Benhar I, Bergenstråhle J, Bergenstråhle L, Bolt L, Braun E, Bui LT, Callori S, Chaffin M, Chichelnitskiy E, Chiou J, Conlon TM, Cuoco MS, Cuomo ASE, Deprez M, Duclos G, Fine D, Fischer DS, Ghazanfar S, Gillich A, Giotti B, Gould J, Guo M, Gutierrez AJ, Habermann AC, Harvey T, He P, Hou X, Hu L, Hu Y, Jaiswal A, Ji L, Jiang P, Kapellos TS, Kuo CS, Larsson L, Leney-Greene MA, Lim K, Litviňuková M, Ludwig LS, Lukassen S, Luo W, Maatz H, Madissoon E, Mamanova L, Manakongtreecheep K, Leroy S, Mayr CH, Mbano IM, McAdams AM, Nabhan AN, Nyquist SK, Penland L, Poirion OB, Poli S, Qi C, Queen R, Reichart D, Rosas I, Schupp JC, Shea CV, Shi X, Sinha R, Sit RV, Slowikowski K, Slyper M, Smith NP, Sountoulidis A, Strunz M, Sullivan TB, Sun D, Talavera-López C, Tan P, Tantivit J, Travaglini KJ, Tucker NR, Vernon KA, Wadsworth MH, Waldman J, Wang X, Xu K, Yan W, Zhao W, and Ziegler CGK
- Subjects
- Adult, Aged, Aged, 80 and over, Alveolar Epithelial Cells metabolism, Alveolar Epithelial Cells virology, Angiotensin-Converting Enzyme 2 genetics, Angiotensin-Converting Enzyme 2 metabolism, COVID-19 pathology, COVID-19 virology, Cathepsin L genetics, Cathepsin L metabolism, Datasets as Topic statistics & numerical data, Demography, Female, Gene Expression Profiling statistics & numerical data, Humans, Lung metabolism, Lung virology, Male, Middle Aged, Organ Specificity genetics, Respiratory System metabolism, Respiratory System virology, Sequence Analysis, RNA methods, Serine Endopeptidases genetics, Serine Endopeptidases metabolism, Single-Cell Analysis methods, COVID-19 epidemiology, COVID-19 genetics, Host-Pathogen Interactions genetics, SARS-CoV-2 physiology, Sequence Analysis, RNA statistics & numerical data, Single-Cell Analysis statistics & numerical data, Virus Internalization
- Abstract
Angiotensin-converting enzyme 2 (ACE2) and accessory proteases (TMPRSS2 and CTSL) are needed for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) cellular entry, and their expression may shed light on viral tropism and impact across the body. We assessed the cell-type-specific expression of ACE2, TMPRSS2 and CTSL across 107 single-cell RNA-sequencing studies from different tissues. ACE2, TMPRSS2 and CTSL are coexpressed in specific subsets of respiratory epithelial cells in the nasal passages, airways and alveoli, and in cells from other organs associated with coronavirus disease 2019 (COVID-19) transmission or pathology. We performed a meta-analysis of 31 lung single-cell RNA-sequencing studies with 1,320,896 cells from 377 nasal, airway and lung parenchyma samples from 228 individuals. This revealed cell-type-specific associations of age, sex and smoking with expression levels of ACE2, TMPRSS2 and CTSL. Expression of entry factors increased with age and in males, including in airway secretory cells and alveolar type 2 cells. Expression programs shared by ACE2
+ TMPRSS2+ cells in nasal, lung and gut tissues included genes that may mediate viral entry, key immune functions and epithelial-macrophage cross-talk, such as genes involved in the interleukin-6, interleukin-1, tumor necrosis factor and complement pathways. Cell-type-specific expression patterns may contribute to the pathogenesis of COVID-19, and our work highlights putative molecular pathways for therapeutic intervention.- Published
- 2021
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23. Single-cell transcriptomics in cancer: computational challenges and opportunities.
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Fan J, Slowikowski K, and Zhang F
- Subjects
- Biomarkers, Tumor, Cell Communication genetics, Genomics methods, High-Throughput Nucleotide Sequencing methods, Humans, Neoplasm Grading, Neoplasms pathology, Organ Specificity genetics, Sequence Analysis, RNA, Tumor Microenvironment genetics, Computational Biology methods, Gene Expression Profiling methods, Neoplasms genetics, Single-Cell Analysis methods, Transcriptome
- Abstract
Intratumor heterogeneity is a common characteristic across diverse cancer types and presents challenges to current standards of treatment. Advancements in high-throughput sequencing and imaging technologies provide opportunities to identify and characterize these aspects of heterogeneity. Notably, transcriptomic profiling at a single-cell resolution enables quantitative measurements of the molecular activity that underlies the phenotypic diversity of cells within a tumor. Such high-dimensional data require computational analysis to extract relevant biological insights about the cell types and states that drive cancer development, pathogenesis, and clinical outcomes. In this review, we highlight emerging themes in the computational analysis of single-cell transcriptomics data and their applications to cancer research. We focus on downstream analytical challenges relevant to cancer research, including how to computationally perform unified analysis across many patients and disease states, distinguish neoplastic from nonneoplastic cells, infer communication with the tumor microenvironment, and delineate tumoral and microenvironmental evolution with trajectory and RNA velocity analysis. We include discussions of challenges and opportunities for future computational methodological advancements necessary to realize the translational potential of single-cell transcriptomic profiling in cancer.
- Published
- 2020
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24. Synoviocyte-targeted therapy synergizes with TNF inhibition in arthritis reversal.
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Svensson MND, Zoccheddu M, Yang S, Nygaard G, Secchi C, Doody KM, Slowikowski K, Mizoguchi F, Humby F, Hands R, Santelli E, Sacchetti C, Wakabayashi K, Wu DJ, Barback C, Ai R, Wang W, Sims GP, Mydel P, Kasama T, Boyle DL, Galimi F, Vera D, Tremblay ML, Raychaudhuri S, Brenner MB, Firestein GS, Pitzalis C, Ekwall AH, Stanford SM, and Bottini N
- Subjects
- Animals, Cells, Cultured, Fibroblasts metabolism, Mice, Tumor Necrosis Factor-alpha metabolism, Antirheumatic Agents therapeutic use, Arthritis, Rheumatoid, Synoviocytes metabolism, Synoviocytes pathology
- Abstract
Fibroblast-like synoviocytes (FLS) are joint-lining cells that promote rheumatoid arthritis (RA) pathology. Current disease-modifying antirheumatic agents (DMARDs) operate through systemic immunosuppression. FLS-targeted approaches could potentially be combined with DMARDs to improve control of RA without increasing immunosuppression. Here, we assessed the potential of immunoglobulin-like domains 1 and 2 (Ig1&2), a decoy protein that activates the receptor tyrosine phosphatase sigma (PTPRS) on FLS, for RA therapy. We report that PTPRS expression is enriched in synovial lining RA FLS and that Ig1&2 reduces migration of RA but not osteoarthritis FLS. Administration of an Fc-fusion Ig1&2 attenuated arthritis in mice without affecting innate or adaptive immunity. Furthermore, PTPRS was down-regulated in FLS by tumor necrosis factor (TNF) via a phosphatidylinositol 3-kinase-mediated pathway, and TNF inhibition enhanced PTPRS expression in arthritic joints. Combination of ineffective doses of TNF inhibitor and Fc-Ig1&2 reversed arthritis in mice, providing an example of synergy between FLS-targeted and immunosuppressive DMARD therapies., (Copyright © 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).)
- Published
- 2020
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25. A positively selected FBN1 missense variant reduces height in Peruvian individuals.
