Jikang Wu (Presenter)
The Ohio State University
Bio: Jikang Wu obtained his bachelor degree in Biology from Wuhan University, China in 2013. In the same year, he joined the chemistry Ph.D. program at The Ohio State University. He works as a graduate research assistant in Dr. Vicki Wysocki's group. He is currently focusing on using LC-MS techniques to better understand infectious diseases and to facilitate the diagnosis and the prevention of diseases. One of his projects is using metabolomics method, integrated with metagenomics and metatranscriptomics approaches, to reveal the interactions between Salmonella and chemical and biological environment. Another project is using proteomics method to discover biomarkers for invasive aspergillosis caused by Aspergillus infection.
Authorship: Jikang Wu (1), Mikayla Borton (2), Anice Sabag-Daigle (3), Brian Ahmer (3), Kelly Wrighton (2) and Vicki Wysocki (1)
(1) Department of Chemistry and Biochemistry, The Ohio State University, Columbus, OH (2) Department of Microbiology, The Ohio State University Columbus OH (3) Department of Microbial Infection and Immunity, The Ohio State University, Columbus, OH
| Short Abstract Salmonella is the leading cause of death from foodborne illness in the United States. While it is well documented that Salmonella infection triggers host inflammation, little is known about the interaction between Salmonella and the gut environment. Here we applied non-targeted metabolomics and metagenomics approaches to reveal the chemical and biological environment in the murine intestine with inflammation induced by a chemical (DSS) or Salmonella. Several metabolites were found to be significantly changed with inflammation and they were correlated with the changes in the abundance of some microorganisms. The enhanced understanding of Salmonella’s interaction with the environment may reveal new therapeutic strategies for prebiotics or probiotics for maintaining or restoring the microbiota in response to Salmonella perturbation. |
Long Abstract
Introduction
Salmonella is the leading cause of death from foodborne illness in the United States [1]. While it is well documented that Salmonella infection triggers host inflammation, little is known about the interaction between Salmonella and the chemical and biological environment in the gut [2]. The gut is a dynamic ecosystem in which spatial and temporal heterogeneity in the nutrient landscape shape the composition of the microbiota [3], which in turn can change the chemical composition of the gut. Multi-omics approaches provide researchers with a greater understanding of the flow of information, from the original cause to the functional consequences [4]. Here we applied non-targeted metabolomics and metagenomics approaches to reveal the chemical and biological environment in the murine intestine with inflammation induced by a chemical (DSS) or Salmonella. The enhanced understanding of Salmonella’s interaction with the environment may reveal new therapeutic strategies for prebiotics or probiotics for maintaining or restoring the microbiota in response to Salmonella perturbation.
Methods
CBA/J mice (from Taconic Farms, Inc., room RB11) were divided into three groups. The control group did not receive any treatment. The chemical inflammation group received 4% DSS (dextran sulfate sodium) in drinking water for the duration of the experiment. The pathogen-induced inflammation group was orally inoculated with 10^9 CFU of a washed overnight culture of S. enterica serovar Typhimurium strain ATCC14028 on day 0. Mouse fecal pellets were collected daily, flash frozen in liquid nitrogen, and stored at -80 °C until further processing. Pretreatment (day -3 to -2), early (day 6 to 7) and late (day 10 to 14) time point fecal pellets were used for non-targeted metabolic profiling and metagenomic profiling. Metabolic profiling experiment with samples number (n=39) was performed, using 5 mice each in the control and DSS-treated groups and 3 mice in the Salmonella-infected group, with experiments including early, middle and late time points for each sample group. Metabolites were extracted with water, chilled methanol, and dichloromethane. After nitrogen drying, the extracts were resuspended, filtered and introduced into two LC-MS/MS platforms. A data-dependent acquisition mode was used on a Q-Exactive Plus Orbitrap mass spectrometer coupled with an Ultimate 3000 RSLC. Progenesis QI was used for data processing and analyzing. Total nucleic acids were extracted using the PowerSoil DNA Isolation kit, eluted in 100 μl of the provided elution buffer, and stored at −20 °C until sequencing [2]. DNA was submitted for sequencing at Argonne National Lab at the Next Generation Sequencing facility using Illumina MiSeq with 2 × 251 bp paired end reads following established HMP protocols [5]. Universal primers 515F and 806R were used for PCR amplification of the V4 hypervariable region of 16S rRNA gene using 35 cycles. To measure inflammation, lipocalin-2 was quantified in fecal sample supernatants using the Duoset murine Lcn-2 ELISA kit (R&D Systems, Minneapolis, MN) [2].
