1,659 results on '"Hafler, David A."'
Search Results
2. IgM N-glycosylation correlates with COVID-19 severity and rate of complement deposition
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Ozonoff, Al, Ehrlich, Lauren IR, Melamed, Esther, Sesma, Ana Fernandez, Simon, Viviana, Pulendran, Bali, Nadeau, Kari C, Davis, Mark M, McCoey, Grace A, Sekaly, Rafick, Baden, Lindsey R, Levy, Ofer, Schaenman, Joanna, Reed, Elaine F, Shaw, Albert C, Hafler, David A, Montgomery, Ruth R, Kleinstein, Steven H, Becker, Patrice M, Augustine, Alison D, Calfee, Carolyn S, Erle, David J, DeBakey, Michael E, Corry, David B, Kheradmand, Farrah, Atkinson, Mark A, Brakenridge, Scott C, Higuita, Nelson I Agudelo, Metcalf, Jordan P, Hough, Catherine L, Messer, William B, Kraft, Monica, Bime, Chris, Peters, Bjoern, Milliren, Carly E, Syphurs, Caitlin, McEnaney, Kerry, Barton, Brenda, Lentucci, Claudia, Saluvan, Mehmet, Chang, Ana C, Hoch, Annmarie, Albert, Marisa, Shaheen, Tanzia, Kho, Alvin T, Liu, Shanshan, Thomas, Sanya, Chen, Jing, Murphy, Maimouna D, Cooney, Mitchell, Hayati, Arash Nemati, Bryant, Robert, Abraham, James, Jayavelu, Naresh Doni, Presnell, Scott, Jancsyk, Tomasz, Maguire, Cole, Qi, Jingjing, Lee, Brian, Fourati, Slim, Esserman, Denise A, Guan, Leying, Gygi, Jeremy, Pawar, Shrikant, Brito, Anderson, Fragiadakis, Gabriela K, Patel, Ravi, Overton, James A, Vita, Randi, Westendorf, Kerstin, Shannon, Casey P, Tebbutt, Scott J, Thyagarajan, Rama V, Rousseau, Justin F, Wylie, Dennis, Triplett, Todd A, Kojic, Erna, Chinthrajah, Sharon, Ahuja, Neera, Rogers, Angela J, Artandi, Maja, Geng, Linda, Yendewa, George, Powell, Debra L, Kim, James N, Simmons, Brent, Goonewardene, I Michael, Smith, Cecilia M, Martens, Mark, Sherman, Amy C, Walsh, Stephen R, Issa, Nicolas C, Salehi-Rad, Ramin, Dela Cruz, Charles, Farhadian, Shelli, Iwasaki, Akiko, Ko, Albert I, Anderson, Evan J, Mehta, Aneesh K, and Sevransky, Jonathan E
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Biological Sciences ,Biomedical and Clinical Sciences ,Immunology ,Infectious Diseases ,Coronaviruses ,Lung ,Emerging Infectious Diseases ,Inflammatory and immune system ,Good Health and Well Being ,Humans ,COVID-19 ,Glycosylation ,SARS-CoV-2 ,Glycosyltransferases ,Complement System Proteins ,Immunoglobulin M ,IMPACC Network - Abstract
The glycosylation of IgG plays a critical role during human severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, activating immune cells and inducing cytokine production. However, the role of IgM N-glycosylation has not been studied during human acute viral infection. The analysis of IgM N-glycosylation from healthy controls and hospitalized coronavirus disease 2019 (COVID-19) patients reveals increased high-mannose and sialylation that correlates with COVID-19 severity. These trends are confirmed within SARS-CoV-2-specific immunoglobulin N-glycan profiles. Moreover, the degree of total IgM mannosylation and sialylation correlate significantly with markers of disease severity. We link the changes of IgM N-glycosylation with the expression of Golgi glycosyltransferases. Lastly, we observe antigen-specific IgM antibody-dependent complement deposition is elevated in severe COVID-19 patients and modulated by exoglycosidase digestion. Taken together, this work links the IgM N-glycosylation with COVID-19 severity and highlights the need to understand IgM glycosylation and downstream immune function during human disease.
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- 2024
3. Genetic mapping across autoimmune diseases reveals shared associations and mechanisms
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Lincoln, Matthew R., Connally, Noah, Axisa, Pierre-Paul, Gasperi, Christiane, Mitrovic, Mitja, van Heel, David, Wijmenga, Cisca, Withoff, Sebo, Jonkers, Iris H., Padyukov, Leonid, Rich, Stephen S., Graham, Robert R., Gaffney, Patrick M., Langefeld, Carl D., Vyse, Timothy J., Hafler, David A., Chun, Sung, Sunyaev, Shamil R., and Cotsapas, Chris
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- 2024
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4. Prior cycles of anti-CD20 antibodies affect antibody responses after repeated SARS-CoV-2 mRNA vaccination.
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Asashima, Hiromitsu, Kim, Dongjoo, Wang, Kaicheng, Lele, Nikhil, Buitrago-Pocasangre, Nicholas, Lutz, Rachel, Cruz, Isabella, Raddassi, Khadir, Ruff, William, Racke, Michael, Wilson, JoDell, Givens, Tara, Grifoni, Alba, Sette, Alessandro, Kleinstein, Steven, Montgomery, Ruth, Shaw, Albert, Li, Fangyong, Fan, Rong, Hafler, David, Tomayko, Mary, Longbrake, Erin, and Weiskopf, Daniela
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Autoimmune diseases ,Autoimmunity ,COVID-19 ,Multiple sclerosis ,Humans ,Aged ,Antibody Formation ,SARS-CoV-2 ,BNT162 Vaccine ,COVID-19 ,Vaccination ,Antibodies ,Monoclonal ,Antilymphocyte Serum ,RNA ,Messenger - Abstract
BACKGROUNDWhile B cell depletion is associated with attenuated antibody responses to SARS-CoV-2 mRNA vaccination, responses vary among individuals. Thus, elucidating the factors that affect immune responses after repeated vaccination is an important clinical need.METHODSWe evaluated the quality and magnitude of the T cell, B cell, antibody, and cytokine responses to a third dose of BNT162b2 or mRNA-1273 mRNA vaccine in patients with B cell depletion.RESULTSIn contrast with control individuals (n = 10), most patients on anti-CD20 therapy (n = 48) did not demonstrate an increase in spike-specific B cells or antibodies after a third dose of vaccine. A third vaccine elicited significantly increased frequencies of spike-specific non-naive T cells. A small subset of B cell-depleted individuals effectively produced spike-specific antibodies, and logistic regression models identified time since last anti-CD20 treatment and lower cumulative exposure to anti-CD20 mAbs as predictors of those having a serologic response. B cell-depleted patients who mounted an antibody response to 3 vaccine doses had persistent humoral immunity 6 months later.CONCLUSIONThese results demonstrate that serial vaccination strategies can be effective for a subset of B cell-depleted patients.FUNDINGThe NIH (R25 NS079193, P01 AI073748, U24 AI11867, R01 AI22220, UM 1HG009390, P01 AI039671, P50 CA121974, R01 CA227473, U01CA260507, 75N93019C00065, K24 AG042489), NIH HIPC Consortium (U19 AI089992), the National Multiple Sclerosis Society (CA 1061-A-18, RG-1802-30153), the Nancy Taylor Foundation for Chronic Diseases, Erase MS, and the Claude D. Pepper Older Americans Independence Center at Yale (P30 AG21342).
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- 2023
5. Meta-analysis identifies common gut microbiota associated with multiple sclerosis
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Lin, Qingqi, Dorsett, Yair, Mirza, Ali, Tremlett, Helen, Piccio, Laura, Longbrake, Erin E., Choileain, Siobhan Ni, Hafler, David A., Cox, Laura M., Weiner, Howard L., Yamamura, Takashi, Chen, Kun, Wu, Yufeng, and Zhou, Yanjiao
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- 2024
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6. Features of acute COVID-19 associated with post-acute sequelae of SARS-CoV-2 phenotypes: results from the IMPACC study
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Ozonoff, Al, Jayavelu, Naresh Doni, Liu, Shanshan, Melamed, Esther, Milliren, Carly E., Qi, Jingjing, Geng, Linda N., McComsey, Grace A., Cairns, Charles B., Baden, Lindsey R., Schaenman, Joanna, Shaw, Albert C., Samaha, Hady, Seyfert-Margolis, Vicki, Krammer, Florian, Rosen, Lindsey B., Steen, Hanno, Syphurs, Caitlin, Dandekar, Ravi, Shannon, Casey P., Sekaly, Rafick P., Ehrlich, Lauren I. R., Corry, David B., Kheradmand, Farrah, Atkinson, Mark A., Brakenridge, Scott C., Higuita, Nelson I. Agudelo, Metcalf, Jordan P., Hough, Catherine L., Messer, William B., Pulendran, Bali, Nadeau, Kari C., Davis, Mark M., Sesma, Ana Fernandez, Simon, Viviana, van Bakel, Harm, Kim-Schulze, Seunghee, Hafler, David A., Levy, Ofer, Kraft, Monica, Bime, Chris, Haddad, Elias K., Calfee, Carolyn S., Erle, David J., Langelier, Charles R., Eckalbar, Walter, Bosinger, Steven E., Peters, Bjoern, Kleinstein, Steven H., Reed, Elaine F., Augustine, Alison D., Diray-Arce, Joann, Maecker, Holden T., Altman, Matthew C., Montgomery, Ruth R., Becker, Patrice M., and Rouphael, Nadine
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- 2024
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7. Neutrophils insert elastase into hepatocytes to regulate calcium signaling in alcohol-associated hepatitis
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Ogino, Noriyoshi, Leite, M. Fatima, Guerra, Mateus T., Kruglov, Emma, Asashima, Hiromitsu, Hafler, David A., Ito, Takeshi, Pereira, Joao P., Peiffer, Brandon J., Sun, Zhaoli, Ehrlich, Barbara E., and Nathanson, Michael H.
