Small extracellular vesicles from young plasma reverse age-related functional declines by improving mitochondrial energy metabolism.

Recent investigations into heterochronic parabiosis have unveiled robust rejuvenating effects of young blood on aged tissues. However, the specific rejuvenating mechanisms remain incompletely elucidated. Here we demonstrate that small extracellular vesicles (sEVs) from the plasma of young mice counteract pre-existing aging at molecular, mitochondrial, cellular and physiological levels. Intravenous injection of young sEVs into aged mice extends their lifespan, mitigates senescent phenotypes and ameliorates age-associated functional declines in multiple tissues. Quantitative proteomic analyses identified substantial alterations in the proteomes of aged tissues after young sEV treatment, and these changes are closely associated with metabolic processes. Mechanistic investigations reveal that young sEVs stimulate PGC-1α expression in vitro and in vivo through their miRNA cargoes, thereby improving mitochondrial functions and mitigating mitochondrial deficits in aged tissues. Overall, this study demonstrates that young sEVs reverse degenerative changes and age-related dysfunction, at least in part, by stimulating PGC-1α expression and enhancing mitochondrial energy metabolism.

Effects of flaxseed oil supplementation on metaphase II oocyte rates in IVF cycles with decreased ovarian reserve: a randomized controlled trial.

Background: This study was designed to explore the effects of flaxseed oil on the metaphase II (MII) oocyte rates in women with decreased ovarian reserve (DOR). Methods: The women with DOR were divided into a study group (n = 108, flaxseed oil treatment) and a control group (n = 110, no treatment). All patients were treated with assisted reproductive technology (ART). Subsequently, the ART stimulation cycle parameters, embryo transfer (ET) results, and clinical reproductive outcomes were recorded. The influencing factors affecting the MII oocyte rate were analyzed using univariate analysis and multivariate analysis. Results: Flaxseed oil reduced the recombinant human follicle-stimulating hormone (r-hFSH) dosage and stimulation time and increased the peak estradiol (E2) concentration in DOR women during ART treatment. The MII oocyte rate, fertilization rate, cleavage rate, high-quality embryo rate, and blastocyst formation rate were increased after flaxseed oil intervention. The embryo implantation rate of the study group was higher than that of the control group (p = 0.05). Additionally, the female age [odds ratio (OR): 0.609, 95% confidence interval (CI): 0.52-0.72, p < 0.01] was the hindering factor of MII oocyte rate, while anti-Müllerian hormone (AMH; OR: 100, 95% CI: 20.31-495, p < 0.01), peak E2 concentration (OR: 1.00, 95% CI: 1.00-1.00, p = 0.01), and the intake of flaxseed oil (OR: 2.51, 95% CI: 1.06-5.93, p = 0.04) were the promoting factors for MII oocyte rate. Conclusion: Flaxseed oil improved ovarian response and the quality of oocytes and embryos, thereby increasing the fertilization rate and high-quality embryo rate in DOR patients. The use of flaxseed oil was positively correlated with MII oocyte rate in women with DOR. Clinical trial number: https://www.chictr.org.cn/, identifier ChiCTR2300073785.

Isothermal strand displacement polymerase reaction (ISDPR)-assisted microchip electrophoresis for highly sensitive detection of cancer associated microRNAs.

More and more studies have found that microRNAs (miRNAs) are markers of cancer, and detection of miRNAs is beneficial for early diagnosis and prognosis of cancer. In this paper, the isothermal strand displacement polymerase reaction (ISDPR), which is an enzyme-assisted nucleic acid amplification method, was studied to combine with microchip electrophoresis (MCE) for a simultaneously detection of two cancer related miRNAs named microRNA-21 (miR-21) and microRNA-221 (miR-221). In the ISDPR amplification, two different DNA hairpins (HPs) were specifically designed, so that miR-21 and miR-221 could respectively bind to HPs and started ISDPR amplification to generate two different products which were ultimately detected by MCE. The optimal conditions of ISDPR were carefully investigated, and the limits of detection (LOD) of miR-21 and miR-221 were as low as 0.35 fM and 0.25 fM (S/N = 3) respectively under these conditions. The human lung tumor cells and serum samples were analyzed by this ISDPR-MCE method and satisfactory results were obtained, which means that this method is of high sensitivity, high efficiency, low reagent consumption and simple operation in miRNAs detection.

Cyclic Multiple Primer Generation Rolling Circle Amplification Assisted Capillary Electrophoresis for Simultaneous and Ultrasensitive Detection of Multiple Pathogenic Bacteria.

