The impact of histone lactylation on the tumor microenvironment and metabolic pathways and its potential in cancer therapy.

BACKGROUND: The complexity of cancer is intricately linked to its multifaceted biological processes, including the roles of the tumor microenvironment (TME) as well as genetic and metabolic regulation. Histone lactylation has recently emerged as a novel epigenetic modification mechanism that plays a pivotal role in regulating cancer initiation, proliferation, invasion, and metastasis. OBJECTIVE: This review aims to elucidate the role of histone lactylation in modulating various aspects of tumor biology, including DNA repair mechanisms, glycolytic metabolic abnormalities, functions of non-tumor cells in the TME, and the promotion of tumor inflammatory responses and immune escape. Additionally, the review explores potential therapeutic strategies targeting histone lactylation. METHODS: A comprehensive literature review was performed, analyzing recent findings on histone lactylation and its impact on cancer biology. This involved a systematic examination of studies focusing on biochemical pathways, cellular interactions, and clinical implications related to histone lactylation. RESULTS: Histone lactylation was identified as a critical regulator of tumor cell DNA repair mechanisms and glycolytic metabolic abnormalities. It also significantly influences the functions of non-tumor cells within the TME, promoting tumor inflammatory responses and immune escape. Moreover, histone lactylation acts as a multifunctional biological signaling molecule impacting immune responses within the TME. Various cell types within the TME, including T cells and macrophages, were found to regulate tumor growth and immune escape mechanisms through lactylation. CONCLUSION: Histone lactylation offers a novel perspective on tumor metabolism and its role in cancer development. It presents promising opportunities for the development of innovative cancer therapies. This review underscores the potential of histone lactylation as a therapeutic target, paving the way for new strategies in cancer treatment.

Improvement of Dscam homophilic binding affinity throughout Drosophila evolution.

BACKGROUND: Drosophila Dscam1 is a cell-surface protein that plays important roles in neural development and axon tiling of neurons. It is known that thousands of isoforms bind themselves through specific homophilic interactions, a process which provides the basis for cellular self-recognition. Detailed biochemical studies of specific isoforms strongly suggest that homophilic binding, i.e. the formation of homodimers by identical Dscam1 isomers, is of great importance for the self-avoidance of neurons. Due to experimental limitations, it is currently impossible to measure the homophilic binding affinities for all 19,000 potential isoforms. RESULTS: Here we reconstructed the DNA sequences of an ancestral Dscam form (which likely existed approximately 40 ~ 50 million years ago) using a comparative genomic approach. On the basis of this sequence, we established a working model to predict the self-binding affinities of all isoforms in both the current and the ancestral genome, using machine-learning methods. Detailed computational analysis was performed to compare the self-binding affinities of all isoforms present in these two genomes. Our results revealed that 1) isoforms containing newly derived variable domains exhibit higher self-binding affinities than those with conserved domains, and 2) current isoforms display higher self-binding affinities than their counterparts in the ancient genome. As thousands of Dscam isoforms are needed for the self-avoidance of the neuron, we propose that an increase in self-binding affinity provides the basis for the successful evolution of the arthropod brain. CONCLUSIONS: Our data presented here provide an excellent model for future experimental studies of the binding behavior of Dscam isoforms. The results of our analysis indicate that evolution favored the rise of novel variable domains thanks to their higher self-binding affinities, rather than selection merely on the basis of simple expansion of isoform diversity, as that this particular selection process would have established the powerful mechanisms required for neuronal self-avoidance. Thus, we reveal here a new molecular mechanism for the successful evolution of arthropod brains.

NURD: an implementation of a new method to estimate isoform expression from non-uniform RNA-seq data.

BACKGROUND: RNA-Seq technology has been used widely in transcriptome study, and one of the most important applications is to estimate the expression level of genes and their alternative splicing isoforms. There have been several algorithms published to estimate the expression based on different models. Recently Wu et al. published a method that can accurately estimate isoform level expression by considering position-related sequencing biases using nonparametric models. The method has advantages in handling different read distributions, but there hasn't been an efficient program to implement this algorithm. RESULTS: We developed an efficient implementation of the algorithm in the program NURD. It uses a binary interval search algorithm. The program can correct both the global tendency of sequencing bias in the data and local sequencing bias specific to each gene. The correction makes the isoform expression estimation more reliable under various read distributions. And the implementation is computationally efficient in both the memory cost and running time and can be readily scaled up for huge datasets. CONCLUSION: NURD is an efficient and reliable tool for estimating the isoform expression level. Given the reads mapping result and gene annotation file, NURD will output the expression estimation result. The package is freely available for academic use at http://bioinfo.au.tsinghua.edu.cn/software/NURD/.