| Literature DB >> 35895716 |
Chiaki Iwamura1, Kiyoshi Hirahara1, Masahiro Kiuchi1, Sanae Ikehara2, Kazuhiko Azuma2, Tadanaga Shimada3, Sachiko Kuriyama1, Syota Ohki2, Emiri Yamamoto2, Yosuke Inaba4, Yuki Shiko4, Ami Aoki1, Kota Kokubo1, Rui Hirasawa1, Takahisa Hishiya1, Kaori Tsuji1, Tetsutaro Nagaoka5, Satoru Ishikawa6, Akira Kojima6, Haruki Mito7, Ryota Hase7, Yasunori Kasahara8, Naohide Kuriyama9, Tetsuya Tsukamoto10, Sukeyuki Nakamura11, Takashi Urushibara12, Satoru Kaneda13, Seiichiro Sakao14, Minoru Tobiume15, Yoshio Suzuki15, Mitsuhiro Tsujiwaki16, Terufumi Kubo16, Tadashi Hasegawa16, Hiroshi Nakase17, Osamu Nishida9, Kazuhisa Takahashi5, Komei Baba18, Yoko Iizumi19, Toshiya Okazaki19, Motoko Y Kimura20, Ichiro Yoshino21, Hidetoshi Igari22,23, Hiroshi Nakajima23,24, Takuji Suzuki14, Hideki Hanaoka4, Taka-Aki Nakada3, Yuzuru Ikehara2,25, Koutaro Yokote26, Toshinori Nakayama1,27.
Abstract
The mortality of coronavirus disease 2019 (COVID-19) is strongly correlated with pulmonary vascular pathology accompanied by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection-triggered immune dysregulation and aberrant activation of platelets. We combined histological analyses using field emission scanning electron microscopy with energy-dispersive X-ray spectroscopy analyses of the lungs from autopsy samples and single-cell RNA sequencing of peripheral blood mononuclear cells to investigate the pathogenesis of vasculitis and immunothrombosis in COVID-19. We found that SARS-CoV-2 accumulated in the pulmonary vessels, causing exudative vasculitis accompanied by the emergence of thrombospondin-1-expressing noncanonical monocytes and the formation of myosin light chain 9 (Myl9)-containing microthrombi in the lung of COVID-19 patients with fatal disease. The amount of plasma Myl9 in COVID-19 was correlated with the clinical severity, and measuring plasma Myl9 together with other markers allowed us to predict the severity of the disease more accurately. This study provides detailed insight into the pathogenesis of vasculitis and immunothrombosis, which may lead to optimal medical treatment for COVID-19.Entities:
Keywords: COVID-19; exudative vasculitis; microthrombi; nonconventional monocytes; plasma Myl9
Mesh:
Substances:
Year: 2022 PMID: 35895716 PMCID: PMC9388124 DOI: 10.1073/pnas.2203437119
Source DB: PubMed Journal: Proc Natl Acad Sci U S A ISSN: 0027-8424 Impact factor: 12.779
Clinical characteristics of COVID-19 patients with autopsy
| Variable | Case #1 | Case #2 | Case #3 |
|---|---|---|---|
| Age (y) | 86 | 58 | 80 |
| Sex | Male | Male | Male |
| Comorbidity | None | Diabetes | Diabetes |
| Hypertension | Hypertension | ||
| Idiopathic pulmonary fibrosis | Dyslipidemia | ||
| Dyslipidemia | Prostatic hyperplasia | ||
| Treatments | |||
| Steroid | None | + | + |
| Antivirus | + | + | |
| Antibiotics | + | + | |
| Anticoagulant | — | + | |
| Hospitalization (on admission-deceased) | 3/7/21 to 3/7/21 | 11/28/20 to 12/30/20 | 3/11/20 to 03/28/20 |
| PCR test result | Positive (3/7/20), negative (not tested) | Positive (11/27/20), negative (12/1/20) | Positive (3/12/20), negative (not tested) |
| MV (d) | 0 | 30 | 15 |
| ECMO (d) | 0 | 21 | 0 |
| Complication | None | Mucormycosis | Renal failure |
| Pancreatitis | Anemia | ||
| Pulmonary and renal infarction | Liver dysfunction | ||
| Nonocclusive mesenteric ischemia | |||
| Cerebral hemorrhage | |||
| Cause of death | Acute respiratory distress syndrome | Multiple organ failure | COVID-19 pneumonia |
ECMO, Extracorporeal membrane oxygenation; MV, mechanical ventilation.
*The details about case #2 are described in a published case report (28).
