multi cohort analysis of host immune response identifies conserved protective and CORD-Papers-2021-10-25 (Version 1)

Title: Multi-cohort analysis of host immune response identifies conserved protective and detrimental modules associated with severity across viruses
Abstract: Viral infections induce a conserved host response distinct from bacterial infections. We hypothesized that the conserved response is associated with disease severity and is distinct between patients with different outcomes. To test this, we integrated 4,780 blood transcriptome profiles from patients aged 0 to 90 years infected with one of 16 viruses, including SARS-CoV-2, Ebola, chikungunya, and influenza, across 34 cohorts from 18 countries, and single-cell RNA sequencing profiles of 702,970 immune cells from 289 samples across three cohorts. Severe viral infection was associated with increased hematopoiesis, myelopoiesis, and myeloid-derived suppressor cells. We identified protective and detrimental gene modules that defined distinct trajectories associated with mild versus severe outcomes. The interferon response was decoupled from the protective host response in patients with severe outcomes. These findings were consistent, irrespective of age and virus, and provide insights to accelerate the development of diagnostics and host-directed therapies to improve global pandemic preparedness.
Published: 3/24/2021
Journal: Immunity
DOI: 10.1016/j.immuni.2021.03.002
DOI_URL: http://doi.org/10.1016/j.immuni.2021.03.002
Author Name: Zheng, Hong
Author link: https://covid19-data.nist.gov/pid/rest/local/author/zheng_hong
Author Name: Rao, Aditya M
Author link: https://covid19-data.nist.gov/pid/rest/local/author/rao_aditya_m
Author Name: Dermadi, Denis
Author link: https://covid19-data.nist.gov/pid/rest/local/author/dermadi_denis
Author Name: Toh, Jiaying
Author link: https://covid19-data.nist.gov/pid/rest/local/author/toh_jiaying
Author Name: Murphy Jones, Lara
Author link: https://covid19-data.nist.gov/pid/rest/local/author/murphy_jones_lara
Author Name: Donato, Michele
Author link: https://covid19-data.nist.gov/pid/rest/local/author/donato_michele
Author Name: Liu, Yiran
Author link: https://covid19-data.nist.gov/pid/rest/local/author/liu_yiran
Author Name: Su, Yapeng
Author link: https://covid19-data.nist.gov/pid/rest/local/author/su_yapeng
Author Name: Dai, Cheng L
Author link: https://covid19-data.nist.gov/pid/rest/local/author/dai_cheng_l
Author Name: Kornilov, Sergey A
Author link: https://covid19-data.nist.gov/pid/rest/local/author/kornilov_sergey_a
Author Name: Karagiannis, Minas
Author link: https://covid19-data.nist.gov/pid/rest/local/author/karagiannis_minas
Author Name: Marantos, Theodoros
Author link: https://covid19-data.nist.gov/pid/rest/local/author/marantos_theodoros
Author Name: Hasin Brumshtein, Yehudit
Author link: https://covid19-data.nist.gov/pid/rest/local/author/hasin_brumshtein_yehudit
Author Name: He, Yudong D
Author link: https://covid19-data.nist.gov/pid/rest/local/author/he_yudong_d
Author Name: Giamarellos Bourboulis, Evangelos J
Author link: https://covid19-data.nist.gov/pid/rest/local/author/giamarellos_bourboulis_evangelos_j
Author Name: Heath, James R
Author link: https://covid19-data.nist.gov/pid/rest/local/author/heath_james_r
Author Name: Khatri, Purvesh
Author link: https://covid19-data.nist.gov/pid/rest/local/author/khatri_purvesh
sha: 78fa25bda40519dfc8daf36f0608798e41c4ecf0
license: no-cc
license_url: [no creative commons license associated]
source_x: Elsevier; Medline; PMC; WHO
source_x_url: https://www.elsevier.com/https://www.medline.com/https://www.ncbi.nlm.nih.gov/pubmed/https://www.who.int/
pubmed_id: 33765435
pubmed_id_url: https://www.ncbi.nlm.nih.gov/pubmed/33765435
pmcid: PMC7988739
pmcid_url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7988739
url: https://www.sciencedirect.com/science/article/pii/S107476132100114X https://doi.org/10.1016/j.immuni.2021.03.002 https://api.elsevier.com/content/article/pii/S107476132100114X https://www.ncbi.nlm.nih.gov/pubmed/33765435/
has_full_text: TRUE