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Liver cells -- Health aspects ,Calcium channels -- Health aspects ,Elastases -- Health aspects ,Hepatitis, Alcoholic -- Diagnosis -- Care and treatment - Abstract
Neutrophil infiltration occurs in a variety of liver diseases, but it is unclear how neutrophils and hepatocytes interact. Neutrophils generally use granule proteases to digest phagocytosed bacteria and foreign substances or neutralize them in neutrophil extracellular traps. In certain pathological states, granule proteases play a destructive role against the host as well. More recently, nondestructive actions of neutrophil granule proteins have been reported, such as modulation of tissue remodeling and metabolism. Here, we report a completely different mechanism by which neutrophils act nondestructively, by inserting granules directly into hepatocytes. Specifically, elastase-containing granules were transferred to hepatocytes where elastase selectively degraded intracellular calcium channels to reduce cell proliferation without cytotoxicity. In response, hepatocytes increased expression of Serpin E2 and A3, which inhibited elastase activity. Elastase insertion was seen in patient specimens of alcohol-associated hepatitis, and the relationship between elastase-mediated ITPR2 degradation and reduced cell proliferation was confirmed in mouse models. Moreover, neutrophils from patients with alcohol-associated hepatitis were more prone to degranulation and more potent in reducing calcium channel expression than neutrophils from healthy individuals. This nondestructive and reversible action on hepatocytes defines a previously unrecognized role for neutrophils in the transient regulation of epithelial calcium signaling mechanisms., Introduction Neutrophils are the most abundant type of polymorphonucleated leukocytes in circulation, typically accounting for around 60% of the total in humans. When organs such as the liver, lungs, and [...]
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- 2024
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8. Multi-omic longitudinal study reveals immune correlates of clinical course among hospitalized COVID-19 patients
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Diray-Arce, Joann, Fourati, Slim, Jayavelu, Naresh Doni, Patel, Ravi, Maguire, Cole, Chang, Ana C, Dandekar, Ravi, Qi, Jingjing, Lee, Brian H, van Zalm, Patrick, Schroeder, Andrew, Chen, Ernie, Konstorum, Anna, Brito, Anderson, Gygi, Jeremy P, Kho, Alvin, Chen, Jing, Pawar, Shrikant, Gonzalez-Reiche, Ana Silvia, Hoch, Annmarie, Milliren, Carly E, Overton, James A, Westendorf, Kerstin, Network, IMPACC, Abraham, James, Adkisson, Michael, Albert, Marisa, Torres, Luz Altamirano, Alvarenga, Bonny, Anderson, Matthew L, Anderson, Evan J, Arnett, Azlann, Asashima, Hiromitsu, Atkinson, Mark A, Baden, Lindsey R, Barton, Brenda, Beach, Katherine, Beagle, Elizabeth, Becker, Patrice M, Bell, Matthew R, Bernui, Mariana, Bime, Chris, Kumar, Arun Boddapati, Booth, Leland J, Borresen, Brittney, Brakenridge, Scott C, Bristow, Laurel, Bryant, Robert, Calfee, Carolyn S, Manuel, Juan Carreño, Carrillo, Sidney, Chak, Suzanna, Chang, Iris, Connors, Jennifer, Conway, Michelle, Corry, David B, Cowan, David, Croen, Brett, Dela Cruz, Charles S, Cusimano, Gina, Eaker, Lily, Edwards, Carolyn, Ehrlich, Lauren IR, Elashoff, David, Erickson, Heidi, Erle, David J, Farhadian, Shelli, Farrugia, Keith, Fatou, Benoit, Fernandes, Andrea, Fernandez-Sesma, Ana, Fragiadakis, Gabriela K, Furukawa, Sara, Geltman, Janelle N, Ghale, Rajani, Bermúdez, Maria González Carolina, Goonewardene, Michael I, Sanchez, Estella Guerrero, Guirgis, Faheem W, Hafler, David A, Hamilton, Sydney, Harris, Paul, Nemati, Arash Hayati, Hendrickson, Carolyn M, Agudelo, Nelson I Higuita, Hodder, Thomas, Holland, Steven M, Hough, Catherine L, Huerta, Christopher, Hurley, Kerin C, Hutton, Scott R, Iwasaki, Akiko, Jauregui, Alejandra, Jha, Meenakshi, Johnson, Brandi, Joyner, David, Kangelaris, Kirsten N, Kelly, Geoffrey, Khalil, Zain, and Khan, Zenab
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Biomedical and Clinical Sciences ,Immunology ,Prevention ,Rare Diseases ,Clinical Research ,2.1 Biological and endogenous factors ,Detection ,screening and diagnosis ,Aetiology ,4.1 Discovery and preclinical testing of markers and technologies ,Good Health and Well Being ,Humans ,COVID-19 ,SARS-CoV-2 ,Longitudinal Studies ,Multiomics ,Disease Progression ,IMPACC Network ,immunophenotyping ,longitudinal modeling ,multi-omics ,systems immunology ,Biomedical and clinical sciences - Abstract
The IMPACC cohort, composed of >1,000 hospitalized COVID-19 participants, contains five illness trajectory groups (TGs) during acute infection (first 28 days), ranging from milder (TG1-3) to more severe disease course (TG4) and death (TG5). Here, we report deep immunophenotyping, profiling of >15,000 longitudinal blood and nasal samples from 540 participants of the IMPACC cohort, using 14 distinct assays. These unbiased analyses identify cellular and molecular signatures present within 72 h of hospital admission that distinguish moderate from severe and fatal COVID-19 disease. Importantly, cellular and molecular states also distinguish participants with more severe disease that recover or stabilize within 28 days from those that progress to fatal outcomes (TG4 vs. TG5). Furthermore, our longitudinal design reveals that these biologic states display distinct temporal patterns associated with clinical outcomes. Characterizing host immune responses in relation to heterogeneity in disease course may inform clinical prognosis and opportunities for intervention.
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- 2023
9. Differential effects of anti-CD20 therapy on CD4 and CD8 T cells and implication of CD20-expressing CD8 T cells in MS disease activity
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Shinoda, Koji, Li, Rui, Rezk, Ayman, Mexhitaj, Ina, Patterson, Kristina R, Kakara, Mihir, Zuroff, Leah, Bennett, Jeffrey L, von Büdingen, H-Christian, Carruthers, Robert, Edwards, Keith R, Fallis, Robert, Giacomini, Paul S, Greenberg, Benjamin M, Hafler, David A, Ionete, Carolina, Kaunzner, Ulrike W, Lock, Christopher B, Longbrake, Erin E, Pardo, Gabriel, Piehl, Fredrik, Weber, Martin S, Ziemssen, Tjalf, Jacobs, Dina, Gelfand, Jeffrey M, Cross, Anne H, Cameron, Briana, Musch, Bruno, Winger, Ryan C, Jia, Xiaoming, Harp, Christopher T, Herman, Ann, and Bar-Or, Amit
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Neurodegenerative ,Neurosciences ,Multiple Sclerosis ,Brain Disorders ,2.1 Biological and endogenous factors ,Aetiology ,Humans ,CD8-Positive T-Lymphocytes ,Leukocytes ,Mononuclear ,Flow Cytometry ,Recurrence ,Antigens ,CD20 ,CD20-expressing T cells ,CD20dim T cells ,CD20dimCD8+ T cells ,anti-CD20 therapy ,ocrelizumab - Abstract
A small proportion of multiple sclerosis (MS) patients develop new disease activity soon after starting anti-CD20 therapy. This activity does not recur with further dosing, possibly reflecting deeper depletion of CD20-expressing cells with repeat infusions. We assessed cellular immune profiles and their association with transient disease activity following anti-CD20 initiation as a window into relapsing disease biology. Peripheral blood mononuclear cells from independent discovery and validation cohorts of MS patients initiating ocrelizumab were assessed for phenotypic and functional profiles using multiparametric flow cytometry. Pretreatment CD20-expressing T cells, especially CD20dimCD8+ T cells with a highly inflammatory and central nervous system (CNS)-homing phenotype, were significantly inversely correlated with pretreatment MRI gadolinium-lesion counts, and also predictive of early disease activity observed after anti-CD20 initiation. Direct removal of pretreatment proinflammatory CD20dimCD8+ T cells had a greater contribution to treatment-associated changes in the CD8+ T cell pool than was the case for CD4+ T cells. Early disease activity following anti-CD20 initiation was not associated with reconstituting CD20dimCD8+ T cells, which were less proinflammatory compared with pretreatment. Similarly, this disease activity did not correlate with early reconstituting B cells, which were predominantly transitional CD19+CD24highCD38high with a more anti-inflammatory profile. We provide insights into the mode-of-action of anti-CD20 and highlight a potential role for CD20dimCD8+ T cells in MS relapse biology; their strong inverse correlation with both pretreatment and early posttreatment disease activity suggests that CD20-expressing CD8+ T cells leaving the circulation (possibly to the CNS) play a particularly early role in the immune cascades involved in relapse development.