- Author
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Asgari S, Luo Y, Akbari A, Belbin GM, Li X, Harris DN, Selig M, Bartell E, Calderon R, Slowikowski K, Contreras C, Yataco R, Galea JT, Jimenez J, Coit JM, Farroñay C, Nazarian RM, O'Connor TD, Dietz HC, Hirschhorn JN, Guio H, Lecca L, Kenny EE, Freeman EE, Murray MB, and Raychaudhuri S
- Subjects
- Female, Gene Frequency, Genome-Wide Association Study, Heredity, Humans, Indians, South American genetics, Male, Microfibrils chemistry, Microfibrils genetics, Peru, Body Height genetics, Fibrillin-1 genetics, Mutation, Missense, Selection, Genetic
- Abstract
On average, Peruvian individuals are among the shortest in the world
1 . Here we show that Native American ancestry is associated with reduced height in an ethnically diverse group of Peruvian individuals, and identify a population-specific, missense variant in the FBN1 gene (E1297G) that is significantly associated with lower height. Each copy of the minor allele (frequency of 4.7%) reduces height by 2.2 cm (4.4 cm in homozygous individuals). To our knowledge, this is the largest effect size known for a common height-associated variant. FBN1 encodes the extracellular matrix protein fibrillin 1, which is a major structural component of microfibrils. We observed less densely packed fibrillin-1-rich microfibrils with irregular edges in the skin of individuals who were homozygous for G1297 compared with individuals who were homozygous for E1297. Moreover, we show that the E1297G locus is under positive selection in non-African populations, and that the E1297 variant shows subtle evidence of positive selection specifically within the Peruvian population. This variant is also significantly more frequent in coastal Peruvian populations than in populations from the Andes or the Amazon, which suggests that short stature might be the result of adaptation to factors that are associated with the coastal environment in Peru.- Published
- 2020
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26. Using genetics to prioritize diagnoses for rheumatology outpatients with inflammatory arthritis.
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Knevel R, le Cessie S, Terao CC, Slowikowski K, Cui J, Huizinga TWJ, Costenbader KH, Liao KP, Karlson EW, and Raychaudhuri S
- Subjects
- Humans, Outpatients, Arthritis, Rheumatoid, Lupus Erythematosus, Systemic diagnosis, Lupus Erythematosus, Systemic genetics, Rheumatic Diseases diagnosis, Rheumatic Diseases genetics, Rheumatology
- Abstract
It is challenging to quickly diagnose slowly progressing diseases. To prioritize multiple related diagnoses, we developed G-PROB (Genetic Probability tool) to calculate the probability of different diseases for a patient using genetic risk scores. We tested G-PROB for inflammatory arthritis-causing diseases (rheumatoid arthritis, systemic lupus erythematosus, spondyloarthropathy, psoriatic arthritis, and gout). After validating on simulated data, we tested G-PROB in three cohorts: 1211 patients identified by International Classification of Diseases (ICD) codes within the eMERGE database, 245 patients identified through ICD codes and medical record review within the Partners Biobank, and 243 patients first presenting with unexplained inflammatory arthritis and with final diagnoses by record review within the Partners Biobank. Calibration of G-probabilities with disease status was high, with regression coefficients from 0.90 to 1.08 (1.00 is ideal). G-probabilities discriminated true diagnoses across the three cohorts with pooled areas under the curve (95% CI) of 0.69 (0.67 to 0.71), 0.81 (0.76 to 0.84), and 0.84 (0.81 to 0.86), respectively. For all patients, at least one disease could be ruled out, and in 45% of patients, a likely diagnosis was identified with a 64% positive predictive value. In 35% of cases, the clinician's initial diagnosis was incorrect. Initial clinical diagnosis explained 39% of the variance in final disease, which improved to 51% ( P < 0.0001) after adding G-probabilities. Converting genotype information before a clinical visit into an interpretable probability value for five different inflammatory arthritides could potentially be used to improve the diagnostic efficiency of rheumatic diseases in clinical practice., (Copyright © 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.)
- Published
- 2020
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27. CUX1 and IκBζ (NFKBIZ) mediate the synergistic inflammatory response to TNF and IL-17A in stromal fibroblasts.
- Author
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Slowikowski K, Nguyen HN, Noss EH, Simmons DP, Mizoguchi F, Watts GFM, Gurish MF, Brenner MB, and Raychaudhuri S
- Subjects
- Adaptor Proteins, Signal Transducing genetics, Arthritis, Rheumatoid genetics, Cells, Cultured, Chemokine CXCL1 genetics, Chemokine CXCL2 genetics, Chemokines, CXC genetics, Chemotactic Factors genetics, Fibroblasts drug effects, Homeodomain Proteins genetics, Humans, Inflammation genetics, Interleukin-17 pharmacology, Interleukin-6 genetics, Matrix Metalloproteinase 3 metabolism, Monocytes drug effects, Monocytes physiology, RNA, Small Interfering genetics, Repressor Proteins genetics, Stromal Cells drug effects, Stromal Cells metabolism, Synovial Fluid, Transcription Factor RelA metabolism, Transcription Factors genetics, Transcriptome radiation effects, Tumor Necrosis Factor-alpha pharmacology, Adaptor Proteins, Signal Transducing metabolism, Arthritis, Rheumatoid metabolism, Fibroblasts metabolism, Homeodomain Proteins metabolism, Inflammation metabolism, Interleukin-17 physiology, Repressor Proteins metabolism, Transcription Factors metabolism, Transcriptome physiology, Tumor Necrosis Factor-alpha physiology
- Abstract
The role of stromal fibroblasts in chronic inflammation is unfolding. In rheumatoid arthritis, leukocyte-derived cytokines TNF and IL-17A work together, activating fibroblasts to become a dominant source of the hallmark cytokine IL-6. However, IL-17A alone has minimal effect on fibroblasts. To identify key mediators of the synergistic response to TNF and IL-17A in human synovial fibroblasts, we performed time series, dose-response, and gene-silencing transcriptomics experiments. Here we show that in combination with TNF, IL-17A selectively induces a specific set of genes mediated by factors including cut-like homeobox 1 (CUX1) and IκBζ (NFKBIZ). In the promoters of CXCL1 , CXCL2 , and CXCL3 , we found a putative CUX1-NF-κB binding motif not found elsewhere in the genome. CUX1 and NF-κB p65 mediate transcription of these genes independent of LIFR, STAT3, STAT4, and ELF3. Transcription of NFKBIZ , encoding the atypical IκB factor IκBζ, is IL-17A dose-dependent, and IκBζ only mediates the transcriptional response to TNF and IL-17A, but not to TNF alone. In fibroblasts, IL-17A response depends on CUX1 and IκBζ to engage the NF-κB complex to produce chemoattractants for neutrophil and monocyte recruitment., Competing Interests: The authors declare no competing interest.
- Published
- 2020
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28. Fast, sensitive and accurate integration of single-cell data with Harmony.
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Korsunsky I, Millard N, Fan J, Slowikowski K, Zhang F, Wei K, Baglaenko Y, Brenner M, Loh PR, and Raychaudhuri S
- Subjects
- Algorithms, Animals, Base Sequence, Datasets as Topic, HEK293 Cells, Humans, Jurkat Cells, Mice, Single-Cell Analysis methods
- Abstract
The emerging diversity of single-cell RNA-seq datasets allows for the full transcriptional characterization of cell types across a wide variety of biological and clinical conditions. However, it is challenging to analyze them together, particularly when datasets are assayed with different technologies, because biological and technical differences are interspersed. We present Harmony (https://github.com/immunogenomics/harmony), an algorithm that projects cells into a shared embedding in which cells group by cell type rather than dataset-specific conditions. Harmony simultaneously accounts for multiple experimental and biological factors. In six analyses, we demonstrate the superior performance of Harmony to previously published algorithms while requiring fewer computational resources. Harmony enables the integration of ~10
6 cells on a personal computer. We apply Harmony to peripheral blood mononuclear cells from datasets with large experimental differences, five studies of pancreatic islet cells, mouse embryogenesis datasets and the integration of scRNA-seq with spatial transcriptomics data.- Published
- 2019
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29. Author Correction: Tubular cell and keratinocyte single-cell transcriptomics applied to lupus nephritis reveal type I IFN and fibrosis relevant pathways.
