The m⁵C modification is one of the widely occurring modifications on RNA. In recent years, m⁵C modification on RNA has increasingly become a focal point in cancer research. Nevertheless, investigating scientific and quantitative research on the publication trends in this field can help us understand the research background and emerging hotspots, providing insights for targeting RNA m⁵C sites in cancer therapy. Co-occurrence analyses and visualizations, including authorship, keywords, genes and diseases, were performed using VOSviewer. CiteSpace was used to identify bursting institutions, keywords and references. R packages, including clusterProfiler, enrichplot and ggplot2, were used to visualize the enrichment results of GO and KEGG. The top contributors to this field were the United States and China, and the journals with the most publications were Frontiers in Genetics and Frontiers in Oncology. The most common keyword was “m⁵C methylation” and the most related genes were “NSUN2”, “AKT1” and “METTL3”. This study conducted a bibliometric analysis covering the development process of RNA m⁵C modification in cancer progression, identifying the countries, institutions, authors, journals, and publications in this field. Additionally, we found that the genes most closely associated with m⁵C are likely to play a significant role in the process of viral oncogenesis. These findings provide a comprehensive overview of RNA m⁵C modification during cancer research and insights into RNA m⁵C-tageted cancer therapy.

The emergence of advanced genome editing technologies has revolutionized research in life sciences, offering an unprecedented way to uncover unknown biological functions and innovative therapeutic strategies. Among all genome editing tools, CRISPR-Cas-based technologies play a pivotal role in this revolution, particularly Class 2 effectors such as Cas9 and Cas12, owing to their high efficacy and ease of programmability. With the advancements in genome sequencing and metagenomics, an increasing number of novel CRISPR-Cas systems have been discovered, including those found in extreme environments and viruses. Furthermore, recent studies have revealed an unexpected role of non-Cas accessory genes, such as the Tn7-like transposon and Pro-CRISPR factors (Pcr), in conferring additional functionalities to the CRISPR system, providing new insights into the understanding of CRISPR-mediated bacterial immunity and advancing the development of genome editing technologies. Therefore, it is essential to develop comprehensive methods for characterizing the Cas proteins and Pro-CRISPR factors with a growing diversity. In this protocol, we provide a method encompassing protein purification, biochemical characterization, validation of protein-protein interactions, and preliminary in vivo functional assays in bacteria for Cas nuclease and its associated Pro-CRISPR factor. We hope this protocol will not only assist in the characterization of the CRISPR-Cas system, but also provide valuable guidance for the characterization of other nucleases or nucleic acid modification systems.

T cell receptors (TCRs) can recognize peptides presented by major histocompatibility complex (MHC) molecules, referred to as HLA in humans, which enables the targeted eradication of tumor cells expressing specific antigens. In recent years, TCR-engineered T cell (TCR-T) cell therapy has demonstrated substantial advancements in clinical trials targeting solid tumors. Notably, in August 2024, the U.S. FDA approved the first TCR-T drug for the treatment of advanced synovial sarcoma, representing a pivotal milestone in the field. For the development of TCR-T therapy, identifying tumor-associated antigen epitopes and high-functional TCRs are critical. Here, we present a comprehensive protocol outlining the process of identification of immunogenic epitopes and the efficient screening of antigen-specific TCRs from HLA transgenic mice. Additionally, the protocol encompasses methodologies for TCR-T cell preparation and their functional evaluation in vitro. These approaches provide a robust framework for advancing the development of tumor-specific TCRs and fostering the clinical translation of TCR-T therapies.

Ferroptosis is a new form of cell death driven by iron-dependent lipid peroxidation. Thus, it is closely related to the lipid and iron metabolism. Accumulating evidence has suggested mitochondria, the center of cell metabolism, are important regulators of ferroptosis. This is not surprising as mitochondria are also the center for lipid metabolism and iron metabolism, as well as redox balance. As the essential way of mitochondrial quality control, mitophagy may alleviate ferroptosis. On the other hand, the digestion of iron-rich mitochondria may provide ample sources for the activation of ferroptosis. This review describes these new findings about the interplay of mitophagy and ferroptosis and demonstrates the dual role of mitophagy in ferroptosis.

Although artificial intelligence (AI) has begun to be applied in synthetic biology, it is limited by its reliance on large amounts of high-quality data, which presents a significant challenge in synthetic biology. Pre-trained models have profoundly influenced natural language processing by enabling systems to understand and generate human language with remarkable accuracy and efficiency by capturing complex linguistic patterns and contextual nuances. This study applies the concept of pre-trained models to promoter sequence analysis through an innovative pre-training and fine-tuning paradigm. Our analysis reveals that pre-trained DNA models, particularly DNABERT, consistently outperform non-pre-trained models in predicting promoter expression levels across various dataset sizes. Building on DNABERT's strengths, we developed the AI model Pymaker, which specializes in predicting yeast promoter expression levels. Additionally, we introduced a novel base mutation model to simulate promoter mutations, enabling the generation of new promoter sequences. By integrating Pymaker with this mutation model, we effectively screened for high-expression, mutation-resistant promoters. Experimental validation in Saccharomyces cerevisiae showed that these selected promoters significantly enhanced LTB protein expression. Notably, Pymaker’s predictions demonstrated superior accuracy, achieving a three-fold increase in protein expression compared to traditional promoters. Our findings highlight the potential of Pymaker not only to identify robust promoters but also to significantly reduce reliance on conventional, labor-intensive experimental methods, heralding a new era in synthetic biology and genetic engineering with practical applications in biopharmaceuticals.