Limits...
Functional Characteristics of the Gut Microbiome in C57BL/6 Mice Differentially Susceptible to Plasmodium yoelii

View Article: PubMed Central - PubMed

ABSTRACT

C57BL/6 mice are widely used for in vivo studies of immune function and metabolism in mammals. In a previous study, it was observed that when C57BL/6 mice purchased from different vendors were infected with Plasmodium yoelii, a causative agent of murine malaria, they exhibited both differential immune responses and significantly different parasite burdens: these patterns were reproducible when gut contents were transplanted into gnotobiotic mice. To gain insight into the mechanism of resistance, we removed whole ceca from mice purchased from two vendors, Taconic Biosciences (low parasitemia) and Charles River Laboratories (high parasitemia), to determine the combined host and microflora metabolome and metatranscriptome. With the exception of two Charles River samples, we observed ≥90% similarity in overall bacterial gene expression within vendors and ≤80% similarity between vendors. In total 33 bacterial genes were differentially expressed in Charles River mice (p-value < 0.05) relative to the mice purchased from Taconic. Included among these, fliC, ureABC, and six members of the nuo gene family were overrepresented in microbiomes susceptible to more severe malaria. Moreover, 38 mouse genes were differentially expressed in these purported genetically identical mice. Differentially expressed genes included basigin, a cell surface receptor required for P. falciparum invasion of red blood cells. Differences in metabolite pools were detected, though their relevance to malaria infection, microbial community activity, or host response is not yet understood. Our data have provided new targets that may connect gut microbial activity to malaria resistance and susceptibility phenotypes in the C57BL/6 model organism.

No MeSH data available.


Related in: MedlinePlus

Heatmap representing metabolite abundances normalized to sample tissue mass and log transformed. Metabolites displayed are significantly different with a p-value cutoff of 0.1. (A) Five columns represent metabolite abundances for each of five Taconic Biosciences mice. (B) Six columns represent each of the six Charles River mice. (C) Columns represent the mean abundances for Taconic (A) and Charles River (B).
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC5037233&req=5

Figure 7: Heatmap representing metabolite abundances normalized to sample tissue mass and log transformed. Metabolites displayed are significantly different with a p-value cutoff of 0.1. (A) Five columns represent metabolite abundances for each of five Taconic Biosciences mice. (B) Six columns represent each of the six Charles River mice. (C) Columns represent the mean abundances for Taconic (A) and Charles River (B).

Mentions: Relative metabolite concentrations were normalized by mass of the processed tissue sample, and these data were used to calculate fold change and cluster analyses. Comparison of normalized metabolite abundances determined that differences in the metabolome of Charles River and Taconic mice were present (p = 0.082). Normalized abundance of significantly different metabolites are presented in Figure 7. Of the 129 metabolites detected in the samples, 36 were found in significantly higher relative concentrations in Charles River mice, and two (NADH and N-acetyl-L-alanine) were found in higher concentrations in Taconic mice (p < 0.1). All statistically significant metabolites exhibited an effect size greater than 1.0, with the lowest being 1.17. The majority of significant metabolites were nucleotides, amino acids, or the substrates involved in the biosynthesis of these compounds. While a number of additional transcripts and metabolites were differentially abundant between mouse substrains, we have restricted our discussion to only those where a mechanism influential in gut microbial symbiosis, immune regulation, and malaria infection are clear.


Functional Characteristics of the Gut Microbiome in C57BL/6 Mice Differentially Susceptible to Plasmodium yoelii
Heatmap representing metabolite abundances normalized to sample tissue mass and log transformed. Metabolites displayed are significantly different with a p-value cutoff of 0.1. (A) Five columns represent metabolite abundances for each of five Taconic Biosciences mice. (B) Six columns represent each of the six Charles River mice. (C) Columns represent the mean abundances for Taconic (A) and Charles River (B).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5037233&req=5

Figure 7: Heatmap representing metabolite abundances normalized to sample tissue mass and log transformed. Metabolites displayed are significantly different with a p-value cutoff of 0.1. (A) Five columns represent metabolite abundances for each of five Taconic Biosciences mice. (B) Six columns represent each of the six Charles River mice. (C) Columns represent the mean abundances for Taconic (A) and Charles River (B).
Mentions: Relative metabolite concentrations were normalized by mass of the processed tissue sample, and these data were used to calculate fold change and cluster analyses. Comparison of normalized metabolite abundances determined that differences in the metabolome of Charles River and Taconic mice were present (p = 0.082). Normalized abundance of significantly different metabolites are presented in Figure 7. Of the 129 metabolites detected in the samples, 36 were found in significantly higher relative concentrations in Charles River mice, and two (NADH and N-acetyl-L-alanine) were found in higher concentrations in Taconic mice (p < 0.1). All statistically significant metabolites exhibited an effect size greater than 1.0, with the lowest being 1.17. The majority of significant metabolites were nucleotides, amino acids, or the substrates involved in the biosynthesis of these compounds. While a number of additional transcripts and metabolites were differentially abundant between mouse substrains, we have restricted our discussion to only those where a mechanism influential in gut microbial symbiosis, immune regulation, and malaria infection are clear.

View Article: PubMed Central - PubMed

ABSTRACT

C57BL/6 mice are widely used for in vivo studies of immune function and metabolism in mammals. In a previous study, it was observed that when C57BL/6 mice purchased from different vendors were infected with Plasmodium yoelii, a causative agent of murine malaria, they exhibited both differential immune responses and significantly different parasite burdens: these patterns were reproducible when gut contents were transplanted into gnotobiotic mice. To gain insight into the mechanism of resistance, we removed whole ceca from mice purchased from two vendors, Taconic Biosciences (low parasitemia) and Charles River Laboratories (high parasitemia), to determine the combined host and microflora metabolome and metatranscriptome. With the exception of two Charles River samples, we observed &ge;90% similarity in overall bacterial gene expression within vendors and &le;80% similarity between vendors. In total 33 bacterial genes were differentially expressed in Charles River mice (p-value &lt; 0.05) relative to the mice purchased from Taconic. Included among these, fliC, ureABC, and six members of the nuo gene family were overrepresented in microbiomes susceptible to more severe malaria. Moreover, 38 mouse genes were differentially expressed in these purported genetically identical mice. Differentially expressed genes included basigin, a cell surface receptor required for P. falciparum invasion of red blood cells. Differences in metabolite pools were detected, though their relevance to malaria infection, microbial community activity, or host response is not yet understood. Our data have provided new targets that may connect gut microbial activity to malaria resistance and susceptibility phenotypes in the C57BL/6 model organism.

No MeSH data available.


Related in: MedlinePlus