Efficient, accurate, and economical detection of pathogenic bacteria is crucial in ensuring food safety and preventing foodborne illnesses. How to fulfill the highly sensitive and simultaneous detection of multiple trace pathogenic bacteria is a big challenge. In this work, capillary electrophoresis coupled with a cyclic multiple primer generation rolling circle amplification (cyclic MPG-RCA) was studied for highly sensitive and simultaneous detection of three kinds of pathogenic bacteria. The cyclic MPG-RCA was based on a carefully designed clover-shaped DNA probe, in which three "leaves" corresponded to three types of aimed pathogenic bacteria: Shigella dysenteriae (S. dysenteriae), Salmonella enterica subsp. enterica serovar Typhi (S. Typhi), and Vibrio parahaemolyticus (V. parahaemolyticus). Under the optimal experimental conditions, the limits of detection (S/N = 3) of this method for bacterial target DNA were 11.4 amol·L-1 (S. dysenteriae), 4.88 amol·L-1 (S. Typhi), and 14.9 amol·L-1 (V. parahaemolyticus), and the conversion concentrations for the target bacteria were 10 colony-forming units (CFU)·mL-1 (S. dysenteriae), 3 CFU·mL-1 (S. Typhi), and 12 CFU·mL-1 (V. parahaemolyticus). This method had been applied to the detection of tap water samples with good results, which proved that it could be used as an effective tool for trace pathogenic bacteria monitoring in foods, environments, and medicines.

Bibliometric and Visual Analysis of the Research Status and Knowledge Structure of Assisted Reproductive Therapy for Patients with Premature Ovarian Insufficiency.

Objective: A large proportion of patients undergoing assisted reproductive therapy (ART) suffer from premature ovarian insufficiency (POI). The knowledge structure, research hotspots, and research trends related to ART for patients with POI are still unclear and have not been systematically summarized. We aimed to analyze the research status of ART for patients with POI and deeply explore its knowledge structure and research trends. Our findings may provide treatment recommendations for clinicians and guidance for researchers in further research. Methods: The PubMed database for publications on ART for patients with POI was searched. The Bibliographic Item Co-occurrence Matrix Builder (BICOMB) obtained the Co-word matrix and co-occurrence matrix. The H-index method was used to extract high-frequency main Medical Subject Headings (MeSH) terms/subheadings. Then we used software such as graphical clustering toolkit (gCluto), Microsoft Excel, Ucinet and NetDraw to carry out the biclustering analysis, strategic diagram analysis and social network analysis of the major MeSH terms/subheadings. Results: The high-frequency major MeSH terms/subheadings were analyzed by biclustering, strategic diagram, and social network analyses. A total of 431 articles from 1983 to 2023 were retrieved. Analysis showed that a total of 176 journals published relevant papers, including FERTILITY AND STERILITY, ranking first. In addition, we extracted 20 high-frequency major MeSH terms/subheadings. We grouped them into five categories: cryopreservation of oocyte and ovarian tissue, oocyte donation, in vitro activation (IVA) of primordial follicles, overview of therapy for patients with POI, therapy of iatrogenic POI. Within these five categories, there were 4, 4, 3, 4, and 5 major MeSH terms/subheadings, respectively. The major MeSH terms/subheadings were evenly distributed, and no particular group had a particular central tendency. Conclusion: The therapy of Iatrogenic POI is in the core position of research and is becoming increasingly mature. Oocyte donation and IVA of primordial follicles are the trends of future research. This study is helpful to understand the current research status, knowledge structure, and research trends of ART for patients with POI, and provide reference for improving ART for patients with POI in the future. Our study may guide clinicians to apply more established research to treat patients, which may lead to better treatment outcomes for patients. At the same time, we also suggest that researchers can conduct research in the field of future research trends, which may lead to greater research results.

Data-driven machine learning prediction of glass transition temperature and the glass-forming ability of metallic glasses.