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- 2023
10. High-plex protein and whole transcriptome co-mapping at cellular resolution with spatial CITE-seq
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Liu, Yang, DiStasio, Marcello, Su, Graham, Asashima, Hiromitsu, Enninful, Archibald, Qin, Xiaoyu, Deng, Yanxiang, Nam, Jungmin, Gao, Fu, Bordignon, Pino, Cassano, Marco, Tomayko, Mary, Xu, Mina, Halene, Stephanie, Craft, Joseph E., Hafler, David, and Fan, Rong
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- 2023
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11. Phenotypes of disease severity in a cohort of hospitalized COVID-19 patients: Results from the IMPACC study
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Ozonoff, Al, Schaenman, Joanna, Jayavelu, Naresh Doni, Milliren, Carly E, Calfee, Carolyn S, Cairns, Charles B, Kraft, Monica, Baden, Lindsey R, Shaw, Albert C, Krammer, Florian, van Bakel, Harm, Esserman, Denise A, Liu, Shanshan, Sesma, Ana Fernandez, Simon, Viviana, Hafler, David A, Montgomery, Ruth R, Kleinstein, Steven H, Levy, Ofer, Bime, Christian, Haddad, Elias K, Erle, David J, Pulendran, Bali, Nadeau, Kari C, Davis, Mark M, Hough, Catherine L, Messer, William B, Higuita, Nelson I Agudelo, Metcalf, Jordan P, Atkinson, Mark A, Brakenridge, Scott C, Corry, David, Kheradmand, Farrah, Ehrlich, Lauren IR, Melamed, Esther, McComsey, Grace A, Sekaly, Rafick, Diray-Arce, Joann, Peters, Bjoern, Augustine, Alison D, Reed, Elaine F, Altman, Matthew C, Becker, Patrice M, Rouphael, Nadine, Bime, Chris, McEnaney, Kerry, Barton, Brenda, Lentucci, Claudia, Saluvan, Mehmet, Chang, Ana C, Hoch, Annmarie, Albert, Marisa, Shaheen, Tanzia, Kho, Alvin T, Thomas, Sanya, Chen, Jing, Murphy, Maimouna D, Cooney, Mitchell, Presnell, Scott, and Fragiadakis, Gabriela K
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Biomedical and Clinical Sciences ,Clinical Sciences ,Lung ,Clinical Research ,Emerging Infectious Diseases ,Prevention ,Infectious Diseases ,Genetics ,Coronaviruses ,Good Health and Well Being ,COVID-19 ,Creatinine ,Female ,Hospitalization ,Humans ,Male ,Phenotype ,Prospective Studies ,RNA ,Viral ,SARS-CoV-2 ,Severity of Illness Index ,Troponin ,Post-Acute COVID-19 Syndrome ,Viral load ,Antibody ,IMPACC study group members ,Public Health and Health Services ,Clinical sciences ,Epidemiology - Abstract
BackgroundBetter understanding of the association between characteristics of patients hospitalized with coronavirus disease 2019 (COVID-19) and outcome is needed to further improve upon patient management.MethodsImmunophenotyping Assessment in a COVID-19 Cohort (IMPACC) is a prospective, observational study of 1164 patients from 20 hospitals across the United States. Disease severity was assessed using a 7-point ordinal scale based on degree of respiratory illness. Patients were prospectively surveyed for 1 year after discharge for post-acute sequalae of COVID-19 (PASC) through quarterly surveys. Demographics, comorbidities, radiographic findings, clinical laboratory values, SARS-CoV-2 PCR and serology were captured over a 28-day period. Multivariable logistic regression was performed.FindingsThe median age was 59 years (interquartile range [IQR] 20); 711 (61%) were men; overall mortality was 14%, and 228 (20%) required invasive mechanical ventilation. Unsupervised clustering of ordinal score over time revealed distinct disease course trajectories. Risk factors associated with prolonged hospitalization or death by day 28 included age ≥ 65 years (odds ratio [OR], 2.01; 95% CI 1.28-3.17), Hispanic ethnicity (OR, 1.71; 95% CI 1.13-2.57), elevated baseline creatinine (OR 2.80; 95% CI 1.63- 4.80) or troponin (OR 1.89; 95% 1.03-3.47), baseline lymphopenia (OR 2.19; 95% CI 1.61-2.97), presence of infiltrate by chest imaging (OR 3.16; 95% CI 1.96-5.10), and high SARS-CoV2 viral load (OR 1.53; 95% CI 1.17-2.00). Fatal cases had the lowest ratio of SARS-CoV-2 antibody to viral load levels compared to other trajectories over time (p=0.001). 589 survivors (51%) completed at least one survey at follow-up with 305 (52%) having at least one symptom consistent with PASC, most commonly dyspnea (56% among symptomatic patients). Female sex was the only associated risk factor for PASC.InterpretationIntegration of PCR cycle threshold, and antibody values with demographics, comorbidities, and laboratory/radiographic findings identified risk factors for 28-day outcome severity, though only female sex was associated with PASC. Longitudinal clinical phenotyping offers important insights, and provides a framework for immunophenotyping for acute and long COVID-19.FundingNIH.
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- 2022
12. scNAT: a deep learning method for integrating paired single-cell RNA and T cell receptor sequencing profiles
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Zhu, Biqing, Wang, Yuge, Ku, Li-Ting, van Dijk, David, Zhang, Le, Hafler, David A., and Zhao, Hongyu
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- 2023
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13. TCR-sequencing in cancer and autoimmunity: barcodes and beyond
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Pauken, Kristen E, Lagattuta, Kaitlyn A, Lu, Benjamin Y, Lucca, Liliana E, Daud, Adil I, Hafler, David A, Kluger, Harriet M, Raychaudhuri, Soumya, and Sharpe, Arlene H
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Cancer ,Genetics ,Generic health relevance ,Inflammatory and immune system ,Autoimmunity ,Humans ,Neoplasms ,Receptors ,Antigen ,T-Cell ,T-Lymphocytes ,PD-1 immunotherapy ,T cell ,TCR sequencing ,cancer ,molecular barcode ,single cell sequencing ,Immunology - Abstract
The T cell receptor (TCR) endows T cells with antigen specificity and is central to nearly all aspects of T cell function. Each naïve T cell has a unique TCR sequence that is stably maintained during cell division. In this way, the TCR serves as a molecular barcode that tracks processes such as migration, differentiation, and proliferation of T cells. Recent technological advances have enabled sequencing of the TCR from single cells alongside deep molecular phenotypes on an unprecedented scale. In this review, we discuss strengths and limitations of TCR sequences as molecular barcodes and their application to study immune responses following Programmed Death-1 (PD-1) blockade in cancer. Additionally, we consider applications of TCR data beyond use as a barcode.
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- 2022
14. Phase 2 Trial of Rituximab in Acetylcholine Receptor Antibody-Positive Generalized Myasthenia Gravis: The BeatMG Study
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Nowak, Richard J, Coffey, Christopher S, Goldstein, Jonathan M, Dimachkie, Mazen M, Benatar, Michael, Kissel, John T, Wolfe, Gil I, Burns, Ted M, Freimer, Miriam L, Nations, Sharon, Granit, Volkan, Smith, A Gordon, Richman, David P, Ciafaloni, Emma, Al-Lozi, Muhammad T, Sams, Laura Ann, Quan, Dianna, Ubogu, Eroboghene, Pearson, Brenda, Sharma, Aditi, Yankey, Jon W, Uribe, Liz, Shy, Michael, Amato, Anthony A, Conwit, Robin, O'Connor, Kevin C, Hafler, David A, Cudkowicz, Merit E, Barohn, Richard J, and Team, on behalf of the NeuroNEXT NN103 BeatMG Study
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Clinical Trials and Supportive Activities ,Clinical Research ,Neurosciences ,Cancer ,Rare Diseases ,Evaluation of treatments and therapeutic interventions ,6.1 Pharmaceuticals ,NeuroNEXT NN103 BeatMG Study Team ,Clinical Sciences ,Cognitive Sciences ,Neurology & Neurosurgery - Abstract
ObjectiveTo determine whether rituximab is safe and potentially beneficial, warranting further investigation in an efficacy trial for acetylcholine receptor antibody-positive generalized MG (AChR-Ab+ gMG).MethodsThe B-Cell Targeted Treatment in MG (BeatMG) study was a randomized, double-blind, placebo-controlled, multicenter phase-2 trial that utilized a futility design. Individuals 21-90 years of age, with AChR-Ab+ gMG (MG Foundation of America Class II-IV) and receiving prednisone ≥15 mg/day were eligible. The primary outcome was a measure of steroid-sparing effect, defined as the proportion achieving ≥75% reduction in mean daily prednisone dose in the 4-weeks prior to week 52 and with clinical improvement or no significant worsening as compared to the 4-week period prior to randomization. The co-primary outcome was safety. Secondary outcomes included MG-specific clinical assessments. Fifty-two individuals were randomized (1:1) to either a two-cycle rituximab/placebo regimen, with follow-up through 52-weeks.ResultsOf the 52 participants included, mean (±SD) age at enrollment was 55.1 (±17.1) years; 23 (44.2%) were female, and 31 (59.6%) were MGFA Class II. The mean (±SD) baseline prednisone dose was 22.1 (±9.7) mg/day. The primary steroid-sparing outcome was achieved in 60% of those on rituximab vs. 56% on placebo. The study reached its futility endpoint (p=0.03) suggesting that the pre-defined clinically meaningful improvement of 30% due to rituximab over placebo was unlikely to be achieved in a subsequent, larger trial. No safety issues identified.ConclusionsWhile rituximab was safe and well-tolerated, these results suggest that there is a low probability of observing the defined clinically meaningful steroid-sparing effect over a 12-month period in a phase-3 trial of mild-moderately symptomatic AChR-Ab+ gMG.Classification of evidenceThis study provides Class I evidence that for mild-to-moderate AChR-Ab+ gMG, compared with placebo, rituximab is safe but unlikely to reduce steroid use by an absolute difference of at least 30% at 1 year.Trial registrationClinicalTrials.gov Identifier: NCT02110706.
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- 2022
15. Immunophenotyping assessment in a COVID-19 cohort (IMPACC): A prospective longitudinal study
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Rouphael, Nadine, Maecker, Holden, Montgomery, Ruth R, Diray-Arce, Joann, Kleinstein, Steven H, Altman, Matthew C, Bosinger, Steven E, Eckalbar, Walter, Guan, Leying, Hough, Catherine L, Krammer, Florian, Langelier, Charles, Levy, Ofer, McEnaney, Kerry, Peters, Bjoern, Rahman, Adeeb, Rajan, Jayant V, Sigelman, Steven, Steen, Hanno, van Bakel, Harm, Ward, Alyssa, Wilson, Michael R, Woodruff, Prescott, Zamecnik, Colin R, Augustine, Alison D, Ozonoff, Al, Reed, Elaine F, Becker, Patrice M, Higuita, Nelson Agudelo, Atkinson, Mark A, Baden, Lindsey R, Bime, Christian, Brakenridge, Scott C, Calfee, Carolyn S, Cairns, Charles B, Corry, David, Davis, Mark M, Ehrlich, Lauren IR, Haddad, Elias K, Erle, David J, Fernandez-Sesma, Ana, Hafler, David A, Kheradmand, Farrah, Kraft, Monica, McComsey, Grace A, Melamed, Esther, Messer, William, Metcalf, Jordan, Nadeau, Kari C, Pulendran, Bali, Sarwal, Minnie, Schaenman, Joanna, Sekaly, Rafick, Shaw, Albert C, and Simon, Viviana
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Biomedical and Clinical Sciences ,Clinical Sciences ,Clinical Research ,Biomarkers ,COVID-19 ,Computational Biology ,Data Analysis ,Gene Expression Profiling ,Hospitalization ,Humans ,Immunophenotyping ,Longitudinal Studies ,Molecular Diagnostic Techniques ,Prospective Studies ,Proteomics ,SARS-CoV-2 ,United States ,IMPACC Manuscript Writing Team ,IMPACC Network Steering Committee ,Clinical sciences ,Immunology - Abstract
The IMmunoPhenotyping Assessment in a COVID-19 Cohort (IMPACC) is a prospective longitudinal study designed to enroll 1000 hospitalized patients with COVID-19 (NCT04378777). IMPACC collects detailed clinical, laboratory and radiographic data along with longitudinal biologic sampling of blood and respiratory secretions for in depth testing. Clinical and lab data are integrated to identify immunologic, virologic, proteomic, metabolomic and genomic features of COVID-19-related susceptibility, severity and disease progression. The goals of IMPACC are to better understand the contributions of pathogen dynamics and host immune responses to the severity and course of COVID-19 and to generate hypotheses for identification of biomarkers and effective therapeutics, including optimal timing of such interventions. In this report we summarize the IMPACC study design and protocols including clinical criteria and recruitment, multi-site standardized sample collection and processing, virologic and immunologic assays, harmonization of assay protocols, high-level analyses and the data sharing plans.