Characteristics of the enrolled COVID-19 patients
| Total | Moderate | Severe | Critical | Fatal | |
|---|---|---|---|---|---|
| Demographics and sex | |||||
| Age, median (± SD) | 57 (15.2) | 48 (15.1) | 61.5 (11.6) | 58 (13.3) | 68.5 (8.9) |
| 20–29 y old | 11 (8.9) | 10 (16.7) | 1 (4.2) | 0 (0.0) | 0 (0.0) |
| 30–39 y old | 12 (9.8) | 10 (16.7) | 0 (0.0) | 2 (7.4) | 0 (0.0) |
| 40–49 y old | 19 (15.4) | 12 (20.0) | 0 (0.0) | 6 (22.2) | 1 (8.3) |
| 50–59 y old | 33 (26.8) | 15 (25.0) | 10 (41.7) | 6 (22.2) | 2 (16.7) |
| 60–69 y old | 28 (22.8) | 8 (13.3) | 8 (33.3) | 8 (29.6) | 4 (33.3) |
| 70–79 y old | 16 (13.0) | 4 (6.7) | 4 (16.7) | 3 (11.1) | 5 (41.7) |
| 80–89 y old | 4 (3.3) | 1 (1.7) | 1(4.2) | 2 (7.4) | 0 (0.0) |
| Female | 33 (26.8) | 24 (40.0) | 2 (8.3) | 5 (18.5) | 2 (16.7) |
| BMI | |||||
| <25 | 56 (45.5) | 36 (60.0) | 8 (33.3) | 10 (37.0) | 2 (16.7) |
| 25 to <30 | 48 (39.0) | 20 (33.3) | 12 (50.0) | 11(40.7) | 5 (41.7) |
| 30 to <40 | 10 (8.1) | 3 (5.0) | 2 (8.3) | 2 (7.4) | 3 (25.0) |
| ≥40 | 4 (3.3) | 0 (0.0) | 0 (0.0) | 3 (11.1) | 1 (8.3) |
| Not recorded | 5 (4.1) | 1 (1.7) | 2 (8.3) | 1 (3.7) | 1 (8.3) |
| BMI, Average (± SD) | 25.6 (6.0) | 23.8 (3.9) | 25.7 (2.9) | 28.2 (9.3) | 28.9 (5.7) |
| Complications | |||||
| Hypertension | 36 (29.3) | 11 (18.3) | 9 (37.5) | 10 (37.0) | 6 (50.0) |
| Diabetes | 31 (25.2) | 6 (10.0) | 7 (29.2) | 13 (48.1) | 5 (41.7) |
| Dyslipidemia | 26 (21.1) | 8 (13.3) | 10 (41.7) | 6 (22.2) | 2 (16.7) |
| Hyperuricemia | 16 (13.0) | 5 (8.3) | 7 (29.2) | 2 (7.4) | 2 (16.7) |
| Respiratory diseases | 12 (9.8) | 2 (3.3) | 1 (4.2) | 8 (29.6) | 1 (8.3) |
| Duration of hospital stay, d (IQR) | 11 (9–22) | 9 (8–10) | 12 (11–18) | 31 (17–48) | 30 (17–41) |
| Medications | |||||
| Corticosteroid | 69 (56.1) | 9 (15.0) | 22 (91.7) | 26 (96.3) | 12 (100.0) |
| Antiviral | 74 (60.2) | 15 (25.0) | 24 (100.0) | 24 (88.9) | 11 (91.7) |
| Antibiotic | 26 (21.1) | 1 (1.7) | 2 (8.3) | 16 (59.3) | 7 (58.3) |
| Anticoagulant | 43 (35.0) | 3 (5.0) | 8 (33.3) | 22 (81.5) | 10 (83.3) |
| Tocilizumab | 18 (14.6) | 0 (0.0) | 2 (8.3) | 10 (37.0) | 6 (50.0) |
| Treatments | |||||
| Mechanical ventilation | 37 (30.1) | 0 (0.0) | 0 (0.0) | 26 (96.3) | 11 (91.7) |
| ECMO | 15 (12.2) | 0 (0.0) | 0 (0.0) | 7 (25.9) | 8 (66.7) |
| CHDF | 14 (11.42) | 0 (0.0) | 0 (0.0) | 6 (22.2) | 8 (66.7) |
| Apheresis | 7 (5.7) | 0 (0.0) | 1 (4.2) | 4 (14.8) | 2 (16.7) |
| Nitric oxide | 16 (13.0) | 0 (0.0) | 0 (0.0) | 11 (40.7) | 5 (41.7) |
| Prone position | 10 (8.1) | 0 (0.0) | 0 (0.0) | 6 (22.2) | 4 (33.3) |
Data are presented as No. (%) unless indicated otherwise. CHDF, continuous hemodiafiltration.