- Author
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Der E, Suryawanshi H, Morozov P, Kustagi M, Goilav B, Ranabothu S, Izmirly P, Clancy R, Belmont HM, Koenigsberg M, Mokrzycki M, Rominieki H, Graham JA, Rocca JP, Bornkamp N, Jordan N, Schulte E, Wu M, Pullman J, Slowikowski K, Raychaudhuri S, Guthridge J, James J, Buyon J, Tuschl T, and Putterman C
- Abstract
An amendment to this paper has been published and can be accessed via a link at the top of the paper.
- Published
- 2019
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30. Publisher Correction: The immune cell landscape in kidneys of patients with lupus nephritis.
- Author
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Arazi A, Rao DA, Berthier CC, Davidson A, Liu Y, Hoover PJ, Chicoine A, Eisenhaure TM, Jonsson AH, Li S, Lieb DJ, Zhang F, Slowikowski K, Browne EP, Noma A, Sutherby D, Steelman S, Smilek DE, Tosta P, Apruzzese W, Massarotti E, Dall'Era M, Park M, Kamen DL, Furie RA, Payan-Schober F, Pendergraft WF 3rd, McInnis EA, Buyon JP, Petri MA, Putterman C, Kalunian KC, Woodle ES, Lederer JA, Hildeman DA, Nusbaum C, Raychaudhuri S, Kretzler M, Anolik JH, Brenner MB, Wofsy D, Hacohen N, and Diamond B
- Abstract
An amendment to this paper has been published and can be accessed via a link at the top of the paper.
- Published
- 2019
- Full Text
- View/download PDF
31. Tubular cell and keratinocyte single-cell transcriptomics applied to lupus nephritis reveal type I IFN and fibrosis relevant pathways.
- Author
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Der E, Suryawanshi H, Morozov P, Kustagi M, Goilav B, Ranabothu S, Izmirly P, Clancy R, Belmont HM, Koenigsberg M, Mokrzycki M, Rominieki H, Graham JA, Rocca JP, Bornkamp N, Jordan N, Schulte E, Wu M, Pullman J, Slowikowski K, Raychaudhuri S, Guthridge J, James J, Buyon J, Tuschl T, and Putterman C
- Subjects
- Biopsy, Cell Lineage genetics, Computational Biology methods, Extracellular Matrix Proteins genetics, Extracellular Matrix Proteins metabolism, Fibrosis, Humans, Lupus Nephritis pathology, Protein Binding, Signal Transduction, Single-Cell Analysis, Skin immunology, Skin metabolism, Skin pathology, Gene Expression Profiling methods, Interferon Type I metabolism, Keratinocytes metabolism, Kidney Tubules cytology, Kidney Tubules metabolism, Lupus Nephritis genetics, Lupus Nephritis metabolism, Transcriptome
- Abstract
The molecular and cellular processes that lead to renal damage and to the heterogeneity of lupus nephritis (LN) are not well understood. We applied single-cell RNA sequencing (scRNA-seq) to renal biopsies from patients with LN and evaluated skin biopsies as a potential source of diagnostic and prognostic markers of renal disease. Type I interferon (IFN)-response signatures in tubular cells and keratinocytes distinguished patients with LN from healthy control subjects. Moreover, a high IFN-response signature and fibrotic signature in tubular cells were each associated with failure to respond to treatment. Analysis of tubular cells from patients with proliferative, membranous and mixed LN indicated pathways relevant to inflammation and fibrosis, which offer insight into their histologic differences. In summary, we applied scRNA-seq to LN to deconstruct its heterogeneity and identify novel targets for personalized approaches to therapy.
- Published
- 2019
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- View/download PDF
32. The immune cell landscape in kidneys of patients with lupus nephritis.
- Author
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Arazi A, Rao DA, Berthier CC, Davidson A, Liu Y, Hoover PJ, Chicoine A, Eisenhaure TM, Jonsson AH, Li S, Lieb DJ, Zhang F, Slowikowski K, Browne EP, Noma A, Sutherby D, Steelman S, Smilek DE, Tosta P, Apruzzese W, Massarotti E, Dall'Era M, Park M, Kamen DL, Furie RA, Payan-Schober F, Pendergraft WF 3rd, McInnis EA, Buyon JP, Petri MA, Putterman C, Kalunian KC, Woodle ES, Lederer JA, Hildeman DA, Nusbaum C, Raychaudhuri S, Kretzler M, Anolik JH, Brenner MB, Wofsy D, Hacohen N, and Diamond B
- Subjects
- Biomarkers, Biopsy, Cluster Analysis, Computational Biology methods, Epithelial Cells metabolism, Flow Cytometry, Gene Expression Profiling, Gene Expression Regulation, Humans, Immunophenotyping, Interferons metabolism, Kidney metabolism, Kidney pathology, Leukocytes immunology, Leukocytes metabolism, Lupus Nephritis genetics, Lupus Nephritis metabolism, Lupus Nephritis pathology, Lymphocytes immunology, Lymphocytes metabolism, Molecular Sequence Annotation, Myeloid Cells immunology, Myeloid Cells metabolism, Single-Cell Analysis, Transcriptome, Kidney immunology, Lupus Nephritis immunology
- Abstract
Lupus nephritis is a potentially fatal autoimmune disease for which the current treatment is ineffective and often toxic. To develop mechanistic hypotheses of disease, we analyzed kidney samples from patients with lupus nephritis and from healthy control subjects using single-cell RNA sequencing. Our analysis revealed 21 subsets of leukocytes active in disease, including multiple populations of myeloid cells, T cells, natural killer cells and B cells that demonstrated both pro-inflammatory responses and inflammation-resolving responses. We found evidence of local activation of B cells correlated with an age-associated B-cell signature and evidence of progressive stages of monocyte differentiation within the kidney. A clear interferon response was observed in most cells. Two chemokine receptors, CXCR4 and CX3CR1, were broadly expressed, implying a potentially central role in cell trafficking. Gene expression of immune cells in urine and kidney was highly correlated, which would suggest that urine might serve as a surrogate for kidney biopsies.
- Published
- 2019
- Full Text
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33. Lymphocyte innateness defined by transcriptional states reflects a balance between proliferation and effector functions.
- Author
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Gutierrez-Arcelus M, Teslovich N, Mola AR, Polidoro RB, Nathan A, Kim H, Hannes S, Slowikowski K, Watts GFM, Korsunsky I, Brenner MB, Raychaudhuri S, and Brennan PJ
- Subjects
- Female, Gene Ontology, Humans, Immunity, Innate physiology, Immunophenotyping, Leukocytes, Mononuclear metabolism, Lymphocyte Activation physiology, Male, Natural Killer T-Cells metabolism, T-Lymphocyte Subsets metabolism, Cell Proliferation physiology, Lymphocytes metabolism
- Abstract
How innate T cells (ITC), including invariant natural killer T (iNKT) cells, mucosal-associated invariant T (MAIT) cells, and γδ T cells, maintain a poised effector state has been unclear. Here we address this question using low-input and single-cell RNA-seq of human lymphocyte populations. Unbiased transcriptomic analyses uncover a continuous 'innateness gradient', with adaptive T cells at one end, followed by MAIT, iNKT, γδ T and natural killer cells at the other end. Single-cell RNA-seq reveals four broad states of innateness, and heterogeneity within canonical innate and adaptive populations. Transcriptional and functional data show that innateness is characterized by pre-formed mRNA encoding effector functions, but impaired proliferation marked by decreased baseline expression of ribosomal genes. Together, our data shed new light on the poised state of ITC, in which innateness is defined by a transcriptionally-orchestrated trade-off between rapid cell growth and rapid effector function.
- Published
- 2019
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34. Discovering in vivo cytokine-eQTL interactions from a lupus clinical trial.
