Strain-resolved microbial community proteomics reveals simultaneous aerobic and anaerobic function during gastrointestinal tract colonization of a preterm infant - PubMed (original) (raw)

Strain-resolved microbial community proteomics reveals simultaneous aerobic and anaerobic function during gastrointestinal tract colonization of a preterm infant

Brandon Brooks et al. Front Microbiol. 2015.

Abstract

While there has been growing interest in the gut microbiome in recent years, it remains unclear whether closely related species and strains have similar or distinct functional roles and if organisms capable of both aerobic and anaerobic growth do so simultaneously. To investigate these questions, we implemented a high-throughput mass spectrometry-based proteomics approach to identify proteins in fecal samples collected on days of life 13-21 from an infant born at 28 weeks gestation. No prior studies have coupled strain-resolved community metagenomics to proteomics for such a purpose. Sequences were manually curated to resolve the genomes of two strains of Citrobacter that were present during the later stage of colonization. Proteome extracts from fecal samples were processed via a nano-2D-LC-MS/MS and peptides were identified based on information predicted from the genome sequences for the dominant organisms, Serratia and the two Citrobacter strains. These organisms are facultative anaerobes, and proteomic information indicates the utilization of both aerobic and anaerobic metabolisms throughout the time series. This may indicate growth in distinct niches within the gastrointestinal tract. We uncovered differences in the physiology of coexisting Citrobacter strains, including differences in motility and chemotaxis functions. Additionally, for both Citrobacter strains we resolved a community-essential role in vitamin metabolism and a predominant role in propionate production. Finally, in this case study we detected differences between genome abundance and activity levels for the dominant populations. This underlines the value in layering proteomic information over genetic potential.

Keywords: colonization; infant gut; metaproteomics; microbial ecology; microbiome; physiology.

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Figures

Figure 1

Figure 1

Microbial community composition observed via read and peptide mapping. Relative proportion of reads (A) and unique peptides (spectral counts normalized by protein length and number of proteins per genome for each sample) (B) mapped to a database of metagenomes derived from dominant gut colonizers in a preterm infant. Abbreviations for each organism begin with UC1 (University of Chicago, study code 1), followed by the organism name: CIT, Citrobacter; CITii, Citrobacter minor strain; CITp, Citrobacter plasmid; ENC, Enterococcus; ENCp, Enterococcus plasmid; ENCv, Enterococcus phage; SER, Serratia.

Figure 2

Figure 2

Metabolic potential of microbes colonizing a preterm infant gut. ggKbase lists illustrate the broad metabolic potential of microbes colonizing a preterm infant in the first month of life. Numbers in each cell are the number of features per organism per list. Colors per cell are generated in a heat map fashion, with darker colors representing more features per list relative to other organisms.

Figure 3

Figure 3

Expression over potential (genomic content) ratio of infant gut microbes. A non-redundant count of the number of features identified via proteomics in a metabolic ggKbase list was divided by the number of features in that list and plotted for each organism across time.

Figure 4

Figure 4

Comparison of proteomic profiles of two closely related Citrobacter strains. Unique peptide counts, normalized by the length of the protein, were mapped to UC1CITs' contigs (aligned on the x-axis). Panels are separated by day of life. For a description of ggKbase lists, see the Materials and Methods and Supplemental File 1. Triangles represent genes unique to the respective strain. Annotations marked with arrows indicate features of interest.

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References

    1. Abbad-Andaloussi S., Durr C., Dürr C., Raval G., Petitdemange H. (1996). Carbon and electron flow in Clostridium butyricum grown in chemostat culture on glycerol and on glucose. Microbiology 142, 1149–1158. 10.1099/13500872-142-5-1149 - DOI - PubMed
    1. Albenberg L., Esipova T. V., Judge C. P., Bittinger K., Chen J., Laughlin A., et al. . (2014). Correlation between intraluminal oxygen gradient and radial partitioning of intestinal microbiota. Gastroenterology 147, 1055.e8–1063.e8. 10.1053/j.gastro.2014.07.020 - DOI - PMC - PubMed
    1. Ardissone A. N., de la Cruz D. M., Davis-Richardson A. G., Rechcigl K. T., Li N., Drew J. C., et al. . (2014). Meconium microbiome analysis identifies bacteria correlated with premature birth. PLoS ONE 9:e90784. 10.1371/journal.pone.0090784 - DOI - PMC - PubMed
    1. Bairoch A. (2000). The ENZYME database in 2000. Nucleic Acids Res. 28, 304–305. 10.1093/nar/28.1.304 - DOI - PMC - PubMed
    1. Barnhart M. M., Chapman M. R. (2006). Curli biogenesis and function. Annu. Rev. Microbiol. 60, 131–147. 10.1146/annurev.micro.60.080805.142106 - DOI - PMC - PubMed

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