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- 2021
16. Disease State Prediction From Single-Cell Data Using Graph Attention Networks
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Ravindra, Neal G., Sehanobish, Arijit, Pappalardo, Jenna L., Hafler, David A., and van Dijk, David
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Quantitative Biology - Genomics ,Computer Science - Machine Learning ,Statistics - Machine Learning ,J.3 ,I.2.6 - Abstract
Single-cell RNA sequencing (scRNA-seq) has revolutionized biological discovery, providing an unbiased picture of cellular heterogeneity in tissues. While scRNA-seq has been used extensively to provide insight into both healthy systems and diseases, it has not been used for disease prediction or diagnostics. Graph Attention Networks (GAT) have proven to be versatile for a wide range of tasks by learning from both original features and graph structures. Here we present a graph attention model for predicting disease state from single-cell data on a large dataset of Multiple Sclerosis (MS) patients. MS is a disease of the central nervous system that can be difficult to diagnose. We train our model on single-cell data obtained from blood and cerebrospinal fluid (CSF) for a cohort of seven MS patients and six healthy adults (HA), resulting in 66,667 individual cells. We achieve 92 % accuracy in predicting MS, outperforming other state-of-the-art methods such as a graph convolutional network and a random forest classifier. Further, we use the learned graph attention model to get insight into the features (cell types and genes) that are important for this prediction. The graph attention model also allow us to infer a new feature space for the cells that emphasizes the differences between the two conditions. Finally we use the attention weights to learn a new low-dimensional embedding that can be visualized. To the best of our knowledge, this is the first effort to use graph attention, and deep learning in general, to predict disease state from single-cell data. We envision applying this method to single-cell data for other diseases., Comment: Incorporated suggestions from anonymous reviewers, Accepted at ACM CHIL 2020, comments welcome
- Published
- 2020
17. Early cellular and molecular signatures correlate with severity of West Nile virus infection
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Lee, Ho-Joon, Zhao, Yujiao, Fleming, Ira, Mehta, Sameet, Wang, Xiaomei, Wyk, Brent Vander, Ronca, Shannon E., Kang, Heather, Chou, Chih-Hung, Fatou, Benoit, Smolen, Kinga K., Levy, Ofer, Clish, Clary B., Xavier, Ramnik J., Steen, Hanno, Hafler, David A., Love, J. Christopher, Shalek, Alex K., Guan, Leying, Murray, Kristy O., Kleinstein, Steven H., and Montgomery, Ruth R.
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- 2023
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18. Single-Cell Transcriptomic Analyses of Brain Parenchyma in Patients With New-Onset Refractory Status Epilepticus (NORSE)
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Hanin, Aurélie, primary, Zhang, Le, additional, Huttner, Anita J., additional, Plu, Isabelle, additional, Mathon, Bertrand, additional, Bielle, Franck, additional, Navarro, Vincent, additional, Hirsch, Lawrence J., additional, and Hafler, David A., additional
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- 2024
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19. Impaired TIGIT expression on B cells drives circulating follicular helper T cell expansion in multiple sclerosis
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Asashima, Hiromitsu, Axisa, Pierre-Paul, Pham, Thi Hong Giang, Longbrake, Erin E., Ruff, William E., Lele, Nikhil, Cohen, Inessa, Raddassi, Khadir, Sumida, Tomokazu S., and Hafler, David A.
- Subjects
Multiple sclerosis -- Development and progression ,B cells -- Physiological aspects -- Health aspects ,CD4 lymphocytes -- Physiological aspects -- Health aspects ,Health care industry - Abstract
B cell depletion in patients with relapsing-remitting multiple sclerosis (RRMS) markedly prevents new MRI-detected lesions and disease activity, suggesting the hypothesis that altered B cell function leads to the activation of T cells driving disease pathogenesis. Here, we performed comprehensive analyses of CD40 ligand- (CD40L-) and IL-21-stimulated memory B cells from patients with MS and healthy age-matched controls, modeling the help of follicular helper T cells (Tfh cells), and found a differential gene expression signature in multiple B cell pathways. Most striking was the impaired TIGIT expression on MS-derived B cells mediated by dysregulation of the transcription factor TCF4. Activated circulating Tfh cells (cTfh cells) expressed CD155, the ligand of TIGIT, and TIGIT on B cells revealed their capacity to suppress the proliferation of IL-17- producing cTfh cells via the TIGIT/CD155 axis. Finally, [CCR6.sup.+] cTfh cells were significantly increased in patients with MS, and their frequency was inversely correlated with that of [TIGIT.sup.+] B cells. Together, these data suggest that the dysregulation of negative feedback loops between [TIGIT.sup.+] memory B cells and cTfh cells in MS drives the activated immune system in this disease., Introduction Relapsing-remitting multiple sclerosis (RRMS) is a genetically mediated autoimmune disease mediated by mye[Lin.sup.-]reactive T cells attacking the CNS (1-3) and is characterized by inflammatory lesions predominantly in the white [...]
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- 2022
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20. Two genetic variants explain the association of European ancestry with multiple sclerosis risk in African-Americans.
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Nakatsuka, Nathan, Patterson, Nick, Patsopoulos, Nikolaos A, Altemose, Nicolas, Tandon, Arti, Beecham, Ashley H, McCauley, Jacob L, Isobe, Noriko, Hauser, Stephen, De Jager, Philip L, Hafler, David A, Oksenberg, Jorge R, and Reich, David
- Subjects
Humans ,Multiple Sclerosis ,Genetic Predisposition to Disease ,Odds Ratio ,Haplotypes ,Polymorphism ,Single Nucleotide ,African Americans ,European Continental Ancestry Group ,Female ,Male ,Genome-Wide Association Study - Abstract
Epidemiological studies have suggested differences in the rate of multiple sclerosis (MS) in individuals of European ancestry compared to African ancestry, motivating genetic scans to identify variants that could contribute to such patterns. In a whole-genome scan in 899 African-American cases and 1155 African-American controls, we confirm that African-Americans who inherit segments of the genome of European ancestry at a chromosome 1 locus are at increased risk for MS [logarithm of odds (LOD) = 9.8], although the signal weakens when adding an additional 406 cases, reflecting heterogeneity in the two sets of cases [logarithm of odds (LOD) = 2.7]. The association in the 899 individuals can be fully explained by two variants previously associated with MS in European ancestry individuals. These variants tag a MS susceptibility haplotype associated with decreased CD58 gene expression (odds ratio of 1.37; frequency of 84% in Europeans and 22% in West Africans for the tagging variant) as well as another haplotype near the FCRL3 gene (odds ratio of 1.07; frequency of 49% in Europeans and 8% in West Africans). Controlling for all other genetic and environmental factors, the two variants predict a 1.44-fold higher rate of MS in European-Americans compared to African-Americans.
- Published
- 2020
21. Multi-omic longitudinal study reveals immune correlates of clinical course among hospitalized COVID-19 patients
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Abraham, James, Adkisson, Michael, Albert, Marisa, Altamirano, Luz Torres, Alvarenga, Bonny, Anderson, Matthew L., Anderson, Evan J., Arnett, Azlann, Asashima, Hiromitsu, Atkinson, Mark A., Baden, Lindsey R., Barton, Brenda, Beach, Katherine, Beagle, Elizabeth, Becker, Patrice M., Bell, Matthew R., Bernui, Mariana, Bime, Christian, Boddapati, Arun Kumar, Booth, J. Leland, Borresen, Brittney, Brakenridge, Scott C., Bristow, Laurel, Bryant, Robert, Calfee, Carolyn S., Carreño, Juan Manuel, Carrillo, Sidney, Chak, Suzanna, Chang, Iris, Connors, Jennifer, Conway, Michelle, Corry, David B., Cowan, David, Croen, Brett, Dela Cruz, Charles S., Cusimano, Gina, Eaker, Lily, Edwards, Carolyn, Ehrlich, Lauren I.R., Elashoff, David, Erickson, Heidi, Erle, David J., Farhadian, Shelli, Farrugia, Keith, Fatou, Benoit, Fernandes, Andrea, Fernandez-Sesma, Ana, Fragiadakis, Gabriela K., Furukawa, Sara, Geltman, Janelle N., Ghale, Rajani, Bermúdez González, Maria Carolina, Goonewardene, I. Michael, Guerrero, Estella Sanchez, Guirgis, Faheem W., Hafler, David A., Hamilton, Sydney, Harris, Paul, Hayati, Arash Nemati, Hendrickson, Carolyn M., Agudelo Higuita, Nelson I., Hodder, Thomas, Holland, Steven M., Hough, Catherine L., Huerta, Christopher, Hurley, Kerin C., Hutton, Scott R., Iwasaki, Akiko, Jauregui, Alejandra, Jha, Meenakshi, Johnson, Brandi, Joyner, David, Kangelaris, Kirsten N., Kelly, Geoffrey, Khalil, Zain, Khan, Zenab, Kheradmand, Farrah, Kim, James N., Kimura, Hiroki, Ko, Albert I., Kohr, Bernard, Kraft, Monica, Krummel, Matthew, Kutzler, Michele A., Lasky-Su, Jessica, Lee, Serena, Lee, Deanna, Leipold, Michael, Lentucci, Claudia, Leroux, Carolyn, Lin, Edward, Liu, Shanshan, Love, Christina, Lu, Zhengchun, Maliskova, Lenka, Manning, Brittany Roth, Manohar, Monali, Martens, Mark, McComsey, Grace A., McEnaney, Kerry, McLin, Renee, Melamed, Esther, Melnyk, Nataliya, Mendez, Kevin, Messer, William B., Metcalf, Jordan P., Michelotti, Gregory, Mick, Eran, Mohanty, Subhasis, Mosier, Jarrod, Mulder, Lubbertus C.F., Murphy, Maimouna, Nadeau, Kari R.C., Nelson, Ebony, Nelson, Allison, Nguyen, Viet, Oberhaus, Jordan, Panganiban, Bernadine, Pellegrini, Kathryn L., Pickering, Harry C., Powell, Debra L., Presnell, Scott, Pulendran, Bali, Rahman, Adeeb H., Rashid, Ahmad Sadeed, Raskin, Ariel, Reed, Elaine F., Ribeiro, Susan Pereira, Rivera, Adreanne M., Rogers, Jacob E., Rogers, Angela, Rogowski, Brandon, Rooks, Rebecca, Rosenberg-Hasson, Yael, Rothman, Jessica, Rousseau, Justin F., Salehi-Rad, Ramin, Saluvan, Mehmet, Samaha, Hady, Schaenman, Joanna, Schunk, Ron, Semenza, Nicholas C., Sen, Subha, Sevransky, Jonathan, Seyfert-Margolis, Vicki, Shaheen, Tanzia, Shaw, Albert C., Sieg, Scott, Siegel, Sarah A.R., Sigal, Natalia, Siles, Nadia, Simmons, Brent, Simon, Viviana, Singh, Gagandeep, Sinko, Lauren, Smith, Cecilia M., Smolen, Kinga K., Song, Li-Zhen, Srivastava, Komal, Sullivan, Peter, Syphurs, Caitlin, Tcheou, Johnstone, Tegos, George P., Tharp, Greg K., Tong, Alexandra, Tsitsiklis, Alexandra, Ungaro, Ricardo F., Vaysman, Tatyana, Viode, Arthur, Vita, Randi, Wang, Xiaomei, Ward, Alyssa, Ward, Dawn C., Willmore, Andrew, Woloszczuk, Kyra, Wong, Kari, Woodruff, Prescott G., Xu, Leqi, van Haren, Simon, van de Guchte, Adriana, Zhao, Yujiao, Diray-Arce, Joann, Fourati, Slim, Doni Jayavelu, Naresh, Patel, Ravi, Maguire, Cole, Chang, Ana C., Dandekar, Ravi, Qi, Jingjing, Lee, Brian H., van Zalm, Patrick, Schroeder, Andrew, Chen, Ernie, Konstorum, Anna, Brito, Anderson, Gygi, Jeremy P., Kho, Alvin, Chen, Jing, Pawar, Shrikant, Gonzalez-Reiche, Ana Silvia, Hoch, Annmarie, Milliren, Carly E., Overton, James A., Westendorf, Kerstin, Cairns, Charles B., Rouphael, Nadine, Bosinger, Steven E., Kim-Schulze, Seunghee, Krammer, Florian, Rosen, Lindsey, Grubaugh, Nathan D., van Bakel, Harm, Wilson, Michael, Rajan, Jayant, Steen, Hanno, Eckalbar, Walter, Cotsapas, Chris, Langelier, Charles R., Levy, Ofer, Altman, Matthew C., Maecker, Holden, Montgomery, Ruth R., Haddad, Elias K., Sekaly, Rafick P., Esserman, Denise, Ozonoff, Al, Augustine, Alison D., Guan, Leying, Peters, Bjoern, and Kleinstein, Steven H.
