Proteomic and Metabolomic Characterization of COVID-19 Patient Sera.

Early detection and effective treatment of severe COVID-19 patients remain major challenges. Here, we performed proteomic and metabolomic profiling of sera from 46 COVID-19 and 53 control individuals. We then trained a machine learning model using proteomic and metabolomic measurements from a training cohort of 18 non-severe and 13 severe patients. The model was validated using 10 independent patients, 7 of which were correctly classified. Targeted proteomics and metabolomics assays were employed to further validate this molecular classifier in a second test cohort of 19 COVID-19 patients, leading to 16 correct assignments. We identified molecular changes in the sera of COVID-19 patients compared to other groups implicating dysregulation of macrophage, platelet degranulation, complement system pathways, and massive metabolic suppression. This study revealed characteristic protein and metabolite changes in the sera of severe COVID-19 patients, which might be used in selection of potential blood biomarkers for severity evaluation.

Pulmonary and renal long COVID at two-year revisit.

This study investigated host responses to long COVID by following up with 89 of the original 144 cohorts for 1-year (N = 73) and 2-year visits (N = 57). Pulmonary long COVID, characterized by fibrous stripes, was observed in 8.7% and 17.8% of patients at the 1-year and 2-year revisits, respectively, while renal long COVID was present in 15.2% and 23.9% of patients, respectively. Pulmonary and renal long COVID at 1-year revisit was predicted using a machine learning model based on clinical and multi-omics data collected during the first month of the disease with an accuracy of 87.5%. Proteomics revealed that lung fibrous stripes were associated with consistent down-regulation of surfactant-associated protein B in the sera, while renal long COVID could be linked to the inhibition of urinary protein expression. This study provides a longitudinal view of the clinical and molecular landscape of COVID-19 and presents a predictive model for pulmonary and renal long COVID.

Side-chain specific isotopic labeling of proteins for infrared structural biology: the case of ring-D4-tyrosine isotope labeling of photoactive yellow protein.

An important bottleneck in the use of infrared spectroscopy as a powerful tool for obtaining detailed information on protein structure is the assignment of vibrational modes to specific amino acid residues. Side-chain specific isotopic labeling is a general approach towards obtaining such assignments. We report a method for high yield isotope editing of the bacterial blue light sensor photoactive yellow protein (PYP) containing ring-D(4)-Tyr. PYP was heterologously overproduced in Escherichia coli in minimal media containing ring-D(4)-Tyr in the presence of glyphosate, which inhibits endogenous biosynthesis of aromatic amino acids (Phe, Trp, and Tyr). Mass spectrometry of the intact protein and of tryptic peptides unambiguously demonstrated highly specific labeling of all five Tyr residues in PYP with 98% incorporation and undetectable isotopic scrambling. FTIR spectroscopy of the protein reveals a characteristic Tyr ring vibrational mode at 1515 cm(-1) that is shifted to 1436 cm(-1), consistent with that from ab initio calculations. PYP is a model system for protein structural dynamics and for receptor activation in biological signaling. The results described here open the way to the analysis of PYP using isotope-edited FTIR spectroscopy with side-chain specific labeling.

Enhanced inflammation and suppressed adaptive immunity in COVID-19 with prolonged RNA shedding.

Little is known regarding why a subset of COVID-19 patients exhibited prolonged positivity of SARS-CoV-2 infection. Here, we found that patients with long viral RNA course (LC) exhibited prolonged high-level IgG antibodies and higher regulatory T (Treg) cell counts compared to those with short viral RNA course (SC) in terms of viral load. Longitudinal proteomics and metabolomics analyses of the patient sera uncovered that prolonged viral RNA shedding was associated with inhibition of the liver X receptor/retinoid X receptor (LXR/RXR) pathway, substantial suppression of diverse metabolites, activation of the complement system, suppressed cell migration, and enhanced viral replication. Furthermore, a ten-molecule learning model was established which could potentially predict viral RNA shedding period. In summary, this study uncovered enhanced inflammation and suppressed adaptive immunity in COVID-19 patients with prolonged viral RNA shedding, and proposed a multi-omic classifier for viral RNA shedding prediction.