Keywords Extracted from Text Content: endosomal vesicles CTSG IFN-induced monocytes PI3K type I IFN anti-inflammatory macrophages HLA Coronavirus Seurat (Figures 2A-2D Figure 6A plasma Figures 2J-2K nasopharyngeal CMV participants UMAP2 Figures S5A adenovirus human rhinovirus À1.16 T antigen macrophages Vallania Andres-Terre CEA-CAM8 Whiskers Figure 1C dSpace matrix anti-IL6 Lead Contact dSpace Dermadi SARS-CoV-2, 1 polymorphonuclear myeloid-derived CD14 monocytes GSE103842 GSE38900 granulocytes CD14+ monocytes Figure 5D cDCs IFNG PBMC samples patient samples TCEAL9 AZU1 MAFB UBE2L6, NAPA lymphoid cells C GE Figure S7D Figure 5H 702,970 immune cells convalescent patients À0.85 human immunodeficiency NK cell Figure 5M IFITM3 nasopharyngeal samples Figures 2A CASP10 76,929 immune cells transmembrane EBV À0.48 between-dataset CONormalization CD16 CCL2 GC ITGAM TXN 3.38e-02 5O Fujii, 2019 immune cell H3N2 myeloid lineage type I IFN-induced class II HLA IFITM1 Figure 3C human HSPCs CAMP Figure 3B S7B arginase Figure S4C ArrayExpress lymphoid compartments Figure 1A S4F Giamarellos-Bourboulis 8.97e-05 Figure S2A IFN Seq-Well 6.16e-15 cell surface phosphoinositide-3-Kinase II IFN receptors 3.6e-07 alarmins Figure 1E B cells RSV pDCs COVID-19 patients S5D Figure S1A Figures S4E UMAP1 Figures S2A-S2C S100A9 line immune cells DNA myeloid-derived killer cell lectin-like receptor DEFA4 |r| 3 neutrophils blood samples 5.13e-08 S4D low-density samples Figures 5J-5K OLR1 ROS/RNS ENA KLRD1 5e-05 TMEM123 Figure S4A proliferating T/NK cells cytotoxic cells children hepatitis B dendritic cells NK cellspecific T cells PIK3R1 S5B Bekerman monocytic MDSCs GSE77087 M-MDSCs VRK2 NK cells pkhatri@stanford.edu Figures S4G cell monocytes/macrophages IFN-gamma cellular Figure S4B https://metasignature.stanford.edu mDCs LCN2 ORM1 multiorgan 1,509 samples Figure 1D Violin plots Cellular Figures S4C 6E 557,240 immune cells HSPCs anti-inflammatory M2b macrophages tSpace BCAT1 antigen-presenting cells Venet S2C KLRD1-expressing NK cells 1,507 À0.88 NK cell-associated dPC2 PMN-MDSCs KIF15 Figure 6C type I and II IFN receptors lymphocytes H1N1 influenza UMAP Figures S2B Immune cells cells coronavirus 2 S4H monocyte myeloid peripheral blood CEP55 Myeloid cells H1N1 Figures S5C anti-inflammatory (M2) macrophages lymphoid HLA-DPB1 coronavirus antiinflammatory macrophages blood PBMCs Module 3 host cell À0.31 plasma samples oxygen MetaSignature database cell membrane Figure S4D HCs IFITM2 Figure 5G Biotechnology Information GSE101702 Silvin IFNinduced transmembrane (IFITM) genes myeloid dendritic cells CD14 GSE66099 dysfunctional myeloid cells HMMR IFN-stimulated conTrols peripheral blood mononuclear cells HLA class II myeloid cells CD16+ monocytes convalescent SARS-CoV-2 Zika HLA-DR patients CASP7 immunoStates IL-6 T cell OASL COVID-19 tissue Figure 2G CASP3 ARG1 Figures 5N NK ISGs IFITMs KLRG1 LOX-1 type I interferon SARS-CoV-2 MDSCs patient 1.8e-05 IL-4R PRC1 plasma |effects Andres-Terre R.Module PRJNA390289 GENCODE v.2.2.2 hg38 Qualimap v32 cell SARS-CoV-2 v0.6.5 Dobin patient Inflammatix human peripheral blood
Extracted Text Content in Record: First 5000 Characters:Viral infections induce a conserved host response distinct from bacterial infections, but whether this conserved response distinguishes severity is unclear. Zheng et al. analyzed >5000 bulk and single-cell transcriptome profiles from patients infected with one of 16 viruses, including SARS-CoV-2, Ebola, and chikungunya. They identified protective and detrimental host response modules that distinguish patients with mild or severe outcomes. Outbreaks of infectious diseases globally have been increasing steadily over the last 40 years (Christiansen, 2018) . The first two decades of the 21 st century have been marked by seven outbreaks of novel viruses, including severe acute respiratory syndrome coronavirus (SARS-CoV-1), H1N1 influenza, Middle East Respiratory Syndrome Coronavirus (MERS-CoV), chikungunya, Ebola, Zika, and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Four of these outbreaks