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- 2023
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22. Sodium perturbs mitochondrial respiration and induces dysfunctional Tregs
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Côrte-Real, Beatriz F., Hamad, Ibrahim, Arroyo Hornero, Rebeca, Geisberger, Sabrina, Roels, Joris, Van Zeebroeck, Lauren, Dyczko, Aleksandra, van Gisbergen, Marike W., Kurniawan, Henry, Wagner, Allon, Yosef, Nir, Weiss, Susanne N.Y., Schmetterer, Klaus G., Schröder, Agnes, Krampert, Luka, Haase, Stefanie, Bartolomaeus, Hendrik, Hellings, Niels, Saeys, Yvan, Dubois, Ludwig J., Brenner, Dirk, Kempa, Stefan, Hafler, David A., Stegbauer, Johannes, Linker, Ralf A., Jantsch, Jonathan, Müller, Dominik N., and Kleinewietfeld, Markus
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- 2023
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23. Basic principles of neuroimmunology
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Yoshida, Tomomi M., Wang, Andrew, and Hafler, David A.
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- 2022
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24. 23 Na imaging : Worth its salt for understanding multiple sclerosis
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Longbrake, Erin E. and Hafler, David A.
- Published
- 2021
25. Differential Results of Polygenic Risk Scoring for Multiple Sclerosis in European and African American Populations
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Rivier, Cyprien A, primary, Payabvash, Seyedmehdi, additional, Zhao, Hong-yu, additional, Hafler, David A, additional, Falcone, Guido J., additional, and Longbrake, Erin E, additional
- Published
- 2024
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- View/download PDF
26. 38. Identifying Novel Molecular Drivers of Autoimmunity in Paraneoplastic Anti-NMDAR Encephalitis
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Gardin, Tova, primary, DiStasio, Marcello, additional, Mandel-Brehm, Caleigh, additional, Bhatta, Sagar, additional, and Hafler, David, additional
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- 2024
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27. Type I interferon transcriptional network regulates expression of coinhibitory receptors in human T cells
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Sumida, Tomokazu S., Dulberg, Shai, Schupp, Jonas C., Lincoln, Matthew R., Stillwell, Helen A., Axisa, Pierre-Paul, Comi, Michela, Unterman, Avraham, Kaminski, Naftali, Madi, Asaf, Kuchroo, Vijay K., and Hafler, David A.
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- 2022
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28. The CELLO trial: Protocol of a planned phase 4 study to assess the efficacy of Ocrelizumab in patients with radiologically isolated syndrome
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Longbrake, Erin E., Hua, Le H., Mowry, Ellen M., Gauthier, Susan A., Alvarez, Enrique, Cross, Anne H., Pei, Jinglan, Priest, Jessica, Raposo, Catarina, Hafler, David A., and Winger, Ryan C.
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- 2022
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- View/download PDF
29. Author Correction: A systems biology approach uncovers cell-specific gene regulatory effects of genetic associations in multiple sclerosis
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Madireddy, Lohith, Patsopoulos, Nikolaos A, Cotsapas, Chris, Bos, Steffan D, Beecham, Ashley, McCauley, Jacob, Kim, Kicheol, Jia, Xiaoming, Santaniello, Adam, Caillier, Stacy J, Andlauer, Till FM, Barcellos, Lisa F, Berge, Tone, Bernardinelli, Luisa, Martinelli-Boneschi, Filippo, Booth, David R, Briggs, Farren, Celius, Elisabeth G, Comabella, Manuel, Comi, Giancarlo, Cree, Bruce AC, D'Alfonso, Sandra, Dedham, Katrina, Duquette, Pierre, Dardiotis, Efthimios, Esposito, Federica, Fontaine, Bertrand, Gasperi, Christiane, Goris, An, Dubois, Benedicte, Gourraud, Pierre-Antoine, Hadjigeorgiou, Georgios, Haines, Jonathan, Hawkins, Clive, Hemmer, Bernhard, Hintzen, Rogier, Horakova, Dana, Isobe, Noriko, Kalra, Seema, Kira, Jun-ichi, Khalil, Michael, Kockum, Ingrid, Lill, Christina M, Lincoln, Matthew R, Luessi, Felix, Martin, Roland, Oturai, Annette, Palotie, Aarno, Pericak-Vance, Margaret A, Henry, Roland, Saarela, Janna, Ivinson, Adrian, Olsson, Tomas, Taylor, Bruce V, Stewart, Graeme J, Harbo, Hanne F, Compston, Alastair, Hauser, Stephen L, Hafler, David A, Zipp, Frauke, De Jager, Philip, Sawcer, Stephen, Oksenberg, Jorge R, and Baranzini, Sergio E
- Subjects
International Multiple Sclerosis Genetics Consortium - Abstract
An amendment to this paper has been published and can be accessed via a link at the top of the paper.
- Published
- 2019
30. A systems biology approach uncovers cell-specific gene regulatory effects of genetic associations in multiple sclerosis
- Author
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Madireddy, Lohith, Patsopoulos, Niklaos A, Cotsapas, Chris, Bos, Steffan D, Beecham, Ashley, McCauley, Jacob, Kim, Kicheol, Jia, Xiaoming, Santaniello, Adam, Caillier, Stacy J, Andlauer, Till FM, Barcellos, Lisa F, Berge, Tone, Bernardinelli, Luisa, Martinelli-Boneschi, Filippo, Booth, David R, Briggs, Farren, Celius, Elisabeth G, Comabella, Manuel, Comi, Giancarlo, Cree, Bruce AC, D'Alfonso, Sandra, Dedham, Katrina, Duquette, Pierre, Efthimios, Dardiotis, Esposito, Federica, Fontaine, Bertrand, Gasperi, Christiane, Goris, An, Dubois, Benedicte, Gourraud, Pierre-Antoine, Hadjigeorgiou, Georgios, Haines, Jonathan, Hawkins, Clive, Hemmer, Bernhard, Hintzen, Rogier, Horakova, Dana, Isobe, Noriko, Kalra, Seema, Kira, Jun-ichi, Khalil, Michael, Kockum, Ingrid, Lill, Christina M, Lincoln, Matthew R, Luessi, Felix, Martin, Roland, Oturai, Annette, Palotie, Aarno, Pericak-Vance, Margaret A, Henry, Roland, Saarela, Janna, Ivinson, Adrian, Olsson, Tomas, Taylor, Bruce V, Stewart, Graeme J, Harbo, Hanne F, Compston, Alastair, Hauser, Stephen L, Hafler, David A, Zipp, Frauke, De Jager, Philip, Sawcer, Stephen, Oksenberg, Jorge R, and Baranzini, Sergio E
- Subjects
Human Genome ,Autoimmune Disease ,Multiple Sclerosis ,Genetics ,Biotechnology ,Brain Disorders ,Neurodegenerative ,Clinical Research ,Neurosciences ,2.1 Biological and endogenous factors ,Aetiology ,Inflammatory and immune system ,Gene Expression Regulation ,Genes ,Regulator ,Genetic Predisposition to Disease ,Genome-Wide Association Study ,Genotype ,Humans ,Polymorphism ,Single Nucleotide ,Systems Biology ,International Multiple Sclerosis Genetics Consortium - Abstract
Genome-wide association studies (GWAS) have identified more than 50,000 unique associations with common human traits. While this represents a substantial step forward, establishing the biology underlying these associations has proven extremely difficult. Even determining which cell types and which particular gene(s) are relevant continues to be a challenge. Here, we conduct a cell-specific pathway analysis of the latest GWAS in multiple sclerosis (MS), which had analyzed a total of 47,351 cases and 68,284 healthy controls and found more than 200 non-MHC genome-wide associations. Our analysis identifies pan immune cell as well as cell-specific susceptibility genes in T cells, B cells and monocytes. Finally, genotype-level data from 2,370 patients and 412 controls is used to compute intra-individual and cell-specific susceptibility pathways that offer a biological interpretation of the individual genetic risk to MS. This approach could be adopted in any other complex trait for which genome-wide data is available.
- Published
- 2019
31. Neuroimmune interactions in health and disease
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Hafler, David A. and Sansing, Lauren H.
