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Heterosis is the phenomenon that the hybrid offspring outperform two-parent population. Hybridisation has been widely used in plant and animal production as it effectively improves the growth and developmental performance, reproductive performance and disease resistance of the offspring. Hybridization can effectively improve the growth and development performance, reproductive performance and disease resistance of offspring, so it is widely used in animal and plant production. Researchers have used cross-breeding techniques to cultivate excellent new agricultural and animal husbandry strains and supporting lines such as super-excellent Chaoyou 1000 hybrid rice, Xiaoyan No.6 hybrid wheat, Dumeng sheep, and Shanxia black pigs. However, there are still some urgent problems in the current hybrid dominance research: the existing hybrid dominance theory can only partially explain the phenomenon of plant and animal hybrid dominance, and the theory of animal hybrid dominance is less researched, and the accuracy of the existing hybrid dominance prediction methods is limited. China is the world's largest pork production and consumption country. Heterosis can effectively improve the production performance of pigs, and its application in the pig industry has important economic and research value. However, the existing research on pig hybrid production is in its infancy and needs to be further studied. In this review, we summarize the existing heterosis theory, heterosis prediction methods, and their application in pig production, to provide a reference for the application of heterosis in pig breeding.

Uterine leiomyosarcoma (uLMS) is a type of malignant soft-tissue tumor, which is developed from myometrium in the female reproductive system. This disease is difficult to be distinguished from benign uterine leiomyoma in the early stages, but it progresses aggressively and relentlessly. Hence, uLMS has a dismal prognosis and high rates of both misdiagnosis and missed diagnosis. Unfortunately, current studies of uLMS pathogenesis and disease biology are inadequate. uLMS disease models are also very limited, hindering the development of effective therapeutics. In this review, we focus on the pathological molecular biology of uLMS, and systematically review the molecular genetic features, epigenetic variants, experimental models, and clinical research progress of uLMS. We further discuss the development direction and potential needs of uLMS in the fields of tumor evolution, tumor microenvironment, and tumor therapy, with the aim of providing a better understanding of the pathobiological mechanism of uLMS and providing a reference for the development of potential diagnostic and therapeutic strategies.

The identification of enzyme functions plays a crucial role in understanding the mechanisms of biological activities and advancing the development of life sciences. However, existing enzyme EC number prediction methods did not fully utilize protein sequence information and still had shortcomings in identification accuracy. To address this issue, we proposed an EC number prediction network using hierarchical features and global features (ECPN-HFGF). This method first utilized residual networks to extract generic features from protein sequences, and then employed hierarchical feature extraction modules and global feature extraction modules to further extract hierarchical and global features of protein sequences. Subsequently, the prediction results of both feature types were combined, and a multitask learning framework was utilized to achieve accurate prediction of enzyme EC numbers. Experimental results indicated that the ECPN-HFGF method performed best in the task of predicting EC numbers for protein sequences, achieving macro F1 and micro F1 scores of 95.5% and 99.0%, respectively. The ECPN-HFGF method effectively combined hierarchical and global features of protein sequences, allowing for rapid and accurate EC number prediction. Compared to current commonly used methods, this method offers significantly higher prediction accuracy, providing an efficient approach for the advancement of enzymology research and enzyme engineering applications.

Self-transcribing active regulatory region sequencing (STARR-seq) is a high-throughput sequencing method capable of simultaneously discovering and validating all enhancers within the genome. In this method, candidate sequences are inserted into plasmid vectors and electroporated into cells. Acting as both enhancers and target genes, the self-transcription of these sequences will also be enhanced by themselves. By sequencing the transcriptome and comparing the results with the non-inserted control, the locations and activity of enhancers can be determined. In traditional enhancer discovery strategies, the chromatin open regions and transcription active regions were sequenced and predicted as enhancers. However, the activity of these putative enhancers could only be validated one by one without a high-throughput method. STARR-seq solved this limitation, allowing simultaneous enhancers discovery and activity validation in a high-throughput manner. Since the introduction of STARR-seq, it has been widely used to discover enhancers and validate enhancer activity in a number of organisms and cells. In this review, we present the traditional enhancer prediction methods and the basic principles, development history, specific applications of STARR-seq, and its future prospects, aiming to provide a reference for researchers in related fields conducting enhancer studies.

Nonsense-mediated mRNA decay (NMD) is an important RNA quality control pathway. It aids in degrading harmful erroneous mRNA, thereby preserving a stable and healthy internal environment. In this study, we employed CRISPR/Cas9 and amiRNA technology to generate knock out or knock down mutants of realted genes in the rice NMD pathway. Through transcriptome sequencing and observing phenotype changes, the study explored the impact of NMD pathway defects on rice gene expression and alternative splicing. The results suggest that even partial defects will induce phenotypic changes such as plant height and pollen vitality to different degrees, showing necessity of NMD factors. Gene expression analysis reveals that most differentially expressed genes are upregulated in the mutants, with ko-upf1-like and kd-upf1 defects having a more significant impact than kd-upf2 and kd-upf3. Specifically, NMD pathway defects result in increased expression levels of rice defense response-related genes and decreased expression levels of secondary metabolism-related genes, with a wider range of affected genes observed in 60-day-old senescence mutants. Transcript analysis indicates that different NMD related genes defects alter hundreds of alternative splicing events, mostly enriched in genes involving alternative splicing regulatory pathways. Approximately half of these events are shared among different mutants, and a substantial number of affected transcripts show NMD target features. NMD could affect both the transcript abundance and their splicing subtypes to regulate the defense response and early-senescence associated pathways, which plays a vital role in rice growth and reproduction.

