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Enregistrement W3080092565 · doi:10.1111/imm.13247

Immunological partners: the gut microbiome in homeostasis and disease

2020· editorial· en· W3080092565 sur OpenAlexaff
Anne Camille La Flamme, Simon Milling

Notice bibliographique

RevueImmunology · 2020
Typeeditorial
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiqueGut microbiota and health
Établissements canadiensInstitute of Infection and Immunity
Organismes subventionnairesnon disponible
Mots-clésMicrobiomeImmune systemBiologyImmunologyGeneral partnershipAcquired immune systemDiseasePolitical scienceMedicineBioinformatics

Résumé

récupéré en direct d'OpenAlex

Our world is changing fast. Microbes, once considered our adversaries, are now recognized as partners. Well, not all microbes, but a vast number that live in the unique niches of our bodies and, in particular, the gut. Recently, we have come to appreciate how our normal microbiota contribute to our health, and how disruption of this partnership can open the way for disease (both infectious and non-infectious). This Joint Virtual Issue on ‘Immunology and the Microbiome’ celebrates this partnership and is a joint initiative by the immunological societies of Australia and New Zealand (ASI) and Britain (BSI) and their journals, Immunology & Cell Biology and Immunology, respectively. This collection contains six recent articles from each journal (three reviews and three original research articles) that highlight specific aspects of the microbiome–immune partnership. Focusing on the articles from Immunology, there are key central themes that arise: the changing nature of this relationship over time, how diet influences the microbiota and consequently the immune system, and how individual components of the innate and adaptive immune systems interact with the gut microbiome to regulate and balance the complex network. To start this collection off, Zhao et al.1 begin at the beginning – the education of the adaptive immune system by gut microbial antigens (Fig. 1 ①). This education starts at birth with the first major seeding of flora from the mother; colonization begins and so does the development of adaptive cells. Zhao et al.1 detail how these processes are intertwined with the neonate’s immune system regulating the microbial community and the microbes shaping the developing immune cells. This period is a critical ‘window of opportunity’, which impacts one’s health and homeostasis throughout life. Within the large community of microbes, specific microbes can support the differentiation or expansion of different functional immune subsets such as the support of regulatory T (Treg) cell expansion by specific Clostridium clusters and Bacteroides fragilis or the induction of T helper type 17 cells by epithelial-adhesive bacteria like Escherichia coli O157.1 Zhao et al. conclude by discussing how the mis-education of adaptive immune cells during this critical period promotes the development of chronic inflammatory diseases such as Crohn’s disease and multiple sclerosis (MS)1 and end by highlighting how the potential of probiotics or prebiotics to ‘normalize’ the gut microbiota presents an attractive therapeutic strategy. Partnerships take a lot of work; they need the right fuel to keep them alive and to enable both partners to benefit. That is how probiotics and prebiotics work – seeding the right microbes and then providing the right nutrients to fuel them. Gudi et al. and Haase et al.2, 3 investigate how specific microbial partnerships are fueled and sustained such that their metabolites can regulate chronic inflammatory diseases such as MS. The complex dietary polysaccharide, yeast β-glucan (YBG), is a prebiotic that Gudi et al.2 investigate in their original article (Fig. 1 ②), and they find that administration of YBG expands FoxP3+ IL-10+ IL-17+ T cells ex vivo and modulates the T-cell compartment in vivo, leading to suppression of type 1 diabetes in non-obese diabetic mice. Key to this effect is the finding that YBG enhances, not suppresses, local cytokine production including interleukin (IL)-10, tumor necrosis factor-α, and IL-17 with a targeted decrease in some cytokines (e.g. interferon-γ).2 Promoting the growth of the ‘right’ kind of bacteria has flow-on effects not only locally but also systemically, by the production of immune-modifying microbial metabolites as discussed by Haase et al. (Fig. 1 ③).3 In particular, the ability of these microbially produced compounds such as short-chain fatty acids or tryptophan metabolites to alter neuroinflammation in diseases such as MS speaks to the importance of the microbiota–gut–brain axis in maintaining health and homeostasis throughout the body. This axis involves not just the immune system but is a complex network encompassing enteroendocrine cells, vagal nerve signals, as well as astrocytes and microglia within the brain with short-chain fatty acids exerting their effects through stimulation of retinoic acid production, histone deacetylase inhibition and direct activation of free fatty acid receptors like GPR43, GPR41 and GPR109A.3 This review centers on the specific benefits of short-chain fatty acids and tryptophan metabolites, but these microbially-derived products are