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LitCovid-PD-FMA-UBERON

LitCovid-PD-UBERON

{"project":"LitCovid-PD-UBERON","denotations":[{"id":"T23","span":{"begin":283,"end":300},"obj":"Body_part"},{"id":"T24","span":{"begin":3488,"end":3493},"obj":"Body_part"},{"id":"T25","span":{"begin":3539,"end":3544},"obj":"Body_part"},{"id":"T26","span":{"begin":3825,"end":3830},"obj":"Body_part"},{"id":"T27","span":{"begin":5083,"end":5096},"obj":"Body_part"},{"id":"T28","span":{"begin":5453,"end":5458},"obj":"Body_part"},{"id":"T29","span":{"begin":5907,"end":5912},"obj":"Body_part"}],"attributes":[{"id":"A23","pred":"uberon_id","subj":"T23","obj":"http://purl.obolibrary.org/obo/UBERON_0000065"},{"id":"A24","pred":"uberon_id","subj":"T24","obj":"http://purl.obolibrary.org/obo/UBERON_0000178"},{"id":"A25","pred":"uberon_id","subj":"T25","obj":"http://purl.obolibrary.org/obo/UBERON_0000178"},{"id":"A26","pred":"uberon_id","subj":"T26","obj":"http://purl.obolibrary.org/obo/UBERON_0000178"},{"id":"A27","pred":"uberon_id","subj":"T27","obj":"http://purl.obolibrary.org/obo/UBERON_0034922"},{"id":"A28","pred":"uberon_id","subj":"T28","obj":"http://purl.obolibrary.org/obo/UBERON_0000178"},{"id":"A29","pred":"uberon_id","subj":"T29","obj":"http://purl.obolibrary.org/obo/UBERON_0001977"}],"text":"Discussion\nThe T and B cell response to SARS-CoV2 infection remains poorly understood. Some studies suggest an overaggressive immune response leading to immunopathology (51) whereas others suggest T cell exhaustion or dysfunction (12–14). Autopsies revealed high virus levels in the respiratory tract and other tissues (52), suggesting ineffective immune responses. Nevertheless, non-hospitalized subjects who recovered from COVID-19 had evidence of virus-specific T cell memory (53). SARS-CoV2-specific antibodies are also found in convalescent subjects and patients are currently being treated with convalescent plasma therapy (30, 54). However, COVID-19 ICU patients have SARS-CoV2-specific antibodies (30), raising the question of why patients with these antibody responses are not controlling disease. In general, these studies report on single patients or small cohorts and comprehensive deep immune profiling of a large number of COVID-19 hospitalized patients is limiting. Such knowledge would address the critical question of whether there is a common profile of immune dysfunction in critically ill patients. Such data would also help guide testing of therapeutics to enhance, inhibit, or otherwise tailor the immune response in COVID-19 patients.\nTo interrogate the immune response patterns of COVID-19 hospitalized patients, we studied a cohort of ~125 COVID-19 patients. We used high dimensional flow cytometry to perform deep immune profiling of individual B and T cell populations, with temporal analysis of immune changes during infection, and combined this profiling with extensive clinical data to understand the relationships between immune responses to SARS-CoV2 and disease severity. Using this approach, we made several key findings. First, a defining feature of COVID-19 disease in hospitalized patients was heterogeneity of the immune response. Many COVID-19 patients displayed robust CD8 T cell and/or CD4 T cell activation and proliferation and PB responses, though a considerable subgroup of patients (~20%) had minimal detectable response compared to controls. Furthermore, even within those patients who mounted detectable B and T cell responses during COVID-19 disease, the immune characteristics of this response were heterogeneous. By deep immune profiling, we identified three immunotypes in hospitalized COVID-19 patients including: (1) patients with robust activation and proliferation of CD4 T cells, relative lack of cTfh, together with modest activation of TEMRA-like as well as highly activated or exhausted CD8 T cells and a signature of T-bet+ PB; (2) Tbetbright effector-like CD8 T cell responses, less robust CD4 T cell responses, and Ki67+ PB and memory B cells; and (3) an immunotype largely lacking detectable lymphocyte response to infection, suggesting a failure of immune activation. UMAP embedding further resolved the T cell activation immunotype, suggesting a link between CD4 T cell activation, Immunotype 1, and increased severity score. Although differences in age and race existed between the cohorts and could impact some immune variables, the major UMAP relationships were preserved even when correcting for these variables. Thus, these immunotypes may reflect fundamental differences in the ways patients respond to SARS-CoV2 infection.