The limited glass-forming ability (GFA) poses a significant challenge for the practical applications of metallic glasses (MGs). The development of high-GFA MGs typically involves trial-and-error processes to screen materials with a large critical diameter (Dmax), which serves as a criterion for determining the GFA. The formation and stability of MGs are influenced by the glass transition temperature (Tg). Over the past decade, the emergence of machine learning (ML) has shown great promise in the exploration of high-GFA materials. However, the contribution of material features to Tg and Dmax predictions, as well as their correlations, remains ambiguous, posing a challenge to achieving high prediction accuracy. Herein, we present a comprehensive dataset consisting of 1764 datapoints for Tg and 1296 datapoints for Dmax. The governing rules for GFA have been established through feature significance analysis. The light gradient boosting (LGB) model exhibits remarkable accuracy in predicting Tg, utilizing sixteen features, achieving a coefficient of determination (R2) score of 0.984 and a root mean square error (RMSE) of 20.196 K. An integrated ML model, based on the weighted voting of three basic models, is developed to enhance the accuracy of Dmax prediction, achieving an R2 score of 0.767 and an RMSE of 2.331 mm. Additionally, a GFA rule is proposed to explore materials with large Dmax values, defined by satisfying the criteria of a thermal conductivity difference ranging from 0.60 to 1.32 and an entropy density exceeding 1.05. Our work provides valuable insights into Tg and Dmax predictions and facilitates the exploration of potential high-GFA MGs through the implementation of a well-established ML model and GFA rules.

Detection of Escherichia coli by capillary electrophoresis assisted by large volume sample stacking and nicking endonuclease signal amplification.

Efficient, accurate and economical detection of pathogenic bacteria is crucial in ensuring food safety and preventing foodborne illnesses. In this study, a capillary electrophoresis coupled laser-induced fluorescence assay (CE-LIF) was developed for the detection of Escherichia coli (E. coli) by detecting its specific DNA. The CE-LIF was assisted by both online enrichment and offline amplification to improve the detection sensitivity of bacterial DNA. Here the online amplification was performed by large volume sample stacking (LVSS), while the offline amplification was nicking endonuclease signal amplification (NESA). Under the optimal experimental conditions, the detection limit of bacterial target DNA was 2.5 fM, and the conversion concentration of E. coli was 3 CFU · mL-1. The method had been applied to the detection of commercially available skim milk samples with good results, which proved that it could be used as an effective tool for food and environmental bacteria monitoring.

Ultrasensitive detection of exosomes by microchip electrophoresis combining with triple amplification strategies.

The analysis of exosomes is significant as they can be used for various pathophysiological processes, especially cancer related intercellular communication. Therefore, a convenient, reliable, and sensitive detection method is urgently needed. Strand displacement amplification (SDA) and catalytic hairpin assembly (CHA) are two kinds of effective isothermal nucleic acid amplification methods. In this article, an efficient quantitative MCE method for detecting human breast cancer cell (MCF-7) exosomes assisted by triple amplification strategies combining cholesterol probe (Chol-probe) with SDA-CHA was first developed. CD63 aptamer was immobilized on the avidin magnetic beads to specifically capture exosomes and then Chol-probe with high affinity was spontaneously inserted into the exosome membrane, which was the first step of amplification strategy to improve detection sensitivity. After magnetic separation, Chol-probe could complement ssDNA and trigger SDA, producing a large number of DNA sequences (Ta) to trigger CHA, achieving SDA-CHA amplification. Under optimal conditions, the detection limit (LOD) for MCF-7 exosomes was as low as 26 particle/µL (S/N = 3). This method provides an effective approach for sensitive and accurate quantification of tumor exosomes, and can be expected to detect exosomes in clinical samples.

Quantitative proteomics reveals PPAR signaling pathway regulates the cardiomyocyte activity of neonatal mouse heart.

Cardiovascular diseases (CVDs) are among the most morbid and deadly types of diseases worldwide, while the existing therapeutic approaches all have their limitations. Mouse heart undergoes a very complex postnatal developmental process, including the 1-week window in which cardiomyocytes (CMs) maintain relatively high cell activity. The underlying mechanism provides an attractive direction for CVDs treatment. Herein, we collected ventricular tissues from mice of different ages from E18.5D to P8W and performed iTRAQ-based quantitative proteomics to characterize the atlas of cardiac development. A total of 3422 proteins were quantified at all selected time points, revealing critical proteomic changes related to cardiac developmental events such as the metabolic transition from glycolysis to beta-oxidation. A cluster of significantly dysregulated proteins containing proteins that have already been reported to be associated with cardiac regeneration (Erbb2, Agrin, and Hmgb) was identified. Meanwhile, the peroxisome proliferator-activated receptor (PPAR) signaling pathway (Cpt1α, Hmgcs2, Plin2, and Fabp4) was also found specifically enriched. We further revealed that bezafibrate, a pan-activator of PPAR signaling pathway markedly enhanced H9C2 cardiomyocyte activity via enhancing Cpt1α expression. This work provides new hint that activation of PPAR signaling pathway could potentially be a therapeutic strategy for the treatment of CVDs.

