scholarly journals Gut microbiota modulation induced by Zika virus infection in immunocompetent mice

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Rafael Corrêa ◽  
Igor de Oliveira Santos ◽  
Heloísa Antoniella Braz-de-Melo ◽  
Lívia Pimentel de Sant’Ana ◽  
Raquel das Neves Almeida ◽  
...  
Keyword(s):  
Gut Microbiota ◽  
Gut Microbiome ◽  
Zika Virus ◽  
Hypervariable Region ◽  
Microbiota Composition ◽  
Colon Tissue ◽  
Zikv Infection ◽  
Immunological Responses ◽  
Immunocompetent Mice ◽  
Gut Microbiota Composition

AbstractGut microbiota composition can modulate neuroendocrine function, inflammation, and cellular and immunological responses against different pathogens, including viruses. Zika virus (ZIKV) can infect adult immunocompetent individuals and trigger brain damage and antiviral responses. However, it is not known whether ZIKV infection could impact the gut microbiome from adult immunocompetent mice. Here, we investigated modifications induced by ZIKV infection in the gut microbiome of immunocompetent C57BL/6J mice. Adult C57BL/6J mice were infected with ZIKV and the gut microbiota composition was analyzed by next-generation sequencing of the V4 hypervariable region present in the bacterial 16S rDNA gene. Our data showed that ZIKV infection triggered a significant decrease in the bacteria belonging to Actinobacteria and Firmicutes phyla, and increased Deferribacteres and Spirochaetes phyla components compared to uninfected mice. Interestingly, ZIKV infection triggered a significant increase in the abundance of bacteria from the Spirochaetaceae family in the gut microbiota. Lastly, we demonstrated that modulation of microbiota induced by ZIKV infection may lead to intestinal epithelium damage and intense leukocyte recruitment to the intestinal mucosa. Taken together, our data demonstrate that ZIKV infection can impact the gut microbiota composition and colon tissue homeostasis in adult immunocompetent mice.

scholarly journals 2581. An Invertebrate Model to Study Gut Microbiome Dysbiosis

2019 ◽  
Vol 6 (Supplement_2) ◽  
pp. S896-S897
Author(s):  
Faris S Alnezary ◽  
Tasnuva Rashid ◽  
Khurshida Begum ◽  
Travis J Carlson ◽  
Anne J Gonzales-Luna ◽  
...  
Keyword(s):  
Gut Microbiota ◽  
Gut Microbiome ◽  
Galleria Mellonella ◽  
Nigella Sativa ◽  
Gut Contents ◽  
In Vivo Model ◽  
Microbiota Composition ◽  
Invertebrate Model ◽  
Gut Microbiota Composition

Abstract Background Antimicrobials disrupt the gut microbiota by reducing gut microbiome diversity and quantity. Galleria mellonella provides an invertebrate model that is inexpensive, easy to maintain, and does not require specialized equipment. This study investigated the feasibility of using G. mellonella as an in vivo model to evaluate the effect of different antimicrobials on gut microbiota. Methods To determine baseline gut microbiota composition, the gut contents of G. mellonella were extracted and genomic DNA underwent shotgun meta-genomic sequencing. To determine the effect of infection and antibiotic use, 30 larvae were injected (left proleg) with ~1 × 105 colony-forming unit (cfu) of methicillin-resistant Staphylococcus aureus (MRSA) and were randomized 1:1:1 to treatment with vancomycin (20 mg/kg) or a natural antimicrobial (Nigella sativa seed oil, 70 mg/kg; NS oil), or a combination. The larvae were kept at 37°C post-infection and monitored daily for 72 hours for activity, extent of cocoon formation/growth, melanization, and survival. Two larvae from each group were randomly selected and homogenized with PBS as controls. After 24 hours of incubation, gut contents were extracted and plated for MRSA and Enterococcus cfu counts. Results Metagenomics analysis showed the gut microbiota composition of G. mellonella larvae was dominated by a subset of closely-related Enterococcus species. After 24 hours of exposure, mean Enterococcus counts were 4 × 103 cfu in the vancomycin arm and 6.2 × 104 cfu in the NS oil arm. Mean MRSA counts were 3.3 × 105 cfu in vancomycin arm and 1.5 × 104 cfu in NS oil arm. The combination of vancomycin and NS oil had higher Enterococcus counts than the vancomycin alone arm (6.3 × 104 cfu vs. 4 × 103 cfu, respectively), suggesting that NS oil may have a role in protecting the gut microbiota. Conclusion This study provides preliminary evidence to support the potential use of G. mellonella to assess the in vivo effect of a natural and synthetic antimicrobial on the gut microbiota. Disclosures All authors: No reported disclosures.

