| Literature DB >> 30975748 |
Jonathon L Baker1, Erik L Hendrickson2, Xiaoyu Tang1, Renate Lux3, Xuesong He4, Anna Edlund1, Jeffrey S McLean2, Wenyuan Shi5.
Abstract
It is well-understood that many bacteria have evolved to survive catastrophic events using a variety of mechanisms, which include expression of stress-response genes, quiescence, necrotrophy, and metabolic advantages obtained through mutation. However, the dynamics of individuals leveraging these abilities to gain a competitive advantage in an ecologically complex setting remain unstudied. In this study, we observed the saliva microbiome throughout the ecological perturbation of long-term starvation, allowing only the species best equipped to access and use the limited resources to survive. During the first several days, the community underwent a death phase that resulted in a ∼50-100-fold reduction in the number of viable cells. Interestingly, after this death phase, only three species, Klebsiella pneumoniae, Klebsiella oxytoca, and Providencia alcalifaciens, all members of the family Enterobacteriaceae, appeared to be transcriptionally active and recoverable. Klebsiella are significant human pathogens, frequently resistant to multiple antibiotics, and recently, ectopic colonization of the gut by oral Klebsiella was documented to induce dysbiosis and inflammation. MetaOmics analyses provided several leads for further investigation regarding the ecological success of the Enterobacteriaceae. The isolates accumulated single nucleotide polymorphisms in known growth advantage in stationary phase alleles and produced natural products closely resembling antimicrobial cyclic depsipeptides. The results presented in this study suggest that pathogenic Enterobacteriaceae persist much longer than their more benign neighbors in the salivary microbiome when faced with starvation. This is particularly significant, given that hospital surfaces contaminated with oral fluids, especially sinks and drains, are well-established sources of outbreaks of drug-resistant Enterobacteriaceae.Entities:
Keywords: Klebsiella; microbial ecology; oral microbiome
Year: 2019 PMID: 30975748 PMCID: PMC6486781 DOI: 10.1073/pnas.1820594116
Source DB: PubMed Journal: Proc Natl Acad Sci U S A ISSN: 0027-8424 Impact factor: 11.205
Fig. 1.Shifts in diversity and relative abundances of constituents of the saliva microbiome during long-term starvation. (A) Number of viable cells in the community, as determined by colony-forming units during starvation in PBS:saliva or PBS over the course of 100 d. (n = 3). (B) Alpha diversity of the PBS:saliva or PBS communities across 84 d of starvation. ***P < 0.001, unpaired t test. (C) Alpha diversity of the PBS:saliva and PBS starvation communities over the course of 84 d of starvation. ****P < 0.0001, Brown-Forsythe test. (D) PCoA plot of unweighted UNIFRAC distances illustrating beta diversity of the PBS:saliva and PBS communities over the course of 84 d of starvation. (E) Relative abundances of bacterial genera in the PBS:saliva starvation experiment, and the starvation community after overnight outgrowth in fresh media, as determined by Illumina sequencing of 16S amplicons. (F) Relative abundances of bacterial genera in the PBS starvation experiment, and the starvation community after overnight outgrowth in fresh media, as determined by Illumina sequencing of 16S amplicons.
Fig. 2.Colony morphology during long-term starvation. Representative image of the PBS:saliva community on SHI agar after the indicated number of days of long-term starvation.
Fig. 3.Relative abundances of actively transcribing taxa in the PBS:saliva long-term starvation community. At the indicated day based on Metaphlan2 analysis of mRNA.
Fig. 4.Accumulation of valine and production of cyclic depsipeptides during long-term starvation. (A) Heat map illustrating the number of liquid chromatography mass spectrometry spectra associated with the indicated spectral cluster after the indicated number of days of starvation. Spectral clusters are grouped into molecule classes (e.g., amino acids) based on molecular networking to GNPS library hits. Row clustering within molecule classes was performed using a Pearson similarity distance matrix. Spectral clusters are named using the GNPS library hit where applicable; otherwise, clusters are named using the liquid chromatography mass spectrometry parent mass. The Solubility column indicates the percent of the spectra (0–100%) within a cluster that were associated with the samples extracted from the culture supernatant (Super.) versus the cell pellet (CP). The Bacterial Family column indicates the percentage of spectra within a cluster that were associated with samples extracted from isolates of Streptococcaceae (Strep.) versus Enterobacteriaceae (Entero.). Only amino acids and cyclic depsipeptides are shown here; the full heat map with all spectral clusters is presented in the . (B) Molecular subnetwork of the four cyclic depsipeptide spectral clusters. Node size corresponds to the total number of spectra in each cluster. Node label indicates parent mass. Edge width indicates the cosine score between spectral cluster nodes. Black outlines denote nodes which mapped to the GNPS library hit, cyclo-MeGlu-O-ξIle-Phe-Pro-Gly-MeVal-MeGlu-ξIle-Pro-Val, with indicated chemical structure. The complete molecular network is available in the . (C) Comparison of MS/MS spectra from spectral cluster with parent mass 1,124.61 (black bars) to GNPS library spectrum for cyclo-MeGlu-O-ξIle-Phe-Pro-Gly-MeVal-MeGlu-ξIle-Pro-Val (green bars).