Bacteria: Driving polar growth
Profiling the phenotype of 200,000 mutants revealed a new cofactor that may help a group of rod-shaped bacteria elongate and grow.
- Global Health Institute, Ecole Polytechnique Fédérale de Lausanne, Switzerland
Bacteria come in a variety of shapes and sizes – some are round, some are spiral and some are rod-shaped. The mechanisms that bacteria use to generate and maintain these diverse shapes as they grow is an area of active research (Kysela et al., 2016). The cell wall of bacteria contains a net-like structure called peptidoglycan, and it is thought that this structure maintains the shape of the cell (Egan et al., 2017). As bacteria grow, nascent peptidoglycans and other materials are inserted into the cell wall in a complicated process involving multiprotein complexes that contain various enzymes (Höltje, 1998; Pazos et al., 2017).
The process of cell wall growth has been widely studied in rod-shaped bacteria such as Escherichia coli and Bacillus subtilis, which grow by adding new material to the long sidewalls of the cell rather than to the ends (Daniel and Errington, 2003). But not all rod-shaped bacteria grow this way. For example, bacteria belonging to the Actinobacteria phylum – which includes the pathogens that cause tuberculosis, leprosy, and diphtheria – grow by adding new material to the ends (or poles) of the cell (Kieser and Rubin, 2014; Cameron et al., 2015).
It has been suggested that scaffold proteins and intermediate filaments target the machinery that synthesizes peptidoglycans to the cell poles, in order to restrict growth to this region (Letek et al., 2008; Fiuza et al., 2010). However, many aspects of polar growth, including the composition of the enzyme complexes and the cofactors involved, are still unknown. Now, in eLife, Joel Sher, Hoong Chuin Lim and Thomas Bernhardt from Harvard Medical School report how a newly discovered cofactor localizes a peptidoglycan synthase enzyme to the poles of bacterial cells (Sher et al., 2020).
To identify the components involved in polar growth, Sher et al. studied a library of 200,000 mutants of Corynebacterium glutamicum (a member of the Actinobacteria phylum) in which each strain is mutated for a specific gene or pathway. The bacteria were exposed to various stressful conditions or antibiotics, and a phenotypic profile was generated for each mutant based on how they responded. Further analysis revealed that genes which have a similar role, or work together in the same pathway, exhibit similar characteristics when mutated. This allowed Sher et al. to identify which genes are involved in polar growth.
Sher et al. studied the behavior of a previously unknown gene that codes for a protein named CofA. The phenotypic profile of CofA mutants was highly correlated with the profiles of strains carrying a genetic mutation in an enzyme called PBP1a, which synthesizes peptidoglycans. Fluorescent tagging revealed that CofA was localized at the cell poles of Corynebacterium, which is consistent with previous studies that found peptidoglycan synthases (such as PBP1a) to also be located at this region (Valbuena et al., 2007).
Individually deleting the genes that code for CofA and PBP1a showed that these two proteins depend on each other for their localization. Further experiments revealed that CofA acts as a cofactor and binds to the transmembrane domain of PBP1a, helping the enzyme accumulate at the tips of the cell.
Paralogs of the gene coding for CofA and its transmembrane domains are found throughout the Actinobacteria phylum. Sher et al. showed that CofA proteins in pathogenic bacteria, such as C. jeikium and M. tuberculosis, also interacted with their PBP1a counterpart in a specific manner. The CofA protein in M. tuberculosis was found to have an extended N-terminal cytoplasmic domain and deleting this region facilitated the interaction between CofA and PBP1a. However, the role of this N-terminal domain was not investigated further.
This study is the first to identify a conserved cofactor that modulates the behavior of peptidoglycan synthases in Actinobacteria. It also raises several tantalizing questions: Would removing CofA cause the growth of Corynebacterium to be less polar? Does deleting CofA and PBP1a change how peptidoglycan units are inserted into the cell wall? It would also be useful to mine the phenotypic profiles of other mutants to see if there are other unidentified cofactors of the PBP proteins.
Several disease-causing pathogens use this mode of polar growth, which is why it is important to study the components and mechanisms involved. The bacterial cell wall is repeatedly used as a target for antibiotic development. These findings could identify new drug targets, which may help combat the rising rates of antibiotic resistance, especially in the case of tuberculosis. Furthermore, the phenotype profiling approach used by Sher et al. could be used to determine the role of previously uncharacterized proteins and identify which proteins and genes participate in the same biological pathway.