Accurate and efficient machine learning models for predicting hydrogen evolution reaction catalysts based on structural and electronic feature engineering in alloys.

Predictive materials design of high-performance alloy electrocatalysts is a grand challenge in hydrogen production via water electrolysis. The vast combinatorial space of element substitutions in alloy electrocatalysts offers a wealth of candidate materials, but presents a significant challenge in terms of experimental and computational exploration of all possible options. Recent scientific and technological developments in machine learning (ML) have offered a new opportunity to accelerate such electrocatalyst materials design. Herein, by incorporating both the electronic and structural properties of alloys, we are able to construct accurate and efficient ML models and predict high-performance alloy catalysts for the hydrogen evolution reaction (HER). We have identified the light gradient boosting (LGB) algorithm as the best-performed method, with an excellent coefficient of determination (R2) value reaching 0.921 and the corresponding root-mean-square error (RMSE) being 0.224 eV. The average marginal contributions of alloy features towards ΔGH* values are estimated to determine the importance of various features during the prediction processes. Our results indicate that both the electronic properties of constituent elements and the structural adsorption site features play the most critical roles in the ΔGH* prediction. Furthermore, 84 potential alloys with |ΔGH*| values less than 0.1 eV are successfully screened out of 2290 candidates selected from the Material Project (MP) database. It is reasonable to expect that the ML models with structural and electronic feature engineering developed in this work would provide new insights in future electrocatalyst developments for the HER and other heterogeneous reactions.

Deep Generative Model for Inverse Design of High-Temperature Superconductor Compositions with Predicted Tc > 77 K.

Identifying new superconductors with high transition temperatures (Tc > 77 K) is a major goal in modern condensed matter physics. The inverse design of high Tc superconductors relies heavily on an effective representation of the superconductor hyperspace due to the underlying complexity involving many-body physics, doping chemistry and materials, and defect structures. In this study, we propose a deep generative model that combines two widely used machine learning algorithms, namely, the variational auto-encoder (VAE) and the generative adversarial network (GAN), to systematically generate unknown superconductors under the given high Tc condition. After training, we successfully identified the distribution of the representative hyperspace of superconductors with different Tc, in which many superconductor constituent elements were found adjacent to each other with their neighbors in the periodic table. Equipped with the conditional distribution of Tc, our deep generative model predicted hundreds of superconductors with Tc > 77 K, as predicted by the published Tc prediction models in the literature. For the copper-based superconductors, our results reproduced the variation in Tc as a function of the Cu concentration and predicted an optimal Tc = 129.4 K, when the Cu concentration reached 2.41 in Hg0.37Ba1.73Ca1.18Cu2.41O6.93Tl0.69. We expect that such an inverse design model and comprehensive list of potential high Tc superconductors would greatly facilitate future research activities in superconductors.

A regulatory circuit comprising the CBP and SIRT7 regulates FAM134B-mediated ER-phagy.

Macroautophagy (autophagy) utilizes a serial of receptors to specifically recognize and degrade autophagy cargoes, including damaged organelles, to maintain cellular homeostasis. Upstream signals spatiotemporally regulate the biological functions of selective autophagy receptors through protein post-translational modifications (PTM) such as phosphorylation. However, it is unclear how acetylation directly controls autophagy receptors in selective autophagy. Here, we report that an ER-phagy receptor FAM134B is acetylated by CBP acetyltransferase, eliciting intense ER-phagy. Furthermore, FAM134B acetylation promoted CAMKII-mediated phosphorylation to sustain a mode of milder ER-phagy. Conversely, SIRT7 deacetylated FAM134B to temper its activities in ER-phagy to avoid excessive ER degradation. Together, this work provides further mechanistic insights into how ER-phagy receptor perceives environmental signals for fine-tuning of ER homeostasis and demonstrates how nucleus-derived factors are programmed to control ER stress by modulating ER-phagy.

Curvature effects on the bifunctional oxygen catalytic performance of single atom metal-N-C.

