Evaluation of gidB alterations responsible for streptomycin resistance in Mycobacterium tuberculosis.

OBJECTIVES: To evaluate gidB alterations for possible impact on the cumulative mechanism underlying the acquisition of high-level streptomycin resistance in Mycobacterium tuberculosis. METHODS: Fifty-two isolates with high streptomycin resistance and 23 isolates with low streptomycin resistance were sequenced for mutational analysis in the rpsL, rrs and gidB region. As the gidB protein has a complex substrate and no activity assay has yet been formulated, mutants of interest were subjected to in silico modelling and were structurally mapped together with active-site amino acid residues for assessment of the relevance to activity of the mutations found. RESULTS: Eight novel sense mutations and four novel mis-sense mutations in gidB were identified. Findings showed that active-site morphology is not only greatly affected by mutants lying in close proximity to the active-site pocket, but also by other mutations altering secondary-structure motifs and having an overall effect on protein structure. CONCLUSIONS: We conclude that gidB mutations address many unanswered questions and explain the whole story behind phenotypic streptomycin-resistant strains exhibiting no mutation in rpsL or rrs. They also validate the hypothesis of sequential progression of resistance from low to high due to the existence of gidB alterations in the genetic background.

Multi-Cellular Immunological Interactions Associated With COVID-19 Infections.

To rapidly prognosticate and generate hypotheses on pathogenesis, leukocyte multi-cellularity was evaluated in SARS-CoV-2 infected patients treated in India or the United States (152 individuals, 384 temporal observations). Within hospital (<90-day) death or discharge were retrospectively predicted based on the admission complete blood cell counts (CBC). Two methods were applied: (i) a "reductionist" one, which analyzes each cell type separately, and (ii) a "non-reductionist" method, which estimates multi-cellularity. The second approach uses a proprietary software package that detects distinct data patterns generated by complex and hypothetical indicators and reveals each data pattern's immunological content and associated outcome(s). In the Indian population, the analysis of isolated cell types did not separate survivors from non-survivors. In contrast, multi-cellular data patterns differentiated six groups of patients, including, in two groups, 95.5% of all survivors. Some data structures revealed one data point-wide line of observations, which informed at a personalized level and identified 97.8% of all non-survivors. Discovery was also fostered: some non-survivors were characterized by low monocyte/lymphocyte ratio levels. When both populations were analyzed with the non-reductionist method, they displayed results that suggested survivors and non-survivors differed immunologically as early as hospitalization day 1.