- Author
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Davenport EE, Amariuta T, Gutierrez-Arcelus M, Slowikowski K, Westra HJ, Luo Y, Shen C, Rao DA, Zhang Y, Pearson S, von Schack D, Beebe JS, Bing N, John S, Vincent MS, Zhang B, and Raychaudhuri S
- Subjects
- Humans, Cytokines genetics, Gene Expression Regulation, Lupus Erythematosus, Systemic genetics, Quantitative Trait Loci genetics
- Abstract
Background: Cytokines are critical to human disease and are attractive therapeutic targets given their widespread influence on gene regulation and transcription. Defining the downstream regulatory mechanisms influenced by cytokines is central to defining drug and disease mechanisms. One promising strategy is to use interactions between expression quantitative trait loci (eQTLs) and cytokine levels to define target genes and mechanisms., Results: In a clinical trial for anti-IL-6 in patients with systemic lupus erythematosus, we measure interferon (IFN) status, anti-IL-6 drug exposure, and whole blood genome-wide gene expression at three time points. We show that repeat transcriptomic measurements increases the number of cis eQTLs identified compared to using a single time point. We observe a statistically significant enrichment of in vivo eQTL interactions with IFN status and anti-IL-6 drug exposure and find many novel interactions that have not been previously described. Finally, we find transcription factor binding motifs interrupted by eQTL interaction SNPs, which point to key regulatory mediators of these environmental stimuli and therefore potential therapeutic targets for autoimmune diseases. In particular, genes with IFN interactions are enriched for ISRE binding site motifs, while those with anti-IL-6 interactions are enriched for IRF4 motifs., Conclusions: This study highlights the potential to exploit clinical trial data to discover in vivo eQTL interactions with therapeutically relevant environmental variables.
- Published
- 2018
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35. Mixed-effects association of single cells identifies an expanded effector CD4 + T cell subset in rheumatoid arthritis.
- Author
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Fonseka CY, Rao DA, Teslovich NC, Korsunsky I, Hannes SK, Slowikowski K, Gurish MF, Donlin LT, Lederer JA, Weinblatt ME, Massarotti EM, Coblyn JS, Helfgott SM, Todd DJ, Bykerk VP, Karlson EW, Ermann J, Lee YC, Brenner MB, and Raychaudhuri S
- Subjects
- Aged, Cell Proliferation, Cytotoxicity, Immunologic, Female, HLA-DR Antigens metabolism, Humans, Immunologic Memory, Male, Middle Aged, Th1 Cells immunology, Transcriptome genetics, Tumor Necrosis Factor Receptor Superfamily, Member 7 metabolism, Arthritis, Rheumatoid immunology, Arthritis, Rheumatoid pathology, CD4-Positive T-Lymphocytes immunology, T-Lymphocyte Subsets immunology
- Abstract
High-dimensional single-cell analyses have improved the ability to resolve complex mixtures of cells from human disease samples; however, identifying disease-associated cell types or cell states in patient samples remains challenging because of technical and interindividual variation. Here, we present mixed-effects modeling of associations of single cells (MASC), a reverse single-cell association strategy for testing whether case-control status influences the membership of single cells in any of multiple cellular subsets while accounting for technical confounders and biological variation. Applying MASC to mass cytometry analyses of CD4
+ T cells from the blood of rheumatoid arthritis (RA) patients and controls revealed a significantly expanded population of CD4+ T cells, identified as CD27- HLA-DR+ effector memory cells, in RA patients (odds ratio, 1.7; P = 1.1 × 10-3 ). The frequency of CD27- HLA-DR+ cells was similarly elevated in blood samples from a second RA patient cohort, and CD27- HLA-DR+ cell frequency decreased in RA patients who responded to immunosuppressive therapy. Mass cytometry and flow cytometry analyses indicated that CD27- HLA-DR+ cells were associated with RA (meta-analysis P = 2.3 × 10-4 ). Compared to peripheral blood, synovial fluid and synovial tissue samples from RA patients contained about fivefold higher frequencies of CD27- HLA-DR+ cells, which comprised ~10% of synovial CD4+ T cells. CD27- HLA-DR+ cells expressed a distinctive effector memory transcriptomic program with T helper 1 (TH 1)- and cytotoxicity-associated features and produced abundant interferon-γ (IFN-γ) and granzyme A protein upon stimulation. We propose that MASC is a broadly applicable method to identify disease-associated cell populations in high-dimensional single-cell data., (Copyright © 2018 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.)- Published
- 2018
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36. Methods for high-dimensional analysis of cells dissociated from cryopreserved synovial tissue
- Author
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Donlin LT, Rao DA, Wei K, Slowikowski K, McGeachy MJ, Turner JD, Meednu N, Mizoguchi F, Gutierrez-Arcelus M, Lieb DJ, Keegan J, Muskat K, Hillman J, Rozo C, Ricker E, Eisenhaure TM, Li S, Browne EP, Chicoine A, Sutherby D, Noma A, Nusbaum C, Kelly S, Pernis AB, Ivashkiv LB, Goodman SM, Robinson WH, Utz PJ, Lederer JA, Gravallese EM, Boyce BF, Hacohen N, Pitzalis C, Gregersen PK, Firestein GS, Raychaudhuri S, Moreland LW, Holers VM, Bykerk VP, Filer A, Boyle DL, Brenner MB, and Anolik JH
- Subjects
- Cryopreservation, Humans, Arthritis, Rheumatoid pathology, Flow Cytometry methods, High-Throughput Screening Assays methods, Synovial Membrane pathology
- Abstract
Background: Detailed molecular analyses of cells from rheumatoid arthritis (RA) synovium hold promise in identifying cellular phenotypes that drive tissue pathology and joint damage. The Accelerating Medicines Partnership RA/SLE Network aims to deconstruct autoimmune pathology by examining cells within target tissues through multiple high-dimensional assays. Robust standardized protocols need to be developed before cellular phenotypes at a single cell level can be effectively compared across patient samples., Methods: Multiple clinical sites collected cryopreserved synovial tissue fragments from arthroplasty and synovial biopsy in a 10% DMSO solution. Mechanical and enzymatic dissociation parameters were optimized for viable cell extraction and surface protein preservation for cell sorting and mass cytometry, as well as for reproducibility in RNA sequencing (RNA-seq). Cryopreserved synovial samples were collectively analyzed at a central processing site by a custom-designed and validated 35-marker mass cytometry panel. In parallel, each sample was flow sorted into fibroblast, T-cell, B-cell, and macrophage suspensions for bulk population RNA-seq and plate-based single-cell CEL-Seq2 RNA-seq., Results: Upon dissociation, cryopreserved synovial tissue fragments yielded a high frequency of viable cells, comparable to samples undergoing immediate processing. Optimization of synovial tissue dissociation across six clinical collection sites with ~ 30 arthroplasty and ~ 20 biopsy samples yielded a consensus digestion protocol using 100 μg/ml of Liberase™ TL enzyme preparation. This protocol yielded immune and stromal cell lineages with preserved surface markers and minimized variability across replicate RNA-seq transcriptomes. Mass cytometry analysis of cells from cryopreserved synovium distinguished diverse fibroblast phenotypes, distinct populations of memory B cells and antibody-secreting cells, and multiple CD4
+ and CD8+ T-cell activation states. Bulk RNA-seq of sorted cell populations demonstrated robust separation of synovial lymphocytes, fibroblasts, and macrophages. Single-cell RNA-seq produced transcriptomes of over 1000 genes/cell, including transcripts encoding characteristic lineage markers identified., Conclusions: We have established a robust protocol to acquire viable cells from cryopreserved synovial tissue with intact transcriptomes and cell surface phenotypes. A centralized pipeline to generate multiple high-dimensional analyses of synovial tissue samples collected across a collaborative network was developed. Integrated analysis of such datasets from large patient cohorts may help define molecular heterogeneity within RA pathology and identify new therapeutic targets and biomarkers.- Published
- 2018
- Full Text
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37. Heritability enrichment of specifically expressed genes identifies disease-relevant tissues and cell types.
