Microbiota-gut-brain axis: enteroendocrine cells and the enteric nervous system form an interface between the microbiota and the central nervous system

Kuwahara, Atsukazu; Matsuda, Kyoko; Kuwahara, Yuko; Asano, Shinji; Inui, Toshio; Marunaka, Yoshinori

doi:10.2220/biomedres.41.199

Cited by 69 publications

(44 citation statements)

References 181 publications

(223 reference statements)

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“…It has been reported that intestinal microbiota disorders may promote the development and deterioration of the symptoms of irritable bowel syndrome, 24 and intestinal microbiota may affect the two-way communication between the enteric nervous system and central nervous system. 25 Previous studies have demonstrated that immune system activation and proinflammatory cytokines may indirectly affect skin. 26,27 This may be the possible mechanism underlying the indirect impact of intestinal skin microbiota on sensitive skin.…”

Section: Discussionmentioning

confidence: 99%

Correlation Between Facial Skin Microbiota and Skin Barriers in a Chinese Female Population with Sensitive Skin

Bai¹,

Wang²,

Zheng³

et al. 2021

IDR

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Background and Aim:The association of microbiota changes with sensitive skin remains controversial until now. Although a strong correlation is detected between skin microbiota distribution and biophysical parameters, there is little knowledge on the link between sensitive skin and skin microbiota in Chinese women. This study aimed to unravel the correlation between facial skin microbiota distribution and skin barriers in Chinese women with sensitive skin. Materials and Methods: In total, 34 volunteers were enrolled, including 24 subjects with sensitive skin (SS group) and 10 subjects with non-sensitive skin (NS group). The cuticle moisture content, transepidermal water loss (TEWL), and facial skin sebum secretion were measured, and the facial skin surface morphology was evaluated. Sensitive skin samples were collected from the facial (SS-F group) and chest skin of subjects in the SS group (SS-C group), while non-sensitive skin samples were collected from the facial skin of subjects in the NS group (NS-F group). All skin samples were subjected to 16S rRNA sequencing. Results: 16S rRNA sequencing detected Actinobacteria, Firmicutes, and Proteobacteria as the three most common microbiota phyla and Propionibacterium, Paracoccus, and Corynebacterium as the three most common microbiota genera, and there were no significant differences in the relative frequency of Actinobacteria, Firmicutes, or Proteobacteria, or Propionibacterium, Paracoccus, or Corynebacterium among the SS-F, SS-C, and NS-F groups (P>0.05). We detected no significant difference in the diversity of bacterial communities among the SS-F, SS-C, and NS-F groups; however, the Shannon's diversity index was significantly higher in the NS-F group than in the SS-C group. In addition, Spearman correlation analysis showed a correlation between the microbiota genera and skin physiological parameters (P<0.05). Conclusion:This study preliminarily unravels the skin microbiota of sensitive skin using a high-throughput tool, and there are no microbiota genera with strong associations with skin physiological parameters.

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Section: Discussionmentioning

confidence: 99%

Correlation Between Facial Skin Microbiota and Skin Barriers in a Chinese Female Population with Sensitive Skin

Bai¹,

Wang²,

Zheng³

et al. 2021

IDR

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“…For example, the administration of GABA-producing Lactobacillus brevis via soybean milk was revealed to be effective as an antidepressant in rats, such as fluoxetine [ 15 ]. Neuroactive compounds derived from gut microbiota metabolism act as potential mediators of communication between the gut microbiota and the host [ 16 ]. As evidence, some receptors and transporters critical for neuroactive compounds are found in enterocytes.…”

Section: Introductionmentioning

confidence: 99%

“…As evidence, some receptors and transporters critical for neuroactive compounds are found in enterocytes. For example, GABA transporters are present in the rat gastrointestinal tract [ 16 ]; dopamine receptors are widespread throughout the intestine [ 9 , 17 ].…”

Section: Introductionmentioning

confidence: 99%

“…In addition, the brain-gut communication pathways include ANS involvement: extrinsic afferent nerves are able to stimulate efferent nerve fibers, by directly or indirectly affecting local gut functions: motility, secretion, and thereby, microbiota composition [ 16 ]. Specifically, the ANS is closely connected with the enteric nervous system (ENS), a large and complex compartment of the peripheral nervous system, which integrates signals from the CNS through connections with the parasympathetic and sympathetic branches of the ANS, and sends information to CNS, via intrinsic primary afferent neurons projecting to motor neurons and interneurons [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

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Deep Transcranial Magnetic Stimulation Affects Gut Microbiota Composition in Obesity: Results of Randomized Clinical Trial