scholarly journals Liver-specific knockdown of ANGPTL8 alters the structure of the gut microbiota in mice

2020 ◽  
Vol 70 (1) ◽  
Author(s):  
Yinlong Cheng ◽  
Yining Li ◽  
Yonghong Xiong ◽  
Yixin Zou ◽  
Siyu Chen ◽  
...  
Keyword(s):  
Gut Microbiota ◽  
Gut Microbiome ◽  
Shannon Index ◽  
Microbiota Composition ◽  
Functional Prediction ◽  
Adeno Associated Virus ◽  
Virus Serotype ◽  
Significant Difference ◽  
And Function ◽  
Gut Microbiota Composition

Abstract Purpose To investigate the effect of liver-specific knockdown of ANGPTL8 on the structure of the gut microbiota. Methods We constructed mice with liver-specific ANGPTL8 knockdown by using an adeno-associated virus serotype 8 (AAV8) system harbouring an ANGPTL8 shRNA. We analysed the structure and function of the gut microbiome through pyrosequencing and KEGG (Kyoto Encyclopedia of Genes and Genomes) functional prediction. Results Compared with controls, ANGPTL8 shRNA reduced the Simpson index and Shannon index (p < 0.01) of the gut microbiota in mice. At the phylum level, the sh-ANGPTL8 group showed a healthier gut microbiota composition than controls (Bacteroidetes: controls 67.52%, sh-ANGPTL8 80.75%; Firmicutes: controls 10.96%, sh-ANGPTL8 8.58%; Proteobacteria: controls 9.29%, sh-ANGPTL8 0.98%; F/B ratio: controls 0.16, sh-ANGPTL8 0.11). PCoA and UPGMA analysis revealed a significant difference in microbiota composition, while KEGG analysis revealed a significant difference in microbiota function between controls and the sh-ANGPTL8 group. Conclusion Our results revealed that inhibition of ANGPTL8 signalling altered the structure of the gut microbiome, which might further affect the metabolism of mice. We have thus identified ANGPTL8 as a novel hepatogenic hormone potentially involving the liver-gut axis and regulating the structure of the gut microbiota.

scholarly journals Microbiome transfer between IL-1RI-/- and wild-type mice during high or low-fat feeding alters metabolic tissue functionality but not glucose homeostasis.

2020 ◽  
Vol 79 (OCE2) ◽  
Author(s):  
Jessica C. Ralston ◽  
Kathleen A.J. Mitchelson ◽  
Gina M. Lynch ◽  
Tam T.T. Tran ◽  
Conall R. Strain ◽  
...  
Keyword(s):  
Glucose Tolerance ◽  
Gut Microbiota ◽  
Glucose Homeostasis ◽  
Gut Microbiome ◽  
Short Chain Fatty Acids ◽  
Microbiota Composition ◽  
Wild Type ◽  
Low Fat ◽  
Unifrac Distance ◽  
Gut Microbiota Composition