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- 2022
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32. Dissection of artifactual and confounding glial signatures by single-cell sequencing of mouse and human brain
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Marsh, Samuel E., Walker, Alec J., Kamath, Tushar, Dissing-Olesen, Lasse, Hammond, Timothy R., de Soysa, T. Yvanka, Young, Adam M. H., Murphy, Sarah, Abdulraouf, Abdulraouf, Nadaf, Naeem, Dufort, Connor, Walker, Alicia C., Lucca, Liliana E., Kozareva, Velina, Vanderburg, Charles, Hong, Soyon, Bulstrode, Harry, Hutchinson, Peter J., Gaffney, Daniel J., Hafler, David A., Franklin, Robin J. M., Macosko, Evan Z., and Stevens, Beth
- Published
- 2022
- Full Text
- View/download PDF
33. Single-cell multi-omics reveals dyssynchrony of the innate and adaptive immune system in progressive COVID-19
- Author
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Unterman, Avraham, Sumida, Tomokazu S., Nouri, Nima, Yan, Xiting, Zhao, Amy Y., Gasque, Victor, Schupp, Jonas C., Asashima, Hiromitsu, Liu, Yunqing, Cosme, Jr., Carlos, Deng, Wenxuan, Chen, Ming, Raredon, Micha Sam Brickman, Hoehn, Kenneth B., Wang, Guilin, Wang, Zuoheng, DeIuliis, Giuseppe, Ravindra, Neal G., Li, Ningshan, Castaldi, Christopher, Wong, Patrick, Fournier, John, Bermejo, Santos, Sharma, Lokesh, Casanovas-Massana, Arnau, Vogels, Chantal B. F., Wyllie, Anne L., Grubaugh, Nathan D., Melillo, Anthony, Meng, Hailong, Stein, Yan, Minasyan, Maksym, Mohanty, Subhasis, Ruff, William E., Cohen, Inessa, Raddassi, Khadir, Niklason, Laura E., Ko, Albert I., Montgomery, Ruth R., Farhadian, Shelli F., Iwasaki, Akiko, Shaw, Albert C., van Dijk, David, Zhao, Hongyu, Kleinstein, Steven H., Hafler, David A., Kaminski, Naftali, and Dela Cruz, Charles S.
- Published
- 2022
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34. Longitudinal single-cell analysis of a patient receiving adoptive cell therapy reveals potential mechanisms of treatment failure
- Author
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Qu, Rihao, Kluger, Yuval, Yang, Junchen, Zhao, Jun, Hafler, David A., Krause, Diane S., Bersenev, Alexey, Bosenberg, Marcus, Hurwitz, Michael, Lucca, Liliana, and Kluger, Harriet M.
- Published
- 2022
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- View/download PDF
35. An autoimmune transcriptional circuit drives FOXP3+ regulatory T cell dysfunction.
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Sumida, Tomokazu S., Lincoln, Matthew R., He, Liang, Park, Yongjin, Ota, Mineto, Oguchi, Akiko, Son, Raku, Yi, Alice, Stillwell, Helen A., Leissa, Greta A., Fujio, Keishi, Murakawa, Yasuhiro, Kulminski, Alexander M., Epstein, Charles B., Bernstein, Bradley E., Kellis, Manolis, and Hafler, David A.
- Subjects
INTERFERON regulatory factors ,REGULATORY T cells ,ZINC-finger proteins ,AP-1 transcription factor ,CENTRAL nervous system diseases ,TRANSCRIPTION factors - Abstract
Autoimmune diseases, among the most common disorders of young adults, are mediated by genetic and environmental factors. Although CD4
+ FOXP3+ regulatory T cells (Tregs ) play a central role in preventing autoimmunity, the molecular mechanism underlying their dysfunction is unknown. Here, we performed comprehensive transcriptomic and epigenomic profiling of Tregs in the autoimmune disease multiple sclerosis (MS) to identify critical transcriptional programs regulating human autoimmunity. We found that up-regulation of a primate-specific short isoform of PR domain zinc finger protein 1 (PRDM1-S) induces expression of serum and glucocorticoid-regulated kinase 1 (SGK1) independent from the evolutionarily conserved long PRDM1, which led to destabilization of forkhead box P3 (FOXP3) and Treg dysfunction. This aberrant PRDM1-S/SGK1 axis is shared among other autoimmune diseases. Furthermore, the chromatin landscape profiling in Tregs from individuals with MS revealed enriched activating protein–1 (AP-1)/interferon regulatory factor (IRF) transcription factor binding as candidate upstream regulators of PRDM1-S expression and Treg dysfunction. Our study uncovers a mechanistic model where the evolutionary emergence of PRDM1-S and epigenetic priming of AP-1/IRF may be key drivers of dysfunctional Tregs in autoimmune diseases. Editor's summary: Regulatory T cells (Tregs ) are known to be dysfunctional in autoimmune diseases, but the cause of this dysfunction has not been fully elucidated. Here, Sumida et al. found that Tregs isolated from individuals with multiple sclerosis, an autoimmune disease of the central nervous system, had increased expression of the short isoform of PR domain zinc finger protein 1 (PRDM1-S), which destabilized expression of the essential Treg transcription factor FOXP3 and induced Treg dysfunction mediated through SGK1, a salt-sensing kinase. Upstream epigenetic analysis revealed a regulatory role for AP-1 and IRF transcription factors in this circuit. The authors detected similar up-regulation of PRDM1-S in other autoimmune diseases, suggesting that PRDM1-S overexpression may be a common feature of Treg dysfunction. —Courtney Malo [ABSTRACT FROM AUTHOR]- Published
- 2024
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36. Single-cell RNA sequencing reveals microglia-like cells in cerebrospinal fluid during virologically suppressed HIV
- Author
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Farhadian, Shelli F, Mehta, Sameet S, Zografou, Chrysoula, Robertson, Kevin, Price, Richard W, Pappalardo, Jenna, Chiarella, Jennifer, Hafler, David A, and Spudich, Serena S
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Infectious Diseases ,Neurodegenerative ,HIV/AIDS ,Neurosciences ,Pediatric AIDS ,Genetics ,Pediatric ,Aetiology ,2.1 Biological and endogenous factors ,Infection ,Neurological ,Good Health and Well Being ,Adult ,Anti-HIV Agents ,Antiretroviral Therapy ,Highly Active ,Blood ,Central Nervous System ,Cerebrospinal Fluid ,Gene Expression ,HIV Infections ,Humans ,Inflammation ,Microglia ,Myeloid Cells ,Neurodegenerative Diseases ,Sequence Analysis ,RNA ,AIDS/HIV ,Neurological disorders - Abstract
Central nervous system (CNS) immune activation is an important driver of neuronal injury during several neurodegenerative and neuroinflammatory diseases. During HIV infection, CNS immune activation is associated with high rates of neurocognitive impairment, even during sustained long-term suppressive antiretroviral therapy (ART). However, the cellular subsets that drive immune activation and neuronal damage in the CNS during HIV infection and other neurological conditions remain unknown, in part because CNS cells are difficult to access in living humans. Using single-cell RNA sequencing (scRNA-seq) on cerebrospinal fluid (CSF) and blood from adults with and without HIV, we identified a rare (
- Published
- 2018
37. Unsupervised capture and profiling of rare immune cells using multi-directional magnetic ratcheting
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Murray, Coleman, Miwa, Hiromi, Dhar, Manjima, Park, Da Eun, Pao, Edward, Martinez, Jessica, Kaanumale, Sireesha, Loghin, Evelina, Graf, John, Rhaddassi, Khadir, Kwok, William W, Hafler, David, Puleo, Chris, and Di Carlo, Dino
- Subjects
Biotechnology ,Inflammatory and immune system ,Cell Separation ,Cytokines ,Humans ,Immune System ,Magnetic Fields ,Chemical Sciences ,Engineering ,Analytical Chemistry - Abstract
Immunotherapies (IT) require induction, expansion, and maintenance of specific changes to a patient's immune cell repertoire which yield a therapeutic benefit. Recently, mechanistic understanding of these changes at the cellular level has revealed that IT results in complex phenotypic transitions in target cells, and that therapeutic effectiveness may be predicted by monitoring these transitions during therapy. However, monitoring will require unique tools that enable capture, manipulation, and profiling of rare immune cell populations. In this study, we introduce a method of automated and unsupervised separation and processing of rare immune cells, using high-force and multidimensional magnetic ratcheting (MR). We demonstrate capture of target immune cells using samples with up to 1 : 10 000 target cell to background cell ratios from input volumes as small as 25 microliters (i.e. a low volume and low cell frequency sample sparing assay interface). Cell capture is shown to achieve up to 90% capture efficiency and purity, and captured cell analysis is shown using both on-chip culture/activity assays and off-chip ejection and nucleic acid analysis. These results demonstrate that multi-directional magnetic ratcheting offers a unique separation system for dealing with blood cell samples that contain either rare cells or significantly small volumes, and the "sample sparing" capability leads to an expanded spectrum of parameters that can be measured. These tools will be paramount to advancing techniques for immune monitoring under conditions in which both the sample volume and number of antigen-specific target cells are often exceedingly small, including during IT and treatment of allergy, asthma, autoimmunity, immunodeficiency, cell based therapy, transplantation, and infection.
- Published
- 2018
38. Minimum Information about T Regulatory Cells: A Step toward Reproducibility and Standardization
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Fuchs, Anke, Gliwiński, Mateusz, Grageda, Nathali, Spiering, Rachel, Abbas, Abul K, Appel, Silke, Bacchetta, Rosa, Battaglia, Manuela, Berglund, David, Blazar, Bruce, Bluestone, Jeffrey A, Bornhäuser, Martin, Brinke, Anja ten, Brusko, Todd M, Cools, Nathalie, Cuturi, Maria Cristina, Geissler, Edward, Giannoukakis, Nick, Gołab, Karolina, Hafler, David A, van Ham, S Marieke, Hester, Joanna, Hippen, Keli, Di Ianni, Mauro, Ilic, Natasa, Isaacs, John, Issa, Fadi, Iwaszkiewicz-Grześ, Dorota, Jaeckel, Elmar, Joosten, Irma, Klatzmann, David, Koenen, Hans, van Kooten, Cees, Korsgren, Olle, Kretschmer, Karsten, Levings, Megan, Marek-Trzonkowska, Natalia Maria, Martinez-Llordella, Marc, Miljkovic, Djordje, Mills, Kingston HG, Miranda, Joana P, Piccirillo, Ciriaco A, Putnam, Amy L, Ritter, Thomas, Roncarolo, Maria Grazia, Sakaguchi, Shimon, Sánchez-Ramón, Silvia, Sawitzki, Birgit, Sofronic-Milosavljevic, Ljiljana, Sykes, Megan, Tang, Qizhi, Vives-Pi, Marta, Waldmann, Herman, Witkowski, Piotr, Wood, Kathryn J, Gregori, Silvia, Hilkens, Catharien MU, Lombardi, Giovanna, Lord, Phillip, Martinez-Caceres, Eva M, and Trzonkowski, Piotr
- Subjects
Vaccine Related ,Autoimmune Disease ,Inflammatory and immune system ,minimum information model ,T regulatory cells ,immunotherapy ,good manufacturing practice ,cell therapy ,immune tolerance ,Immunology ,Medical Microbiology - Abstract
Cellular therapies with CD4+ T regulatory cells (Tregs) hold promise of efficacious treatment for the variety of autoimmune and allergic diseases as well as posttransplant complications. Nevertheless, current manufacturing of Tregs as a cellular medicinal product varies between different laboratories, which in turn hampers precise comparisons of the results between the studies performed. While the number of clinical trials testing Tregs is already substantial, it seems to be crucial to provide some standardized characteristics of Treg products in order to minimize the problem. We have previously developed reporting guidelines called minimum information about tolerogenic antigen-presenting cells, which allows the comparison between different preparations of tolerance-inducing antigen-presenting cells. Having this experience, here we describe another minimum information about Tregs (MITREG). It is important to note that MITREG does not dictate how investigators should generate or characterize Tregs, but it does require investigators to report their Treg data in a consistent and transparent manner. We hope this will, therefore, be a useful tool facilitating standardized reporting on the manufacturing of Tregs, either for research purposes or for clinical application. This way MITREG might also be an important step toward more standardized and reproducible testing of the Tregs preparations in clinical applications.
