Obesity influences composition of salivary and fecal microbiota and impacts the interactions between bacterial taxa

Bombin, Andrei; Yan, Shun; Bombin, Sergei; Mosley, Jonathan D.; Ferguson, Jane F.

doi:10.14814/phy2.15254

Cited by 14 publications

(8 citation statements)

References 179 publications

(174 reference statements)

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“…It is noteworthy that a previous study confirmed the significance of the oral microbiome by comparing the salivary and fecal microbiota of the same subjects [ 55 ]. They found that the salivary microbiota provided a more distinct pattern for differentiation between obese and healthy weight individuals and was superior to the fecal microbiota.…”

Section: Discussionmentioning

confidence: 76%

“…They found that the salivary microbiota provided a more distinct pattern for differentiation between obese and healthy weight individuals and was superior to the fecal microbiota. Although healthy and overweight groups were not distinct from each other, Actinomyces and Haemophilus were characteristic for the overweight group [ 55 ]. A recent study has proven that oral diseases, particularly periodontitis, can endanger the entire body by inducing gut microbiota dysbiosis and intestinal inflammation through the translocation of salivary microbes [ 56 ].…”

Section: Discussionmentioning

confidence: 99%

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Dysbiosis of the Subgingival Microbiome and Relation to Periodontal Disease in Association with Obesity and Overweight

et al. 2023

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Obesity causes gut dysbiosis; nevertheless, little is known about the oral microbiome. We aimed to identify differences in the subgingival microbiota influenced by body weight and periodontal status. Patients (n = 75) recruited at the University Dental Hospital Sharjah, United Arab Emirates, were distributed into three equal groups (healthy weight, overweight, and obese) sub-divided into having either no-mild (NM) or moderate-severe (MS) periodontitis. Subgingival plaques were collected. Microbiota were identified by 16S rRNA sequencing using nanopore technology. Linear discriminant analysis demonstrated significant bacterial biomarkers for body weight and periodontal health. Unique microbiota signatures were identified, with enrichment of periopathogens in patients with MS periodontitis (Aggregatibacter actinomycetemcomitans in obese, Tannerella forsythia and Treponema denticola in overweight, Porphyromonas gingivalis and Fusobacterium nucleatum in healthy weight), thus reflecting differences in the microbiota affected by body weight. Other pathogenic bacteria, such as Salmonella enterica and Klebsiella pneumoniae, were enriched in overweight subjects with NM periodontitis, suggesting an increase in the relative abundance of pathogens even in patients with good periodontal health if they were overweight. Alpha and beta diversities were significantly different among the groups. Dysbiosis of the subgingival microbiota in obese and overweight individuals was associated with increased prevalence and severity of periodontal disease, which was correlated with the body mass index. This study highlights the immense importance of the oral microbiome and the need for lifestyle and dental interventions to resolve oral dysbiosis and restore normal homeostasis.

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Section: Discussionmentioning

confidence: 76%

Section: Discussionmentioning

confidence: 99%

Dysbiosis of the Subgingival Microbiome and Relation to Periodontal Disease in Association with Obesity and Overweight

et al. 2023

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“…One possible reason for the difference in findings could be body mass index. It has been shown that the intestinal flora and viral communities are altered in obese individuals [ 37 , 38 ], and our analysis removed a limiting value that possibly influenced the conclusion that the clinical characteristic of the children represented by this significantly different value was obesity (body mass index of 26.63 kg/m 2 ), whereas Gondalia did not mention the body mass index of the participants in his article. It is also possible that our experiment was too homogeneous, as the intestinal symptom of these children was constipation (See Supplemental Table 1 ).…”

Section: Discussionmentioning

confidence: 99%

Children with autism show differences in the gut DNA virome compared to non-autistic children: a case control study