- Author
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Finucane HK, Reshef YA, Anttila V, Slowikowski K, Gusev A, Byrnes A, Gazal S, Loh PR, Lareau C, Shoresh N, Genovese G, Saunders A, Macosko E, Pollack S, Perry JRB, Buenrostro JD, Bernstein BE, Raychaudhuri S, McCarroll S, Neale BM, and Price AL
- Subjects
- Bipolar Disorder genetics, Body Mass Index, Brain metabolism, Chromatin genetics, Epigenesis, Genetic, Gene Expression Profiling statistics & numerical data, Genome-Wide Association Study statistics & numerical data, Humans, Immune System Diseases genetics, Linkage Disequilibrium, Models, Genetic, Multifactorial Inheritance, Neurons metabolism, Schizophrenia genetics, Tissue Distribution genetics, Gene Expression, Genetic Predisposition to Disease
- Abstract
We introduce an approach to identify disease-relevant tissues and cell types by analyzing gene expression data together with genome-wide association study (GWAS) summary statistics. Our approach uses stratified linkage disequilibrium (LD) score regression to test whether disease heritability is enriched in regions surrounding genes with the highest specific expression in a given tissue. We applied our approach to gene expression data from several sources together with GWAS summary statistics for 48 diseases and traits (average N = 169,331) and found significant tissue-specific enrichments (false discovery rate (FDR) < 5%) for 34 traits. In our analysis of multiple tissues, we detected a broad range of enrichments that recapitulated known biology. In our brain-specific analysis, significant enrichments included an enrichment of inhibitory over excitatory neurons for bipolar disorder, and excitatory over inhibitory neurons for schizophrenia and body mass index. Our results demonstrate that our polygenic approach is a powerful way to leverage gene expression data for interpreting GWAS signals.
- Published
- 2018
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38. Functionally distinct disease-associated fibroblast subsets in rheumatoid arthritis.
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Mizoguchi F, Slowikowski K, Wei K, Marshall JL, Rao DA, Chang SK, Nguyen HN, Noss EH, Turner JD, Earp BE, Blazar PE, Wright J, Simmons BP, Donlin LT, Kalliolias GD, Goodman SM, Bykerk VP, Ivashkiv LB, Lederer JA, Hacohen N, Nigrovic PA, Filer A, Buckley CD, Raychaudhuri S, and Brenner MB
- Subjects
- Arthritis, Rheumatoid genetics, Cadherins genetics, Cadherins metabolism, Cells, Cultured, Humans, Synovial Membrane cytology, Synovial Membrane metabolism, Thy-1 Antigens genetics, Thy-1 Antigens metabolism, Transcriptome, Arthritis, Rheumatoid metabolism, Fibroblasts metabolism
- Abstract
Fibroblasts regulate tissue homeostasis, coordinate inflammatory responses, and mediate tissue damage. In rheumatoid arthritis (RA), synovial fibroblasts maintain chronic inflammation which leads to joint destruction. Little is known about fibroblast heterogeneity or if aberrations in fibroblast subsets relate to pathology. Here, we show functional and transcriptional differences between fibroblast subsets from human synovial tissues using bulk transcriptomics of targeted subpopulations and single-cell transcriptomics. We identify seven fibroblast subsets with distinct surface protein phenotypes, and collapse them into three subsets by integrating transcriptomic data. One fibroblast subset, characterized by the expression of proteins podoplanin, THY1 membrane glycoprotein and cadherin-11, but lacking CD34, is threefold expanded in patients with RA relative to patients with osteoarthritis. These fibroblasts localize to the perivascular zone in inflamed synovium, secrete proinflammatory cytokines, are proliferative, and have an in vitro phenotype characteristic of invasive cells. Our strategy may be used as a template to identify pathogenic stromal cellular subsets in other complex diseases.
- Published
- 2018
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39. Functional genomics of stromal cells in chronic inflammatory diseases.
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Slowikowski K, Wei K, Brenner MB, and Raychaudhuri S
- Subjects
- Arthritis, Rheumatoid immunology, Cell Differentiation, Chemokines, Endothelial Cells cytology, Endothelial Cells immunology, Fibroblasts cytology, Fibroblasts immunology, Flow Cytometry, Genomics, Humans, Inflammatory Bowel Diseases immunology, Mesenchymal Stem Cells cytology, Mesenchymal Stem Cells immunology, Pericytes cytology, Pericytes immunology, Rheumatic Diseases genetics, Rheumatic Diseases immunology, Sequence Analysis, RNA, Single-Cell Analysis, Stromal Cells immunology, Synovial Membrane cytology, Thy-1 Antigens immunology, Arthritis, Rheumatoid genetics, Inflammatory Bowel Diseases genetics, RNA, Messenger metabolism, Stromal Cells cytology
- Abstract
Purpose of Review: Stroma is a broad term referring to the connective tissue matrix in which other cells reside. It is composed of diverse cell types with functions such as extracellular matrix maintenance, blood and lymph vessel development, and effector cell recruitment. The tissue microenvironment is determined by the molecular characteristics and relative abundances of different stromal cells such as fibroblasts, endothelial cells, pericytes, and mesenchymal precursor cells. Stromal cell heterogeneity is explained by embryonic developmental lineage, stages of differentiation to other cell types, and activation states. Interaction between immune and stromal cell types is critical to wound healing, cancer, and a wide range of inflammatory diseases. Here, we review recent studies of inflammatory diseases that use functional genomics and single-cell technologies to identify and characterize stromal cell types associated with pathogenesis., Recent Findings: High dimensional strategies using mRNA sequencing, mass cytometry, and fluorescence activated cell-sorting with fresh primary tissue samples are producing detailed views of what is happening in diseased tissue in rheumatoid arthritis, inflammatory bowel disease, and cancer. Fibroblasts positive for CD90 (Thy-1) are enriched in the synovium of rheumatoid arthritis patients. Single-cell RNA-seq studies will lead to more discoveries about the stroma in the near future., Summary: Stromal cells form the microenvironment of inflamed and diseased tissues. Functional genomics is producing an increasingly detailed view of subsets of stromal cells with pathogenic functions in rheumatic diseases and cancer. Future genomics studies will discover disease mechanisms by perturbing molecular pathways with chemokines and therapies known to affect patient outcomes. Functional genomics studies with large sample sizes of patient tissues will identify patient subsets with different disease phenotypes or treatment responses.
- Published
- 2018
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- View/download PDF
40. Refining the role of de novo protein-truncating variants in neurodevelopmental disorders by using population reference samples.
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Kosmicki JA, Samocha KE, Howrigan DP, Sanders SJ, Slowikowski K, Lek M, Karczewski KJ, Cutler DJ, Devlin B, Roeder K, Buxbaum JD, Neale BM, MacArthur DG, Wall DP, Robinson EB, and Daly MJ
- Subjects
- Autism Spectrum Disorder genetics, Exome genetics, Genetic Predisposition to Disease genetics, Humans, Intellectual Disability genetics, Phenotype, Genetic Variation genetics, Neurodevelopmental Disorders genetics
- Abstract
Recent research has uncovered an important role for de novo variation in neurodevelopmental disorders. Using aggregated data from 9,246 families with autism spectrum disorder, intellectual disability, or developmental delay, we found that ∼1/3 of de novo variants are independently present as standing variation in the Exome Aggregation Consortium's cohort of 60,706 adults, and these de novo variants do not contribute to neurodevelopmental risk. We further used a loss-of-function (LoF)-intolerance metric, pLI, to identify a subset of LoF-intolerant genes containing the observed signal of associated de novo protein-truncating variants (PTVs) in neurodevelopmental disorders. LoF-intolerant genes also carry a modest excess of inherited PTVs, although the strongest de novo-affected genes contribute little to this excess, thus suggesting that the excess of inherited risk resides in lower-penetrant genes. These findings illustrate the importance of population-based reference cohorts for the interpretation of candidate pathogenic variants, even for analyses of complex diseases and de novo variation.