resulted in pandemics in the last decade (Morens and Fauci, 2020) . With each outbreak, a typical approach has been to pursue a pathogen-specific strategy. With novel viruses, when our biological understanding of the causative agent is poor, the acquisition of sufficient knowledge to manage the disease is time-consuming and expensive. Adopting a pathogen-agnostic strategy, such as through the identi-fication of an underlying conserved host response across patient populations, could greatly accelerate the development of diagnostics and therapies to manage future emerging outbreaks. For instance, most approved antiviral drugs are effective against a small number of viruses, and highly susceptible to resistance (Bekerman and Einav, 2015) . In contrast, identifying conserved host biology, such as host proteins required by multiple viruses, could be used to develop broad-spectrum antivirals. Similarly, a conserved host response to viral infections could be used to develop diagnostics and prognostics. Several studies have repeatedly demonstrated the utility of the host immune response to pathogens to accurately diagnose the presence and type of infections (Andres-Terre et al., Sweeney et al., 2015; 2016b; Mayhew et al., 2020) . We have previously identified a conserved host response to distinguish bacterial and viral infections (Andres-Terre et al., Sweeney et al., 2015; 2016b) . We have also demonstrated that the conserved host immune response to infection is detected earlier than symptom onset (Andres-Terre et al., 2015; Sweeney et al., 2016a; Warsinske et al., 2018; Gupta et al., 2020; Turner et al., 2020) . Here, we hypothesized that our previously described conserved host response signature to respiratory viral infections, called the Meta-Virus Signature (MVS) (Andres-Terre et al., 2015) , is also conserved in viral infections that cause severe disease, including Ebola, SARS-CoV-2, and others, and it could be used to identify common genes associated with detrimental and protective host immune responses, irrespective of the virus. We tested these hypotheses by integrating 34 independent cohorts comprising 4,780 blood transcriptome profiles and single-cell RNA-seq profiles of 702,970 immune cells from 289 samples from healthy controls (HCs) and patients with acute viral infection. We found that the MVS is (1) present in SARS-CoV-2, Ebola, chikungunya, influenza, and other viruses, (2) correlated with severity, and (3) predominantly expressed in myeloid cells. Using a patient trajectory differentiation method, we found that patients with mild or severe viral infection follow different trajectories comprised of four gene modules corresponding to protective and detrimental host immune responses. We defined the severe-or-mild (SoM) score that accurately distinguished patients with non-severe and severe outcomes. By leveraging the biological, clinical, and technical heterogeneity across data, we provide strong evidence of a conserved host immune response to acute viral infection, irrespective of the virus. Further analysis of these conserved host response modules could lead to the development of diagnostics, prognostics, and host-directed therapies for a broad spectrum of viruses that could facilitate risk stratification and targeted treatment of patients during the current pandemic and in novel outbreaks that will inevitably arise in the future. We searched the public repositories for blood transcriptome profiles from patients with viral infection (STAR Methods). After excluding datasets used to discover the MVS previously, we identified 26 datasets composed of 4,780 samples from patients across 18 countries infected with at least one of 16 viruses (Figure 1A, Table S1 , and Data S1). Overall, these datasets included a broad spectrum of biological, clinical, and technical heterogeneity represented by blood samples profiled from children and adults infected with a virus using either microarray or RNA sequencing. We assigned a standardized severity category to each of the 4,780 samples ( Figure 1A and STAR Methods).