Duan

Wang

et al. 2023

BMC Pediatr

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Background Several previous studies have identified a potential role that the gut microbiome can play in autism spectrum disorder (ASD) in children, but little is known about how variations in the virome may be involved in ASD. We aimed to understand the changes in the gut DNA virome of children with ASD. Methods A case–control study was presented, in which 13 two-children families were observed while considering the age, mode of birth, history of antibiotic use, and vaccination history to minimize the influence of confounding factors. DNA viral metagenomic sequencing was successfully performed on stool samples from 11 children with ASD and 12 healthy non-ASD children. The basic composition and gene function of the participants' fecal DNA virome were detected and analyzed. Finally, the abundance and diversity of the DNA virome of children with ASD and their healthy siblings were compared. Results The gut DNA virome in children aged 3–11 years was found to be dominated by the Siphoviridae family of Caudovirales. The proteins encoded by the DNA genes mainly carry out the functions of genetic information transmission and metabolism. Compared the gut DNA virome of ASD and healthy non-ASD children, their abundance of Caudovirales and Petitvirales both showed a significant negative correlation (r = -0.902, P < 0.01), there was no statistically significant difference in the relative abundance of viruses at the order and family levels, and a difference in the relative abundance at the genus level for Skunavirus (Ζ = -2.157, P = 0.031). Viral α diversity was reduced in children with ASD, but α diversity and β diversity did not differ statistically between groups. Conclusions This study indicates that elevated Skunavirus abundance and decreased α diversity in the gut DNA virulence group of children with ASD, but no statistically significant difference in the change in alpha and beta diversity. This provides preliminary cumulative information on virological aspects of the relationship between the microbiome and ASD, and should benefit future multi-omics and large sample studies on the gut microbes in children with ASD.

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“…Perturbations in amino acid metabolism have been observed in diabetes [78,79]. Since the FoCus and PopGen cohort studies include both healthy and obese groups, we focused on KEGG functional categories such as carbohydrate metabolism, amino acid metabolic pathways, and sugar transport which have been reported as enriched in obesity [80,81]. We selected significant KO terms (unadjusted p-value<0.05) in the MGS data under the above-mentioned categories and compared them with KO terms obtained from the functional inference tools (Fig.…”

Section: Many Disease-relevant Ko Terms Were Differentially Abundant ...mentioning

confidence: 99%

On the limits of 16S rRNA gene-based metagenome prediction and functional profiling

Matchado,

Rühlemann,

Reitmeier

et al. 2024

Microbial Genomics

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Molecular profiling techniques such as metagenomics, metatranscriptomics or metabolomics offer important insights into the functional diversity of the microbiome. In contrast, 16S rRNA gene sequencing, a widespread and cost-effective technique to measure microbial diversity, only allows for indirect estimation of microbial function. To mitigate this, tools such as PICRUSt2, Tax4Fun2, PanFP and MetGEM infer functional profiles from 16S rRNA gene sequencing data using different algorithms. Prior studies have cast doubts on the quality of these predictions, motivating us to systematically evaluate these tools using matched 16S rRNA gene sequencing, metagenomic datasets, and simulated data. Our contribution is threefold: (i) using simulated data, we investigate if technical biases could explain the discordance between inferred and expected results; (ii) considering human cohorts for type two diabetes, colorectal cancer and obesity, we test if health-related differential abundance measures of functional categories are concordant between 16S rRNA gene-inferred and metagenome-derived profiles and; (iii) since 16S rRNA gene copy number is an important confounder in functional profiles inference, we investigate if a customised copy number normalisation with the rrnDB database could improve the results. Our results show that 16S rRNA gene-based functional inference tools generally do not have the necessary sensitivity to delineate health-related functional changes in the microbiome and should thus be used with care. Furthermore, we outline important differences in the individual tools tested and offer recommendations for tool selection.

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Obesity influences composition of salivary and fecal microbiota and impacts the interactions between bacterial taxa

Abstract: This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Cited by 14 publications

References 179 publications

Dysbiosis of the Subgingival Microbiome and Relation to Periodontal Disease in Association with Obesity and Overweight

Dysbiosis of the Subgingival Microbiome and Relation to Periodontal Disease in Association with Obesity and Overweight

Children with autism show differences in the gut DNA virome compared to non-autistic children: a case control study

On the limits of 16S rRNA gene-based metagenome prediction and functional profiling

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