- Published
- 2017
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- View/download PDF
41. Pathologically expanded peripheral T helper cell subset drives B cells in rheumatoid arthritis.
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Rao DA, Gurish MF, Marshall JL, Slowikowski K, Fonseka CY, Liu Y, Donlin LT, Henderson LA, Wei K, Mizoguchi F, Teslovich NC, Weinblatt ME, Massarotti EM, Coblyn JS, Helfgott SM, Lee YC, Todd DJ, Bykerk VP, Goodman SM, Pernis AB, Ivashkiv LB, Karlson EW, Nigrovic PA, Filer A, Buckley CD, Lederer JA, Raychaudhuri S, and Brenner MB
- Subjects
- Arthritis, Rheumatoid blood, B-Lymphocytes pathology, Cell Differentiation, Cell Movement, Chemokine CXCL13 metabolism, Gene Expression Profiling, Humans, Inducible T-Cell Co-Stimulator Protein metabolism, Interleukins metabolism, Macrophage-Activating Factors, Positive Regulatory Domain I-Binding Factor 1, Programmed Cell Death 1 Receptor metabolism, Proto-Oncogene Proteins c-bcl-6 metabolism, Receptors, CXCR5 deficiency, Receptors, CXCR5 metabolism, Receptors, Chemokine metabolism, Repressor Proteins metabolism, Signaling Lymphocytic Activation Molecule Family metabolism, Synovial Fluid immunology, T-Lymphocytes, Helper-Inducer metabolism, Arthritis, Rheumatoid immunology, Arthritis, Rheumatoid pathology, B-Lymphocytes immunology, T-Lymphocytes, Helper-Inducer immunology, T-Lymphocytes, Helper-Inducer pathology
- Abstract
CD4
+ T cells are central mediators of autoimmune pathology; however, defining their key effector functions in specific autoimmune diseases remains challenging. Pathogenic CD4+ T cells within affected tissues may be identified by expression of markers of recent activation. Here we use mass cytometry to analyse activated T cells in joint tissue from patients with rheumatoid arthritis, a chronic immune-mediated arthritis that affects up to 1% of the population. This approach revealed a markedly expanded population of PD-1hi CXCR5- CD4+ T cells in synovium of patients with rheumatoid arthritis. However, these cells are not exhausted, despite high PD-1 expression. Rather, using multidimensional cytometry, transcriptomics, and functional assays, we define a population of PD-1hi CXCR5- 'peripheral helper' T (TPH ) cells that express factors enabling B-cell help, including IL-21, CXCL13, ICOS, and MAF. Like PD-1hi CXCR5+ T follicular helper cells, TPH cells induce plasma cell differentiation in vitro through IL-21 secretion and SLAMF5 interaction (refs 3, 4). However, global transcriptomics highlight differences between TPH cells and T follicular helper cells, including altered expression of BCL6 and BLIMP1 and unique expression of chemokine receptors that direct migration to inflamed sites, such as CCR2, CX3CR1, and CCR5, in TPH cells. TPH cells appear to be uniquely poised to promote B-cell responses and antibody production within pathologically inflamed non-lymphoid tissues.- Published
- 2017
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42. 52 Genetic Loci Influencing Myocardial Mass.
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van der Harst P, van Setten J, Verweij N, Vogler G, Franke L, Maurano MT, Wang X, Mateo Leach I, Eijgelsheim M, Sotoodehnia N, Hayward C, Sorice R, Meirelles O, Lyytikäinen LP, Polašek O, Tanaka T, Arking DE, Ulivi S, Trompet S, Müller-Nurasyid M, Smith AV, Dörr M, Kerr KF, Magnani JW, Del Greco M F, Zhang W, Nolte IM, Silva CT, Padmanabhan S, Tragante V, Esko T, Abecasis GR, Adriaens ME, Andersen K, Barnett P, Bis JC, Bodmer R, Buckley BM, Campbell H, Cannon MV, Chakravarti A, Chen LY, Delitala A, Devereux RB, Doevendans PA, Dominiczak AF, Ferrucci L, Ford I, Gieger C, Harris TB, Haugen E, Heinig M, Hernandez DG, Hillege HL, Hirschhorn JN, Hofman A, Hubner N, Hwang SJ, Iorio A, Kähönen M, Kellis M, Kolcic I, Kooner IK, Kooner JS, Kors JA, Lakatta EG, Lage K, Launer LJ, Levy D, Lundby A, Macfarlane PW, May D, Meitinger T, Metspalu A, Nappo S, Naitza S, Neph S, Nord AS, Nutile T, Okin PM, Olsen JV, Oostra BA, Penninger JM, Pennacchio LA, Pers TH, Perz S, Peters A, Pinto YM, Pfeufer A, Pilia MG, Pramstaller PP, Prins BP, Raitakari OT, Raychaudhuri S, Rice KM, Rossin EJ, Rotter JI, Schafer S, Schlessinger D, Schmidt CO, Sehmi J, Silljé HHW, Sinagra G, Sinner MF, Slowikowski K, Soliman EZ, Spector TD, Spiering W, Stamatoyannopoulos JA, Stolk RP, Strauch K, Tan ST, Tarasov KV, Trinh B, Uitterlinden AG, van den Boogaard M, van Duijn CM, van Gilst WH, Viikari JS, Visscher PM, Vitart V, Völker U, Waldenberger M, Weichenberger CX, Westra HJ, Wijmenga C, Wolffenbuttel BH, Yang J, Bezzina CR, Munroe PB, Snieder H, Wright AF, Rudan I, Boyer LA, Asselbergs FW, van Veldhuisen DJ, Stricker BH, Psaty BM, Ciullo M, Sanna S, Lehtimäki T, Wilson JF, Bandinelli S, Alonso A, Gasparini P, Jukema JW, Kääb S, Gudnason V, Felix SB, Heckbert SR, de Boer RA, Newton-Cheh C, Hicks AA, Chambers JC, Jamshidi Y, Visel A, Christoffels VM, Isaacs A, Samani NJ, and de Bakker PIW
- Subjects
- Animals, Humans, Cardiomegaly genetics, Genetic Loci, Genome-Wide Association Study
- Abstract
Background: Myocardial mass is a key determinant of cardiac muscle function and hypertrophy. Myocardial depolarization leading to cardiac muscle contraction is reflected by the amplitude and duration of the QRS complex on the electrocardiogram (ECG). Abnormal QRS amplitude or duration reflect changes in myocardial mass and conduction, and are associated with increased risk of heart failure and death., Objectives: This meta-analysis sought to gain insights into the genetic determinants of myocardial mass., Methods: We carried out a genome-wide association meta-analysis of 4 QRS traits in up to 73,518 individuals of European ancestry, followed by extensive biological and functional assessment., Results: We identified 52 genomic loci, of which 32 are novel, that are reliably associated with 1 or more QRS phenotypes at p < 1 × 10(-8). These loci are enriched in regions of open chromatin, histone modifications, and transcription factor binding, suggesting that they represent regions of the genome that are actively transcribed in the human heart. Pathway analyses provided evidence that these loci play a role in cardiac hypertrophy. We further highlighted 67 candidate genes at the identified loci that are preferentially expressed in cardiac tissue and associated with cardiac abnormalities in Drosophila melanogaster and Mus musculus. We validated the regulatory function of a novel variant in the SCN5A/SCN10A locus in vitro and in vivo., Conclusions: Taken together, our findings provide new insights into genes and biological pathways controlling myocardial mass and may help identify novel therapeutic targets., (Copyright © 2016 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.)
- Published
- 2016
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43. A method to decipher pleiotropy by detecting underlying heterogeneity driven by hidden subgroups applied to autoimmune and neuropsychiatric diseases.
- Author
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Han B, Pouget JG, Slowikowski K, Stahl E, Lee CH, Diogo D, Hu X, Park YR, Kim E, Gregersen PK, Dahlqvist SR, Worthington J, Martin J, Eyre S, Klareskog L, Huizinga T, Chen WM, Onengut-Gumuscu S, Rich SS, Wray NR, and Raychaudhuri S
- Subjects
- Computational Biology, Databases, Genetic, Gene Expression Regulation, Genetic Predisposition to Disease, Humans, Arthritis, Rheumatoid genetics, Autoimmune Diseases genetics, Depressive Disorder, Major genetics, Diabetes Mellitus, Type 1 genetics, Genetic Markers genetics, Genetic Pleiotropy genetics, Models, Statistical, Polymorphism, Single Nucleotide genetics
- Abstract
There is growing evidence of shared risk alleles for complex traits (pleiotropy), including autoimmune and neuropsychiatric diseases. This might be due to sharing among all individuals (whole-group pleiotropy) or a subset of individuals in a genetically heterogeneous cohort (subgroup heterogeneity). Here we describe the use of a well-powered statistic, BUHMBOX, to distinguish between those two situations using genotype data. We observed a shared genetic basis for 11 autoimmune diseases and type 1 diabetes (T1D; P < 1 × 10(-4)) and for 11 autoimmune diseases and rheumatoid arthritis (RA; P < 1 × 10(-3)). This sharing was not explained by subgroup heterogeneity (corrected PBUHMBOX > 0.2; 6,670 T1D cases and 7,279 RA cases). Genetic sharing between seronegative and seropostive RA (P < 1 × 10(-9)) had significant evidence of subgroup heterogeneity, suggesting a subgroup of seropositive-like cases within seronegative cases (PBUHMBOX = 0.008; 2,406 seronegative RA cases). We also observed a shared genetic basis for major depressive disorder (MDD) and schizophrenia (P < 1 × 10(-4)) that was not explained by subgroup heterogeneity (PBUHMBOX = 0.28; 9,238 MDD cases).
