Respiratory immune status and microbiome in recovered COVID-19 patients revealed by metatranscriptomic analyses.

Coronavirus disease 2019 (COVID-19) is currently a severe threat to global public health, and the immune response to COVID-19 infection has been widely investigated. However, the immune status and microecological changes in the respiratory systems of patients with COVID-19 after recovery have rarely been considered. We selected 72 patients with severe COVID-19 infection, 57 recovered from COVID-19 infection, and 65 with non-COVID-19 pneumonia, for metatranscriptomic sequencing and bioinformatics analysis. Accordingly, the differentially expressed genes between the infected and other groups were enriched in the chemokine signaling pathway, NOD-like receptor signaling pathway, phagosome, TNF signaling pathway, NF-kappa B signaling pathway, Toll-like receptor signaling pathway, and C-type lectin receptor signaling pathway. We speculate that IL17RD, CD74, and TNFSF15 may serve as disease biomarkers in COVID-19. Additionally, principal coordinate analysis revealed significant differences between groups. In particular, frequent co-infections with the genera Streptococcus, Veillonella, Gemella, and Neisseria, among others, were found in COVID-19 patients. Moreover, the random forest prediction model with differential genes showed a mean area under the curve (AUC) of 0.77, and KCNK12, IL17RD, LOC100507412, PTPRT, MYO15A, MPDZ, FLRT2, SPEG, SERPINB3, and KNDC1 were identified as the most important genes distinguishing the infected group from the recovered group. Agrobacterium tumefaciens, Klebsiella michiganensis, Acinetobacter pittii, Bacillus sp. FJAT.14266, Brevundimonas naejangsanensis, Pseudopropionibacterium propionicum, Priestia megaterium, Dialister pneumosintes, Veillonella rodentium, and Pseudomonas protegens were selected as candidate microbial markers for monitoring the recovery of COVID patients. These results will facilitate the diagnosis, treatment, and prognosis of COVID patients recovering from severe illness.

Evaluation of a high-sensitivity SARS-CoV-2 antigen test on the fully automated light-initiated chemiluminescent immunoassay platform.

OBJECTIVES: To describe a high-sensitivity SARS-CoV-2 antigen test that is based on the fully automated light-initiated chemiluminescent immunoassay (LiCA®), and to validate its analytical characteristics and clinical agreement on detecting SARS-CoV-2 infection against the reference molecular test. METHODS: Analytical performance was validated and detection limits were determined using different types of nucleocapsid protein samples. 798-pair anterior nasal swab specimens were collected from hospitalized patients and asymptomatic screening individuals. Agreement between LiCA® antigen and real-time reverse transcription polymerase chain reaction (rRT-PCR) was evaluated. RESULTS: Repeatability and within-lab precision were 1.6-2.3%. The C5∼C95 interval was -5.1-4.6% away from C50. Detection limits in average (SD) were 325 (±141) U/mL on the national reference panel, 0.07 (±0.04) TCID50/mL on active viral cultures, 0.27 (±0.09) pg/mL on recombinant nucleocapsid proteins and 1.07 (±1.01) TCID50/mL on inactivated viral suspensions, respectively. LiCA detected a median of 374-fold (IQR 137-643) lower levels of the viral antigen than comparative rapid tests. As reference to the rRT-PCR method, overall sensitivity and specificity were determined to be 97.5% (91.4-99.7%) and 99.9% (99.2-100%), respectively. Total agreement between both methods was 99.6% (98.7-99.9%) with Cohen's kappa 0.98 (0.96-1). A positive detection rate of 100% (95.4-100%) was obtained as Ct≤37.8. CONCLUSIONS: The LiCA® system provides an exceptionally high-sensitivity and fully automated platform for the detection of the SARS-CoV-2 antigen in nasal swabs. The assay may have high potential use for large-scale population screening and surveillance of COVID-19 as an alternative to the rRT-PCR test.

Respiratory immune status and microbiome in recovered COVID-19 patients revealed by metatranscriptomic analyses

Coronavirus disease 2019 (COVID-19) is currently a severe threat to global public health, and the immune response to COVID-19 infection has been widely investigated. However, the immune status and microecological changes in the respiratory systems of patients with COVID-19 after recovery have rarely been considered. We selected 72 patients with severe COVID-19 infection, 57 recovered from COVID-19 infection, and 65 with non-COVID-19 pneumonia, for metatranscriptomic sequencing and bioinformatics analysis. Accordingly, the differentially expressed genes between the infected and other groups were enriched in the chemokine signaling pathway, NOD-like receptor signaling pathway, phagosome, TNF signaling pathway, NF-kappa B signaling pathway, Toll-like receptor signaling pathway, and C-type lectin receptor signaling pathway. We speculate that IL17RD, CD74, and TNFSF15 may serve as disease biomarkers in COVID-19. Additionally, principal coordinate analysis revealed significant differences between groups. In particular, frequent co-infections with the genera Streptococcus, Veillonella, Gemella, and Neisseria, among others, were found in COVID-19 patients. Moreover, the random forest prediction model with differential genes showed a mean area under the curve (AUC) of 0.77, and KCNK12, IL17RD, LOC100507412, PTPRT, MYO15A, MPDZ, FLRT2, SPEG, SERPINB3, and KNDC1 were identified as the most important genes distinguishing the infected group from the recovered group. Agrobacterium tumefaciens, Klebsiella michiganensis, Acinetobacter pittii, Bacillus sp. FJAT.14266, Brevundimonas naejangsanensis, Pseudopropionibacterium propionicum, Priestia megaterium, Dialister pneumosintes, Veillonella rodentium, and Pseudomonas protegens were selected as candidate microbial markers for monitoring the recovery of COVID patients. These results will facilitate the diagnosis, treatment, and prognosis of COVID patients recovering from severe illness.