The decrease in fibroblast collagen is a primary contributor to skin aging. Lactate can participate in collagen synthesis through lysine lactylation by regulating gene transcription. However, the precise mechanism by which lactate influences collagen synthesis requires further investigation. This study demonstrates that the depletion of macrophages mitigates the stimulating effect of lactate on collagen synthesis in fibroblasts. Through joint CUT&Tag and RNA‐sequencing analyses, a feedback loop between H4K12 lactylation (H4K12la) and histone deacetylase 3 (HDAC3) in macrophages that drives lactate‐induced collagen synthesis are identified. Macrophages can uptake extracellular lactate via monocarboxylate transporter‐1 (MCT1), leading to an up‐regulation of H4K12la levels through a KAT5‐KAT8‐dependent mechanism in response to Poly‐L‐Lactic Acid (PLLA) stimulation, a source of low concentration and persistent lactate, thereby promoting collagen synthesis in fibroblasts. Furthermore, H4K12la is enriched at the promoters of TGF‐β1 and TGF‐β3, enhancing their transcription. Hyperlactylation of H4K12la inhibits the expression of the eraser HDAC3, while the activation of HDAC3 reduces H4K12la in macrophages and suppresses collagen synthesis in fibroblasts. In conclusion, this study illustrates that macrophages play a critical role in lactate‐induced collagen synthesis in the skin, and targeting the lactate‐H4K12la‐HDAC3‐TGF‐β axis may represent a novel approach for enhancing collagen production to combat skin aging.

Hypotaurine, an important sulfur-containing and nonpeptidic amino acid, is a precursor of taurine and an antioxidant. Our previous study indicated that hypotaurine levels are associated with the ageing of Caenorhabditis elegans (C. elegans). However, whether hypotaurine plays a role in the lifespan regulation of C. elegans and the mechanism remains undetermined. Here, we found that hypotaurine enhances oxidative stress resistance and ameliorates ageing in C. elegans. Our results show that hypotaurine regulates a variety of pathways and leads to the upregulation of some age-related genes to extend lifespan. We also found that the stress response-related transcription factors DAF-16/FOXO and SKN-1/NRF2 contribute to the beneficial longevity conferred by hypotaurine. Moreover, our results demonstrate that hypotaurine induced lifespan extension by regulating the insulin/IGF-1 signaling (IIS) pathway, the reproductive signaling pathway and DR-like mechanisms. Additionally, our results also indicated that mitochondrial function also plays a crucial role in the lifespan extension induced by hypotaurine. Taken together, these data indicate that hypotaurine delays the ageing of C. elegans, due, at least in part, to its antioxidant activity, which in turn regulates IIS, and reproductive and DR-related pathways, thereby inducing the activity of the transcription factors DAF-16 and SKN-1.

Background: Rheumatic heart disease (RHD) accounts for a large proportion of Intensive Care Unit (ICU) deaths. Early prediction of RHD can help with timely and appropriate treatment to improve survival outcomes, and the XGBoost machine learning technology can be used to identify predictive factors; however, its use has been limited in the past. We compared the performance of logistic regression and XGBoost in predicting hospital mortality among patients with RHD from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database.Methods: The patients with RHD in the MIMIC-IV database were divided into two groups retrospectively according to the availability of data and its clinical significance based on whether they survived or died. Backward stepwise regression was used to analyze the independent factors influencing patients with RHD, and to compare the differences between the two groups. The XGBoost algorithm and logistic regression were used to establish two prediction models, and the areas under the receiver operating characteristic curves (AUCs) and decision-curve analysis (DCA) were used to test and compare the models. Finally, DCA and the clinical impact curve (CIC) were used to validate the model.Results: Data on 1,634 patients with RHD were analyzed, comprising 207 who died during hospitalization and 1,427 survived. According to estimated results for the two models using AUCs [0.838 (95% confidence interval = 0.786–0.891) and 0.815 (95% confidence interval = 0.765–0.865)] and DCA, the logistic regression model performed better. DCA and CIC verified that the logistic regression model had convincing predictive value.Conclusions: We used logistic regression analysis to establish a more meaningful prediction model for the final outcome of patients with RHD. This model might be clinically useful for patients with RHD and help clinicians to provide detailed treatments and precise management.