Understanding the fundamental relationship between the structural information of electrocatalysts and their catalytic activities plays a key role in controlling many important electrochemical processes. Recently, single-atom catalysts (SACs) with the so-called MN4 structure, consisting of a central transition metal quadruply bound to four pyridine nitrogen atoms all situated in an extended carbon-based matrix, have attracted intensive scientific attention owing to their exceptional catalytic performance. In this work, we perform the first-principles density functional theory (DFT) calculations to explore the curvature effects of the carbon matrix surfaces on the catalytic activities for two fundamental electrochemical processes, namely, the oxygen reduction reaction (ORR) and the oxygen evolution reaction (OER). Our DFT results suggest that the curved surface structure can weaken the interaction between the metal atom and the N-doped carbon matrix, modify the electronic structure of the metal atom, and thus increase the adsorption strength of the reaction intermediates, resulting in enhanced OER and ORR catalytic activities of MN4 catalysts. More importantly, a prediction model is developed to evaluate the bifunctional catalytic activities of such catalysts based on their directly obtained parameters including the surface curvature of the catalysts, the number of d electrons of the metal element, and the electronegativity of the metal atom and its coordination atoms in MN4 catalysts. This prediction model not only provides some candidates, for example, FeN4, CoN4 and OsN4 for the ORR; CoN4, NiN4, RuN4, RhN4 and IrN4 for the OER; and CoN4, RuN4, IrN4 and OsN4 for the bifunctional ORR and OER, but also reasonably links the structure of catalysts with their catalytic performance, providing new possibilities for the quick design of high-performance catalysts.

Large Data Set-Driven Machine Learning Models for Accurate Prediction of the Thermoelectric Figure of Merit.

The figure of merit (zT) is a key parameter to measure the performance of thermoelectric materials. At present, the prediction of zT values via machine leaning has emerged as a promising method for exploring high-performance materials. However, the machine learning-based predictions still suffer from unsatisfactory accuracy, and this is related to the size of the data set, the hyperparameters of models, and the quality of the data. In this work, 5038 pieces of data of thermoelectric materials were selected, and several regression models were generated to predict zT values. This large data set-driven light gradient boosting (LGB) model with 57 features performed with an excellent accuracy, achieving a coefficient of determination (R2) value of 0.959, a root mean squared error (RMSE) of 0.094, a mean absolute error (MAE) of 0.057, and a correlation coefficient (R) of 0.979. Owing to the large size of the data set, the prediction accuracy exceeds that of most reported zT predictions via machine learning. The "ME Lattice Parameter" was verified as the most important feature in the zT prediction. Furthermore, nine potential candidates were screened out from among one million pieces of data. This study solves the problem of the data set size, adjusts the hyperparameters of the models, uses feature engineering to improve data quality, and provides an efficient strategy to perform wide-ranging screening for promising materials.

Quantitative Proteomics Reveals Down-Regulated Glycolysis/Gluconeogenesis in the Large-Duct Type Intrahepatic Cholangiocarcinoma.

Intrahepatic cholangiocarcinoma (iCCA) is a lethal hepatobiliary malignancy that arises from the epithelial cells of the intrahepatic bile ducts, accounting for approximately 10% of cholangiocarcinoma (CCA). According to the 2019 World Health Organization (WHO) classification of tumors of the digestive system, iCCA is divided into small-duct type (SD-type) and large-duct type (LD-type). However, it remains unknown which molecular events contribute to the disparity. To explore the proteomic characteristics of iCCA, we used an isobaric tag for relative and absolute quantitation (iTRAQ) based quantitative proteomics strategy to investigate stably dysregulated proteins in the SD-type and LD-type of iCCA tissues. Importantly, we found three glycolysis/gluconeogenesis-related enzymes, triosephosphate isomerize 1 (TPI1), glyceraldehyde-3-phosphate dehydrogenase (GAPDH), and phosphoglycerate kinase 1 (PGK1), were significantly down-regulated in the LD-type iCCA, which were further confirmed by immunohistochemistry using tissue microarray. Moreover, we demonstrated that the knockdown of these three candidate proteins by siRNAs notably increased the ability of proliferation in two CCA cell lines (HuH28 and RBE), suggesting that effective down-regulation of the glycolysis/gluconeogenesis pathway might be an underlying novel mechanism contributing to the LD-type iCCA.

Development of functional anti-Gn nanobodies specific for SFTSV based on next-generation sequencing and proteomics.

