农学最新文献

Effective delivery of engineered proteins into mitochondria is of great significance for developing efficient mitochondrial DNA editing tools and realizing accurate treatment of mitochondrial diseases. Here, the candidate genes, eGFP and Cas9, were engineered with different mitochondrial localization signal (MLS) sequences introduced at their up- or/and down-streams. The corresponding expression vectors for the engineered proteins were constructed respectively, and HEK293T cells were transfected with these vectors. The fluorescence colocalization and Western blotting assays were used to analyze the mitochondrial targeting presentation effect of different engineered proteins. The results demonstrated that the daul-MLS modification of the eGFP and Cas9 proteins significantly improved the efficiency of mitochondrial targeted presentation, compared with the engineered proteins with single MLS added. Hence, it is speculated that dual MLS strategy can enhance the mitochondrial targeting of engineered proteins, which lays a theoretical foundation for the future development of efficient mitochondrial DNA editing tools.

Angiogenesis refers to the process of forming a new network of blood vessels from existing ones through the migration, proliferation, and differentiation of endothelial cells. This process is crucial for the growth and spread of solid tumors, particularly once the tumor volume exceeds 2 mm³, as the newly formed vascular network provides essential oxygen, nutrients, and growth factors to the tumor. Anti-angiogenesis therapy has become one of the commonly used targeted treatments for cancer in clinical practice. Bevacizumab, the first anti-angiogenesis drug, has been widely applied in the treatment of various solid tumors. However, due to acquired resistance, its efficacy is typically sustained for only 1 to 2 years. Despite the relative genomic stability of endothelial cells, which makes resistance less likely, various types of resistance phenomena have been observed in clinical practice, indicating that resistance to anti-angiogenic therapy remains a challenging research area. This review focuses on the latest advances in the mechanisms of resistance to anti-angiogenic therapy in tumors and explores new prospects for anti-tumor angiogenesis treatment, in order to provide strong theoretical support and guidance for clinical practice.

Single-cell DNA methylation sequencing technology has seen rapid advancements in recent years, playing a crucial role in uncovering cellular heterogeneity and the mechanisms of epigenetic regulation. As sequencing technologies have progressed, the quality and quantity of single-cell methylation data have also increased, making standardized preprocessing workflows and appropriate analysis methods essential for ensuring data comparability and result reliability. However, a comprehensive data analysis pipeline to guide researchers in mining existing data has yet to be established. This review systematically summarizes the preprocessing steps and analysis methods for single-cell methylation data, introduces relevant algorithms and tools, and explores the application prospects of single-cell methylation technology in neuroscience, hematopoietic differentiation, and cancer research. The aim is to provide guidance for researchers in data analysis and to promote the development and application of single-cell methylation sequencing technology.

The composition of T cell subsets and tumor-specific T cell interactions within the tumor microenvironment (TME) contribute to the heterogeneity observed in breast cancer. Moreover, aberrant tumor metabolism is often intimately linked to dysregulated anti-tumor immune function of T cells. Identifying key metabolic genes that affect immune cell interactions thus holds promise for uncovering potential therapeutic targets in the treatment of breast cancer. This study leverages single-cell transcriptomic data from breast cancer to investigate tumor-specific T-cell subsets and their interacting subnetworks in the TME during cancer progression. We further assess the metabolic pathway activities of tumor-specifically activated T-cell subsets. The results reveal that metabolic pathways involved in insulin synthesis, secretion, degradation, as well as fructose catabolism, significantly influence multiple T cell interactions. By integrating the metabolic pathways that significantly up-regulate T cells in tumors and influence their interactions, we identify key abnormal metabolic genes associated with T-cell collaboration and further develop a breast cancer risk assessment model. Additionally, using gene expression profiles of prognosis-related genes significantly associated with aberrant metabolism and drug IC50 values, we predict targeted drugs, yielding potential candidates like GSK-J4 and PX-12. This study integrate the analysis of abnormal T-cell interactions and metabolic pathway abnormalities in the breast cancer TME, elucidating their roles in cancer progression and providing leads for novel breast cancer therapeutic strategies.

The high heterogeneity within and between breast cancer patients complicates treatment determination and prognosis assessment. Treatment decision-making is influenced by various factors, such as tumor subtype, histological grade, and genotype, necessitating personalized treatment strategies. Prognostic outcomes vary significantly depending on patient-specific conditions. As a critical branch of artificial intelligence, machine learning efficiently handles large datasets and automates decision-making processes. The introduction of machine learning offers new solutions for breast cancer treatment selection and prognosis assessment. In the field of cancer therapy, traditional methods for predicting treatment and survival outcomes often rely on single or few biomarkers, limiting their ability to capture the complexity of biological processes comprehensively. Machine learning analyzes patients' multi-omic data and the intricate patterns of variations during cancer initiation and progression to predict patients' survival and treatment outcomes. Consequently, it facilitates the selection of appropriate therapeutic interventions to implement early intervention and improve treatment efficacy for patients. Here, we first introduce common machine learning methods, and then elaborate on the application of machine learning in the field of survival prediction and prognosis from two aspects: evaluating survival and predicting treatment outcomes for breast cancer patients. The aim is to provide breast cancer patients with precise treatment strategies to improve therapeutic outcomes and quality of life.

With the rapid development of high-throughput sequencing technology in the past decade, an increasing number of sequencing methods targeting different types of DNA damage have been developed and widely used in the field. These technologies not only help to elucidate the dynamic processes of repair pathways corresponding to different types of lesions, understand the underlying mechanisms of key factors and identify new hotspots prone to damage, but also greatly advanced our knowledge of crucial physiological processes such as meiotic homologous recombination, antibody generation and cytosine demethylation. These advancements hold significant potential for broader applications in exploring disease initiation and drug development. However, understanding and selecting the appropriate techniques have become difficult. This article reviews the main sequencing detection methods for the most common DNA lesions and introduce their principles, thereby providing valuable insights for the selection, application, further development and optimization of these technologies.