AbstractReduced inflammatory signaling (IL-1RI-/-) alters metabolic responses to dietary challenges (1). Inflammasome deficiency (e.g. IL-18-/-, Asc-/-) can modify gut microbiota concomitant with hepatosteatosis; an effect that was transferable to wild-type (WT) mice by co-housing (2). Taken together, this evidence suggests that links between diet, microbiota and IL-1RI-signaling can influence metabolic health. Our aim was to determine whether IL-1RI-mediated signaling interacted with the gut microbiome to impact metabolic tissue functionality in a diet-specific fashion. Male WT (C57BL/J6) and IL-1RI-/- mice were fed either high-fat diet (HFD; 45% kcal) or low-fat diet (LFD; 10% kcal) for 24 weeks and were housed i) separately by genotype or ii) with genotypes co-housed together (i.e. isolated vs shared microbial environment; n = 8–10 mice per group). Glucose tolerance and insulin secretion response (1.5 g/kg i.p.), gut microbiota composition and caecal short-chain fatty acids (SCFA) were assessed. Liver and adipose tissue were harvested and examined for triacylglycerol (TAG) formation, cholesterol and metabolic markers (Fasn, Cpt1α, Pparg, Scd1, Dgat1/2), using histology, gas-chromatography and RT-PCR, respectively. Statistical analysis included 1-way or 2-way ANOVA, where appropriate, with Bonferroni post-hoc correction. Co-housing significantly affected gut microbiota composition, illustrated by clustering in PCoA (unweighted UniFrac distance) of co-housed mice but not their single-housed counterparts, on both HFD and LFD. The taxa driving these differences were primarily from Lachnospiraceae and Ruminococcaceae families. Single-housed WT had lower hepatic weight, TAG, cholesterol levels and Fasn despite HFD, an effect lost in their co-housed counterparts, who aligned more to IL-1RI-/- hepatic lipid status. Hepatic Cpt1α was lowest in co-housed WT. Adipose from IL-1RI-/- groups on HFD displayed increased adipocyte size and reduced adipocyte number compared to WT groups, but greater lipogenic potential (Pparg, Scd1, Dgat2) alongside a blunted IL-6 response to pro-inflammatory stimuli (~32%, P = 0.025). Whilst caecal SCFA concentrations were not different between groups, single-housed IL-1RI-/- adipocytes showed greatest sensitivity to SCFA-induced lipogenesis. Interestingly, differences in tissue functionality and gut microbiome occurred despite unaltered glucose tolerance; although there was a trend for phenotypic transfer of body weight via co-housing. For all endpoints examined, similar genotype/co-housing effects were observed for both HFD and LFD with the greatest impacts seen in HFD-fed mice. In conclusion, while the gut microbiome may be an important consideration in dietary interventions, these results question the magnitude of its impact in relation to the IL-1RI-dependent immunometabolism-glucose homeostasis axis.

scholarly journals Comparative Analysis of Fecal Microbiota Composition Between Rheumatoid Arthritis and Osteoarthritis Patients

Genes ◽  
2019 ◽  
Vol 10 (10) ◽  
pp. 748 ◽  
Author(s):  
Jin-Young Lee ◽  
Mohamed Mannaa ◽  
Yunkyung Kim ◽  
Jehun Kim ◽  
Geun-Tae Kim ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Gut Microbiota ◽  
Gut Microbiome ◽  
Bacterial Species ◽  
Amplicon Sequencing ◽  
Fecal Microbiota ◽  
Rrna Gene ◽  
Microbiota Composition ◽  
Stool Samples ◽  
Gut Microbiota Composition

The aim of this study was to investigate differences between the gut microbiota composition in patients with rheumatoid arthritis (RA) and those with osteoarthritis (OA). Stool samples from nine RA patients and nine OA patients were collected, and DNA was extracted. The gut microbiome was assessed using 16S rRNA gene amplicon sequencing. The structures and differences in the gut microbiome between RA and OA were analyzed. The analysis of diversity revealed no differences in the complexity of samples. The RA group had a lower Bacteroidetes: Firmicutes ratio than did the OA group. Lactobacilli and Prevotella, particularly Prevotella copri, were more abundant in the RA than in the OA group, although these differences were not statistically significant. The relative abundance of Bacteroides and Bifidobacterium was lower in the RA group. At the species level, the abundance of certain bacterial species was significantly lower in the RA group, such as Fusicatenibacter saccharivorans, Dialister invisus, Clostridium leptum, Ruthenibacterium lactatiformans, Anaerotruncus colihominis, Bacteroides faecichinchillae, Harryflintia acetispora, Bacteroides acidifaciens, and Christensenella minuta. The microbial properties of the gut differed between RA and OA patients, and the RA dysbiosis revealed results similar to those of other autoimmune diseases, suggesting that a specific gut microbiota pattern is related to autoimmunity.

scholarly journals Analyzing Type 2 Diabetes Associations with the Gut Microbiome in Individuals from Two Ethnic Backgrounds Living in the Same Geographic Area

Nutrients ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 3289
Author(s):  
Manon Balvers ◽  
Mélanie Deschasaux ◽  
Bert-Jan van den Born ◽  
Koos Zwinderman ◽  
Max Nieuwdorp ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Gut Microbiota ◽  
South Asian ◽  
Gut Microbiome ◽  
Geographical Area ◽  
Microbiota Composition ◽  
Α Diversity ◽  
Functional Pathways ◽  
Gut Microbiota Composition