\nA second key observation from these studies was the robust PB response. Some patients had PB frequencies rivaling those found in acute Ebola or Dengue infection (34, 42, 43, 55). Furthermore, blood PB frequencies are typically correlated with blood activated cTfh responses (40). However, in COVID-19 patients, this relationship between PB and activated cTfh was weak. The lack of relationship between these two cell types in this disease could be due to T cell-independent B cell responses, lack of activated cTfh in peripheral blood at this time point, or lower CXCR5 expression observed across lymphocyte populations, making it more difficult to identify cTfh. Indeed, activated (CD38+HLA-DR+) CD4 T cells could play a role in providing B cell help, perhaps as part of an extrafollicular response, but such a connection was also not robust in the current data. Most ICU patients made SARS-CoV2-specific antibodies, suggesting that at least part of the PB response was antigen-specific. Indeed, the cTfh response did correlate with antibodies suggesting that at least some of the humoral response is targeted against the virus. Future studies will be needed to address the antigen specificity, ontogeny, and role in pathogenesis for these robust PB responses.\nA striking feature of some patients with strong T and B cell activation and proliferation was the durability of this response. This T and B activation was interesting considering clinical lymphopenia in many patients. This lymphopenia, however, was preferential for CD8 T cells. It may be notable that such focal lymphopenia preferentially affecting CD8 T cells is also a feature of acute Ebola infection of macaques and is associated with CD95 expression and severe disease (55). Indeed CD95 was associated with activated T cell clusters in COVID-19 disease. Nevertheless, the frequency of the KI67+ or CD38+HLA-DR+ CD8 and CD4 T cell responses in COVID-19 patients was similar in magnitude to other acute viral infections or live attenuated vaccines in humans (47–49). However, during many acute viral infections, peak CD8 or CD4 T cell responses and the window of detectable PB in peripheral blood are relatively short (43, 56, 57). The stability of CD8 and CD4 T cell activation and PB responses during COVID-19 disease suggests a prolonged period of peak immune responses at the time of hospitalization or perhaps a failure to appropriately down-regulate responses in some patients. These ideas would fit with an overaggressive immune response and/or “cytokine storm” (2) in this subset of patients. Indeed, in some patients, we found elevated serum cytokines and that stimulation of T cells in vitro provoked cytokines and chemokines capable of activating and recruiting myeloid cells. A key question will be how to identify these patients for selected immune regulatory treatment while avoiding treating patients with already weak T and B cell responses.\nAn additional major finding was the ability to connect immune features not only to disease severity at the time of sampling but also to the trajectory of disease severity change over time. Using correlative analyses, we observed relationships between features of the different immunotypes, patient comorbidities, and clinical features of COVID-19 disease. By integrating ~200 immune features with extensive clinical data, disease severity scores, and temporal changes, we built an integrated computational model that connected patient immune response phenotype to disease severity. Moreover, this UMAP embedding approach allowed us to connect these integrated immune signatures back to specific clinically measurable features of disease. The integrated immune signatures captured by Components 1 and 2 in this UMAP model provided support for the notion of Immunotypes 1 and 2. These analyses suggested that Immunotype 1, comprised of robust CD4 T cell activation, paucity of cTfh with proliferating effector/exhausted CD8 T cells and T-bet+ PB involvement, was connected to more severe disease whereas Immunotype 2, characterized by more traditional effector CD8 T cells subsets, less CD4 T cell activation and proliferating PB and memory B cells, was better captured by UMAP Component 2. Immunotype 3, in which minimal lymphocyte activation response was observed, may represent ~20% of COVID-19 patients and is a potentially important scenario to consider as patients who may have failed to mount a robust antiviral T and B cell response. This UMAP integrated modeling approach could be improved in the future with additional data on other immune cell types and/or comprehensive data for circulating inflammatory mediators for all patients. Nevertheless, these findings provoke the idea of the tailoring clinical treatments or future immune-based clinical trials to patients whose immunotype suggests greater potential benefit.\nRespiratory viral infections can cause pathology as a result of an immune response that is too weak and results in virus-induced pathology, or an immune response that is too strong and leads to immunopathology (58). Our data suggest that the immune response of hospitalized COVID-19 patients may fall across this spectrum of immune response patterns, presenting as distinct immunotypes linked to clinical features, disease severity, and temporal changes in response and pathogenesis. This study provides a compendium of immune response data and also an integrated framework as a “map” for connecting immune features to disease. By localizing patients on an immune topology map built on this dataset, we can begin to infer which types of therapeutic interventions may be most useful in specific patients."}