only subset of metabolites that contributes to immune homeostasis and the development of a beneficial microbial partnership. Immune control of the microbial populations in the gut is essential to the maintenance of a healthy gut microbiome, and control of gut microbes by gut eosinophils and secretory IgA (sIgA) are the focus of the articles by Singh et al. and Hoces et al.4, 5 Using mice deficient in eosinophils (i.e. ΔdblGATA-1−/− mice), Singh and his colleagues report that an absence of eosinophils did not alter gut architecture, barrier integrity or sIgA levels; however, the loss of eosinophils significantly changed microbial diversity with the greatest effects found in the mucus-associated communities (Fig. 1 ④).4 Although these investigators did not find that these changes in microbial communities resulted in any apparent negative health effects in this controlled experimental system, it remains to be seen whether a loss or impairment of eosinophil function in a more complex and chaotic environment that more closely parallels real life would reveal a role for these innate cells in maintaining gut health. Looking in more detail into how sIgA regulates microbial colonization, Hoces et al.5 not only discuss the direct interaction between sIgA and gut microbes but also consider the contributions of gut physiology and environment into the seemingly contradictory actions of sIgA in preventing disease but promoting colonization (Fig. 1 ⑤). They discuss the function of sIgA in the context of the changing gut environment, which is promoted by the high flow rate through the gastrointestinal system, as well as the constant molecular evolution that microbes undergo in adapting to this changing environment. Specifically, they propose a model whereby high microbial densities lead to classical agglutination by sIgA, whereas low bacterial densities promote ‘enchained’ clonal growth.5 This enchained growth or clumping provides specific benefits including selective clonal growth and extinction as well as reducing horizontal gene transfer, suggesting an impact on local microbial evolution.5 By understanding how sIgA regulates microbial communities in the gut, we can better design vaccines or targeted therapeutics that harness the complex activities of sIgA to regulate gut health. The last Immunology article in this Joint Virtual issue highlights how sustaining a beneficial partnership requires constant work and effort. In the context of gut health, Kehrmann et al.6 investigate the involvement of Treg cells in shaping the microbial communities in the gut (Fig. 1 ⑥). While Treg cells are critical regulators of immune cells, this original article looks at how a loss of Treg cells, using DEpletion of REGulatory T cells (DEREG) mice, changes the microbiota. One of the key findings of this study was the increase in abundance of bacteria from the phylum Firmicutes; however, underscoring this finding was recognition of the contribution of inter-subject variability influenced by cage, breeding, sex and experiment to even this controlled experimental system.6 To complement these articles from Immunology, are six from Immunology & Cell Biology that investigate and discuss the involvement of immune factors (interferon inducible transmembrane genes)7 or immune cells (MAIT cells)8 in regulating the local gut environment to maintain epithelial homeostasis and immune–microbial balance. The benefit of a balanced and regulated microbiota is also discussed by Malone et al. in the context of how the gut–brain axis influences stroke outcomes9 and by McCoy et al.10 in how the microbiome shapes immune memory. These reviews highlight the potential of targeted microbiome interventions in the treatment of stroke or to maximize vaccine efficacy. Finally, studies by Poyntz et al. and Mullaney et al. examine the individual contributions of genetic factors versus microbiota in experimental models of antibody responsiveness11 or autoimmunity,12 and find that in these instances, the microbiome cannot overcome genetic susceptibility. Taken together, this collection of articles and editorials from Immunology1-6 and Immunology & Cell Biology7-13 begin to dissect the complex partnership that has evolved, and continues to evolve, between microbes and humans. The consequences of an unhealthy relationship (i.e. dysbiosis) are far-reaching, as shown by the effects of the gut microbiome on chronic inflammatory (e.g. Crohn's disease) or autoimmune (type 1 diabetes, MS) diseases, but by understanding this complex network, we can design interventions (diet, vaccines, therapeutics) to prevent disease and promote a healthy homeostasis.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Éditorial · Signal consensuel: Éditorial
Score de désaccord entre enseignants0,201
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,001
Communication savante0,0000,000
Science ouverte0,0010,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,010
Tête enseignante GPT0,284
Écart entre enseignants0,275 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeSans objet
Domainenon disponible
GenreÉditorial

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations8
Publié2020
Routes d'admission1
Résumé présentoui

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