It is currently unknown whether associations between gut microbiota composition and type 2 diabetes (T2D) differ according to the ethnic background of individuals. Thus, we studied these associations in participants from two ethnicities characterized by a high T2D prevalence and living in the same geographical area, using the Healthy Life In Urban Settings (HELIUS) study. We included 111 and 128 T2D participants on metformin (Met-T2D), 78 and 49 treatment-naïve T2D (TN-T2D) participants, as well as a 1:1 matched group of healthy controls from, respectively, African Surinamese and South-Asian Surinamese descent. Fecal microbiome profiles were obtained through 16S rRNA gene sequencing. Univariate and machine learning analyses were used to explore the associations between T2D and the composition and function of the gut microbiome in both ethnicities, comparing Met-T2D and TN-T2D participants to their respective healthy control. We found a lower α-diversity for South-Asian Surinamese TN-T2D participants but no significant associations between TN-T2D status and the abundance of bacterial taxa or functional pathways. In African Surinamese participants, we did not find any association between TN-T2D status and the gut microbiome. With respect to Met-T2D participants, we identified several bacterial taxa and functional pathways with a significantly altered abundance in both ethnicities. More alterations were observed in South-Asian Surinamese. Some altered taxa and pathways observed in both ethnicities were previously related to metformin use. This included a strong negative association between the abundance of Romboutsia and Met-T2D status. Other bacterial taxa were consistent with previous observations in T2D, including reduced butyrate producers such as Anaerostipes hadrus. Hence, our results highlighted both shared and unique gut microbial biomarkers of Met-T2D in individuals from different ethnicities but living in the same geographical area. Future research using higher-resolution shotgun sequencing is needed to clarify the role of ethnicity in the association between T2D and gut microbiota composition.

Novel Interactions Between Circulating microRNAs and Gut Microbiota Composition in Human Obesity.

2020 ◽  
Author(s):  
Taís Silveira Assmann ◽  
Amanda Cuevas-Sierra ◽  
José Ignacio Riezu-Boj ◽  
Fermin Milagro ◽  
J Alfredo Martínez
Keyword(s):  
Gut Microbiota ◽  
Gut Microbiome ◽  
Target Genes ◽  
Metabolic Diseases ◽  
Bacterial Species ◽  
Microbiota Composition ◽  
Circulating Mirnas ◽  
Clinical Trial Registration ◽  
Body Adiposity ◽  
Gut Microbiota Composition

Abstract Background: Unbalances in microRNAs (miRNA) and gut microbiota patterns have been proposed as putative factors concerning onset and development of obesity and other metabolic diseases. However, the determinants that mediate the interactions between miRNAs and the gut microbiome impacting on obesity are scarcely understood. Thus, the aim of this article was to investigate possible interactions between circulating miRNAs and gut microbiota composition in obesity. Method: The analyzed sample comprised 78 subjects with obesity [cases, body mass index (BMI): 30 – 40 kg/m2] and 25 eutrophic individuals (controls, BMI £ 25 kg/m2). The expression of 96 miRNAs was investigated in plasma of all individuals using miRCURY LNA miRNA Custom PCR Panels (Exiqon). Bacterial DNA sequencing was performed following the Illumina 16S protocol. The FDR (Benjamini-Hochberg test, q-value) correction was used for multiple comparison analyses.Results: A total of 26 circulating miRNAs and 12 bacterial species were found differentially expressed between cases and controls. Interestingly, an interaction among three miRNAs (miR-130b-3p, miR-185-5p, and miR-21-5p) with Bacteroides eggerthi, and BMI levels was evidenced (r2= 0.148, P= 0.004). Those miRNAs that correlated with obesity-associated gut bacteria abundance are known to regulate target genes that participate in metabolism-related pathways, such as fatty acid degradation, carbohydrate digestion and absorption, insulin signaling, and glycerolipid metabolism. Conclusion: This study characterized an interaction between the abundance of 4 bacterial species and 14 circulating miRNAs in relation to body adiposity. Moreover, the current study also suggests that miRNAs may serve as a communication mechanism between the gut microbiome and human hosts. Clinical trial registration: clinicaltrials.gov (reg. no. NCT02737267).

scholarly journals A Partnership in the Making: Metabolic Phenotype Determined by the Combination of Dietary Fiber and the Gut Microbiome (FS07-03-19)

2019 ◽  
Vol 3 (Supplement_1) ◽  
Author(s):  
Tzu-Wen Cross ◽  
Evan Hutchison ◽  
Jacob Coulthurst ◽  
Federico Rey
Keyword(s):  
Body Weight ◽  
Gut Microbiota ◽  
Microbial Diversity ◽  
Dietary Fiber ◽  
Gut Microbiome ◽  
Microbiota Composition ◽  
Lower Body ◽  
Germ Free ◽  
Lower Body Weight ◽  
Gut Microbiota Composition