Keywords Extracted from PMC Text: KLRG1 's Bongen lambda Dist R Figures S4E conTrols IFITM2 Qualimap Figure 1D Module 3 dense matrix CD14 monocytes ARG1 CASP10 Coronavirus Figures 2J–2K IFITMs cellular NK cell dSpace peripheral blood mononuclear cells A–2D cell pkhatri@stanford.edu GSE103842 S5B type I and II IFN receptors MDSCs line convalescent patients MetaSignature database 5.13e-08 anti-inflammatory (M2) macrophages Lead Contact children ISGs Figure S6B Figures S4G CASP7 T C Alevin Figures S5A Fujii, 2019 H1N1 influenza participants CCL2 HCs, 2 CD66B SARS-CoV-2 granulocytes NK Figures S5C anti-inflammatory macrophages 5e-05 peripheral blood Inflammatix princurve R Figure S1A 6.16e-15 702,970 myeloid cells AZU1 Figure 5A GSE101702 BCAT1 Figure 6 II IFN receptors CTSG MAFB S4H nasopharyngeal immune cells GSE38900 NCBI GEO PRJNA252396 https://metasignature.stanford.edu CONormalization Figure S2A KNN SingleR Figures 5J–5K Figure 1A CD14+ monocytes HSPCs immunoStates patient monocytes TCEAL9 Biotechnology Information human LCN2 EBV coronavirus immune cell Figure 5H CEP55 GE human rhinovirus MetaIntegrator R tPC1 tSpace Figure 6C antigen Figures S4C Figure 3B RSV type I IFN-induced cDCs HMMR PRJNA390289 cell surface IFN-gamma TMEM123 PMN-MDSCs blood samples CD16 v32 IL-4R Immune cells myeloid GENCODE Figure 1E hepatitis B COVID-19 patients Cellular monocytic MDSCs myeloid-derived ENA −0.88 dPC2 Figure S4D IFITM1 ArrayExpress CAMP OASL LOX-1 |effects H3N2 tPC2 samples CMV 5O H1N1 NCBI virus database Dermadi BCL6 76,929 immune cells S4D Figures 5N 557,240 immune cells CD16+ monocytes UBE2L6, NAPA PI3K lymphoid cells ORM1 mDCs HCs Figures 2A Figure S4A Figure 5D SARS-CoV-2, 1 −0.85 plasma p ≤ 1.8e-05 M-MDSCs GSE77087 neutrophils DNA UMAP Figure 2G space matrix macrophages ITGAM PRC1 DEFA4 IFN-stimulated anti-inflammatory M2b macrophages dSpace matrix 6E IFN-induced transmembrane (IFITM) 3.6e-07 low-density Bekerman B cells 1,509 samples S4F IFITM3 dendritic cells CEACAM8 polymorphonuclear myeloid-derived proliferating T/NK cells S2D Seq-Well nasopharyngeal samples VRK2 GC TXN matrix AUROC30.929 GSE66099 OLR1 coronavirus 2 lymphocytes " human immunodeficiency myeloid lineage S7B Figure S7D v0.6.5 v1.2.1 −0.48 p ≤ 1e-06 Figure 1C UMAP1 Zika HLA-DPB1 NK cells plasma samples S5D adenovirus HLA class II CD14 Figure 5M type I IFN multiorgan Figure S4B Figure 5G pro- killer cell lectin-like receptor v.2.2.2 PRJNA507472 Andres-Terre hg38 IFNG KLRD1 myeloid dendritic cells patients KIF15 monocyte T cells phosphoinositide-3-Kinase blood Figures S2A–S2C UMAP2 cell membrane oxygen 1,507 Figure 3C 8.97e-05 SRR4888654 Dobin |r|30.43 Princurve patient samples S2C cells PBMC samples PIK3R1 NK cell-associated Vallania 3.38e-02 Figures 2I–2K K nearest neighbors type I interferon between-dataset Cell convalescent SARS-CoV-2 IFN CASP3 PBMCs
Extracted PMC Text Content in Record: First 5000 Characters:Outbreaks of infectious diseases globally have been increasing steadily over the last 40 years (Christiansen, 2018). The first two decades of the 21st century have been marked by seven outbreaks of novel viruses, including severe acute respiratory syndrome coronavirus (SARS-CoV-1), H1N1 influenza, Middle East Respiratory Syndrome Coronavirus (MERS-CoV), chikungunya, Ebola, Zika, and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Four of these outbreaks resulted in pandemics in the last decade (Morens and Fauci, 2020). With each outbreak, a typical approach has been to pursue a pathogen-specific strategy. With novel viruses, when our biological understanding of the causative agent is poor, the acquisition of sufficient knowledge to manage the disease is time-consuming and expensive. Adopting a pathogen-agnostic strategy, such as through the identification of an underlying conserved host response across patient populations, could greatly accelerate the development of diagnostics and therapies to manage future emerging outbreaks. For