- Published
- 2018
39. Circulating Tumor Reactive KIR+CD8+ T cells Suppress Anti-Tumor Immunity in Patients with Melanoma
- Author
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Hafler, David, primary, Lu, Benjamin, additional, Lucca, Liliana, additional, Lewis, Wesley, additional, Wang, Jiping, additional, Nogeuira, Catarina, additional, Heer, Sebastian, additional, Axisa, Pierre-Paul, additional, Buitrago-Pocasangre, Nicholas, additional, Pham, Giang, additional, Kojima, Mina, additional, Wei, Wei, additional, Aizenbud, Lilach, additional, Bacchiocchi, Antonietta, additional, Zhang, Lin, additional, Walewski, Joseph, additional, Chiang, Veronica, additional, Olino, Kelly, additional, Clune, James, additional, Halaban, Ruth, additional, Kluger, Yuval, additional, Coyle, Anthony, additional, Kisielow, Jan, additional, Obermair, Franz-Josef, additional, and Kluger, Harriet, additional
- Published
- 2024
- Full Text
- View/download PDF
40. Systems Analysis of Immune Changes after B-cell Depletion in Autoimmune Multiple Sclerosis
- Author
-
Wei, Jessica, primary, Moon, Jeonghyeon, additional, Yasumizu, Yoshiaki, additional, Zhang, Le, additional, Radassi, Khadir, additional, Buitrago-Pocasangre, Nicholas, additional, Deerhake, M. Elizabeth, additional, Strauli, Nicholas, additional, Chan, Alan, additional, Herman, Ann, additional, Pedotte, Rosetta, additional, Raposo, Catarina, additional, Tackenberg, Björn, additional, Yim, Isaiah, additional, Pappalardo, Jenna, additional, Longbrake, Erin E., additional, Sumida, Tomokazu S., additional, Axisa, Pierre-Paul, additional, and Hafler, David A., additional
- Published
- 2024
- Full Text
- View/download PDF
41. Solving Immunology?
- Author
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Vodovotz, Yoram, Xia, Ashley, Read, Elizabeth L, Bassaganya-Riera, Josep, Hafler, David A, Sontag, Eduardo, Wang, Jin, Tsang, John S, Day, Judy D, Kleinstein, Steven H, Butte, Atul J, Altman, Matthew C, Hammond, Ross, and Sealfon, Stuart C
- Subjects
Biological Sciences ,Genetics ,Inflammatory and immune system ,Good Health and Well Being ,Animals ,Computational Biology ,Database Management Systems ,High-Throughput Screening Assays ,Humans ,Immune System ,Immunity ,Models ,Immunological ,Systems Biology ,Translational Research ,Biomedical ,autoimmune disease ,conference ,mathematical modeling ,personalized medicine ,translation ,Immunology ,Biochemistry and cell biology - Abstract
Emergent responses of the immune system result from the integration of molecular and cellular networks over time and across multiple organs. High-content and high-throughput analysis technologies, concomitantly with data-driven and mechanistic modeling, hold promise for the systematic interrogation of these complex pathways. However, connecting genetic variation and molecular mechanisms to individual phenotypes and health outcomes has proven elusive. Gaps remain in data, and disagreements persist about the value of mechanistic modeling for immunology. Here, we present the perspectives that emerged from the National Institute of Allergy and Infectious Disease (NIAID) workshop 'Complex Systems Science, Modeling and Immunity' and subsequent discussions regarding the potential synergy of high-throughput data acquisition, data-driven modeling, and mechanistic modeling to define new mechanisms of immunological disease and to accelerate the translation of these insights into therapies.
- Published
- 2017
42. Minimum Information about T Regulatory Cells: A Step toward Reproducibility and Standardization.
- Author
-
Fuchs, Anke, Gliwiński, Mateusz, Grageda, Nathali, Spiering, Rachel, Abbas, Abul K, Appel, Silke, Bacchetta, Rosa, Battaglia, Manuela, Berglund, David, Blazar, Bruce, Bluestone, Jeffrey A, Bornhäuser, Martin, Ten Brinke, Anja, Brusko, Todd M, Cools, Nathalie, Cuturi, Maria Cristina, Geissler, Edward, Giannoukakis, Nick, Gołab, Karolina, Hafler, David A, van Ham, S Marieke, Hester, Joanna, Hippen, Keli, Di Ianni, Mauro, Ilic, Natasa, Isaacs, John, Issa, Fadi, Iwaszkiewicz-Grześ, Dorota, Jaeckel, Elmar, Joosten, Irma, Klatzmann, David, Koenen, Hans, van Kooten, Cees, Korsgren, Olle, Kretschmer, Karsten, Levings, Megan, Marek-Trzonkowska, Natalia Maria, Martinez-Llordella, Marc, Miljkovic, Djordje, Mills, Kingston HG, Miranda, Joana P, Piccirillo, Ciriaco A, Putnam, Amy L, Ritter, Thomas, Roncarolo, Maria Grazia, Sakaguchi, Shimon, Sánchez-Ramón, Silvia, Sawitzki, Birgit, Sofronic-Milosavljevic, Ljiljana, Sykes, Megan, Tang, Qizhi, Vives-Pi, Marta, Waldmann, Herman, Witkowski, Piotr, Wood, Kathryn J, Gregori, Silvia, Hilkens, Catharien MU, Lombardi, Giovanna, Lord, Phillip, Martinez-Caceres, Eva M, and Trzonkowski, Piotr
- Subjects
T regulatory cells ,cell therapy ,good manufacturing practice ,immune tolerance ,immunotherapy ,minimum information model ,Autoimmune Disease ,Vaccine Related ,Inflammatory and Immune System ,Immunology ,Medical Microbiology - Abstract
Cellular therapies with CD4+ T regulatory cells (Tregs) hold promise of efficacious treatment for the variety of autoimmune and allergic diseases as well as posttransplant complications. Nevertheless, current manufacturing of Tregs as a cellular medicinal product varies between different laboratories, which in turn hampers precise comparisons of the results between the studies performed. While the number of clinical trials testing Tregs is already substantial, it seems to be crucial to provide some standardized characteristics of Treg products in order to minimize the problem. We have previously developed reporting guidelines called minimum information about tolerogenic antigen-presenting cells, which allows the comparison between different preparations of tolerance-inducing antigen-presenting cells. Having this experience, here we describe another minimum information about Tregs (MITREG). It is important to note that MITREG does not dictate how investigators should generate or characterize Tregs, but it does require investigators to report their Treg data in a consistent and transparent manner. We hope this will, therefore, be a useful tool facilitating standardized reporting on the manufacturing of Tregs, either for research purposes or for clinical application. This way MITREG might also be an important step toward more standardized and reproducible testing of the Tregs preparations in clinical applications.
- Published
- 2017
43. Emerging Cerebrospinal Fluid Biomarkers of Disease Activity and Progression in Multiple Sclerosis.
- Author
-
Cross, Anne H., Gelfand, Jeffrey M., Thebault, Simon, Bennett, Jeffrey L., von Büdingen, H. Christian, Cameron, Briana, Carruthers, Robert, Edwards, Keith, Fallis, Robert, Gerstein, Rachel, Giacomini, Paul S., Greenberg, Benjamin, Hafler, David A., Ionete, Carolina, Kaunzner, Ulrike W., Kodama, Lay, Lock, Christopher, Longbrake, Erin E., Musch, Bruno, and Pardo, Gabriel
- Published
- 2024
- Full Text
- View/download PDF
44. Corrigendum to “Phenotypes of disease severity in a cohort of hospitalized COVID-19 patients: results from the IMPACC study” [eBioMedicine 83 (2022) 104208]
- Author
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Ozonoff, Al, primary, Schaenman, Joanna, additional, Jayavelu, Naresh Doni, additional, Milliren, Carly E., additional, Calfee, Carolyn S., additional, Cairns, Charles B., additional, Kraft, Monica, additional, Baden, Lindsey R., additional, Shaw, Albert C., additional, Krammer, Florian, additional, van Bakel, Harm, additional, Esserman, Denise A., additional, Liu, Shanshan, additional, Sesma, Ana Fernandez, additional, Simon, Viviana, additional, Hafler, David A., additional, Montgomery, Ruth R., additional, Kleinstein, Steven H., additional, Levy, Ofer, additional, Bime, Chris, additional, Haddad, Elias K., additional, Erle, David J., additional, Pulendran, Bali, additional, Nadeau, Kari C., additional, Davis, Mark M., additional, Hough, Catherine L., additional, Messer, William B., additional, Agudelo Higuita, Nelson I., additional, Metcalf, Jordan P., additional, Atkinson, Mark A., additional, Brakenridge, Scott C., additional, Corry, David, additional, Kheradmand, Farrah, additional, Ehrlich, Lauren I.R., additional, Melamed, Esther, additional, McComsey, Grace A., additional, Sekaly, Rafick, additional, Diray-Arce, Joann, additional, Peters, Bjoern, additional, Augustine, Alison D., additional, Reed, Elaine F., additional, Altman, Matthew C., additional, Becker, Patrice M., additional, and Rouphael, Nadine, additional