Severe fever with thrombocytopenia syndrome (SFTS) is an acute infectious disease caused by novel bunyavirus (SFTSV), with a mortality rate of 6.3% ~ 30%. To date, there is no specific treatment for SFTS. Previously, we demonstrated that SFTSV surface glycoprotein (Glycoprotein N, Gn) was a potential target for the development of SFTS vaccine or therapeutic antibodies, and anti-Gn neutralizing antibodies played a protective role in SFTS infection. Compared with traditional antibodies, nanobodies from camelids have various advantages, including small molecular weight, high affinity, low immunogenicity, convenient production by gene engineering, etc. In this study, we combined next-generation sequencing (NGS) with proteomics technology based on affinity purification-mass spectrometry (AP-MS) and bioinformatics analysis to high-throughput screen monoclonal anti-Gn nanobodies from camel immunized with Gn protein. We identified 19 anti-Gn monoclonal nanobody sequences, of which six sequences were selected for recombinant protein expression and purification. Among these six anti-Gn nanobodies, nanobody 57,493 was validated to be highly specific for Gn. The innovative high-throughput technical route developed in this study could also be expanded to the production of nanobodies specific for other viruses like SARS-CoV-2.

Highly sensitive detection of MUC1 by microchip electrophoresis combining with target recycling amplification and strand displacement amplification.

Mucin 1 (MUC1) is usually overexpressed in a variety of malignant tumors, and quantitative analysis of MUC1 plays an important role in the early diagnosis of cancer. In this work, a highly sensitive MUC1 assay was developed by integrating microchip electrophoresis (MCE) with target recycling amplification (TRA) and strand displacement amplification (SDA). Specifically, the presence of MUC1 can trigger the exposure of the designed hairpin probe (HP) to initiate SDA and an amplified amount of ssDNA is produced finally. The amount of these ssDNA can be detected by MCE, then the concentration of MUC1 can be obtained through the correlation between MUC1 concentration and ssDNA concentration. The experimental results show that the MCE signal had a good linear relationship with MUC1 concentration in the range of 1.0 pg/mL - 1.0 × 103 pg/mL with a low limit of detection of 0.23 pg/mL under the optimal conditions (S/N = 3). Additionally, the assay had been successfully applied to detect MUC1 in biological samples with satisfactory results, providing an alternative assay for the detection of other tumor markers owing to the high sensitivity, high selectivity, simple operation and low sample consumption.

TRIM24 is an insulin-responsive regulator of P-bodies.

Insulin is a potent inducer of mRNA transcription and translation, contributing to metabolic regulation. Insulin has also been suggested to regulate mRNA stability through the processing body (P-body) molecular machinery. However, whether and how insulin regulates mRNA stability via P-bodies is not clear. Here we show that the E3-ligase TRIM24 is a critical factor linking insulin signalling to P-bodies. Upon insulin stimulation, protein kinase B (PKB, also known as Akt) phosphorylates TRIM24 and stimulates its shuttling from the nucleus into the cytoplasm. TRIM24 interacts with several critical components of P-bodies in the cytoplasm, promoting their polyubiquitylation, which consequently stabilises Pparγ mRNA. Inactivation of TRIM24 E3-ligase activity or prevention of its phosphorylation via knockin mutations in mice promotes hepatic Pparγ degradation via P-bodies. Consequently, both knockin mutations alleviate hepatosteatosis in mice fed on a high-fat diet. Our results demonstrate the critical role of TRIM24 in linking insulin signalling to P-bodies and have therapeutic implications for the treatment of hepatosteatosis.

The RalGAPα1-RalA signal module protects cardiac function through regulating calcium homeostasis.

Sarcoplasmic/endoplasmic reticulum calcium ATPase SERCA2 mediates calcium re-uptake from the cytosol into sarcoplasmic reticulum, and its dysfunction is a hallmark of heart failure. Multiple factors have been identified to modulate SERCA2 activity, however, its regulation is still not fully understood. Here we identify a Ral-GTPase activating protein RalGAPα1 as a critical regulator of SERCA2 in cardiomyocytes through its downstream target RalA. RalGAPα1 is induced by pressure overload, and its deficiency causes cardiac dysfunction and exacerbates pressure overload-induced heart failure. Mechanistically, RalGAPα1 regulates SERCA2 through direct interaction and its target RalA. Deletion of RalGAPα1 decreases SERCA2 activity and prolongs calcium re-uptake into sarcoplasmic reticulum. GDP-bound RalA, but not GTP-bound RalA, binds to SERCA2 and activates the pump for sarcoplasmic reticulum calcium re-uptake. Overexpression of a GDP-bound RalAS28N mutant in the heart preserves cardiac function in a mouse model of heart failure. Our findings have therapeutic implications for treatment of heart failure.