No abstract

One-third of the world's population has been infected with Mycobacterium tuberculosis (M. tuberculosis), a primary pathogen of the mammalian respiratory system, while about 10% of latent infections progress to active tuberculosis (TB), indicating that host and environmental factors may determine the outcomes such as infection clearance/persistence and treatment prognosis. The gut microbiota is essential for development of host immunity, defense, nutrition and metabolic homeostasis. Thus, the pattern of gut microbiota may contribute to M. tuberculosis infection and prognosis. In current study we characterized the differences in gut bacterial communities in new tuberculosis patients (NTB), recurrent tuberculosis patients (RTB), and healthy control. The abundance-based coverage estimator (ACE) showed the diversity index of the gut microbiota in the patients with recurrent tuberculosis was increased significantly compared with healthy controls (p < 0.05). At the phyla level, Actinobacteria and Proteobacteria, which contain many pathogenic species, were significantly enriched in the feces RTB patients. Conversely, phylum Bacteroidetes, containing a variety of beneficial commensal organisms, was reduced in the patients with the recurrent tuberculosis compared to healthy controls. The Gram-negative genus Prevotella of oral origin from phylum of Bacteroidetes and genus Lachnospira from phylum of Firmicutes were significantly decreased in both the new and recurrent TB patient groups, compared with the healthy control group (p < 0.05). We also found that there was a positive correlation between the gut microbiota and peripheral CD4+ T cell counts in the patients. This study, for the first time, showed associations between gut microbiota with tuberculosis and its clinical outcomes. Maintaining eubiosis, namely homeostasis of gut microbiota, may be beneficial for host recovery and prevention of recurrence of M. tuberculosis infection.

SARS-CoV-2 is the underlying cause for the COVID-19 pandemic. Like most enveloped RNA viruses, SARS-CoV-2 uses a homotrimeric surface antigen to gain entry into host cells. Here we describe S-Trimer, a native-like trimeric subunit vaccine candidate for COVID-19 based on Trimer-Tag technology. Immunization of S-Trimer with either AS03 (oil-in-water emulsion) or CpG 1018 (TLR9 agonist) plus alum adjuvants induced high-level of neutralizing antibodies and Th1-biased cellular immune responses in animal models. Moreover, rhesus macaques immunized with adjuvanted S-Trimer were protected from SARS-CoV-2 challenge compared to vehicle controls, based on clinical observations and reduction of viral loads in lungs. Trimer-Tag may be an important platform technology for scalable production and rapid development of safe and effective subunit vaccines against current and future emerging RNA viruses.

The outbreak of COVID‐19 coronavirus disease around the end of 2019 has become a pandemic. The preferred method for COVID‐19 detection is the real‐time polymerase chain reaction (RT‐PCR)‐based technique; however, it also has certain limitations, such as sample‐dependent procedures with a relatively high false negative ratio. We propose a safe and efficient method for screening COVID‐19 based on Raman spectroscopy. A total of 177 serum samples are collected from 63 confirmed COVID‐19 patients, 59 suspected cases, and 55 healthy individuals as a control group. Raman spectroscopy is adopted to analyze these samples, and a machine learning support‐vector machine (SVM) method is applied to the spectrum dataset to build a diagnostic algorithm. Furthermore, 20 independent individuals, including 5 asymptomatic COVID‐19 patients and 5 symptomatic COVID‐19 patients, 5 suspected patients, and 5 healthy patients, were sampled for external validation. In these three groups—confirmed COVID‐19, suspected, and healthy individuals—the distribution of statistically significant points of difference showed highly consistency for intergroups after repeated sampling processes. The classification accuracy between the COVID‐19 cases and the suspected cases is 0.87 (95% confidence interval [CI]: 0.85–0.88), and the accuracy between the COVID‐19 and the healthy controls is 0.90 (95% CI: 0.89–0.91), while the accuracy between the suspected cases and the healthy control group is 0.68 (95% CI: 0.67–0.73). For the independent test dataset, we apply the obtained SVM model to the classification of the independent test dataset to have all the results correctly classified. Our model showed that the serum‐level classification results were all correct for independent test dataset. Our results suggest that Raman spectroscopy could be a safe and efficient technique for COVID‐19 screening.