LitCovid-PubTator

LitCovid-PD-MONDO

LitCovid-PD-CLO

LitCovid-PD-CHEBI

LitCovid-PD-GO-BP

LitCovid-PD-HP

{"project":"LitCovid-PD-HP","denotations":[{"id":"T15","span":{"begin":4746,"end":4757},"obj":"Phenotype"},{"id":"T16","span":{"begin":4781,"end":4792},"obj":"Phenotype"},{"id":"T17","span":{"begin":4871,"end":4882},"obj":"Phenotype"},{"id":"T18","span":{"begin":5815,"end":5829},"obj":"Phenotype"}],"attributes":[{"id":"A15","pred":"hp_id","subj":"T15","obj":"http://purl.obolibrary.org/obo/HP_0001888"},{"id":"A16","pred":"hp_id","subj":"T16","obj":"http://purl.obolibrary.org/obo/HP_0001888"},{"id":"A17","pred":"hp_id","subj":"T17","obj":"http://purl.obolibrary.org/obo/HP_0001888"},{"id":"A18","pred":"hp_id","subj":"T18","obj":"http://purl.obolibrary.org/obo/HP_0033041"}],"text":"Discussion\nThe T and B cell response to SARS-CoV2 infection remains poorly understood. Some studies suggest an overaggressive immune response leading to immunopathology (51) whereas others suggest T cell exhaustion or dysfunction (12–14). Autopsies revealed high virus levels in the respiratory tract and other tissues (52), suggesting ineffective immune responses. Nevertheless, non-hospitalized subjects who recovered from COVID-19 had evidence of virus-specific T cell memory (53). SARS-CoV2-specific antibodies are also found in convalescent subjects and patients are currently being treated with convalescent plasma therapy (30, 54). However, COVID-19 ICU patients have SARS-CoV2-specific antibodies (30), raising the question of why patients with these antibody responses are not controlling disease. In general, these studies report on single patients or small cohorts and comprehensive deep immune profiling of a large number of COVID-19 hospitalized patients is limiting. Such knowledge would address the critical question of whether there is a common profile of immune dysfunction in critically ill patients. Such data would also help guide testing of therapeutics to enhance, inhibit, or otherwise tailor the immune response in COVID-19 patients.\nTo interrogate the immune response patterns of COVID-19 hospitalized patients, we studied a cohort of ~125 COVID-19 patients. We used high dimensional flow cytometry to perform deep immune profiling of individual B and T cell populations, with temporal analysis of immune changes during infection, and combined this profiling with extensive clinical data to understand the relationships between immune responses to SARS-CoV2 and disease severity. Using this approach, we made several key findings. First, a defining feature of COVID-19 disease in hospitalized patients was heterogeneity of the immune response. Many COVID-19 patients displayed robust CD8 T cell and/or CD4 T cell activation and proliferation and PB responses, though a considerable subgroup of patients (~20%) had minimal detectable response compared to controls. Furthermore, even within those patients who mounted detectable B and T cell responses during COVID-19 disease, the immune characteristics of this response were heterogeneous. By deep immune profiling, we identified three immunotypes in hospitalized COVID-19 patients including: (1) patients with robust activation and proliferation of CD4 T cells, relative lack of cTfh, together with modest activation of TEMRA-like as well as highly activated or exhausted CD8 T cells and a signature of T-bet+ PB; (2) Tbetbright effector-like CD8 T cell responses, less robust CD4 T cell responses, and Ki67+ PB and memory B cells; and (3) an immunotype largely lacking detectable lymphocyte response to infection, suggesting a failure of immune activation. UMAP embedding further resolved the T cell activation immunotype, suggesting a link between CD4 T cell activation, Immunotype 1, and increased severity score. Although differences in age and race existed between the cohorts and could impact some immune variables, the major UMAP relationships were preserved even when correcting for these variables. Thus, these immunotypes may reflect fundamental differences in the ways patients respond to SARS-CoV2 infection.\nA second key observation from these studies was the robust PB response. Some patients had PB frequencies rivaling those found in acute Ebola or Dengue infection (34, 42, 43, 55). Furthermore, blood PB frequencies are typically correlated with blood activated cTfh responses (40). However, in COVID-19 patients, this relationship between PB and activated cTfh was weak. The lack of relationship between these two cell types in this disease could be due to T cell-independent B cell responses, lack of activated cTfh in peripheral blood at this time point, or lower CXCR5 expression observed across lymphocyte populations, making it more difficult to identify cTfh. Indeed, activated (CD38+HLA-DR+) CD4 T cells could play a role in providing B cell help, perhaps as part of an extrafollicular response, but such a connection was also not robust in the current data. Most ICU patients made SARS-CoV2-specific antibodies, suggesting that at least part of the PB response was antigen-specific. Indeed, the cTfh response did correlate with antibodies suggesting that at least some of the humoral response is targeted against the virus. Future studies will be needed to address the antigen specificity, ontogeny, and role in pathogenesis for these robust PB responses.