Co-infecting microorganisms dramatically alter pathogen gene essentiality during polymicrobial infection

Ibberson, Carolyn B.; Stacy, Apollo; Fleming, Derek; Dees, Justine L.; Rumbaugh, Kendra P.; Gilmore, Michael S.; Whiteley, Marvin

doi:10.1038/nmicrobiol.2017.79

Cited by 96 publications

(102 citation statements)

References 40 publications

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“…This rich dataset provided both general gene fitness trends and some specific molecular insights into the mutualistic relationship, such as the importance or lack thereof of various regulatory and metabolic pathways and the existence of unexpected purine cross-feeding. Similar to findings from TnSeq experiments on cocultures mimicking coinfections (2, 23, 33), we observed that diverse cellular functions impact E. coli fitness in coculture with R. palustris . These observations underscore that the genetic and physiological architectures underlying microbial interactions, cooperative or otherwise, are complex and often difficult to predict, even in engineered systems.…”

Section: Resultssupporting

confidence: 84%

Genome-wide analysis ofEscherichia colifitness determinants in a cross-feeding mutualism withRhodopseudomonas palustris

LaSarre

Deutschbauer

Love

et al. 2020

Preprint

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Microbial interactions abound in natural ecosystems and shape community structure and function. Substantial attention has been given to cataloging mechanisms by which microbes interact, but there is a limited understanding of the genetic landscapes that promote or hinder microbial interactions. We previously developed a mutualistic coculture pairing Escherichia coli and Rhodopseudomonas palustris, wherein E. coli provides carbon to R. palustris in the form of glucose fermentation products and R. palustris fixes N2 gas and provides nitrogen to E. coli in the form of NH4+. The stable coexistence and reproducible trends exhibited by this coculture make it ideal for interrogating the genetic underpinnings of a cross-feeding mutualism. Here, we used random barcode transposon sequencing (RB-TnSeq) to conduct a genome-wide search for E. coli genes that influence fitness during cooperative growth with R. palustris. RB-TnSeq revealed hundreds of genes that increased or decreased E. coli fitness in a mutualism-dependent manner. Some identified genes were involved in nitrogen sensing and assimilation, as expected given the coculture design. The other identified genes were involved in diverse cellular processes, including energy production and cell wall and membrane biogenesis. Additionally, we discovered unexpected purine cross-feeding from R. palustris to E. coli, with coculture rescuing growth of an E. coli purine auxotroph. Our data provide insight into the genes and gene networks that can influence a cross-feeding mutualism and underscore that microbial interactions are not necessarily predictable a priori.IMPORTANCEMicrobial communities impact life on earth in profound ways, including driving global nutrient cycles and influencing human health and disease. These community functions depend on the interactions that resident microbes have with the environment and each other. Thus, identifying genes that influence these interactions will aid the management of natural communities and the use of microbial consortia as biotechnology. Here, we identified genes that influenced Escherichia coli fitness during cooperative growth with a mutualistic partner, Rhodospeudomonas palustris. Although this mutualism centers on the bidirectional exchange of essential carbon and nitrogen, E. coli fitness was positively and negatively affected by genes involved in diverse cellular processes. Furthermore, we discovered an unexpected purine cross-feeding interaction. These results contribute knowledge on the genetic foundation of a microbial cross-feeding interaction and highlight that unanticipated interactions can occur even within engineered microbial communities.

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Section: Resultssupporting

confidence: 84%

Genome-wide analysis ofEscherichia colifitness determinants in a cross-feeding mutualism withRhodopseudomonas palustris

LaSarre

Deutschbauer

Love

et al. 2020

Preprint

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“…S2). The idea of pathway sharing is in line with the observation that the gene essentiality for a specific taxon is dependent on its community partners (45). It is possible that interactions between bacteria may indeed lead to other than only additive effects on the phenotypic population structure of the individual taxa.…”

