Bacterial coinfections contribute to severe COVID-19 in winter.
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ABSTRACT:
SUBMITTER: Fan H
PROVIDER: S-EPMC10204009 | biostudies-literature | 2023 May
REPOSITORIES: biostudies-literature
Publications
Bacterial coinfections contribute to severe COVID-19 in winter.
Fan Hui H Zhou Li L Lv Jingjun J Yang Shimin S Chen Guozhong G Liu Xinjin X Han Chunyan C Tan Xue X Qian Shengnan S Wu Zegang Z Yu Shan S Guo Ming M Zhu Chengliang C Chen Yu Y Lan Ke K
Cell research 20230523 7
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Project description:BackgroundBacterial coinfection in patients with SARS-CoV-2 infection is an important risk factor for death. This study investigated whether there were differences in levels of serum inflammatory markers in COVID-19 patients with bacterial coinfections compared with those without bacterial infection.MethodsA total of 235 inpatients with SARS-CoV-2 infection admitted to Qingdao Central Hospital from December 7, 2022, to August 7, 2024, were included. Patients were divided into a bacteria-positive group (115 cases) and a bacteria-negative group (120 cases) according to whether they had bacterial coinfections. PCT, CRP, and 12 kinds of cytokines were compared between groups, and the distribution of bacterial species in the positive group was statistically analyzed.ResultsThe serum levels of CRP (Z = 8.94, p < 0.001), PCT (Z = 5.59, p < 0.001), IL-1β (t = 4.863, p < 0.001), IL-2 (t = 5.810, p < 0.001), IL-5 (t = 3.837, p < 0.001), IL-6 (t = 4.910, p < 0.001), IL-8 (t = 3.325, p < 0.001), ILIL-12p70 (t = 4.722, p < 0.001), IL-17 (t = 3.315, p = 0.001) and TNF-α (t = 4.251, p < 0.001) between the two groups were significantly different. IL-4, IL-10, IFN-α, and IFN-γ were not statistically significant (p > 0.05). Among the 115 bacteria-positive patients, 56 patients were positive for one species and 59 patients were multiple infections. Acinetobacter baumannii, Klebsiella pneumoniae, Pseudomonas aeruginosa, Staphylococcus aureus, and Haemophilus influenzae were common species.ConclusionsSerum PCT and CRP levels in COVID-19 patients with bacterial coinfection are higher than those without bacterial infection. Cytokines such as IL-1β, IL-2, IL-5, IL-6, IL-8, IL-12p70, IL-17, and TNF-α may be involved in the progression of COVID-19 combined with bacterial infection. They can be used as potential markers to evaluate the disease condition and prognosis.
| S-EPMC11653711 | biostudies-literature
Project description: Not available
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Project description:The study aims at investigating the specifical transcriptional signatures of severe COVID-19
2021-01-15 | GSE164805 | GEO
Project description:Early in the COVID-19 pandemic, type 2 diabetes (T2D) was marked as a risk-factor for severe disease. Inflammation is central to the aetiology of both conditions where immune responses influence disease course. Identifying at-risk groups through immuno-inflammatory signatures can direct personalised care and help develop potential targets for precision therapy. This observational study characterised immunophenotypic variation associated with COVID-19 severity in T2D. Broad-spectrum immunophenotyping quantified 15 leukocyte populations in circulation from a cohort of 45 hospitalised COVID-19 patients with and without T2D. Lymphocytopenia, of CD8+ lymphocytes, was associated with severe COVID-19 and intensive care admission in non-diabetic and T2D patients. A morphological anomaly of increased monocyte size and monocytopenia of classical monocytes were specifically associated with severe COVID-19 in patients with T2D requiring intensive care. Over-expression of inflammatory markers reminiscent of the type-1 interferon pathway underlaid the immunophenotype associated with T2D. These changes may contribute to severity of COVID-19 in T2D. These findings show characteristics of severe COVID-19 in T2D as well as provide evidence that type-1 interferons may be actionable targets for future studies.
| S-SCDT-EMM-2020-13038 | biostudies-other
Project description:Analysis of COVID-19 hospitalized patients, with different kind of symptoms, by human rectal swabs collection and 16S sequencing approach.
