Novel DNA chip based on a modified DigiTag2 assay for high-throughput species identification and genotyping of Mycobacterium tuberculosis complex isolates.

A multipurpose high-throughput genotyping tool for the assessment of recent epidemiological data and evolutional pattern in Mycobacterium tuberculosis complex (MTBC) clinical isolates was developed in this study. To facilitate processing, 51 highly informative single nucleotide polymorphisms (SNPs) were selected for discriminating the clinically most relevant MTBC species and genotyping M. tuberculosis into its principle genetic groups (PGGs) and SNP cluster groups (SCGs). Because of the high flexibility of the DigiTag2 assay, the identical protocol and DNA array containing the identical set of probes were applied to the highly GC-rich mycobacterial genome. The specific primers with multiplex amplification and hybridization conditions based on the DigiTag2 principle were optimized and evaluated with 14 MTBC reference strains, 4 nontuberculous mycobacteria (NTM) isolates, and 322 characterized M. tuberculosis clinical isolates. The DNA chip that was developed revealed a 99.85% call rate, a 100% conversion rate, and 99.75% reproducibility. For the accuracy rate, 98.94% of positive calls were consistent with previous molecular characterizations. Our cost-effective technology was capable of simultaneously identifying the MTBC species and the genotypes of 96 M. tuberculosis clinical isolates within 6 h using only simple instruments, such as a thermal cycler, a hybridization oven, and a DNA chip scanner, and less technician skill was required than for other techniques. We demonstrate this approach's potential as a simple, flexible, and rapid tool for providing clearer information regarding circulating MTBC isolates.

HLA-A*11:01:01:01, HLA*C*12:02:02:01-HLA-B*52:01:02:02, age and sex are associated with severity of Japanese COVID-19 with respiratory failure

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus causing coronavirus disease 2019 (COVID-19) was announced as an outbreak by the World Health Organization (WHO) in January 2020 and as a pandemic in March 2020. The majority of infected individuals have experienced no or only mild symptoms, ranging from fully asymptomatic cases to mild pneumonic disease. However, a minority of infected individuals develop severe respiratory symptoms. The objective of this study was to identify susceptible HLA alleles and clinical markers for the early identification of severe COVID-19 among hospitalized COVID-19 patients. A total of 137 patients with mild COVID-19 (mCOVID-19) and 53 patients with severe COVID-19 (sCOVID-19) were recruited from the Center Hospital of the National Center for Global Health and Medicine (NCGM), Tokyo, Japan for the period of February-August 2020. High-resolution sequencing-based typing for eight HLA genes was performed using next-generation sequencing. In the HLA association studies, HLA-A*11:01:01:01 [Pc = 0.013, OR = 2.26 (1.27-3.91)] and HLA-C*12:02:02:01-HLA-B*52:01:01:02 [Pc = 0.020, OR = 2.25 (1.24-3.92)] were found to be significantly associated with the severity of COVID-19. After multivariate analysis controlling for other confounding factors and comorbidities, HLA-A*11:01:01:01 [P = 3.34E-03, OR = 3.41 (1.50-7.73)], age at diagnosis [P = 1.29E-02, OR = 1.04 (1.01-1.07)] and sex at birth [P = 8.88E-03, OR = 2.92 (1.31-6.54)] remained significant. Early identification of potential sCOVID-19 could help clinicians prioritize medical utility and significantly decrease mortality from COVID-19.