Section: Discussionmentioning

confidence: 83%

Coculturing Bacteria Leads to Reduced Phenotypic Heterogeneities

Heyse

Buysschaert

Props

et al. 2019

Appl Environ Microbiol

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Isogenic bacterial populations are known to exhibit phenotypic heterogeneity at the single-cell level. Because of difficulties in assessing the phenotypic heterogeneity of a single taxon in a mixed community, the importance of this deeper level of organization remains relatively unknown for natural communities. In this study, we have used membrane-based microcosms that allow the probing of the phenotypic heterogeneity of a single taxon while interacting with a synthetic or natural community. Individual taxa were studied under axenic conditions, as members of a coculture with physical separation, and as a mixed culture. Phenotypic heterogeneity was assessed through both flow cytometry and Raman spectroscopy. Using this setup, we investigated the effect of microbial interactions on the individual phenotypic heterogeneities of two interacting drinking water isolates. Through flow cytometry we have demonstrated that interactions between these bacteria lead to a reduction of their individual phenotypic diversities and that this adjustment is conditional on the bacterial taxon. Single-cell Raman spectroscopy confirmed a taxondependent phenotypic shift due to the interaction. In conclusion, our data suggest that bacterial interactions may be a general driver of phenotypic heterogeneity in mixed microbial populations. IMPORTANCE Laboratory studies have shown the impact of phenotypic heterogeneity on the survival and functionality of isogenic populations. Because phenotypic heterogeneity plays an important role in pathogenicity and virulence, antibiotic resistance, biotechnological applications, and ecosystem properties, it is crucial to understand its influencing factors. An unanswered question is whether bacteria in mixed communities influence the phenotypic heterogeneity of their community partners. We found that coculturing bacteria leads to a reduction in their individual phenotypic heterogeneities, which led us to the hypothesis that the individual phenotypic diversity of a taxon is dependent on the community composition.

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“…S5 ). The idea of pathway sharing is in line with the observation that the gene-essentiality for a specific taxon is dependent on its community partners (54). Asides these cooperative interactions, competition may also explain the reduction in phenotypic diversity.…”

Section: Discussionmentioning

confidence: 85%

Coculturing bacteria leads to reduced phenotypic heterogeneities

Heyse

Buysschaert

Props

et al. 2018

Preprint

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19Isogenic bacterial populations are known to exhibit phenotypic heterogeneity at the 20 single cell level. Because of difficulties in assessing the phenotypic heterogeneity of a 21 65 5 difficult to assess the heterogeneity of a single taxon within a mixed community. 66 Recently, several experimental approaches that assess the metabolic diversity of a 67 single taxon in natural communities have been developed (19, 20). However, these 68 approaches rely on FISH-probes that bind to 16S rRNA gene sequences for 69 identification of the taxon of interest. Hence, they do not allow to exclude the possibility 70 that some of the observed phenotypic differences are caused by minor genetic 71 differences between bacteria with very similar 16S rRNA genes. 72Two laser-based methods that are suitable for assessing phenotypes are flow cytometry 73 and Raman spectroscopy (21)(22)(23). Two types of light can be detected by the flow 74 cytometer, that is scattered light and fluorescence. The scattered light provides 75 information about the basic characteristics of the cells (e.g. size, shape and surface 76 properties), while the fluorescence data provides additional information about the cell 77 properties for which it has been stained (e.g. nucleic acid content, metabolic activity, 78 etc.) (24). Flow cytometry thus gives information regarding morphological as well as 79 specific physiological properties of single-cells. The Raman spectrum of a single cell 80 consists of a combination of the individual spectra of all the compounds that make up 81 this cell (e.g. proteins, nucleic acids, fatty acids, etc.). This results in a complex spectrum, 82 which can be interpreted as a chemical fingerprint of the cell (25, 26). Hence, single-cell 83 Raman spectra offer an in depth view on the biochemical composition of each 84 phenotype. 85A tool that can help to answer questions that are difficult to study directly in natural 86 communities is a synthetic ecosystem. A synthetic ecosystem consists of a selected set 87 of species under specific conditions. They are controllable and have a reduced 88 complexity in comparison to natural communities (27). Hence, they provide a way to 89 6 test ecological theories in order to better understand the rules of nature (28). A specific 90 setup for these synthetic ecosystems are co-cultures. The principle of such a system is 91 that two or more bacterial populations are cultivated together with some degree of 92 contact between them, which allows to study their interactions (29). 93An unanswered question, and the focus of this study, is whether bacteria in mixed 94 communities influence the phenotypic heterogeneity of their community partners. Here, 95 we used a synthetic community setup where two isolates were used as model 96 organisms. Four synthetic communities were created. The isolates were grown in 97 axenic cultures as a reference for non-interacting genotypes. To be able to study the 98 individual community members separately after they have been interacting via their 99 joint medi...

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Co-infecting microorganisms dramatically alter pathogen gene essentiality during polymicrobial infection

Cited by 96 publications

References 40 publications

Genome-wide analysis ofEscherichia colifitness determinants in a cross-feeding mutualism withRhodopseudomonas palustris

Genome-wide analysis ofEscherichia colifitness determinants in a cross-feeding mutualism withRhodopseudomonas palustris

Coculturing Bacteria Leads to Reduced Phenotypic Heterogeneities

Coculturing bacteria leads to reduced phenotypic heterogeneities

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