- Published
- 2016
- Full Text
- View/download PDF
44. Disentangling the Effects of Colocalizing Genomic Annotations to Functionally Prioritize Non-coding Variants within Complex-Trait Loci.
- Author
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Trynka G, Westra HJ, Slowikowski K, Hu X, Xu H, Stranger BE, Klein RJ, Han B, and Raychaudhuri S
- Subjects
- Arthritis, Rheumatoid genetics, Breast Neoplasms genetics, Histones genetics, Histones metabolism, Humans, Gene Expression Regulation genetics, Genetic Variation, Genome, Human genetics, Molecular Sequence Annotation methods, Quantitative Trait Loci genetics
- Abstract
Identifying genomic annotations that differentiate causal from trait-associated variants is essential to fine mapping disease loci. Although many studies have identified non-coding functional annotations that overlap disease-associated variants, these annotations often colocalize, complicating the ability to use these annotations for fine mapping causal variation. We developed a statistical approach (Genomic Annotation Shifter [GoShifter]) to assess whether enriched annotations are able to prioritize causal variation. GoShifter defines the null distribution of an annotation overlapping an allele by locally shifting annotations; this approach is less sensitive to biases arising from local genomic structure than commonly used enrichment methods that depend on SNP matching. Local shifting also allows GoShifter to identify independent causal effects from colocalizing annotations. Using GoShifter, we confirmed that variants in expression quantitative trail loci drive gene-expression changes though DNase-I hypersensitive sites (DHSs) near transcription start sites and independently through 3' UTR regulation. We also showed that (1) 15%-36% of trait-associated loci map to DHSs independently of other annotations; (2) loci associated with breast cancer and rheumatoid arthritis harbor potentially causal variants near the summits of histone marks rather than full peak bodies; (3) variants associated with height are highly enriched in embryonic stem cell DHSs; and (4) we can effectively prioritize causal variation at specific loci., (Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.)
- Published
- 2015
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- View/download PDF
45. SNPsea: an algorithm to identify cell types, tissues and pathways affected by risk loci.
- Author
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Slowikowski K, Hu X, and Raychaudhuri S
- Subjects
- Erythroid Cells metabolism, Genetic Loci, Genomics, Humans, Risk, Algorithms, Linkage Disequilibrium, Polymorphism, Single Nucleotide
- Abstract
Unlabelled: We created a fast, robust and general C+ + implementation of a single-nucleotide polymorphism (SNP) set enrichment algorithm to identify cell types, tissues and pathways affected by risk loci. It tests trait-associated genomic loci for enrichment of specificity to conditions (cell types, tissues and pathways). We use a non-parametric statistical approach to compute empirical P-values by comparison with null SNP sets. As a proof of concept, we present novel applications of our method to four sets of genome-wide significant SNPs associated with red blood cell count, multiple sclerosis, celiac disease and HDL cholesterol., Availability and Implementation: http://broadinstitute.org/mpg/snpsea., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author 2014. Published by Oxford University Press.)
- Published
- 2014
- Full Text
- View/download PDF
46. Genetic association study of QT interval highlights role for calcium signaling pathways in myocardial repolarization.
- Author
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Arking DE, Pulit SL, Crotti L, van der Harst P, Munroe PB, Koopmann TT, Sotoodehnia N, Rossin EJ, Morley M, Wang X, Johnson AD, Lundby A, Gudbjartsson DF, Noseworthy PA, Eijgelsheim M, Bradford Y, Tarasov KV, Dörr M, Müller-Nurasyid M, Lahtinen AM, Nolte IM, Smith AV, Bis JC, Isaacs A, Newhouse SJ, Evans DS, Post WS, Waggott D, Lyytikäinen LP, Hicks AA, Eisele L, Ellinghaus D, Hayward C, Navarro P, Ulivi S, Tanaka T, Tester DJ, Chatel S, Gustafsson S, Kumari M, Morris RW, Naluai ÅT, Padmanabhan S, Kluttig A, Strohmer B, Panayiotou AG, Torres M, Knoflach M, Hubacek JA, Slowikowski K, Raychaudhuri S, Kumar RD, Harris TB, Launer LJ, Shuldiner AR, Alonso A, Bader JS, Ehret G, Huang H, Kao WH, Strait JB, Macfarlane PW, Brown M, Caulfield MJ, Samani NJ, Kronenberg F, Willeit J, Smith JG, Greiser KH, Meyer Zu Schwabedissen H, Werdan K, Carella M, Zelante L, Heckbert SR, Psaty BM, Rotter JI, Kolcic I, Polašek O, Wright AF, Griffin M, Daly MJ, Arnar DO, Hólm H, Thorsteinsdottir U, Denny JC, Roden DM, Zuvich RL, Emilsson V, Plump AS, Larson MG, O'Donnell CJ, Yin X, Bobbo M, D'Adamo AP, Iorio A, Sinagra G, Carracedo A, Cummings SR, Nalls MA, Jula A, Kontula KK, Marjamaa A, Oikarinen L, Perola M, Porthan K, Erbel R, Hoffmann P, Jöckel KH, Kälsch H, Nöthen MM, den Hoed M, Loos RJ, Thelle DS, Gieger C, Meitinger T, Perz S, Peters A, Prucha H, Sinner MF, Waldenberger M, de Boer RA, Franke L, van der Vleuten PA, Beckmann BM, Martens E, Bardai A, Hofman N, Wilde AA, Behr ER, Dalageorgou C, Giudicessi JR, Medeiros-Domingo A, Barc J, Kyndt F, Probst V, Ghidoni A, Insolia R, Hamilton RM, Scherer SW, Brandimarto J, Margulies K, Moravec CE, del Greco M F, Fuchsberger C, O'Connell JR, Lee WK, Watt GC, Campbell H, Wild SH, El Mokhtari NE, Frey N, Asselbergs FW, Mateo Leach I, Navis G, van den Berg MP, van Veldhuisen DJ, Kellis M, Krijthe BP, Franco OH, Hofman A, Kors JA, Uitterlinden AG, Witteman JC, Kedenko L, Lamina C, Oostra BA, Abecasis GR, Lakatta EG, Mulas A, Orrú M, Schlessinger D, Uda M, Markus MR, Völker U, Snieder H, Spector TD, Ärnlöv J, Lind L, Sundström J, Syvänen AC, Kivimaki M, Kähönen M, Mononen N, Raitakari OT, Viikari JS, Adamkova V, Kiechl S, Brion M, Nicolaides AN, Paulweber B, Haerting J, Dominiczak AF, Nyberg F, Whincup PH, Hingorani AD, Schott JJ, Bezzina CR, Ingelsson E, Ferrucci L, Gasparini P, Wilson JF, Rudan I, Franke A, Mühleisen TW, Pramstaller PP, Lehtimäki TJ, Paterson AD, Parsa A, Liu Y, van Duijn CM, Siscovick DS, Gudnason V, Jamshidi Y, Salomaa V, Felix SB, Sanna S, Ritchie MD, Stricker BH, Stefansson K, Boyer LA, Cappola TP, Olsen JV, Lage K, Schwartz PJ, Kääb S, Chakravarti A, Ackerman MJ, Pfeufer A, de Bakker PI, and Newton-Cheh C
- Subjects
- Adult, Aged, Arrhythmias, Cardiac genetics, Arrhythmias, Cardiac metabolism, Death, Sudden, Cardiac etiology, Electrocardiography methods, Female, Genetic Predisposition to Disease, Genome-Wide Association Study methods, Genotype, Heart Ventricles metabolism, Humans, Long QT Syndrome metabolism, Male, Middle Aged, Myocardium metabolism, Polymorphism, Single Nucleotide, Calcium Signaling genetics, Long QT Syndrome genetics
- Abstract
The QT interval, an electrocardiographic measure reflecting myocardial repolarization, is a heritable trait. QT prolongation is a risk factor for ventricular arrhythmias and sudden cardiac death (SCD) and could indicate the presence of the potentially lethal mendelian long-QT syndrome (LQTS). Using a genome-wide association and replication study in up to 100,000 individuals, we identified 35 common variant loci associated with QT interval that collectively explain ∼8-10% of QT-interval variation and highlight the importance of calcium regulation in myocardial repolarization. Rare variant analysis of 6 new QT interval-associated loci in 298 unrelated probands with LQTS identified coding variants not found in controls but of uncertain causality and therefore requiring validation. Several newly identified loci encode proteins that physically interact with other recognized repolarization proteins. Our integration of common variant association, expression and orthogonal protein-protein interaction screens provides new insights into cardiac electrophysiology and identifies new candidate genes for ventricular arrhythmias, LQTS and SCD.
