Two-component Signaling Pathways: A bacterial Goldilocks mechanism

Bacillus subtilis can measure the activity of the enzymes that remodel the cell wall to ensure that the levels of activity are ‘just right’.
  1. Irene M Kim
  2. Hendrik Szurmant  Is a corresponding author
  1. Western University of Health Sciences, United States

When mollusks get bigger, their shells grow with them to accommodate the changing shape and size of the organism being housed. Something similar also happens in bacteria. The cell wall of most bacteria consists of a single macromolecule called peptidoglycan that surrounds the cell and is made up of modified sugars that are crosslinked through peptide side chains. Like sea shells, bacteria come in different sizes and the cell wall dictates their shape (Cabeen and Jacobs-Wagner, 2007). The cell wall also protects bacteria from adverse environmental conditions, but it must be constantly remodeled so that rapid bacterial growth and division can take place. The enzymes in charge of this remodeling process are called autolysins, and their activity must be regulated to stop bacteria from losing their cell wall.

Signal transduction systems are protein systems that detect molecular or physical cues and translate them into an appropriate cellular response. In bacteria, signal transduction is commonly regulated by two-component systems, known as TCS for short (Zschiedrich et al., 2016). Usually the two components are a signal detector and a transcription factor that communicate with one another through the transfer of a phosphoryl group.

An important TCS in the soil bacterium Bacillus subtilis and other related bacteria is the WalRK system, which is essential for viability (Szurmant, 2012). The system, which is comprised of the signal-detecting protein WalK and the transcription factor WalR, gets its name from its role in maintaining the cell wall (Dubrac et al., 2007). Together these two components regulate the expression of several autolysin genes, including those for the enzymes LytE and CwlO, which are required for cell elongation (Salzberg et al., 2013). Now, in eLife, David Rudner and colleagues at Harvard Medical School – including Genevieve Dobihal and Yannick Brunet as joint first authors, along with Josué Flores-Kim – report on how the WalRK system in B. subtilis detects and responds to autolysin levels (Dobihal et al., 2019).

Dobihal et al. first observed that B. subtilis can measure the levels of autolysin activity and, if they are too low for the cell to grow, can adjust them accordingly. Next, they examined if the reverse is also true: can the cell identify if autolysin activity is too high to retain the protective shell, and reduce autolysin expression appropriately? Indeed, when the autolysin LytE is artificially overproduced, the cell reduces endogenous production of this enzyme. Thus the bacterium employs homeostatic control to ensure that autolysin activity is 'not too much, not too little, but just right', just like in the tale of Goldilocks and the three bears (Figure 1). This equilibrium is important given that mis-regulated autolysin activity can lead to cell lysis and defects in the permeability of the membrane.

The Goldilocks principle applied to bacterial cell wall homeostasis.

The bacterial cell wall (top left) consists of sugar strands (hexagons) that are crosslinked via peptide bonds between their peptide sidechains (small circles). Cell expansion requires the incorporation of new cell wall material. Autolysin enzymes cleave the peptide crosslinks to allow for expansion. Insufficient autolysin activity prevents expansion and thus growth (bottom left). Uncontrolled autolysin activity results in cell wall destruction and lysis (indicated by yellow stars, top right). When the autolysin activity is ‘just right’, the cell wall expands (red) and its integrity is maintained (bottom right).

Dobihal et al. then used several reporters to measure the expression of different genes regulated by WalR, and found they all responded similarly to the overexpression and deletion of the gene for LytE. This suggests that LytE and CwlO activity is directly detected by the WalRK system, but the precise signal used by the WalRK system to detect this activity remained unknown, as did the mechanism of detection.

WalK is a multi-domain membrane-spanning protein that has two domains commonly associated with signal detection: one of these domains faces the outside of the cell whereas the other faces the inside (Fukushima et al., 2011). WalK interacts with two other proteins that inhibit its activity, WalH and WalI (Szurmant et al., 2007; Szurmant et al., 2008). The signal for autolysin levels could be perceived by either of the two inhibitor proteins or by one of the signal detection domains of WalK. Dobihal et al. deleted domains in WalH, WalI and WalK to determine which protein detected the signal, demonstrating that the WalK domain that faces the outside of the cell is the only one required.

But what is the signal detected by WalK? LytE and CwlO are both able to cleave peptide bonds, probably to reduce crosslinks in the cell wall (Bisicchia et al., 2007). WalK could therefore be responding to a physical signal, such as a change in the tension exerted by a cell wall with too many or too few crosslinks. Alternatively, the signal could be of a chemical nature, such as a peptide being released when the autolysins remodel the cell wall. To distinguish between these two possibilities, Dobihal et al. exposed the purified cell wall of B. subtilis to the CwlO enzyme in vitro, and then applied the cleavage products of the reaction to B. subtilis cultures. The results showed that the cleavage products of CwlO can affect the expression of genes regulated by WalR. Exactly which molecule interacts with WalK to relay the signal remains unknown.

The findings by Dobihal et al. contribute to our understanding of the WalRK two-component system in B. subtilis. The spherical bacteria Staphylococcus aureus and Streptococcus pneumoniae are distant relatives of B. subtilis and also use the WalRK system to modulate autolysin gene expression, despite not growing by cell wall elongation (Ng and Winkler, 2004; Dubrac et al., 2007). Differences in domain architecture of WalK (S. pneumoniae) or cell wall crosslinks (S. aureus) dictate that the signal to modulate WalRK activity must be different from the one used by B. subtilis. Even in B. subtilis previous results suggested that there might be additional signals detected by WalK that are related to cell division (Fukushima et al., 2011). Thus, a unifying theme for the role of WalRK in all these bacteria remains unclear, and requires additional studies to build on these exciting new insights.

References

  1. Book
    1. Szurmant H
    (2012)
    Essential two-component systems of gram-positive bacteria
    In: Gross R, Beier D, editors. Two-Component Systems in Bacteria, 1. Norwich: Caister Scientific Press. pp. 127–147.

Article and author information

Author details

  1. Irene M Kim

    Irene M Kim is at the College of Osteopathic Medicine of the Pacific in the Western University of Health Sciences, Pomona, California

    Competing interests
    No competing interests declared
  2. Hendrik Szurmant

    Hendrik Szurmant is at the College of Osteopathic Medicine of the Pacific in the Western University of Health Sciences, Pomona, California

    For correspondence
    hszurmant@westernu.edu
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-6970-9566

Publication history

  1. Version of Record published:

Copyright

© 2020, Kim and Szurmant

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

  • 1,943
    views
  • 168
    downloads
  • 2
    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)

  1. Irene M Kim
  2. Hendrik Szurmant
(2020)
Two-component Signaling Pathways: A bacterial Goldilocks mechanism
eLife 9:e54244.
https://doi.org/10.7554/eLife.54244

Further reading

    1. Microbiology and Infectious Disease
    Lesia Semenova, Yingfan Wang ... Edward P Browne
    Research Article

    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.

    1. Microbiology and Infectious Disease
    Srinivasan Vijay, Nguyen Le Hoai Bao ... Nguyen Thuy Thuong
    Research Article

    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.