Dataset Information

Gut Microbial and Metabolic Responses to Salmonella enterica Serovar Typhimurium and Candida albicans

ABSTRACT:

The gut microbiota is increasingly recognized for playing a critical role in human health and disease, especially in conferring resistance to both virulent pathogens such as Salmonella, which infects 1.2 million people in the United States every year [1], and opportunistic pathogens like Candida, which causes an estimated 46,000 cases of invasive candidiasis each year in the United States [2]. The dynamics of pathogen-microbiome interactions and the metabolites involved in this process remain largely unknown.

We use gnotobiotic mice infected with the virulent pathogen Salmonella enterica serovar Typhimurium or the opportunistic pathogen Candida albicans in combination with metagenomics and discovery metabolomics to identify changes in the community and metabolome during infection. To isolate the role of the microbiota in response to pathogens, we compared mice monocolonized with the pathogen, uninfected mice 'humanized' with a synthetic human microbiome, or infected humanized mice. We observe that changes in the community and in biosynthetic gene cluster potential occur within 3 days for the virulent Salmonella enterica serovar Typhimurium, but there are minimal changes with a poorly colonizing Candida albicans. In addition, the metabolome shifts depending on infection status, including changes in glutathione metabolites in response to Salmonella infection. The LC-MS metabolomic fingerprint of the cecum differed between mice monocolonized with either pathogen and humanized infected mice. Specifically, we identified an increase in glutathione disulfide, glutathione cysteine disulfide, inosine 5'-monophosphate, and hydroxybutyrylcarnitine in mice infected with Salmonella in contrast to uninfected mice and mice monocolonized with Salmonella. These metabolites potentially play a role in pathogen-induced oxidative stress. These results provide insight into how the microbiota community members interact with each other and with pathogens on a metabolic level.

Ref:

[1] Scallan E, Hoekstra RM, Angulo FJ, Tauxe RV, Widdowson MA, Roy SL, Jones JL, and Griffin PM. Foodborne Illness Acquired in the United States—Major Pathogens. Emerg Infect Dis 2011;17:7-15. doi.org/10.3201/eid1701.P11101

[2] Centers for Disease Control and Prevention, Antibiotic Resistance Threats in the United States, 2013

INSTRUMENT(S): Liquid Chromatography Tandem MS (LC-MS/MS), Liquid Chromatography MS - Positive (LC-MS (Positive))

SUBMITTER: Caitlin Keller

PROVIDER: MTBLS753 | MetaboLights | 2019-09-20

REPOSITORIES: MetaboLights

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Dataset's files

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			Action	DRS
	MTBLS753	Other
	FILES	Other
	a_MTBLS753_microbiome_metabolite_profiling_mass_spectrometry-1.txt	Txt
	a_MTBLS753_microbiome_metabolite_profiling_mass_spectrometry.txt	Txt
	i_Investigation.txt	Txt

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Publications

Gut Microbial and Metabolic Responses to Salmonella enterica Serovar Typhimurium and Candida albicans.

Bratburd Jennifer R JR Keller Caitlin C Vivas Eugenio E Gemperline Erin E Li Lingjun L Rey Federico E FE Currie Cameron R CR

mBio 20181106 6

The gut microbiota confers resistance to pathogens of the intestinal ecosystem, yet the dynamics of pathogen-microbiome interactions and the metabolites involved in this process remain largely unknown. Here, we use gnotobiotic mice infected with the virulent pathogen <i>Salmonella enterica</i> serovar Typhimurium or the opportunistic pathogen <i>Candida albicans</i> in combination with metagenomics and discovery metabolomics to identify changes in the community and metabolome during infection. T ...[more]

PMID: 30401779

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Project description:The interplay between pathogens and hosts has been studied for decades using targeted approaches such as the analysis of mutants and host immunological responses. Although much has been learned from such studies, they focus on individual pathways and fail to reveal the global effects of infection on the host. To alleviate this issue, high-throughput methods such as transcriptomics and proteomics have been used to study host-pathogen interactions. Recently, metabolomics was established as a new method to study changes in the biochemical composition of host tissues. We report a metabolomics study of Salmonella enterica serovar Typhimurium infection. We used Fourier Transform Ion Cyclotron Resonance Mass Spectrometry with Direct Infusion to reveal that dozens of host metabolic pathways are affected by Salmonella in a murine infection model. In particular, multiple host hormone pathways are disrupted. Our results identify unappreciated effects of infection on host metabolism and shed light on mechanisms used by Salmonella to cause disease, and by the host to counter infection. Female C57BL/6 mice were infected with Salmonella enterica serovar Typhimurium SL1344 cells by oral gavage. Feces and livers were collected and metabolites extracted using acetonitrile. For experiments with feces, samples were collected from 4 mice before and after infection. For liver experiments, 11 uninfected and 11 infected mice were used. Samples were combined into 3 groups of 3-4 mice each, resulting in the analysis of 3 group samples of uninfected and 3 of infected mice. Extracts were infused into a 12-T Apex-Qe hybrid quadrupole-FT-ICR mass spectrometer equipped with an Apollo II electrospray ionization source, a quadrupole mass filter and a hexapole collision cell. Raw mass spectrometry data were processed as described elsewhere (Han et al. 2008. Metabolomics. 4:128-140 [PMID 19081807]). To identify differences in metabolite composition between uninfected and infected samples, we filtered the list of masses for metabolites which were present on one set of samples but not the other. Additionally, we calculated the ratios between averaged intensities of metabolites from uninfected and infected mice. To assign possible metabolite identities, monoisotopic neutral masses of interest were queried against MassTrix (http://masstrix.org). Masses were searched against the Mus musculus database within a mass error of 3 ppm. Data were analyzed by unpaired t tests with 95% confidence intervals.