\nA striking feature of some patients with strong T and B cell activation and proliferation was the durability of this response. This T and B activation was interesting considering clinical lymphopenia in many patients. This lymphopenia, however, was preferential for CD8 T cells. It may be notable that such focal lymphopenia preferentially affecting CD8 T cells is also a feature of acute Ebola infection of macaques and is associated with CD95 expression and severe disease (55). Indeed CD95 was associated with activated T cell clusters in COVID-19 disease. Nevertheless, the frequency of the KI67+ or CD38+HLA-DR+ CD8 and CD4 T cell responses in COVID-19 patients was similar in magnitude to other acute viral infections or live attenuated vaccines in humans (47–49). However, during many acute viral infections, peak CD8 or CD4 T cell responses and the window of detectable PB in peripheral blood are relatively short (43, 56, 57). The stability of CD8 and CD4 T cell activation and PB responses during COVID-19 disease suggests a prolonged period of peak immune responses at the time of hospitalization or perhaps a failure to appropriately down-regulate responses in some patients. These ideas would fit with an overaggressive immune response and/or “cytokine storm” (2) in this subset of patients. Indeed, in some patients, we found elevated serum cytokines and that stimulation of T cells in vitro provoked cytokines and chemokines capable of activating and recruiting myeloid cells. A key question will be how to identify these patients for selected immune regulatory treatment while avoiding treating patients with already weak T and B cell responses.\nAn additional major finding was the ability to connect immune features not only to disease severity at the time of sampling but also to the trajectory of disease severity change over time. Using correlative analyses, we observed relationships between features of the different immunotypes, patient comorbidities, and clinical features of COVID-19 disease. By integrating ~200 immune features with extensive clinical data, disease severity scores, and temporal changes, we built an integrated computational model that connected patient immune response phenotype to disease severity. Moreover, this UMAP embedding approach allowed us to connect these integrated immune signatures back to specific clinically measurable features of disease. The integrated immune signatures captured by Components 1 and 2 in this UMAP model provided support for the notion of Immunotypes 1 and 2. These analyses suggested that Immunotype 1, comprised of robust CD4 T cell activation, paucity of cTfh with proliferating effector/exhausted CD8 T cells and T-bet+ PB involvement, was connected to more severe disease whereas Immunotype 2, characterized by more traditional effector CD8 T cells subsets, less CD4 T cell activation and proliferating PB and memory B cells, was better captured by UMAP Component 2. Immunotype 3, in which minimal lymphocyte activation response was observed, may represent ~20% of COVID-19 patients and is a potentially important scenario to consider as patients who may have failed to mount a robust antiviral T and B cell response. This UMAP integrated modeling approach could be improved in the future with additional data on other immune cell types and/or comprehensive data for circulating inflammatory mediators for all patients. Nevertheless, these findings provoke the idea of the tailoring clinical treatments or future immune-based clinical trials to patients whose immunotype suggests greater potential benefit.\nRespiratory viral infections can cause pathology as a result of an immune response that is too weak and results in virus-induced pathology, or an immune response that is too strong and leads to immunopathology (58). Our data suggest that the immune response of hospitalized COVID-19 patients may fall across this spectrum of immune response patterns, presenting as distinct immunotypes linked to clinical features, disease severity, and temporal changes in response and pathogenesis. This study provides a compendium of immune response data and also an integrated framework as a “map” for connecting immune features to disease. By localizing patients on an immune topology map built on this dataset, we can begin to infer which types of therapeutic interventions may be most useful in specific patients."}

LitCovid-sentences

2_test

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PMC:7402624 / 52519-61471 JSON TXT