MétaCan
Menu
← Back to cohort

IGF‐1 Optimizes Mitochondrial Function Through AMP‐Activated Protein Kinase in Adult Sensory Neurons

2018· article· en· W3175377679 on OpenAlexafffund
Mohamad‐Reza Aghanoori, Mohammad Golam Sabbir, Darrell R. Smith, Paul Fernyhough

Bibliographic record

VenueThe FASEB Journal · 2018
Typearticle
Languageen
FieldMedicine
TopicPain Mechanisms and Treatments
Canadian institutionsSt. Boniface HospitalUniversity of Manitoba
FundersCanadian Institutes of Health Research
KeywordsAMPKProtein kinase AEndocrinologyInternal medicineAMP-activated protein kinaseMitochondrionmitochondrial fusionCell biologyBiologyMitochondrial DNAKinaseMedicineGeneBiochemistry

Abstract

fetched live from OpenAlex

Objective Diabetic sensorimotor polyneuropathy (DSPN) affects about half of diabetic patients leading to significant morbidity in the form of ulcers, gangrene and lower limb amputation. Intractable pain is a major problem where therapy is imperfect. Recent studies have shown impaired growth factor signaling together with down‐regulation of AMP‐activated protein kinase (AMPK) and mitochondrial dysfunction in dorsal root ganglia (DRG) in animal models of type 1 and type 2 diabetes. We hypothesized that loss of IGF‐1 signaling in diabetes contributes to loss of AMPK activity and mitochondrial function in DRG neurons. Methodology Expression of genes linking IGF‐1 to mitochondrial function in intact DRGs isolated from streptozotocin (STZ)‐induced type 1 diabetic versus age matched control rats were analyzed using qRT‐PCR array. Adult DRG neurons were cultured under defined conditions from age‐matched control or STZ‐induced diabetic rats. Cultured adult neurons treated with/without IGF‐1 underwent qRT‐PCR and Western blotting for expression analysis of genes downstream from IGF‐1 signaling. In parallel, in IGF‐1 treated vs. control neuron cultures we determined (i) mitochondrial DNA (mtDNA)/nuclear DNA (nDNA) ratio using qRT‐PCR, and (ii) mitochondrial respiration (oxygen consumption rate; OCR) assessed using the Seahorse XF24. Finally, specific inhibitors and siRNAs against IGF‐1‐activated signaling cascades were utilized to dissect the pathways modulated by IGF‐1 to regulate mitochondrial protein expression and organelle function. Results Dysregulation of genes including IGF‐1, AMPKα2, ATP5a1 (subunit of ATPase) and peroxisome proliferator–activated receptor γ coactivator‐1β was observed in intact DRG of diabetic vs. control rats at the mRNA level. Exogenous IGF‐1 up‐regulated mRNA levels of these genes in cultured DRGs derived from control or diabetic rats. Short‐term IGF‐1 treatment (10nM for 15 min‐6h) of cultures significantly (P<0.05) increased phosphorylation of Akt, P70S6K (a downstream target of Akt involved in protein synthesis), AMPK (on T172) and acetyl‐CoA carboxylase (ACC, an endogenous target of AMPK). Mitochondrial gene expression was also augmented. Blockade of AMPK by a pharmacological inhibitor, compound C, suppressed IGF‐1 dependent activation ACC and Akt in DRG cultures from control rats. Twenty‐four hour treatment of DRG cultures with IGF‐1 significantly (P<0.05) enhanced mtDNA/nDNA ratio and mitochondrial OCR. The positive effect of IGF‐1 on OCR was prevented by (i) compound C, (ii) U0126 (ERK inhibitor), or (iii) AMPKα1‐specific siRNA. Conclusions IGF‐1 elevated an array of parameters related to mitochondrial function by acting, in part, through AMPK activation. Optimization of mitochondrial phenotype by IGF‐1 could be an effective therapeutic option in DSPN where mitochondrial dysfunction contributes to pathogenesis. Support or Funding Information Funded by CIHR grant # MOP‐130282 (P.F.) This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.028
GPT teacher head0.274
Teacher spread0.246 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2018
Admission routes2
Has abstractyes

Explore more

Same venueThe FASEB Journal→Same topicPain Mechanisms and Treatments→French-language works237,207→