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Enregistrement W2150550400 · doi:10.1113/jphysiol.2010.189498

Out‐FOX(O)ing proteolysis in sepsis

2010· letter· en· W2150550400 sur OpenAlexaff
Stuart M. Phillips

Notice bibliographique

RevueThe Journal of Physiology · 2010
Typeletter
Langueen
DomaineMedicine
ThématiqueExercise and Physiological Responses
Établissements canadiensMcMaster University
Organismes subventionnairesnon disponible
Mots-clésSepsisWastingInflammationProteolysisMedicineImmunologyTumor necrosis factor alphaEtiologyIntensive care medicineBioinformaticsInternal medicineBiologyBiochemistry

Résumé

récupéré en direct d'OpenAlex

Clinicians often struggle with patients in intensive care suffering from sepsis. Aside from the complex problems of septicaemia itself there a whole host of secondary problems linked to loss of muscle mass and metabolic disturbances. Hypercytokinaemia is a hallmark of sepsis and a number of other inflammatory conditions and we have an increasing appreciation of the effects that these endocrine molecules have on numerous processes (Pedersen, 2009). Two cytokines that are strongly implicated are tumour necrosis factor-α (TNF-α) and interleukin-6 (IL-6), both of which have been shown to be elevated in inflammation and play a role in the aetiology of muscle wasting (Frost & Lang, 2007). It is increasingly clear that blunting the appearance of TNF-α and IL-6 may be beneficial and help abate muscle wasting. The anti-inflammatory effects of glucocorticoids are widely exploited in various clinical scenarios and yet their use in sepsis, even after more than 50 years, is contentious (Annane et al. 2009). Presumably, it would make sense to use glucocorticoids to suppress inflammation during highly pro-inflammatory states, but we also know that glucocorticoids can be in and of themselves activators of proteolysis (Tisdale, 2007). So the answer to the question of glucorticoid efficacy in the treatment of sepsis, at least insofar as muscle wasting is concerned, is a tricky one that appears to be dose dependent (Annane et al. 2009). An intriguing question is still how glucocorticoids would exert their benefit in terms of preserving muscle mass, if indeed they do, during endotoxaemia? A mechanistic light has been shone on this question and some praiseworthy insight is provided in this issue of The Journal of Physiology by Crossland et al. (2010). What the authors found was that a low dose infusion of dexamethasone during lipopolysaccharide (LPS)-induced endotoxaemia in rodents blunted a rise in muscle muscle proteolysis and expression of IL-6 and TNF-α mRNA. Interestingly, the normal endotoxaemia-induced rise in muscle atrophy F-box (MAFbx) and muscle RING finger 1 (MuRF1) mRNA expression, two prototypical proteolytic proteins both implicated in muscle wasting in a variety of conditions (Lecker et al. 2004), remained unchanged with DEX. How then is dexamethasone (DEX) blocking proteolysis? The answer to this question is not forthcoming from Crossland's data, but there was DEX-induced suppression of the rise in cathepsin-L expression and activity as well as metallothionein-1A expression, the significance of which are not known, but both or either could have had an impact on proteolysis. Interestingly, and following up on their previous work (Crossland et al. 2008), the authors showed that DEX also resulted in a reduced rise or the normal LPS triggered rise in pyruvate dehydrogenase kinase 4 (PDK4) mRNA as well as a upregulation of glycogenolysis and lactate accumulation. The metabolic crossover of DEX is intriguing and raises the question of just how the glucocortoids are exerting both anti-proteolytic and metabolic effects? The authors put their money on a cytokine-mediated suppression of Akt (protein kinase B) and forkhead box O1 (FOXO1); the phosphorylation of both proteins was enhanced with low dose DEX compared to the LPL-induced septic condition. The authors propose a scheme whereby cytokine inhibition is the top of the cascade and the nexus is Akt and FOXO; the missing link is still how proteolysis is down-regulated with DEX. Attractive candidates might include the calpains (Tidball & Spencer, 2002) or caspase-3 (Supinski et al. 2009), both of which have been hypothesized to play an initiatory role in mediating further proteolysis by the ubiquitin proteasomal pathway. More definitive experiments on whether the two hypothesized cytokines, TNF-α and IL-6, are actually causative in this process are needed. Aside from proteolytic activation, LPS-induced endotoxaemia also resulted in elevated expression of pyruvate dehydrogenase kinase (PDK) mRNA and protein. Again, this finding is consistent with the previous work from this group (Crossland et al. 2008) and highlights an interesting and important point of convergence for FOXO and its role as an integrative sensor of cellular stress. The administration of DEX markedly reduced PDK mRNA expression and also resulted in less glycogen degradation and lactate accumulation, which is most likely due to less PDK-induced suppression of pyruvate dehydrogenase activity. An important next step to gain a better understanding of how Akt and FOXO are involved in this pathway will be to somehow manipulate, perhaps through receptor blockade, TNF-α, which the authors speculated was the main reason for why DEX inhibited the rise in PDK mRNA and protein. Crossland and her colleagues are to be congratulated for their work in this area. Clearly future work will need to focus back in on the exact role of TNF-α and IL-6 in this process. The authors’ data represents not only an exciting mechanistic advance but one that advances our knowledge potentially leading to new ideas on how sepsis-induced complications might be mitigated in humans.

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)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesIntégrité de la recherche
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,697
Score d'incertitude au seuil0,994

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0010,008
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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 Opus0,033
Tête enseignante GPT0,316
Écart entre enseignants0,283 · 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
GenreEmpirique

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

En bref

Citations3
Publié2010
Routes d'admission1
Résumé présentoui

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