- Published
- 2014
- Full Text
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47. Regulation of gene expression in autoimmune disease loci and the genetic basis of proliferation in CD4+ effector memory T cells.
- Author
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Hu X, Kim H, Raj T, Brennan PJ, Trynka G, Teslovich N, Slowikowski K, Chen WM, Onengut S, Baecher-Allan C, De Jager PL, Rich SS, Stranger BE, Brenner MB, and Raychaudhuri S
- Subjects
- Arthritis, Rheumatoid metabolism, Arthritis, Rheumatoid pathology, Autoimmune Diseases metabolism, Autoimmune Diseases pathology, CD4-Positive T-Lymphocytes immunology, CD4-Positive T-Lymphocytes pathology, Celiac Disease metabolism, Celiac Disease pathology, Cell Proliferation genetics, Diabetes Mellitus, Type 1 metabolism, Diabetes Mellitus, Type 1 pathology, Gene Expression Regulation immunology, Genetic Predisposition to Disease, Genome-Wide Association Study, Genotype, Humans, Phenotype, Polymorphism, Single Nucleotide genetics, Receptors, Antigen, T-Cell biosynthesis, Receptors, Antigen, T-Cell genetics, Arthritis, Rheumatoid genetics, Autoimmune Diseases genetics, Celiac Disease genetics, Diabetes Mellitus, Type 1 genetics, Gene Expression Regulation genetics, Quantitative Trait Loci genetics
- Abstract
Genome-wide association studies (GWAS) and subsequent dense-genotyping of associated loci identified over a hundred single-nucleotide polymorphism (SNP) variants associated with the risk of rheumatoid arthritis (RA), type 1 diabetes (T1D), and celiac disease (CeD). Immunological and genetic studies suggest a role for CD4-positive effector memory T (CD+ TEM) cells in the pathogenesis of these diseases. To elucidate mechanisms of autoimmune disease alleles, we investigated molecular phenotypes in CD4+ effector memory T cells potentially affected by these variants. In a cohort of genotyped healthy individuals, we isolated high purity CD4+ TEM cells from peripheral blood, then assayed relative abundance, proliferation upon T cell receptor (TCR) stimulation, and the transcription of 215 genes within disease loci before and after stimulation. We identified 46 genes regulated by cis-acting expression quantitative trait loci (eQTL), the majority of which we detected in stimulated cells. Eleven of the 46 genes with eQTLs were previously undetected in peripheral blood mononuclear cells. Of 96 risk alleles of RA, T1D, and/or CeD in densely genotyped loci, eleven overlapped cis-eQTLs, of which five alleles completely explained the respective signals. A non-coding variant, rs389862A, increased proliferative response (p=4.75 × 10-8). In addition, baseline expression of seventeen genes in resting cells reliably predicted proliferative response after TCR stimulation. Strikingly, however, there was no evidence that risk alleles modulated CD4+ TEM abundance or proliferation. Our study underscores the power of examining molecular phenotypes in relevant cells and conditions for understanding pathogenic mechanisms of disease variants.
- Published
- 2014
- Full Text
- View/download PDF
48. Common genetic variants modulate pathogen-sensing responses in human dendritic cells.
- Author
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Lee MN, Ye C, Villani AC, Raj T, Li W, Eisenhaure TM, Imboywa SH, Chipendo PI, Ran FA, Slowikowski K, Ward LD, Raddassi K, McCabe C, Lee MH, Frohlich IY, Hafler DA, Kellis M, Raychaudhuri S, Zhang F, Stranger BE, Benoist CO, De Jager PL, Regev A, and Hacohen N
- Subjects
- Adult, Autoimmune Diseases genetics, Communicable Diseases genetics, Dendritic Cells drug effects, Escherichia coli, Female, Genetic Loci, Genome-Wide Association Study, HEK293 Cells, Humans, Influenza A virus, Interferon-beta pharmacology, Lipopolysaccharides immunology, Male, Middle Aged, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Transcriptome, Young Adult, Dendritic Cells immunology, Gene-Environment Interaction, Host-Pathogen Interactions genetics, Interferon Regulatory Factor-7 genetics, STAT Transcription Factors genetics
- Abstract
Little is known about how human genetic variation affects the responses to environmental stimuli in the context of complex diseases. Experimental and computational approaches were applied to determine the effects of genetic variation on the induction of pathogen-responsive genes in human dendritic cells. We identified 121 common genetic variants associated in cis with variation in expression responses to Escherichia coli lipopolysaccharide, influenza, or interferon-β (IFN-β). We localized and validated causal variants to binding sites of pathogen-activated STAT (signal transducer and activator of transcription) and IRF (IFN-regulatory factor) transcription factors. We also identified a common variant in IRF7 that is associated in trans with type I IFN induction in response to influenza infection. Our results reveal common alleles that explain interindividual variation in pathogen sensing and provide functional annotation for genetic variants that alter susceptibility to inflammatory diseases.
- Published
- 2014
- Full Text
- View/download PDF
49. Computational and experimental analyses of retrotransposon-associated minisatellite DNAs in the soybean genome.
- Author
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Mogil LS, Slowikowski K, and Laten HM
- Subjects
- Chromosome Mapping, DNA, Plant chemistry, Genome, Plant, Minisatellite Repeats, Retroelements, Glycine max genetics
- Abstract
Background: Retrotransposons are mobile DNA elements that spread through genomes via the action of element-encoded reverse transcriptases. They are ubiquitous constituents of most eukaryotic genomes, especially those of higher plants. The pericentromeric regions of soybean (Glycine max) chromosomes contain >3,200 intact copies of the Gmr9/GmOgre retrotransposon. Between the 3' end of the coding region and the long terminal repeat, this retrotransposon family contains a polymorphic minisatellite region composed of five distinct, interleaved minisatellite families. To better understand the possible role and origin of retrotransposon-associated minisatellites, a computational project to map and physically characterize all members of these families in the G. max genome, irrespective of their association with Gmr9, was undertaken., Methods: A computational pipeline was developed to map and analyze the organization and distribution of five Gmr9-associated minisatellites throughout the soybean genome. Polymerase chain reaction amplifications were used to experimentally assess the computational outputs., Results: A total of 63,841 copies of Gmr9-associated minisatellites were recovered from the assembled G. max genome. Ninety percent were associated with Gmr9, an additional 9% with other annotated retrotransposons, and 1% with uncharacterized repetitive DNAs. Monomers were tandemly interleaved and repeated up to 149 times per locus., Conclusions: The computational pipeline enabled a fast, accurate, and detailed characterization of known minisatellites in a large, downloaded DNA database, and PCR amplification supported the general organization of these arrays.
- Published
- 2012
- Full Text
- View/download PDF
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