Fig. 1.Exudative vasculitis in the lung of COVID-19 patients. (A) Representative images of the lung of fatal COVID-19 cases and control cases who died of stomach cancer with HE staining (Left). Locations of SARS-CoV-2 in the lungs of fatal COVID-19 cases were determined by immunohistochemistry using anti–SARS-CoV-2 spike glycoprotein antibody (Center and Right). SARS-CoV-2 spike glycoprotein-positive cells are indicted by black arrowheads. The inside of the open rectangles in the Center is magnified, Right. (B) Images of the scanning electron microscope in the area indicated by black arrowheads in the Upper Center image of A. SARS-CoV-2 viruses are shown with light green using anti–SARS-CoV-2 spike glycoprotein antibody (Right). (C) EVG staining of the lung from fatal COVID-19 cases and control cases who died with stomach cancer. Images in the open rectangles in the Left are magnified, Right. (D) Representative image of exudative vasculitis in the lungs of fatal COVID-19 cases and control cases who died with stomach cancer using EVG staining. White arrowheads indicate lymphatic ducts. Dimensions of adventitia (black arrowhead) of a muscular artery (100 to 500 µm) in three COVID-19 patients and two control donors were pooled (Right). Statistical significance was determined by the Mann–Whitney U test. ***P < 0.001.
Fig. 2.A sc RNAseq analysis revealed dysregulation of myeloid cells with biased up-regulation of neutrophil and platelet activation gene sets in the blood of COVID-19 patients. (A) UMAP projection of CD45+ cells at 57,049 single-cell transcriptomes isolated from PBMCs of 21 patients (moderate n = 10, severe n = 6, fatal n = 5) is depicted and colored according to the 8 cellular populations. (B) Percentages of cell compositions in PBMCs for moderate, severe, and fatal COVID-19 cases are shown. (C) The bar graph shows the ssGSVA scores in pathways enriched for fatal COVID-19 cases compared with moderate COVID-19 cases. (D) ssGSVA scores of neutrophil degranulation (Left), platelet activation, signaling, and aggregation (Center), and platelet degranulation (Right) are projected on ridgeline plots. Open rectangles with dashed lines indicate higher ssGSVA scores (neutrophil degranulation: 1.1 > ssGSVA; platelet activation, signaling, and aggregation, and platelet degranulation: 1.5 > ssGSVA). (E) Immune cells gated by ssGSVA scores in D are projected on the UMAP (Upper) and bar plot showing the counts of each cell population (under panel).
Fig. 3.Noncanonical monocytes expressing THBS-1 were accumulated in the lung of COVID-19 patients. (A) The UMAP projection of CD14+ cells from moderate, severe, and critical COVID-19 cases is depicted and colored according to the cellular populations. DC, dendritic cell. (B) The volcano plot depicts the differential gene expression of noncanonical monocytes in comparison to canonical monocytes. (C) The volcano plot depicts the differential gene expressions related to platelet degranulation, activation, signaling, and aggregation between moderate and fatal COVID-19 cases. (D) The UMAP shows the expression of THBS-1 with color intensity. (E) Violin plots show the distribution of THBS-1 in each cell population of moderate, critical, and fatal COVID-19 cases. (F) Identification of noncanonical macrophages expressing CD163, and THBS-1 in the peripheral lung tissue of the fatal COVID-19 case (case #1) and control case who died of stomach cancer. The inside of the open rectangles in the Upper panels is magnified in the Lower panels, respectively.
Fig. 4.Plasma Myl9, a component of the thrombus, indicates the severity of COVID-19. (A) HE and PTAH staining of the peripheral lung tissue from the fatal COVID-19 case (case #1). Brown staining developed by immunohistochemistry indicates platelets (CD41) or Myl9 deposition in the thrombus. (B) Plasma Myl9 levels from healthy controls (HC, n = 30), COVID-19 patients at admission (n = 123), sepsis patients at admission (n = 9), and heart surgery patients on the day when surgery was performed (n = 15). Yellow lines indicate mean values and 95% CIs. Comparisons were performed by the Kruskal–Wallis test. ns, not significant; **P < 0.001; ***P < 0.0001. (C) ROC curve for plasma Myl9 levels between COVID-19 patients and healthy controls. (D) Plasma Myl9 levels in the COVID-19 patients at admission and at discharge (n = 87). The same patients are connected via lines. The comparison was performed by a paired t test. *P < 0.05. (E) A multiple linear regression analysis was performed to evaluate the association between Myl9 and severity (moderate, n = 60; severe, n = 24; critical, n = 27; fatal, n = 12). **P < 0.001; ***P < 0.0001. (F) Correlation of levels of plasma Myl9 and each blood marker using Spearman’s correlation coefficient (r). All blood samples from COVID-19 patients during hospitalization were analyzed. (G) Correlation between plasma Myl9 levels and days after admission in COVID-19 patients. Individual data were adjusted by age and sex. Tukey’s method was applied for pairwise group comparisons. (H) Plasma Myl9 levels at admission when patients with severe, critical, and fatal disease were divided according to the presence of anti–IFN-α2 antibodies (anti–IFN-α2 antibody [−], n = 56; [+] n = 7). The comparison was performed by a paired t test. *P < 0.05. The values indicate the mean ± SEM.