Endogenous biosynthesis of docosahexaenoic acid(DHA)regulates fish oocyte maturation by promoting pregnenolone production

Omega-3 polyunsaturated fatty acids(n-3 PUFAs),particularly docosahexaenoic acid(22:6n-3,DHA),play crucial roles in the reproductive health of vertebrates,including humans.Nevertheless,the underlying mechanism related to this phenomenon remains largely unknown.In this study,we employed two zebrafish genetic models,i.e.,elovl2^(-/-)mutant as an endogenous DHAdeficient model and fat1(omega-3 desaturase encoding gene)transgenic zebrafish as an endogenous DHA-rich model,to investigate the effects of DHA on oocyte maturation and quality.Results show that the elovl2^(-/-)mutants had much lower fecundity and poorer oocyte quality than the wild-type controls,while the fat1 zebrafish had higher fecundity and better oocyte quality than wildtype controls.DHA deficiency in elovl2^(-/-)embryos led to defects in egg activation,poor microtubule stability,and reduced pregnenolone levels.Further study revealed that DHA promoted pregnenolone synthesis by enhancing transcription of cyp11a1,which encodes the cholesterol side-chain cleavage enzyme,thereby stabilizing microtubule assembly during oogenesis.In turn,the hypothalamic-pituitary-gonadal axis was enhanced by DHA.In conclusion,using two unique genetic models,our findings demonstrate that endogenously synthesized DHA promotes oocyte maturation and quality by promoting pregnenolone production via transcriptional regulation of cyp11a1.

Construction of A Prediction Model for Atrial Fibrillation in Patients with Dilated Cardiomyopathy and Heart Failure

Dilated cardiomyopathy(DCM)is a common myocardial disease characterized by enlargement of the heart cavity and decreased systolic function,often leading to heart failure(HF)and arrhythmia.The occurrence of atrial fibrillation(AF)is closely related to the progression and prognosis of the disease.In recent years,with the advancement of medical imaging and biomarkers,models for predicting the occurrence of AF in DCM patients have gradually become a research hotspot.This article aims to review the current situation of AF in DCM patients and explore the importance and possible methods of constructing predictive models to provide reference for clinical prevention and treatment.We comprehensively analyzed the risk factors for AF in DCM patients from epidemiological data,pathophysiological mechanisms,clinical and laboratory indicators,electrocardiogram and imaging parameters,and biomarkers,and evaluated the effectiveness of existing predictive models.Through analysis of existing literature and research,this article proposes a predictive model that integrates multiple parameters to improve the accuracy of predicting AF in DCM patients and provide a scientific basis for personalized treatment.

Identification and characterization of putative CIPK genes in maize

Calcium （Ca） plays a crucial role as a second messenger in intracellular signaling elicited by developmental and environmental cues. Calcineurin B-like proteins （CBLs） and their target proteins, CBL-interacting protein kinases （CIPKs） have emerged as a key Ca^2＋-mediated signaling network in response to stresses in plants. Bioinformatic analysis was used to identify 43 putative ZmC1PK （Zea mays CIPK） genes in the genome of maize inbred line B73. Based on gene structures, these ZmCIPKs were divided into intron-rich and intron-poor groups. Phylogenetic analysis indicated that the ZmCIPK family had a high evolutionary relationship with the rice CIPK family of 30 members. Microarray data and RT-PCR assay showed that ZrnCIPK genes transcriptionally responded to abiotic stresses, and that 24, 31, 20 and 19 ZmCIPK genes were up-regulated by salt, drought, heat and cold stresses, respectively. There were different expression patterns of ZmCIPKs between cold-tolerant inbred line B73 and cold-sensitive inbred line Mo17 under cold stress. Our findings will aid further molecular dissection of biological functions of the CIPKs in maize, and provide new insight into the CBL--CIPK signaling network in plants.