Abstract Objectives Dietary fiber consumption improves cardiometabolic health, partly by enhancing microbial diversity and increasing production of butyrate in the distal gut. However, it is unclear whether the benefits associated with different types of fiber vary based on the gut microbiota composition. We surveyed nine different human gut microbial communities by characterizing them in germ-free mice and selected two communities based on their butyrate-producing capacity (“B”) and diversity (“D”) (i.e., high- vs. low-BD communities). Our objective was to assess the role of high- vs. low-BD communities on the metabolic effects elicited by the consumption of various dietary fibers. Methods We formulated seven diets with different sources of dietary fiber (10% wt/wt): i) resistant starch type 2 (RS2); ii) RS4; iii) inulin; iv) short-chain fructooligosaccharides (scFOS); v) pectin, vi) assorted fiber (a combination of the 5 fermentable fibers), and vii) cellulose (a non-fermentable control). Germ-free C57BL/6 male mice were colonized with either the high- or low-BD communities and fed the assorted fiber diet for 2 weeks to reach stability of microbial engraftment. Mice were then switched to one of the 7 diets for 4 weeks (n = 7–10/group; 117 mice total). We quantified cecal level of short-chain fatty acids and assessed the gut microbiota composition using 16S rRNA gene-based sequencing. Results Mice colonized with the high-BD community have lower body weight and fat mass compared to the low-BD community when fermentable-fiber sources RS2, inulin, or assorted fiber were present in the diet. Body weight did not differ between the two communities when mice were fed RS4, scFOS, pectin, or cellulose diets. Lower body weight and fat mass were associated with greater cecal butyrate concentrations and microbial diversity. Conclusions The efficacy of dietary fiber interventions on metabolic health varies based on the gut microbiota composition. Overall, our results suggest that dietary fiber supplementations need to be matched with the metabolic potential of the gut microbiome. Funding Sources Fondation Leducq, USDA, and NIH.

scholarly journals Comprehensive Analysis Reveals Novel Interactions between Circulating MicroRNAs and Gut Microbiota Composition in Human Obesity

2020 ◽  
Vol 21 (24) ◽  
pp. 9509
Author(s):  
Taís Silveira Assmann ◽  
Amanda Cuevas-Sierra ◽  
José Ignacio Riezu-Boj ◽  
Fermín I. Milagro ◽  
J. Alfredo Martínez
Keyword(s):  
Gut Microbiota ◽  
Gut Microbiome ◽  
Bacterial Species ◽  
Comprehensive Analysis ◽  
Microbiota Composition ◽  
Human Obesity ◽  
Bacterial Dna ◽  
Circulating Micrornas ◽  
Novel Interactions ◽  
Gut Microbiota Composition

Background: The determinants that mediate the interactions between microRNAs and the gut microbiome impacting on obesity are scarcely understood. Thus, the aim of this study was to investigate possible interactions between circulating microRNAs and gut microbiota composition in obesity. Method: The sample comprised 78 subjects with obesity (cases, body mass index (BMI): 30–40 kg/m2) and 25 eutrophic individuals (controls, BMI ≤ 25 kg/m2). The expression of 96 microRNAs was investigated in plasma of all individuals using miRCURY LNA miRNA Custom PCR Panels. Bacterial DNA sequencing was performed following the Illumina 16S protocol. The FDR correction was used for multiple comparison analyses. Results: A total of 26 circulating microRNAs and 12 bacterial species were found differentially expressed between cases and controls. Interestingly, an interaction among three miRNAs (miR-130b-3p, miR-185-5p and miR-21-5p) with Bacteroides eggerthi and BMI levels was evidenced (r2 = 0.148, p = 0.004). Moreover, these microRNAs regulate genes that participate in metabolism-related pathways, including fatty acid degradation, insulin signaling and glycerolipid metabolism. Conclusions: This study characterized an interaction between the abundance of 4 bacterial species and 14 circulating microRNAs in relation to obesity. Moreover, the current study also suggests that miRNAs may serve as a communication mechanism between the gut microbiome and human hosts.

scholarly journals A Bayesian Negative Binomial Hierarchical Model for Identifying Diet–Gut Microbiome Associations

2021 ◽  
Vol 12 ◽  
Author(s):  
Alma Revers ◽  
Xiang Zhang ◽  
Aeilko H. Zwinderman
Keyword(s):  
Gut Microbiota ◽  
Gut Microbiome ◽  
Phylogenetic Relationships ◽  
Negative Binomial ◽  
Urban Setting ◽  
Microbiota Composition ◽  
Human Gut ◽  
Bayesian Hierarchical ◽  
Gut Microbiota Composition