Ferrulli

Drago

Gandini

et al. 2021

IJMS

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Growing evidence highlights the crucial role of gut microbiota in affecting different aspects of obesity. Considering the ability of deep transcranial magnetic stimulation (dTMS) to modulate the cortical excitability, the reward system, and, indirectly, the autonomic nervous system (ANS), we hypothesized a potential role of dTMS in affecting the brain-gut communication pathways, and the gut microbiota composition in obesity. In a hospital setting, 22 subjects with obesity (5 M, 17 F; 44.9 ± 2.2 years; BMI 37.5 ± 1.0 kg/m2) were randomized into three groups receiving 15 sessions (3 per week for 5 weeks) of high frequency (HF), low frequency (LF) dTMS, or sham stimulation. Fecal samples were collected at baseline and after 5 weeks of treatment. Total bacterial DNA was extracted from fecal samples using the QIAamp DNA Stool Mini Kit (Qiagen, Italy) and analyzed by a metagenomics approach (Ion Torrent Personal Genome Machine). After 5 weeks, a significant weight loss was found in HF (HF: −4.1 ± 0.8%, LF: −1.9 ± 0.8%, sham: −1.3 ± 0.6%, p = 0.042) compared to LF and sham groups, associated with a decrease in norepinephrine compared to baseline (HF: −61.5 ± 15.2%, p < 0.01; LF: −31.8 ± 17.1%, p < 0.05; sham: −35.8 ± 21.0%, p > 0.05). Furthermore, an increase in Faecalibacterium (+154.3% vs. baseline, p < 0.05) and Alistipes (+153.4% vs. baseline, p < 0.05) genera, and a significant decrease in Lactobacillus (−77.1% vs. baseline, p < 0.05) were found in HF. Faecalibacterium variations were not significant compared to baseline in the other two groups (LF: +106.6%, sham: +27.6%; p > 0.05) as well as Alistipes (LF: −54.9%, sham: −15.1%; p > 0.05) and Lactobacillus (LF: −26.0%, sham: +228.3%; p > 0.05) variations. Norepinephrine change significantly correlated with Bacteroides (r2 = 0.734; p < 0.05), Eubacterium (r2 = 0.734; p < 0.05), and Parasutterella (r2 = 0.618; p < 0.05) abundance variations in HF. In conclusion, HF dTMS treatment revealed to be effective in modulating gut microbiota composition in subjects with obesity, reversing obesity-associated microbiota variations, and promoting bacterial species representative of healthy subjects with anti-inflammatory properties.

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“…Another aspect of calorie restriction and related microbiome shifts, thus far largely ignored, is the behavioral changes in humans undergoing CR. Physiological changes under CR have been shown to contribute to reduction of ageing rate and oxidative stress, lowering risk factors for cardiovascular diseases and alleviate obesity, and changing endocrinal functions that correlate to metabolism and physical health 8 . Yet, CR is shown to be able to modulate behavioral changes in both human and experimental models, in measurements that include both objective (for instance psychological well-being, cognitive functioning) and subjective (mood, subjective feelings of hunger etc., determined usually by questionnaires) criteria 9 .…”

Section: Introductionmentioning

confidence: 99%

Gut microbiome couples gut and brain during calorie restriction in treating obesity

Zeng

Wang

Xiang

et al. 2020

Preprint

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Calorie restriction (CR) has been widely recognized for its effect in reducing body weight and alleviating diabetes in humans, as well as prolonging life span in animal studies. Gut microbiome shifts contribute to part of the effects of CR, but little is known regarding their influences except on metabolism and immunity. Here we monitored gut microbiome using metagenomics and metatranscriptomics in obese individuals undergoing CR, and revealed microbial determinants that could contribute to successful weight loss. Microbiome changes are linked to changes in blood metabolome and hormones, which eventually correlate to brain functional changes as studied using functional magnetic resonance imaging (fMRI). Brain functional shifts indicate response of central neural system (CNS) to CR, and microbiome constitutes the keystone of gut-brain axis. Animal experiment further reaffirms the gut microbiome changes, metabolic and hormonal shifts of CR, while proteomic analysis of brain tissues suggest that epigenetic modifications of key proteins could explain responses of CNS to CR. Our study establishes linkage between CR, gut microbiome, metabolome/ hormones and CNS function, and demonstrates that CR has multi-facet, coordinated effects on the host, of which many could contribute to weight loss and other beneficial effects.

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Microbiota-gut-brain axis: enteroendocrine cells and the enteric nervous system form an interface between the microbiota and the central nervous system

Cited by 69 publications

References 181 publications

Correlation Between Facial Skin Microbiota and Skin Barriers in a Chinese Female Population with Sensitive Skin

Correlation Between Facial Skin Microbiota and Skin Barriers in a Chinese Female Population with Sensitive Skin

Deep Transcranial Magnetic Stimulation Affects Gut Microbiota Composition in Obesity: Results of Randomized Clinical Trial

Gut microbiome couples gut and brain during calorie restriction in treating obesity

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