Genomic prediction has emerged as a pivotal technology for the genetic evaluation of livestock, crops, and for predicting human disease risks. However, classical genomic prediction methods face challenges in incorporating biological prior information such as the genetic regulation mechanisms of traits. This study introduces a novel approach that integrates mRNA transcript information to predict complex trait phenotypes. To evaluate the accuracy of the new method, we utilized a Drosophila population that is widely employed in quantitative genetics researches globally. Results indicate that integrating mRNA transcript data can significantly enhance the genomic prediction accuracy for certain traits, though it does not improve phenotype prediction accuracy for all traits. Compared with GBLUP, the prediction accuracy for olfactory response to dCarvone in male Drosophila increased from 0.256 to 0.274. Similarly, the accuracy for cafe in male Drosophila rose from 0.355 to 0.401. The prediction accuracy for survival_paraquat in male Drosophila is improved from 0.101 to 0.138. In female Drosophila, the accuracy of olfactory response to 1hexanol increased from 0.147 to 0.210. In conclusion, integrating mRNA transcripts can substantially improve genomic prediction accuracy of certain traits by up to 43%, with range of 7% to 43%. Furthermore, for some traits, considering interaction effects along with mRNA transcript integration can lead to even higher prediction accuracy.

Accurate breed classification is required for the conservation and utilization of farm animal genetic resources. Traditional classification methods mainly rely on phenotypic characterization. However, it is difficult to distinguish between the highly similar breeds due to the challenges in qualifying the phenotypic character. Machine learning algorithms show unique advantages in breed classification using genomic information. To evaluate the classification methods for Chinese cattle breeds, this study utilized genomic SNP data from 213 individuals across seven Chinese local breeds and compared the classification accuracies of three feature selection methods (F_ST value sorting and screening, mRMR, and Relief-F) and three machine learning algorithms (Random Forest, Support Vector Machine, and Naive Bayes). Results showed that: 1) using the F_ST method to screen more than 1500 SNPs, or using the mRMR algorithm to screen more than 1000 SNPs, the SVM classification algorithm can achieve more than 99.47% classification accuracy; 2) the most effective algorithm was SVM, followed by NB, while the best SNP selection method was F_ST and mRMR, followed by Relief-F; 3) species misclassification often occurs between breeds with high similarity. This study demonstrates that machine learning classification models combined with genomic data are effective methods for the classification of local cattle breeds, providing a technical basis for the rapid and accurate classification of cattle breeds in China.

Biodiversity losses along with the exponential growth of global human population and human-provoked over-exploitation of natural resources. Genetic factors played an important role in the conservation of endangered species. Conservation genetics is a cross-field disciplinary of genetics and conservation biology. The course of conservation genetics is not available in colleges and universities, and the course of genetics does not directly reflect the content of biological conservation. We have taught genetics with integrative thoughts of conservation biology. In the form of case studies, we have integrated recent advances of research and technology in the relevant fields into the genetics classroom. As a result, we improved the undergraduates' motivation and interest in active learning, provoked the mutual promotion of "basic knowledge of genetics, awareness of ecological protection, and cultivate interdisciplinary thinking", and set up the groundwork for cultivating interdisciplinary talents who not only master solid basic knowledge, but also have the concept of ecological civilization.

Sperm chromatin not only carries genetic information such as paternal DNA, but also carries structural proteins, epigenetic information, and higher-order chromatin structures (such as matrix attachment regions and telomeres), etc. These information play an important role in embryonic development. This article mainly reviews the effects of these different information carried by sperm chromatin on sperm function and embryonic development and the research progress of related detection methods, in order to provide a theoretical basis and scientific diagnosis and treatment strategies for the etiology screening of clinical infertility, embryo arrest and recurrent miscarriage, so as to improve the pregnancy outcomes of natural conception and assisted reproduction. Keywords: sperm chromatin; epigenetics; sperm DNA damage; sperm function; higher-order chromatin structures.

Duchenne muscular dystrophy (DMD) is a severe X-linked recessive genetic disorder caused by mutations in the DMD gene, which leads to a deficiency of the dystrophin protein. The main mutation types of this gene include exon deletions and duplications, point mutations, and insertions. These mutations disrupt the normal expression of dystrophin, ultimately leading to the disease. In this study, we reported a case of DMD caused by an insertion mutation in exon 59 (E59) of the DMD gene. The affected child exhibited significant abnormalities in related biochemical markers, early symptoms of DMD, and multiple gray hair. His mother and sister were carriers with slightly abnormal biochemical markers. The mother had mild clinical symptoms, while the sister had no clinical symptoms. Other family members were genetically and physically normal. Sequencing and sequence alignment revealed that the inserted fragment was an Alu element from the AluYa5 subfamily. This insertion produced two stop codons and a polyadenylate (polyA) tail. To understand the impact of this insertion on the DMD gene and its association with clinical symptoms, exonic splicing enhancer (ESE) prediction indicated that the insertion did not affect the splicing of E59. Therefore, we speculated that the insertion sequence would be present in the mRNA sequence of the DMD gene. The two stop codons and polyA tail likely terminate translation, preventing the production of functional dystrophin protein, which may be the mechanism leading to DMD. In addition to typical DMD symptoms, the child also exhibited premature graying of hair. This study reports, for the first time, a case of DMD caused by the insertion of an Alu element into the coding region of the DMD gene. This finding provides clues for studying gene mutations induced by Alu sequence insertion and expands the understanding of DMD gene mutations.