Intermittent Cold Exposure Induces Distinct Proteomic Signatures in White Adipose Tissue of Mice
Bibliographic record
Abstract
Adipose tissue exhibits dynamic metabolic and structural changes in response to environmental stimuli, including temperature fluctuations. While continuous cold exposure has been extensively studied, the molecular effects of prolonged intermittent cold exposure (ICE) remain poorly characterized. Here, we present a proteomic analysis of inguinal white adipose tissue (IWAT) from mice subjected to a 16-week regimen of short-term daily ICE (6 °C for 6 h, 5 days per week) without compensatory caloric intake. Mass spectrometry identified 1108 proteins, with 140 differentially expressed between experimental and control groups. ICE significantly upregulated mitochondrial proteins associated with lipid and carbohydrate catabolism, the tricarboxylic acid (TCA) cycle, oxidative phosphorylation, and lipogenesis, including LETM1, AIFM1, PHB, PHB2, ACOT2, NDUA9, and ATP5J. These changes reflect enhanced metabolic activity and mitochondrial remodeling. In contrast, proteins linked to oxidative stress, insulin resistance, inflammation, and extracellular matrix remodeling were downregulated, such as HMGB1, FETUA, SERPH1, RPN1, and AOC3. Notably, gamma-synuclein (SYUG), which inhibits lipolysis, was undetectable in ICE-treated samples. Our findings support the hypothesis that ICE promotes thermogenic reprogramming and metabolic rejuvenation in subcutaneous fat through activation of futile cycles and mitochondrial restructuring. This study offers molecular insights into adaptive thermogenesis and presents intermittent cold exposure as a potential strategy to mitigate adipose tissue aging.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".