| Literature DB >> 24719769 |
Lisa M Runco1, Charles B Stauft2, J Robert Coleman3.
Abstract
The majority of studies focused on the construction and reengineering of bacterial pathogens have mainly relied on the knocking out of virulence factors or deletion/mutation of amino acid residues to then observe the microbe's phenotype and the resulting effect on the host immune response. These knockout bacterial strains have also been proposed as vaccines to combat bacterial disease. Theoretically, knockout strains would be unable to cause disease since their virulence factors have been removed, yet they could induce a protective memory response. While knockout strains have been valuable tools to discern the role of virulence factors in host immunity and bacterial pathogenesis, they have been unable to yield clinically relevant vaccines. The advent of synthetic biology and enhanced user-directed gene customization has altered this binary process of knockout, followed by observation. Recent studies have shown that a researcher can now tailor and customize a given microbe's gene expression to produce a desired immune response. In this commentary, we highlight these studies as a new avenue for controlling the inflammatory response as well as vaccine development.Entities:
Year: 2014 PMID: 24719769 PMCID: PMC3955589 DOI: 10.1155/2014/651568
Source DB: PubMed Journal: J Pathog ISSN: 2090-3057
Figure 1Induction of a “just right” immune response, as explained with regard to the damage-response framework (DRF). This curve, based on the DRF of how a host responds to virulence factors and pathogens, was first put forth by Pirofski and Casadevall [8, 9]. The DRF hypothesizes that human disease resulting from microbial infection manifests in two ways: (1) there is a response that is too robust (wild type), which is deleterious to the host or (2) a response that is insufficient, allowing unchecked invasion and succumbing to infection. Also, the low immune response portion of the curve can be viewed with regard to knockout strains with low efficacy as vaccines. Synthetic strains would be in the minimum of the DRF because they are expressing low levels of the toxin, allowing for an increased immune response as compared to the knockout, but below the disease threshold.
Figure 2Modulation of the immune response by using codon-pair bias to titrate bacterial toxin expression. Codon-pair bias customization provides for the titration of expression and modulation of the host immune response. By reducing Streptococcus pneumoniae (SP) toxin expression (pneumolysin gene, ply) a more controlled, nondeleterious response is induced. The shading of the triangles is supported by data from Coleman et al., whereby ply expression is significantly reduced but not eliminated [2]. The wildtype serotype-3 SP (Wt-SP3) expresses 8 hemolytic units (HU) per mL of supernatant from an eight-hour growing culture, whereas SynSP3 produces 2 HU/mL and Δply-SP3 produces 0 HU/mL (data not shown). The infection of mice with Wt-SP3, which secretes high levels of pneumolysin, induces an excessive, deleterious recruitment of CD45+Ly6G+ neutrophils (PMNs) to the lungs 48 hours postinfection, a known manifestation of pulmonary pneumococcal disease [10]. The left y-axis is log10 scale and corresponds to the total number of PMNs isolated from lungs of mice infected with the indicated strains. Interestingly Δply-SP3 recruited the fewest PMNs and SynSP3 an intermediary quantity, corresponding to the hypothesis of the DRF of “just right” immune response. The right y-axis is a linear axis and corresponds to the quantity of IL-10 isolated via ELISA from lungs of mice infected with the indicated strain (right axis). IL-10 is an anti-inflammatory cytokine, so the increased expression of IL-10 in the lungs of SynSP3-infected mice may allow for the “controlled” nondeleterious PMN infiltration. The SynSP3-infected mice recruit fewer PMNs (left axis) and possess the highest level of IL-10 (right axis). The control Δply knockout strain secretes no PLY and fails to stimulate the immune response, with the least PMNs (left) and IL-10 levels (right axis). The graphs have been placed on a single figure for the ease of comparison and not to read each y-axis simultaneously.