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

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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.


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Relative abundance of SEED subsystems functional categories. Read counts normalized by library size from samples within each group. Blue bars represent abundance in mice purchased from Taconic Biosciences. Red bars represent abundance in mice purchased from Charles River Laboratories. Data (mean ± SD) are from n = 5 Tac and n = 6 CR mice. Asterisks indicate functional categories significantly different (p < 0.05) or trending toward significant (p < 0.8) in a comparison via unpaired Student’s t-test.
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Figure 4: Relative abundance of SEED subsystems functional categories. Read counts normalized by library size from samples within each group. Blue bars represent abundance in mice purchased from Taconic Biosciences. Red bars represent abundance in mice purchased from Charles River Laboratories. Data (mean ± SD) are from n = 5 Tac and n = 6 CR mice. Asterisks indicate functional categories significantly different (p < 0.05) or trending toward significant (p < 0.8) in a comparison via unpaired Student’s t-test.

Mentions: In general, the distribution of sequences within SEED Subsystem categories were consistent between the two mouse groups (Figure 4). Combining 11 metatranscriptomes, the most abundant functional groups are Carbohydrate Metabolism (19.5%), Protein Metabolism (14.0%), and Amino Acid Metabolism (7.7%). A significant portion (13.3%) of the sequences are categorized as clustering-based subsystems, whose functions are bioinformatically identified, but not yet experimentally validated. An unpaired t-test comparing normalized expression of individual Level 1 SEED Subsystem categories between the two treatment groups yielded significant (p < 0.05), or trending toward significant (p < 0.08), differences in Protein Metabolism (p = 0.029), Cell Wall and Capsule synthesis (p = 0.053), Motility and Chemotaxis (p = 0.047), Sulfur Metabolism (p = 0.038), Iron Acquisition and Metabolism (p = 0.077), Secondary Metabolism (p = 0.059), and Potassium Metabolism (p = 0.014).


Functional Characteristics of the Gut Microbiome in C57BL/6 Mice Differentially Susceptible to Plasmodium yoelii
Relative abundance of SEED subsystems functional categories. Read counts normalized by library size from samples within each group. Blue bars represent abundance in mice purchased from Taconic Biosciences. Red bars represent abundance in mice purchased from Charles River Laboratories. Data (mean ± SD) are from n = 5 Tac and n = 6 CR mice. Asterisks indicate functional categories significantly different (p < 0.05) or trending toward significant (p < 0.8) in a comparison via unpaired Student’s t-test.
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC5037233&req=5

Figure 4: Relative abundance of SEED subsystems functional categories. Read counts normalized by library size from samples within each group. Blue bars represent abundance in mice purchased from Taconic Biosciences. Red bars represent abundance in mice purchased from Charles River Laboratories. Data (mean ± SD) are from n = 5 Tac and n = 6 CR mice. Asterisks indicate functional categories significantly different (p < 0.05) or trending toward significant (p < 0.8) in a comparison via unpaired Student’s t-test.
Mentions: In general, the distribution of sequences within SEED Subsystem categories were consistent between the two mouse groups (Figure 4). Combining 11 metatranscriptomes, the most abundant functional groups are Carbohydrate Metabolism (19.5%), Protein Metabolism (14.0%), and Amino Acid Metabolism (7.7%). A significant portion (13.3%) of the sequences are categorized as clustering-based subsystems, whose functions are bioinformatically identified, but not yet experimentally validated. An unpaired t-test comparing normalized expression of individual Level 1 SEED Subsystem categories between the two treatment groups yielded significant (p < 0.05), or trending toward significant (p < 0.08), differences in Protein Metabolism (p = 0.029), Cell Wall and Capsule synthesis (p = 0.053), Motility and Chemotaxis (p = 0.047), Sulfur Metabolism (p = 0.038), Iron Acquisition and Metabolism (p = 0.077), Secondary Metabolism (p = 0.059), and Potassium Metabolism (p = 0.014).

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