The human gut microbiota composition plays an important role in human health. Long-term diet intervention may shape human gut microbiome. Therefore, many studies focus on discovering links between long-term diets and gut microbiota composition. This study aimed to incorporate the phylogenetic relationships between the operational taxonomic units (OTUs) into the diet-microbe association analysis, using a Bayesian hierarchical negative binomial (NB) model. We regularized the dispersion parameter of the negative binomial distribution by assuming a mean-dispersion association. A simulation study showed that, if over-dispersion is present in the microbiome data, our approach performed better in terms of mean squared error (MSE) of the slope-estimates compared to the standard NB regression model or a Bayesian hierarchical NB model without including the phylogenetic relationships. Data of the Healthy Life in an Urban Setting (HELIUS) study showed that for some phylogenetic families the (posterior) variances of the slope-estimates were decreasing when including the phylogenetic relationships into the analyses. In contrast, when OTUs of the same family were not similarly affected by the food item, some bias was introduced, leading to larger (posterior) variances of the slope-estimates. Overall, the Bayesian hierarchical NB model, with a dependency between the mean and dispersion parameters, proved to be a robust method for analyzing diet-microbe associations.

scholarly journals Oral Administration ofPorphyromonas gingivalisAlters the Gut Microbiome and Serum Metabolome

mSphere ◽  
2018 ◽  
Vol 3 (5) ◽  
Author(s):  
Tamotsu Kato ◽  
Kyoko Yamazaki ◽  
Mayuka Nakajima ◽  
Yasuhiro Date ◽  
Jun Kikuchi ◽  
...  
Keyword(s):  
Gut Microbiota ◽  
Periodontal Disease ◽  
Oral Administration ◽  
Gut Microbiome ◽  
Metabolic Diseases ◽  
Microbiota Composition ◽  
Content Type ◽  
Increased Risk ◽  
Metabolite Profiles ◽  
Gut Microbiota Composition

ABSTRACTPeriodontal disease induced by periodontopathic bacteria likePorphyromonas gingivalisis demonstrated to increase the risk of metabolic, inflammatory, and autoimmune disorders. Although precise mechanisms for this connection have not been elucidated, we have proposed mechanisms by which orally administered periodontopathic bacteria might induce changes in gut microbiota composition, barrier function, and immune system, resulting in an increased risk of diseases characterized by low-grade systemic inflammation. Accumulating evidence suggests a profound effect of altered gut metabolite profiles on overall host health. Therefore, it is possible thatP. gingivaliscan affect these metabolites. To test this, C57BL/6 mice were administered withP. gingivalisW83 orally twice a week for 5 weeks and compared with sham-inoculated mice. The gut microbial communities were analyzed by pyrosequencing the 16S rRNA genes. Inferred metagenomic analysis was used to determine the relative abundance of KEGG pathways encoded in the gut microbiota. Serum metabolites were analyzed using nuclear magnetic resonance (NMR)-based metabolomics coupled with multivariate statistical analyses. Oral administration ofP. gingivalisinduced a change in gut microbiota composition. The distributions of metabolic pathways differed between the two groups, including those related to amino acid metabolism and, in particular, the genes for phenylalanine, tyrosine, and tryptophan biosynthesis. Also, alanine, glutamine, histidine, tyrosine, and phenylalanine were significantly increased in the serum ofP. gingivalis-administered mice. In addition to altering immune modulation and gut barrier function, oral administration ofP. gingivalisaffects the host’s metabolic profile. This supports our hypothesis regarding a gut-mediated systemic pathology resulting from periodontal disease.IMPORTANCEIncreasing evidence suggest that alterations of the gut microbiome underlie metabolic disease pathology by modulating gut metabolite profiles. We have shown that orally administeredPorphyromonas gingivalis, a representative periodontopathic bacterium, alters the gut microbiome; that may be a novel mechanism by which periodontitis increases the risk of various diseases. Given the association between periodontal disease and metabolic diseases, it is possible thatP. gingivaliscan affect the metabolites. Metabolite profiling analysis demonstrated that several amino acids related to a risk of developing diabetes and obesity were elevated inP. gingivalis-administered mice. Our results revealed that the increased risk of various diseases byP. gingivalismight be mediated at least in part by alteration of metabolic profiles. The findings should add new insights into potential links between periodontal disease and systemic disease for investigators in periodontal disease and also for investigators in the field of other diseases, such as metabolic diseases.

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