2022-09-22 | E-MTAB-12236 | biostudies-arrayexpress
Project description:Lung transplantation can potentially be a life-saving treatment for patients with non-resolving COVID-19-associated respiratory failure. Concerns limiting transplant include recurrence of SARS-CoV-2 infection in the allograft, technical challenges imposed by viral-mediated injury to the native lung, and potential risk for allograft infection by pathogens associated with ventilator-associated pneumonia in the native lung. Most importantly, the native lung might recover, resulting in long-term outcomes preferable to transplant. Here, we report results of the first successful lung transplantation procedures in patients with non-resolving COVID-19-associated respiratory failure in the United States. We performed sm-FISH to detect both positive and negative strands of SARS-CoV-2 RNA in the explanted lung tissue, extracellular matrix imaging using SHIELD tissue clearance, and single cell RNA-Seq on explant and warm post-mortem lung biopsies from patients who died from severe COVID-19 pneumonia. Lungs from patients with prolonged COVID-19 were free of virus but pathology showed extensive evidence of injury and fibrosis which resembled end-stage pulmonary fibrosis. We used a machine learning approach to project single cell RNA-Seq data from patients with late stage COVID-19 onto a single cell atlas of pulmonary fibrosis, revealing similarities across cell lineages. There was no recurrence of SARS-CoV-2 or pathogens associated with pre-transplant ventilator associated pneumonias following transplantation. Our findings suggest that some patients with severe COVID-19 develop fibrotic lung disease for which lung transplantation is the only option for survival.
2020-10-28 | GSE158127 | GEO
Project description:Previous findings suggest that bacterial coinfections are less common in ICU patients with COVID-19 than with influenza, but evidence is limited.ObjectivesThis study aimed to compare the rate of early bacterial coinfections in ICU patients with COVID-19 or influenza.Design setting and participantsRetrospective propensity score matched cohort study. We included patients admitted to ICUs of a single academic center with COVID-19 or influenza (January 2015 to April 2022).Main outcomes and measuresThe primary outcome was early bacterial coinfection (i.e., positive blood or respiratory culture within 2 d of ICU admission) in the propensity score matched cohort. Key secondary outcomes included frequency of early microbiological testing, antibiotic use, and 30-day all-cause mortality.ResultsOut of 289 patients with COVID-19 and 39 patients with influenza, 117 (n = 78 vs 39) were included in the matched analysis. In the matched cohort, the rate of early bacterial coinfections was similar between COVID-19 and influenza (18/78 [23%] vs 8/39 [21%]; odds ratio, 1.16; 95% CI, 0.42-3.45; p = 0.82). The frequency of early microbiological testing and antibiotic use was similar between the two groups. Within the overall COVID-19 group, early bacterial coinfections were associated with a statistically significant increase in 30-day all-cause mortality (21/68 [30.9%] vs 40/221 [18.1%]; hazard ratio, 1.84; 95% CI, 1.01-3.32).Conclusions and relevanceOur data suggest similar rates of early bacterial coinfections in ICU patients with COVID-19 and influenza. In addition, early bacterial coinfections were significantly associated with an increased 30-day mortality in patients with COVID-19.
| S-EPMC10090795 | biostudies-literature
Project description:iCLIP data to identify OAS1 binding sites in uninfected and SARS-CoV-2 infected cells
2021-10-07 | GSE182394 | GEO
Project description:Single-cell RNA-seq analysis of PBMCs during COVID-19
2022-01-27 | GSE192391 | GEO
Project description:The etiology of severe forms of COVID19, especially in young patients, remains a salient unanswered question. Here we build on a 3-tier cohort where all individuals/patients were strictly below 50 years of age and where a number of comorbidities were excluded at study onset. Besides healthy controls (N=22), these include patients in the intensive care unit with Acute Respiratory Distress Syndrome (ARDS) (“critical group”; N=47), and those in a non-critical care ward under supplemental oxygen (“non-critical group”, N=25). This highly curated cohort allowed us to perform a deep multi-omics approach which included whole genome sequencing, whole blood RNA-sequencing, plasma and peripheral-blood mononuclear cells proteomics, multiplex cytokine profiling, mass-cytometry-based immune cell profiling in conjunction with viral parameters i.e. anti-SARS-Cov-2 neutralizing antibodies and multi-target antiviral serology. Critical patients were characterized by an exacerbated inflammatory state, perturbed lymphoid and myeloid cell compartments, signatures of dysregulated blood coagulation and active regulation of viral entry into the cells. A unique gene signature that differentiates critical from non-critical patients was identified by an ensemble machine learning, deep learning and quantum computing approach. Within this gene network, Structural Causal Modeling identified several ARDS driver genes, among which the up-regulated metalloprotease ADAM9 seems to be a key driver. Inhibition of ADAM9 ex vivo interfered with SARS-Cov-2 uptake and replication in human epithelial cells. Hence we apply a machine learning approach to identify driver genes for severe forms of COVID-19 in a small, uncluttered cohort of patients.
2021-11-25 | PXD025265 | Pride