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.

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.

We performed RNA-Seq and high-resolution mass spectrometry on 128 blood samples from COVID-19 positive and negative patients with diverse disease severities. Over 17,000 transcripts, proteins, metabolites, and lipids were quantified and 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 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 comparative analysis with published data and a machine learning approach for prediction of COVID-19 severity.

The zoopathogenic fungus Histoplasma capsulatum, like other eukaryotic aerobic microorganisms, requires iron for growth. Under conditions of low iron availability, the fungus secretes hydroxamates that function as siderophores (iron chelators). The experiments to be reported were designed to gather further information on the hydroxamate siderophores of H. capsulatum. The fungus was grown in a synthetic medium deferrated with the cationic exchange resin Chelex 100. Siderophores were detected after 4 days of incubation at 37°C in media containing 0.3 to 1.0 M iron. The secretion was suppressed by 10 M iron. The hydroxamates were purified by reverse-phase and size-exclusion chromatography. On the basis of ions observed during electrospray mass spectroscopy, five hydroxamate siderophores were tentatively identified: dimerum acid, acetyl dimerum acid, coprogen B, methyl coprogen B, and fusarinine (monomeric). A polyclonal antibody to dimerum acid was generated. This reagent cross-reacted with coprogen B and fusarinine. Thus, the antibody detects hydroxamates in all three families of siderophores excreted by H. capsulatum.

Ovarian cancer, a highly metastatic disease, is the fifth leading cause of cancer-related deaths in women. Chickens are widely used as a model for human ovarian cancer as they spontaneously develop epithelial ovarian tumors similar to humans. The cellular and molecular biology of chicken ovarian cancer (COVCAR) cells, however, have not been studied. Our objectives were to culture COVCAR cells and to characterize their invasiveness and expression of genes and proteins associated with ovarian cancer. COVCAR cell lines (n = 13) were successfully maintained in culture for up to19 passages, cryopreserved and found to be viable upon thawing and replating. E-cadherin, cytokeratin and α-smooth muscle actin were localized in COVCAR cells by immunostaining. COVCAR cells were found to be invasive in extracellular matrix and exhibited anchorage-independent growth forming colonies, acini and tube-like structures in soft agar. Using RT-PCR, COVCAR cells were found to express E-cadherin, N-cadherin, cytokeratin, vimentin, mesothelin, EpCAM, steroidogenic enzymes/proteins, inhibin subunits-α, βA, βB, anti-müllerian hormone, estrogen receptor [ER]-α, ER-β, progesterone receptor, androgen receptor, and activin receptors. Quantitative PCR analysis revealed greater N-cadherin, vimentin, and VEGF mRNA levels and lesser cytokeratin mRNA levels in COVCAR cells as compared with normal ovarian surface epithelial (NOSE) cells, which was suggestive of epithelial-mesenchymal transformation. Western blotting analyses revealed significantly greater E-cadherin levels in COVCAR cell lines compared with NOSE cells. Furthermore, cancerous ovaries and COVCAR cell lines expressed higher levels of an E-cadherin cleavage product when compared to normal ovaries and NOSE cells, respectively. Cancerous ovaries were found to express significantly higher ovalbumin levels whereas COVCAR cell lines did not express ovalbumin thus suggesting that the latter did not originate from oviduct. Taken together, COVCAR cell lines are likely to improve our understanding of the cellular and molecular biology of ovarian tumors and its metastasis.