Getting Our Fingers on the Pulse of Slow-Growing Bacteria in Hard-To-Reach Places

Mycobacterium tuberculosis grows linearly at the single-cell level with larger variability than model organisms

The ability of bacterial pathogens to regulate growth is crucial to control homeostasis, virulence, and drug response. Yet, we do not understand the growth and cell cycle behaviors of Mycobacterium tuberculosis (Mtb), a slow-growing pathogen, at the single-cell level. Here, we use time-lapse imaging and mathematical modeling to characterize these fundamental properties of Mtb. Whereas most organisms grow exponentially at the single-cell level, we find that Mtb exhibits a unique linear growth mode. Mtb growth characteristics are highly variable from cell-to-cell, notably in their growth speeds, cell cycle timing, and cell sizes. Together, our study demonstrates that growth behavior of Mtb diverges from what we have learned from model bacteria. Instead, Mtb generates a heterogeneous population while growing slowly and linearly. Our study provides a new level of detail into how Mtb grows and creates heterogeneity, and motivates more studies of growth behaviors in bacterial pathogens.

Harnessing the Role of Bacterial Plasma Membrane Modifications for the Development of Sustainable Membranotropic Phytotherapeutics

Membrane-targeted molecules such as cationic antimicrobial peptides (CAMPs) are amongst the most advanced group of antibiotics used against drug-resistant bacteria due to their conserved and accessible targets. However, multi-drug-resistant bacteria alter their plasma membrane (PM) lipids, such as lipopolysaccharides (LPS) and phospholipids (PLs), to evade membrane-targeted antibiotics. Investigations reveal that in addition to LPS, the varying composition and spatiotemporal organization of PLs in the bacterial PM are currently being explored as novel drug targets. Additionally, PM proteins such as Mla complex, MPRF, Lpts, lipid II flippase, PL synthases, and PL flippases that maintain PM integrity are the most sought-after targets for development of new-generation drugs. However, most of their structural details and mechanism of action remains elusive. Exploration of the role of bacterial membrane lipidome and proteome in addition to their organization is the key to developing novel membrane-targeted antibiotics. In addition, membranotropic phytochemicals and their synthetic derivatives have gained attractiveness as popular herbal alternatives against bacterial multi-drug resistance. This review provides the current understanding on the role of bacterial PM components on multidrug resistance and their targeting with membranotropic phytochemicals.

Rapid absolute quantification of pathogens and ARGs by nanopore sequencing

Compositional nature of relative abundance data in the current standard microbiome studies limits microbial dynamics interpretations and cross-sample comparisons. Here, we demonstrate the first rapid (1-h sequencing) method coupling Nanopore metagenomic sequencing with cellular spike-in to facilitate the absolute quantification and removal assessment of pathogens and antibiotic resistance genes (ARGs) in wastewater treatment plants (WWTPs). Nanopore sequencing-based quantification results for both simple mock community and complex real environmental samples showed a high consistency with those from the widely-used Illumina and culture-based approaches. Implementing such method, we quantified 46 predominant putative pathogenic species, and 361 ARGs in three WWTP sample sets. Though high log removals of dominant pathogens (2.23 logs) and ARGs (1.98 logs) were achieved, complete removal of all pathogens and ARGs were not achieved. Noticeably, Mycobacterium spp., Clostridium_P perfringens, and Borrelia hermsii exhibited low removal, and 13 ARGs even increased in absolute abundance after the treatment. Our proposed approach manifested its profound ability in providing absolute quantitation information guiding wastewater-based epidemiological surveillance and quantitative risk assessment facilitating microbial hazards management.

Compensatory evolution of Pseudomonas aeruginosa's slow growth phenotype suggests mechanisms of adaptation in cystic fibrosis

Long-term infection of the airways of cystic fibrosis patients with Pseudomonas aeruginosa is often accompanied by a reduction in bacterial growth rate. This reduction has been hypothesised to increase within-patient fitness and overall persistence of the pathogen. Here, we apply adaptive laboratory evolution to revert the slow growth phenotype of P. aeruginosa clinical strains back to a high growth rate. We identify several evolutionary trajectories and mechanisms leading to fast growth caused by transcriptional and mutational changes, which depend on the stage of adaptation of the strain. Return to high growth rate increases antibiotic susceptibility, which is only partially dependent on reversion of mutations or changes in the transcriptional profile of genes known to be linked to antibiotic resistance. We propose that similar mechanisms and evolutionary trajectories, in reverse direction, may be involved in pathogen adaptation and the establishment of chronic infections in the antibiotic-treated airways of cystic fibrosis patients.

Inference of Bacterial Pathogen Instantaneous Population Growth Dynamics

Although bacterial host colonization is a dynamic process that requires population growth, studies often focus on comparing bacterial populations at a given time point. However, this may not reflect the dynamics of the colonization process. Time-course assays provide important insights into the dynamics of host colonization but are laborious and may still lack resolution for immediate processes affecting populations. An alternative way to address this issue, using widely accessible tools (such as quantitative PCR [qPCR]), is to take advantage of the relationship between bacterial chromosomal replication and cell division to determine population growth status at the sampling time. Conceptually, the ratio between the number of copies at the origin of replication and that at the terminus of replication should be correlated with the measured bacterial growth rate. This peak-to-trough ratio (PTR) to estimate instantaneous population growth status was tested with the slow-growing plant-pathogenic bacterium Xylella fastidiosa. We found no correlation between PTR and the measured growth rate when using genome-level data but overall sequencing depth of coverage trends matched theoretical expectations. On the other hand, the population growth status of X. fastidiosa was predicted by PTR when using qPCR data, which was improved by the pretreatment of cells with a photoreactive DNA-binding dye. Our results suggest that PTR could be used to determine X. fastidiosa growth status both in planta and in insect vectors. We expect PTR will perform better with fast-growing bacterial pathogens, potentially becoming a powerful tool for easily and quickly assessing population growth status.

Mycobacterium tuberculosis Metabolism

Mycobacterium tuberculosis is the cause of tuberculosis (TB), a disease which continues to overwhelm health systems in endemic regions despite the existence of effective combination chemotherapy and the widespread use of a neonatal anti-TB vaccine. For a professional pathogen, M. tuberculosis retains a surprisingly large proportion of the metabolic repertoire found in nonpathogenic mycobacteria with very different lifestyles. Moreover, evidence that additional functions were acquired during the early evolution of the M. tuberculosis complex suggests the organism has adapted (and augmented) the metabolic pathways of its environmental ancestor to persistence and propagation within its obligate human host. A better understanding of M. tuberculosis pathogenicity, however, requires the elucidation of metabolic functions under disease-relevant conditions, a challenge complicated by limited knowledge of the microenvironments occupied and nutrients accessed by bacilli during host infection, as well as the reliance in experimental mycobacteriology on a restricted number of experimental models with variable relevance to clinical disease. Here, we consider M. tuberculosis metabolism within the framework of an intimate host-pathogen coevolution. Focusing on recent advances in our understanding of mycobacterial metabolic function, we highlight unusual adaptations or departures from the better-characterized model intracellular pathogens. We also discuss the impact of these mycobacterial "innovations" on the susceptibility of M. tuberculosis to existing and experimental anti-TB drugs, as well as strategies for targeting metabolic pathways. Finally, we offer some perspectives on the key gaps in the current knowledge of fundamental mycobacterial metabolism and the lessons which might be learned from other systems.