Differentially culturable tubercle bacteria dynamics during standard anti-tuberculosis treatment: A prospective cohort study.

This study aimed to evaluate the dynamics of culture filtrate dependent subpopulations of Mycobacterium tuberculosis in a prospective cohort study following 17 patients through a standard 6-month anti-tuberculosis regimen, performing monthly sputum collection. We performed the limiting dilution method with culture filtrate supplementation of liquid media in pre- and post-treatment sputum samples to assess the bacillary load and to evaluate the Mycobacterium tuberculosis subpopulation dynamics within the 6-months standard anti-tuberculosis regimen. We found that supplementation increased the bacillary load by 30% in pre-treatment samples (p = 0.0005) and 35% in samples after one month of treatment (p = 0.0977). We found a weak linear correlation between the decrease of Mycobacterium tuberculosis growth in liquid media with and without culture filtrate supplementation (ρ = 0.54; p = 0.026). None of the patients had bacilli recovery after two months of treatment. Our study constitutes the first follow-up regarding Mycobacterium tuberculosis subpopulation dynamics throughout a standard 6-month anti-tuberculosis treatment and also supports the use of culture filtrate to increase bacillary load in liquid media. Moreover, it highlights that any new treatment regimens should test the efficacy of the drugs in all Mycobacterium tuberculosis subpopulations.

Small-world networks decrease the speed of Muller's ratchet.

Muller's ratchet is an evolutionary process that has been implicated in the extinction of asexual species, the evolution of non-recombining genomes, such as the mitochondria, the degeneration of the Y chromosome, and the evolution of sex and recombination. Here we study the speed of Muller's ratchet in a spatially structured population which is subdivided into many small populations (demes) connected by migration, and distributed on a graph. We studied different types of networks: regular networks (similar to the stepping-stone model), small-world networks and completely random graphs. We show that at the onset of the small-world network - which is characterized by high local connectivity among the demes but low average path length - the speed of the ratchet starts to decrease dramatically. This result is independent of the number of demes considered, but is more pronounced the larger the network and the stronger the deleterious effect of mutations. Furthermore, although the ratchet slows down with increasing migration between demes, the observed decrease in speed is smaller in the stepping-stone model than in small-world networks. As migration rate increases, the structured populations approach, but never reach, the result in the corresponding panmictic population with the same number of individuals. Since small-world networks have been shown to describe well the real contact networks among people, we discuss our results in the light of the evolution of microbes and disease epidemics.

Muller's ratchet in random graphs and scale-free networks.

Muller's ratchet is an evolutionary process that has been implicated in the extinction of asexual species, the evolution of mitochondria, the degeneration of the Y chromosome, the evolution of sex and recombination and the evolution of microbes. Here we study the speed of Muller's ratchet in a population subdivided into many small subpopulations connected by migration, and distributed on a network. We compare the speed of the ratchet in two distinct types of topologies: scale free networks and random graphs. The difference between the topologies is noticeable when the average connectivity of the network and the migration rate is large. In this situation we observe that the ratchet clicks faster in scale free networks than in random graphs. So contrary to intuition, scale free networks are more prone to loss of genetic information than random graphs. On the other hand, we show that scale free networks are more robust to the random extinction than random graphs. Since these complex networks have been shown to describe well real-life systems, our results open a framework for studying the evolution of microbes and disease epidemics.