Preliminary Analysis of B- and T-Cell Responses to SARS-CoV-2.

BACKGROUND AND OBJECTIVE: Without a specific antiviral treatment or vaccine, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a global pandemic, affecting over 200 countries worldwide. A better understanding of B- and T-cell immunity is critical to the diagnosis, treatment and prevention of coronavirus disease 2019 (COVID-19). METHODS: A cohort of 129 patients with COVID-19 and 20 suspected cases were enrolled in this study, and a lateral flow immunochromatographic assay (LFIA) and a magnetic chemiluminescence enzyme immunoassay (MCLIA) were evaluated for SARS-CoV-2 IgM/IgG detection. Additionally, 127 patients with COVID-19 were selected for the detection of IgM and IgG antibodies to SARS-CoV-2 to evaluate B-cell immunity, and peripheral blood lymphocyte subsets were quantified in 95 patients with COVID-19 to evaluate T-cell immunity. RESULTS: The sensitivity and specificity of LFIA-IgM/IgG and MCLIA-IgM/IgG assays for detecting SARS-CoV infection were > 90%, comparable with reverse transcription polymerase chain reaction detection. IgM antibody levels peaked on day 13 and began to fall on day 21, while IgG antibody levels peaked on day 17 and were maintained until tracking ended. Lymphocyte and subset enumeration suggested that lymphocytopenia occurred in patients with COVID-19. CONCLUSIONS: LFIA-IgM/IgG and MCLIA-IgM/IgG assays can indicate SARS-CoV-2 infection, which elicits an antibody response. Lymphocytopenia occurs in patients with COVID-19, which possibly weakens the T-cell response.

Analysis of serum metabolic profile by ultra-performance liquid chromatography-mass spectrometry for biomarkers discovery: application in a pilot study to discriminate patients with tuberculosis.

BACKGROUND: Tuberculosis (TB) is a chronic wasting inflammatory disease characterized by multisystem involvement, which can cause metabolic derangements in afflicted patients. Metabolic signatures have been exploited in the study of several diseases. However, the serum that is successfully used in TB diagnosis on the basis of metabolic profiling is not by much. METHODS: Orthogonal partial least-squares discriminant analysis was capable of distinguishing TB patients from both healthy subjects and patients with conditions other than TB. Therefore, TB-specific metabolic profiling was established. Clusters of potential biomarkers for differentiating TB active from non-TB diseases were identified using Mann-Whitney U-test. Multiple logistic regression analysis of metabolites was calculated to determine the suitable biomarker group that allows the efficient differentiation of patients with TB active from the control subjects. RESULTS: From among 271 participants, 12 metabolites were found to contribute to the distinction between the TB active group and the control groups. These metabolites were mainly involved in the metabolic pathways of the following three biomolecules: Fatty acids, amino acids, and lipids. The receiver operating characteristic curves of 3D, 7D, and 11D-phytanic acid, behenic acid, and threoninyl-γ-glutamate exhibited excellent efficiency with area under the curve (AUC) values of 0.904 (95% confidence interval [CI]: 0863-0.944), 0.93 (95% CI: 0.893-0.966), and 0.964 (95% CI: 00.941-0.988), respectively. The largest and smallest resulting AUCs were 0.964 and 0.720, indicating that these biomarkers may be involved in the disease mechanisms. The combination of lysophosphatidylcholine (18:0), behenic acid, threoninyl-γ-glutamate, and presqualene diphosphate was used to represent the most suitable biomarker group for the differentiation of patients with TB active from the control subjects, with an AUC value of 0.991. CONCLUSION: The metabolic analysis results identified new serum biomarkers that can distinguish TB from non-TB diseases. The metabolomics-based analysis provides specific insights into the biology of TB and may offer new avenues for TB diagnosis.