- Published
- 2023
- Full Text
- View/download PDF
45. Class II HLA interactions modulate genetic risk for multiple sclerosis.
- Author
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Moutsianas, Loukas, Jostins, Luke, Beecham, Ashley, Dilthey, Alexander, Xifara, Dionysia, Ban, Maria, Shah, Tejas, Patsopoulos, Nikolaos, Alfredsson, Lars, Anderson, Carl, Attfield, Katherine, Barrett, Jeffrey, Binder, Thomas, Booth, David, Buck, Dorothea, Celius, Elisabeth, Cotsapas, Chris, DAlfonso, Sandra, Dendrou, Calliope, Donnelly, Peter, Dubois, Bénédicte, Fontaine, Bertrand, Fugger, Lars, Goris, An, Graetz, Christiane, Hemmer, Bernhard, Hillert, Jan, Kockum, Ingrid, Leslie, Stephen, Lill, Christina, Martinelli-Boneschi, Filippo, Olsson, Tomas, Oturai, Annette, Saarela, Janna, Søndergaard, Helle, Spurkland, Anne, Taylor, Bruce, Winkelmann, Juliane, Zipp, Frauke, Haines, Jonathan, Pericak-Vance, Margaret, Spencer, Chris, Stewart, Graeme, Hafler, David, Ivinson, Adrian, Harbo, Hanne, De Jager, Philip, Compston, Alastair, McCauley, Jacob, Sawcer, Stephen, McVean, Gil, Hauser, Stephen, Oksenberg, Jorge, Baranzini, Sergio, and Gourraud, Pierre-Antoine
- Subjects
Alleles ,Epistasis ,Genetic ,Genetic Predisposition to Disease ,Histocompatibility Antigens Class II ,Humans ,Multiple Sclerosis ,Polymorphism ,Single Nucleotide - Abstract
Association studies have greatly refined the understanding of how variation within the human leukocyte antigen (HLA) genes influences risk of multiple sclerosis. However, the extent to which major effects are modulated by interactions is poorly characterized. We analyzed high-density SNP data on 17,465 cases and 30,385 controls from 11 cohorts of European ancestry, in combination with imputation of classical HLA alleles, to build a high-resolution map of HLA genetic risk and assess the evidence for interactions involving classical HLA alleles. Among new and previously identified class II risk alleles (HLA-DRB1*15:01, HLA-DRB1*13:03, HLA-DRB1*03:01, HLA-DRB1*08:01 and HLA-DQB1*03:02) and class I protective alleles (HLA-A*02:01, HLA-B*44:02, HLA-B*38:01 and HLA-B*55:01), we find evidence for two interactions involving pairs of class II alleles: HLA-DQA1*01:01-HLA-DRB1*15:01 and HLA-DQB1*03:01-HLA-DQB1*03:02. We find no evidence for interactions between classical HLA alleles and non-HLA risk-associated variants and estimate a minimal effect of polygenic epistasis in modulating major risk alleles.
- Published
- 2015
46. NEBULA is a fast negative binomial mixed model for differential or co-expression analysis of large-scale multi-subject single-cell data
- Author
-
He, Liang, Davila-Velderrain, Jose, Sumida, Tomokazu S., Hafler, David A., Kellis, Manolis, and Kulminski, Alexander M.
- Published
- 2021
- Full Text
- View/download PDF
47. Genetic basis of autoimmunity
- Author
-
Marson, Alexander, Housley, William J, and Hafler, David A
- Subjects
Biological Sciences ,Biomedical and Clinical Sciences ,Genetics ,Epidemiology ,Health Sciences ,Biotechnology ,Autoimmune Disease ,Human Genome ,Neurodegenerative ,Aetiology ,2.1 Biological and endogenous factors ,Inflammatory and immune system ,Animals ,Autoimmune Diseases ,Autoimmunity ,Databases ,Genetic ,Gene Expression Regulation ,Gene-Environment Interaction ,Genome-Wide Association Study ,Humans ,Medical and Health Sciences ,Immunology ,Biological sciences ,Biomedical and clinical sciences ,Health sciences - Abstract
Autoimmune diseases affect up to approximately 10% of the population. While rare Mendelian autoimmunity syndromes can result from monogenic mutations disrupting essential mechanisms of central and peripheral tolerance, more common human autoimmune diseases are complex disorders that arise from the interaction between polygenic risk factors and environmental factors. Although the risk attributable to most individual nucleotide variants is modest, genome-wide association studies (GWAS) have the potential to provide an unbiased view of biological pathways that drive human autoimmune diseases. Interpretation of GWAS requires integration of multiple genomic datasets including dense genotyping, cis-regulatory maps of primary immune cells, and genotyped studies of gene expression in relevant cell types and cellular conditions. Improved understanding of the genetic basis of autoimmunity may lead to a more sophisticated understanding of underlying cellular phenotypes and, eventually, novel diagnostics and targeted therapies.
- Published
- 2015
48. Genetic and epigenetic fine mapping of causal autoimmune disease variants
- Author
-
Farh, Kyle Kai-How, Marson, Alexander, Zhu, Jiang, Kleinewietfeld, Markus, Housley, William J, Beik, Samantha, Shoresh, Noam, Whitton, Holly, Ryan, Russell JH, Shishkin, Alexander A, Hatan, Meital, Carrasco-Alfonso, Marlene J, Mayer, Dita, Luckey, C John, Patsopoulos, Nikolaos A, De Jager, Philip L, Kuchroo, Vijay K, Epstein, Charles B, Daly, Mark J, Hafler, David A, and Bernstein, Bradley E
- Subjects
Immunization ,Human Genome ,Vaccine Related ,Autoimmune Disease ,Genetics ,1.1 Normal biological development and functioning ,2.1 Biological and endogenous factors ,Aetiology ,Underpinning research ,Inflammatory and immune system ,Autoimmune Diseases ,Base Sequence ,Chromatin ,Consensus Sequence ,Enhancer Elements ,Genetic ,Epigenesis ,Genetic ,Epigenomics ,Genome-Wide Association Study ,Humans ,Nucleotide Motifs ,Organ Specificity ,Polymorphism ,Single Nucleotide ,T-Lymphocytes ,Transcription Factors ,General Science & Technology - Abstract
Genome-wide association studies have identified loci underlying human diseases, but the causal nucleotide changes and mechanisms remain largely unknown. Here we developed a fine-mapping algorithm to identify candidate causal variants for 21 autoimmune diseases from genotyping data. We integrated these predictions with transcription and cis-regulatory element annotations, derived by mapping RNA and chromatin in primary immune cells, including resting and stimulated CD4(+) T-cell subsets, regulatory T cells, CD8(+) T cells, B cells, and monocytes. We find that ∼90% of causal variants are non-coding, with ∼60% mapping to immune-cell enhancers, many of which gain histone acetylation and transcribe enhancer-associated RNA upon immune stimulation. Causal variants tend to occur near binding sites for master regulators of immune differentiation and stimulus-dependent gene activation, but only 10-20% directly alter recognizable transcription factor binding motifs. Rather, most non-coding risk variants, including those that alter gene expression, affect non-canonical sequence determinants not well-explained by current gene regulatory models.
- Published
- 2015
49. Joint Modeling and Registration of Cell Populations in Cohorts of High-Dimensional Flow Cytometric Data
- Author
-
Pyne, Saumyadipta, Wang, Kui, Irish, Jonathan, Tamayo, Pablo, Nazaire, Marc-Danie, Duong, Tarn, Lee, Sharon, Ng, Shu-Kay, Hafler, David, Levy, Ronald, Nolan, Garry, Mesirov, Jill, and McLachlan, Geoffrey J.
- Subjects
Statistics - Machine Learning - Abstract
In systems biomedicine, an experimenter encounters different potential sources of variation in data such as individual samples, multiple experimental conditions, and multi-variable network-level responses. In multiparametric cytometry, which is often used for analyzing patient samples, such issues are critical. While computational methods can identify cell populations in individual samples, without the ability to automatically match them across samples, it is difficult to compare and characterize the populations in typical experiments, such as those responding to various stimulations or distinctive of particular patients or time-points, especially when there are many samples. Joint Clustering and Matching (JCM) is a multi-level framework for simultaneous modeling and registration of populations across a cohort. JCM models every population with a robust multivariate probability distribution. Simultaneously, JCM fits a random-effects model to construct an overall batch template -- used for registering populations across samples, and classifying new samples. By tackling systems-level variation, JCM supports practical biomedical applications involving large cohorts.
- Published
- 2013
- Full Text
- View/download PDF
50. Decreased RORC-dependent silencing of prostaglandin receptor EP2 induces autoimmune Th17 cells
- Author
-
Kofler, David M, Marson, Alexander, Dominguez-Villar, Margarita, Xiao, Sheng, Kuchroo, Vijay K, and Hafler, David A
- Subjects
Biomedical and Clinical Sciences ,Immunology ,Autoimmune Disease ,Clinical Research ,Genetics ,Aetiology ,2.1 Biological and endogenous factors ,Inflammatory and immune system ,Adult ,Animals ,Autoimmunity ,Case-Control Studies ,Dinoprostone ,Down-Regulation ,Female ,Gene Knockdown Techniques ,Gene Silencing ,Humans ,Male ,Mice ,Mice ,Inbred C57BL ,Mice ,Knockout ,Middle Aged ,Models ,Immunological ,Multiple Sclerosis ,Nuclear Receptor Subfamily 1 ,Group F ,Member 3 ,Phenotype ,Promoter Regions ,Genetic ,Receptors ,Prostaglandin E ,EP2 Subtype ,Signal Transduction ,Th17 Cells ,Medical and Health Sciences ,Biological sciences ,Biomedical and clinical sciences ,Health sciences - Abstract
Prostaglandin E2 (PGE2) promotes Th17 expansion while otherwise inhibiting other CD4+ T cell subsets. Here, we identified a PGE2-dependent pathway that induces pathogenic Th17 cells in autoimmune disease and is regulated by the transcription factor RORC. Compared with other CD4+ cell types from healthy subjects, there is a surprising lack of the prostaglandin receptor EP2 on Th17 cells; therefore, we examined the hypothesis that RORγt, which is highly expressed in Th17 cells, mediates EP2 downregulation. Chromatin immunoprecipitation followed by DNA sequencing revealed that RORγt binds directly to Ptger2 (the gene encoding EP2 receptor) in Th17 cells isolated from WT mice. In Th17 cells isolated from humans, RORC repressed EP2 by directly silencing PTGER2 transcription, and knock down of RORC restored EP2 expression in Th17 cells. Compared with Th17 cells from healthy individuals, Th17 cells from patients with MS exhibited reduced RORC binding to the PTGER2 promoter region, resulting in higher EP2 levels and increased expression of IFN-γ and GM-CSF. Finally, overexpression of EP2 in Th17 cells from healthy individuals induced a specific program of inflammatory gene transcription that produced a pathogenic Th17 cell phenotype. These findings reveal that RORC directly regulates the effects of PGE2 on Th17 cells, and dysfunction of this pathway induces a pathogenic Th17 cell phenotype.
- Published
- 2014
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