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Record W2150550400 · doi:10.1113/jphysiol.2010.189498

Out‐FOX(O)ing proteolysis in sepsis

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

Bibliographic record

VenueThe Journal of Physiology · 2010
Typeletter
Languageen
FieldMedicine
TopicExercise and Physiological Responses
Canadian institutionsMcMaster University
Fundersnot available
KeywordsSepsisWastingInflammationProteolysisMedicineImmunologyTumor necrosis factor alphaEtiologyIntensive care medicineBioinformaticsInternal medicineBiologyBiochemistry

Abstract

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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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0110.004

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.033
GPT teacher head0.316
Teacher spread0.283 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations3
Published2010
Admission routes1
Has abstractyes

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