We performed RNA-seq and high-resolution mass spectrometry on 128 blood samples from COVID-19-positive and COVID-19-negative patients with diverse disease severities and outcomes. Quantified transcripts, proteins, metabolites, and lipids were associated with clinical outcomes in a curated relational database, uniquely enabling systems analysis and cross-ome correlations to molecules and patient prognoses. We mapped 219 molecular features with high significance to COVID-19 status and severity, many of which were involved in complement activation, dysregulated lipid transport, and neutrophil activation. We identified sets of covarying molecules, e.g., protein gelsolin and metabolite citrate or plasmalogens and apolipoproteins, offering pathophysiological insights and therapeutic suggestions. The observed dysregulation of platelet function, blood coagulation, acute phase response, and endotheliopathy further illuminated the unique COVID-19 phenotype. We present a web-based tool (covid-omics.app) enabling interactive exploration of our compendium and illustrate its utility through a machine learning approach for prediction of COVID-19 severity.

A drug-induced sarcoidosis-like reaction (DISR) is a systemic granulomatous reaction that is indistinguishable from sarcoidosis and occurs in a temporal relationship with initiation of an offending drug. DISRs typically improve or resolve after withdrawal of the offending drug. Four common categories of drugs that have been associated with the development of a DISR are immune checkpoint inhibitors, highly active antiretroviral therapy, interferons, and tumor necrosis factor-α antagonists. Similar to sarcoidosis, DISRs do not necessarily require treatment because they may cause no significant symptoms, quality of life impairment, or organ dysfunction. When treatment of a DISR is required, standard antisarcoidosis regimens seem to be effective. Because a DISR tends to improve or resolve when the offending drug is discontinued, this is another effective treatment for a DISR. However, the offending drug need not be discontinued if it is useful, and antigranulomatous therapy can be added. In some situations, the development of a DISR may suggest a beneficial effect of the inducing drug. Understanding the mechanisms leading to DISRs may yield important insights into the immunopathogenesis of sarcoidosis.

Background The incidence of secondary pulmonary infections is not well described in hospitalized COVID-19 patients. Understanding the incidence of secondary pulmonary infections and the associated bacterial and fungal microorganisms identified can improve patient outcomes. Objective This narrative review aims to determine the incidence of secondary bacterial and fungal pulmonary infections in hospitalized COVID-19 patients, and describe the bacterial and fungal microorganisms identified. Method We perform a literature search and select articles with confirmed diagnoses of secondary bacterial and fungal pulmonary infections that occur 48 h after admission, using respiratory tract cultures in hospitalized adult COVID-19 patients. We exclude articles involving co-infections defined as infections diagnosed at the time of admission by non-SARS-CoV-2 viruses, bacteria, and fungal microorganisms. Results The incidence of secondary pulmonary infections is low at 16% (4.8-42.8%) for bacterial infections and lower for fungal infections at 6.3% (0.9-33.3%) in hospitalized COVID-19 patients. Secondary pulmonary infections are predominantly seen in critically ill hospitalized COVID-19 patients. The most common bacterial microorganisms identified in the respiratory tract cultures are Pseudomonas aeruginosa, Klebsiella species, Staphylococcus aureus, Escherichia coli, and Stenotrophomonas maltophilia. Aspergillus fumigatus is the most common microorganism identified to cause secondary fungal pulmonary infections. Other rare opportunistic infection reported such as PJP is mostly confined to small case series and case reports. The overall time to diagnose secondary bacterial and fungal pulmonary infections is 10 days (2-21 days) from initial hospitalization and 9 days (4-18 days) after ICU admission. The use of antibiotics is high at 60-100% involving the studies included in our review. Conclusion The widespread use of empirical antibiotics during the current pandemic may contribute to the development of multidrug-resistant microorganisms, and antimicrobial stewardship programs are required for minimizing and de-escalating antibiotics. Due to the variation in definition across most studies, a large, well-designed study is required to determine the incidence, risk factors, and outcomes of secondary pulmonary infections in hospitalized COVID-19 patients.

Background There are no prior reports that compare differentially methylated regions of DNA in blood samples from COVID-19 patients to samples collected before the SARS-CoV-2 pandemic using a shared epigenotyping platform. We performed a genome-wide analysis of circulating blood DNA CpG methylation using the Infinium Human MethylationEPIC BeadChip on 124 blood samples from hospitalized COVID-19-positive and COVID-19-negative patients and compared these data with previously reported data from 39 healthy individuals collected before the pandemic. Prospective outcome measures such as COVID-19-GRAM risk-score and mortality were combined with methylation data. Results Global mean methylation levels did not differ between COVID-19 patients and healthy pre-pandemic controls. About 75% of acute illness-associated differentially methylated regions were located near gene promoter regions and were hypo-methylated in comparison with healthy pre-pandemic controls. Gene ontology analyses revealed terms associated with the immune response to viral infections and leukocyte activation; and disease ontology analyses revealed a predominance of autoimmune disorders. Among COVID-19-positive patients, worse outcomes were associated with a prevailing hyper-methylated status. Recursive feature elimination identified 77 differentially methylated positions predictive of COVID-19 severity measured by the GRAM-risk score. Conclusion Our data contribute to the awareness that DNA methylation may influence the expression of genes that regulate COVID-19 progression and represent a targetable process in that setting.

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