instance, most approved antiviral drugs are effective against a small number of viruses, and highly susceptible to resistance (Bekerman and Einav, 2015). In contrast, identifying conserved host biology, such as host proteins required by multiple viruses, could be used to develop broad-spectrum antivirals. Similarly, a conserved host response to viral infections could be used to develop diagnostics and prognostics. Several studies have repeatedly demonstrated the utility of the host immune response to pathogens to accurately diagnose the presence and type of infections (Andres-Terre et al., 2015; Sweeney et al., 2015; 2016b; Mayhew et al., 2020). We have previously identified a conserved host response to distinguish bacterial and viral infections (Andres-Terre et al., 2015; Sweeney et al., 2015; 2016b). We have also demonstrated that the conserved host immune response to infection is detected earlier than symptom onset (Andres-Terre et al., 2015; Sweeney et al., 2016a; Warsinske et al., 2018; Gupta et al., 2020; Turner et al., 2020). Here, we hypothesized that our previously described conserved host response signature to respiratory viral infections, called the Meta-Virus Signature (MVS) (Andres-Terre et al., 2015), is also conserved in viral infections that cause severe disease, including Ebola, SARS-CoV-2, and others, and it could be used to identify common genes associated with detrimental and protective host immune responses, irrespective of the virus. We tested these hypotheses by integrating 34 independent cohorts comprising 4,780 blood transcriptome profiles and single-cell RNA-seq profiles of 702,970 immune cells from 289 samples from healthy controls (HCs) and patients with acute viral infection. We found that the MVS is (1) present in SARS-CoV-2, Ebola, chikungunya, influenza, and other viruses, (2) correlated with severity, and (3) predominantly expressed in myeloid cells. Using a patient trajectory differentiation method, we found that patients with mild or severe viral infection follow different trajectories comprised of four gene modules corresponding to protective and detrimental host immune responses. We defined the severe-or-mild (SoM) score that accurately distinguished patients with non-severe and severe outcomes. By leveraging the biological, clinical, and technical heterogeneity across data, we provide strong evidence of a conserved host immune response to acute viral infection, irrespective of the virus. Further analysis of these conserved host response modules could lead to the development of diagnostics, prognostics, and host-directed therapies for a broad spectrum of viruses that could facilitate risk stratification and targeted treatment of patients during the current pandemic and in novel outbreaks that will inevitably arise in the future. We searched the public repositories for blood transcriptome profiles from patients with viral infection (STAR Methods). After excluding datasets used to discover the MVS previously, we identified 26 datasets composed of 4,780 samples from patients across 18 countries infected with at least one of 16 viruses (Figure 1 A, Table S1, and Data S1). Overall, these datasets included a broad spectrum of biological, clinical, and technical heterogeneity represented by blood samples profiled from children and adults infected with a virus using either microarray or RNA sequencing. We assigned a standardized severity category to each of the 4,780 samples (Figure 1A and STAR Methods). Briefly, we divided non-hospitalized samples into "no symptoms" or "mild," and hospitalized patients into "moderate," "serious," "critical," and "fatal" categories based on the level of care required and outcomes as described in the original publications (Figure 1A and STAR Methods). We also defined two broader categories: "non-severe," encompassing patients with mild and moderate viral infection, and "severe," encompassing patients with
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