References
-
The essential features and modes of bacterial polar growthTrends in Microbiology 23:347–353.https://doi.org/10.1016/j.tim.2015.01.003
-
Regulation of bacterial cell wall growthThe FEBS Journal 284:851–867.https://doi.org/10.1111/febs.13959
-
Phosphorylation of a novel cytoskeletal protein (RsmP) regulates rod-shaped morphology in Corynebacterium glutamicumJournal of Biological Chemistry 285:29387–29397.https://doi.org/10.1074/jbc.M110.154427
-
Growth of the stress-bearing and shape-maintaining murein sacculus of Escherichia coliMicrobiology and Molecular Biology Reviews 62:181–203.https://doi.org/10.1128/MMBR.62.1.181-203.1998
-
How sisters grow apart: mycobacterial growth and divisionNature Reviews Microbiology 12:550–562.https://doi.org/10.1038/nrmicro3299
-
DivIVA is required for polar growth in the MreB-lacking rod-shaped actinomycete Corynebacterium glutamicumJournal of Bacteriology 190:3283–3292.https://doi.org/10.1128/JB.01934-07
-
Robust peptidoglycan growth by dynamic and variable multi-protein complexesCurrent Opinion in Microbiology 36:55–61.https://doi.org/10.1016/j.mib.2017.01.006
Article and author information
Author details
Publication history
Copyright
© 2020, Dhar
This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.
Metrics
-
- 1,089
- views
-
- 77
- downloads
-
- 0
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
A two-part list of links to download the article, or parts of the article, in various formats.
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Bacteria: Driving polar growth
eLife 9:e57043.
https://doi.org/10.7554/eLife.57043
Further reading
-
- Microbiology and Infectious Disease
Understanding the interplay between the HIV reservoir and the host immune system may yield insights into HIV persistence during antiretroviral therapy (ART) and inform strategies for a cure. Here, we applied machine learning (ML) approaches to cross-sectional high-parameter HIV reservoir and immunology data in order to characterize host–reservoir associations and generate new hypotheses about HIV reservoir biology. High-dimensional immunophenotyping, quantification of HIV-specific T cell responses, and measurement of genetically intact and total HIV proviral DNA frequencies were performed on peripheral blood samples from 115 people with HIV (PWH) on long-term ART. Analysis demonstrated that both intact and total proviral DNA frequencies were positively correlated with T cell activation and exhaustion. Years of ART and select bifunctional HIV-specific CD4 T cell responses were negatively correlated with the percentage of intact proviruses. A leave-one-covariate-out inference approach identified specific HIV reservoir and clinical–demographic parameters, such as age and biological sex, that were particularly important in predicting immunophenotypes. Overall, immune parameters were more strongly associated with total HIV proviral frequencies than intact proviral frequencies. Uniquely, however, expression of the IL-7 receptor alpha chain (CD127) on CD4 T cells was more strongly correlated with the intact reservoir. Unsupervised dimension reduction analysis identified two main clusters of PWH with distinct immune and reservoir characteristics. Using reservoir correlates identified in these initial analyses, decision tree methods were employed to visualize relationships among multiple immune and clinical–demographic parameters and the HIV reservoir. Finally, using random splits of our data as training-test sets, ML algorithms predicted with approximately 70% accuracy whether a given participant had qualitatively high or low levels of total or intact HIV DNA . The techniques described here may be useful for assessing global patterns within the increasingly high-dimensional data used in HIV reservoir and other studies of complex biology.
-
- Microbiology and Infectious Disease
Antibiotic tolerance in Mycobacterium tuberculosis reduces bacterial killing, worsens treatment outcomes, and contributes to resistance. We studied rifampicin tolerance in isolates with or without isoniazid resistance (IR). Using a minimum duration of killing assay, we measured rifampicin survival in isoniazid-susceptible (IS, n=119) and resistant (IR, n=84) isolates, correlating tolerance with bacterial growth, rifampicin minimum inhibitory concentrations (MICs), and isoniazid-resistant mutations. Longitudinal IR isolates were analyzed for changes in rifampicin tolerance and genetic variant emergence. The median time for rifampicin to reduce the bacterial population by 90% (MDK90) increased from 1.23 days (IS) and 1.31 days (IR) to 2.55 days (IS) and 1.98 days (IR) over 15–60 days of incubation, indicating fast and slow-growing tolerant sub-populations. A 6 log10-fold survival fraction classified tolerance as low, medium, or high, showing that IR is linked to increased tolerance and faster growth (OR = 2.68 for low vs. medium, OR = 4.42 for low vs. high, p-trend = 0.0003). High tolerance in IR isolates was associated with rifampicin treatment in patients and genetic microvariants. These findings suggest that IR tuberculosis should be assessed for high rifampicin tolerance to optimize treatment and prevent the development of multi-drug-resistant tuberculosis.