Results
Metagenomics data showed mice inoculated with the same dose of Salmonella had a varied susceptibility to Salmonella colonization. Based on the relative abundance of Salmonella, mice in the pathogen-induced inflammation group were divided into low-responder and high-responder groups and high- responders were used for metabolic profiling. A preliminary experiment on fecal pellets collected at late time points from two mice of each group detected more than 1200 features on either LC-MS/MS platform. A principal component analysis of all the features indicated that three groups (control, DSS-treated, Salmonella-infection) had different profiles of metabolites. One metabolite of interest, fructose-asparagine (F-Asn), is a novel nutrient for Salmonella [6]. Previously, we used a targeted LC-MS/MS method to quantify the level of F-Asn in mice cecal contents and showed that F-Asn has higher abundance in Salmonella high-responders compared to controls [7]. Our non-targeted metabolic profiling on fecal pellets also indicated an increase of F-Asn with Salmonella infection compared to the control and DSS-treated groups. Metagenomics analysis showed fra gene (which encode enzymes involved in F-Asn utilization) diversity was greatly decreased in the high-responders. Several amino acids, including proline, phenylalanine, alanine, lysine, histidine, glutamic acid, and glutamine increased in the Salmonella-infection group compared with the control and DSS-treated groups. An increase of several flavonoids in the Salmonella-infection group was observed. The metagenomics analysis showed a reduction of Clostridia in the Salmonella-infection group. The increase in flavonoids agrees with a previous report that Clostridium orbiscindens was capable of anaerobically degrading several flavonoids [8]. Several dipeptides including Ser-Tyr, Gln-Phe, and Ser-Met decreased in the Salmonella-infection group compared with control group. Upregulations of some carbohydrates, fatty acyls and polyamines were observed with downregulations of bilirubin reduction products.
Conclusions & Discussion
Our data show changes in the metabolite profiles and microorganism distribution for non-inflamed controls vs. DSS-induced vs. Salmonella-induced inflammation. The accumulation of F-Asn and decrease of fra gene diversity in Salmonella-infected mouse gut suggests that Salmonella eliminates its competitors which also consume F-Asn. Some members of Clostridia contain fra genes [9]. Consistent with the reduced abundance of Clostridia in Salmonella-infected mice, some Clostridia related-metabolites changes are observed. Previous findings show that some Clostridia members can produce SCFAs [10] and some can degrade flavonoids [8], which may explain the observation of decreased short-chain fatty acids (SCFAs) levels with Salmonella infection from our previous targeted LC-MS/MS analysis [2] and an increase of several flavonoids from untargeted metabolomics analysis. Further analysis of the correlation metabolites and microorganism changes were performed and some correlations were found. In conclusion, the integration of metabolomics and metagenomics can better explain the effects caused by pathogens in a biological system.
References & Acknowledgements:
[1] https://www.cdc.gov/foodborneburden/2011-foodborne-estimates.html
[2] Borton et al. Microbiome (2017) 5:47
[3] Pereira et al. Environmental Microbiology (2017) 19(4), 1366-1378.
[4] Hasin et al. Genome Biology 2017 18:83
[5] Caporaso et al. Proc Natl Acad Sci. 2011; 108(Supplement 1):4516–22
[6] Ali et al. PLoS Pathog. 2014 Jun; 10(6): e1004209.
[7] Wu et al. 2017. Salmonella-mediated inflammation eliminates competitors for fructose-asparagine in the gut. submitted
[8] Schoefer et al. Appl Environ Microbiol 2003 Oct; 69(10): 5849–5854.
[9] Daigle et al. 2017. Identification of bacterial species that can utilize fructose-asparagine. submitted.
[10] Louis et al. FEMS Microbiol Lett. 2009; 294:1–8
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