Metabolic predictors of COVID-19 mortality and severity: a survival analysis.

Introduction: The global healthcare burden of COVID-19 pandemic has been unprecedented with a high mortality. Metabolomics, a powerful technique, has been increasingly utilized to study the host response to infections and to understand the progression of multi-system disorders such as COVID-19. Analysis of the host metabolites in response to SARS-CoV-2 infection can provide a snapshot of the endogenous metabolic landscape of the host and its role in shaping the interaction with SARS-CoV-2. Disease severity and consequently the clinical outcomes may be associated with a metabolic imbalance related to amino acids, lipids, and energy-generating pathways. Hence, the host metabolome can help predict potential clinical risks and outcomes. Methods: In this prospective study, using a targeted metabolomics approach, we studied the metabolic signature in 154 COVID-19 patients (males=138, age range 48-69 yrs) and related it to disease severity and mortality. Blood plasma concentrations of metabolites were quantified through LC-MS using MxP Quant 500 kit, which has a coverage of 630 metabolites from 26 biochemical classes including distinct classes of lipids and small organic molecules. We then employed Kaplan-Meier survival analysis to investigate the correlation between various metabolic markers, disease severity and patient outcomes. Results: A comparison of survival outcomes between individuals with high levels of various metabolites (amino acids, tryptophan, kynurenine, serotonin, creatine, SDMA, ADMA, 1-MH and carnitine palmitoyltransferase 1 and 2 enzymes) and those with low levels revealed statistically significant differences in survival outcomes. We further used four key metabolic markers (tryptophan, kynurenine, asymmetric dimethylarginine, and 1-Methylhistidine) to develop a COVID-19 mortality risk model through the application of multiple machine-learning methods. Conclusions: Metabolomics analysis revealed distinct metabolic signatures among different severity groups, reflecting discernible alterations in amino acid levels and perturbations in tryptophan metabolism. Notably, critical patients exhibited higher levels of short chain acylcarnitines, concomitant with higher concentrations of SDMA, ADMA, and 1-MH in severe cases and non-survivors. Conversely, levels of 3-methylhistidine were lower in this context.

A landscape of genomic alterations at the root of a near-untreatable tuberculosis epidemic.

BACKGROUND: Atypical Beijing genotype Mycobacterium tuberculosis strains are widespread in South Africa and have acquired resistance to up to 13 drugs on multiple occasions. It is puzzling that these strains have retained fitness and transmissibility despite the potential fitness cost associated with drug resistance mutations. METHODS: We conducted Illumina sequencing of 211 Beijing genotype M. tuberculosis isolates to facilitate the detection of genomic features that may promote acquisition of drug resistance and restore fitness in highly resistant atypical Beijing forms. Phylogenetic and comparative genomic analysis was done to determine changes that are unique to the resistant strains that also transmit well. Minimum inhibitory concentration (MIC) determination for streptomycin and bedaquiline was done for a limited number of isolates to demonstrate a difference in MIC between isolates with and without certain variants. RESULTS: Phylogenetic analysis confirmed that two clades of atypical Beijing strains have independently developed resistance to virtually all the potent drugs included in standard (pre-bedaquiline) drug-resistant TB treatment regimens. We show that undetected drug resistance in a progenitor strain was likely instrumental in this resistance acquisition. In this cohort, ethionamide (ethA A381P) resistance would be missed in first-line drug-susceptible isolates, and streptomycin (gidB L79S) resistance may be missed due to an MIC close to the critical concentration. Subsequent inadequate treatment historically led to amplification of resistance and facilitated spread of the strains. Bedaquiline resistance was found in a small number of isolates, despite lack of exposure to the drug. The highly resistant clades also carry inhA promoter mutations, which arose after ethA and katG mutations. In these isolates, inhA promoter mutations do not alter drug resistance, suggesting a possible alternative role. CONCLUSION: The presence of the ethA mutation in otherwise susceptible isolates from ethionamide-naïve patients demonstrates that known exposure is not an adequate indicator of drug susceptibility. Similarly, it is demonstrated that bedaquiline resistance can occur without exposure to the drug. Inappropriate treatment regimens, due to missed resistance, leads to amplification of resistance, and transmission. We put these results into the context of current WHO treatment regimens, underscoring the risks of treatment without knowledge of the full drug resistance profile.

Progenitor strain introduction of Mycobacterium bovis at the wildlife-livestock interface can lead to clonal expansion of the disease in a single ecosystem.

Mycobacterium bovis infects multiple wildlife species and domesticated cattle across South Africa, and negatively impacts on livestock trade and movement of wildlife for conservation purposes. M. bovis infection was first reported in the Kruger National Park (KNP) in South Africa during the 1990s, and has since spread to infect numerous animal host species throughout the park and across South Africa. Whole genome sequencing data of 17 M. bovis isolates were analyzed to investigate the genomic diversity among M. bovis isolates causing disease in different animal host species from various locations in South Africa. M. bovis strains analyzed in this study are geographic rather than host species-specific. The clonal expansion of M. bovis in the KNP highlights the effect of an introduction of a transmissible infectious disease leading to a rising epidemic in wildlife, and emphasizes the importance of disease control and movement restriction of species that serve as disease reservoirs. In conclusion, the point source introduction of a single M. bovis strain type in the KNP ecosystem lead to an M. bovis outbreak in this area that affects various host species and poses an infection risk in neighboring rural communities where HIV prevalence is high.

Whole genome sequence analysis of Mycobacterium suricattae.

Tuberculosis occurs in various mammalian hosts and is caused by a range of different lineages of the Mycobacterium tuberculosis complex (MTBC). A recently described member, Mycobacterium suricattae, causes tuberculosis in meerkats (Suricata suricatta) in Southern Africa and preliminary genetic analysis showed this organism to be closely related to an MTBC pathogen of rock hyraxes (Procavia capensis), the dassie bacillus. Here we make use of whole genome sequencing to describe the evolution of the genome of M. suricattae, including known and novel regions of difference, SNPs and IS6110 insertion sites. We used genome-wide phylogenetic analysis to show that M. suricattae clusters with the chimpanzee bacillus, previously isolated from a chimpanzee (Pan troglodytes) in West Africa. We propose an evolutionary scenario for the Mycobacterium africanum lineage 6 complex, showing the evolutionary relationship of M. africanum and chimpanzee bacillus, and the closely related members M. suricattae, dassie bacillus and Mycobacterium mungi.