Evolutionary Genetics of Mycobacterium tuberculosis and HIV-1: "The Tortoise and the Hare".
Ana Santos-PereiraCarlos MagalhãesPedro M M AraújoNuno S OsórioPublished in: Microorganisms (2021)
The already enormous burden caused by Mycobacterium tuberculosis and Human Immunodeficiency Virus type 1 (HIV-1) alone is aggravated by co-infection. Despite obvious differences in the rate of evolution comparing these two human pathogens, genetic diversity plays an important role in the success of both. The extreme evolutionary dynamics of HIV-1 is in the basis of a robust capacity to evade immune responses, to generate drug-resistance and to diversify the population-level reservoir of M group viral subtypes. Compared to HIV-1 and other retroviruses, M. tuberculosis generates minute levels of genetic diversity within the host. However, emerging whole-genome sequencing data show that the M. tuberculosis complex contains at least nine human-adapted phylogenetic lineages. This level of genetic diversity results in differences in M. tuberculosis interactions with the host immune system, virulence and drug resistance propensity. In co-infected individuals, HIV-1 and M. tuberculosis are likely to co-colonize host cells. However, the evolutionary impact of the interaction between the host, the slowly evolving M. tuberculosis bacteria and the HIV-1 viral "mutant cloud" is poorly understood. These evolutionary dynamics, at the cellular niche of monocytes/macrophages, are also discussed and proposed as a relevant future research topic in the context of single-cell sequencing.
Keyphrases
- human immunodeficiency virus
- antiretroviral therapy
- mycobacterium tuberculosis
- hiv aids
- genetic diversity
- hiv infected
- hiv positive
- hepatitis c virus
- hiv testing
- men who have sex with men
- pulmonary tuberculosis
- single cell
- immune response
- endothelial cells
- sars cov
- genome wide
- staphylococcus aureus
- escherichia coli
- dna methylation
- south africa
- rna seq
- risk factors
- emergency department
- induced apoptosis
- electronic health record
- biofilm formation
- machine learning
- gram negative
- artificial intelligence
- cystic fibrosis
- peripheral blood