Small-sample learning reveals propionylation in determining global protein homeostasis

Chromatin lysine acylation: On the path to chromatin homeostasis and genome integrity

The fundamental role of cells in safeguarding the genome's integrity against DNA double-strand breaks (DSBs) is crucial for maintaining chromatin homeostasis and the overall genomic stability. Aberrant responses to DNA damage, known as DNA damage responses (DDRs), can result in genomic instability and contribute significantly to tumorigenesis. Unraveling the intricate mechanisms underlying DDRs following severe damage holds the key to identify therapeutic targets for cancer. Chromatin lysine acylation, encompassing diverse modifications such as acetylation, lactylation, crotonylation, succinylation, malonylation, glutarylation, propionylation, and butyrylation, has been extensively studied in the context of DDRs and chromatin homeostasis. Here, we delve into the modifying enzymes and the pivotal roles of lysine acylation and their crosstalk in maintaining chromatin homeostasis and genome integrity in response to DDRs. Moreover, we offer a comprehensive perspective and overview of the latest insights, driven primarily by chromatin acylation modification and associated regulators.

PEA-m6A: an ensemble learning framework for accurately predicting N6-methyladenosine modifications in plants

N 6-methyladenosine (m6A), which is the mostly prevalent modification in eukaryotic mRNAs, is involved in gene expression regulation and many RNA metabolism processes. Accurate prediction of m6A modification is important for understanding its molecular mechanisms in different biological contexts. However, most existing models have limited range of application and are species-centric. Here we present PEA-m6A, a unified, modularized and parameterized framework that can streamline m6A-Seq data analysis for predicting m6A-modified regions in plant genomes. The PEA-m6A framework builds ensemble learning-based m6A prediction models with statistic-based and deep learning-driven features, achieving superior performance with an improvement of 6.7% to 23.3% in the area under precision-recall curve compared with state-of-the-art regional-scale m6A predictor WeakRM in 12 plant species. Especially, PEA-m6A is capable of leveraging knowledge from pretrained models via transfer learning, representing an innovation in that it can improve prediction accuracy of m6A modifications under small-sample training tasks. PEA-m6A also has a strong capability for generalization, making it suitable for application in within- and cross-species m6A prediction. Overall, this study presents a promising m6A prediction tool, PEA-m6A, with outstanding performance in terms of its accuracy, flexibility, transferability, and generalization ability. PEA-m6A has been packaged using Galaxy and Docker technologies for ease of use and is publicly available at https://github.com/cma2015/PEA-m6A.

ifDEEPre: large protein language-based deep learning enables interpretable and fast predictions of enzyme commission numbers

Accurate understanding of the biological functions of enzymes is vital for various tasks in both pathologies and industrial biotechnology. However, the existing methods are usually not fast enough and lack explanations on the prediction results, which severely limits their real-world applications. Following our previous work, DEEPre, we propose a new interpretable and fast version (ifDEEPre) by designing novel self-guided attention and incorporating biological knowledge learned via large protein language models to accurately predict the commission numbers of enzymes and confirm their functions. Novel self-guided attention is designed to optimize the unique contributions of representations, automatically detecting key protein motifs to provide meaningful interpretations. Representations learned from raw protein sequences are strictly screened to improve the running speed of the framework, 50 times faster than DEEPre while requiring 12.89 times smaller storage space. Large language modules are incorporated to learn physical properties from hundreds of millions of proteins, extending biological knowledge of the whole network. Extensive experiments indicate that ifDEEPre outperforms all the current methods, achieving more than 14.22% larger F1-score on the NEW dataset. Furthermore, the trained ifDEEPre models accurately capture multi-level protein biological patterns and infer evolutionary trends of enzymes by taking only raw sequences without label information. Meanwhile, ifDEEPre predicts the evolutionary relationships between different yeast sub-species, which are highly consistent with the ground truth. Case studies indicate that ifDEEPre can detect key amino acid motifs, which have important implications for designing novel enzymes. A web server running ifDEEPre is available at https://proj.cse.cuhk.edu.hk/aihlab/ifdeepre/ to provide convenient services to the public. Meanwhile, ifDEEPre is freely available on GitHub at https://github.com/ml4bio/ifDEEPre/.

Benzyl isothiocyanate inhibits TNFα-driven lipolysis via suppression of the ERK/PKA/HSL signaling pathway in 3T3-L1 adipocytes

Tumor necrosis factor α (TNFα), an inflammatory cytokine, induces lipolysis and increases circulating concentrations of free fatty acids. In addition, TNFα is the first adipokine produced by adipose tissue in obesity, contributing to obesity-associated metabolic disease. Given that benzyl isothiocyanate (BITC) is a well-known anti-inflammatory agent, we hypothesized that BITC can ameliorate TNFα-induced lipolysis and investigated the working mechanisms involved. We first challenged 3T3-L1 adipocytes with TNFα to induce lipolysis, which was confirmed by increased glycerol release, decreased protein expression of peroxisome proliferator-activated receptor γ (PPARγ) and perilipin 1 (PLIN1), and increased phosphorylation of ERK, protein kinase A (PKA), and hormone-sensitive lipase (HSL). However, inhibition of ERK or PKA significantly attenuated the lipolytic activity of TNFα. Meanwhile, pretreatment with BITC significantly ameliorated the lipolytic activity of TNFα; the TNFα-induced phosphorylation of ERK, PKA, and HSL; the TNFα-induced ubiquitination of PPARγ; the TNFα-induced decrease in PPARγ nuclear protein binding to PPAR response element; and the TNFα-induced decrease in PLIN1 protein expression. Our results indicate that BITC ameliorates TNFα-induced lipolysis by inhibiting the ERK/PKA/HSL signaling pathway, preventing PPARγ proteasomal degradation, and maintaining PLIN1 protein expression.

From beasts to bytes: Revolutionizing zoological research with artificial intelligence

Since the late 2010s, Artificial Intelligence (AI) including machine learning, boosted through deep learning, has boomed as a vital tool to leverage computer vision, natural language processing and speech recognition in revolutionizing zoological research. This review provides an overview of the primary tasks, core models, datasets, and applications of AI in zoological research, including animal classification, resource conservation, behavior, development, genetics and evolution, breeding and health, disease models, and paleontology. Additionally, we explore the challenges and future directions of integrating AI into this field. Based on numerous case studies, this review outlines various avenues for incorporating AI into zoological research and underscores its potential to enhance our understanding of the intricate relationships that exist within the animal kingdom. As we build a bridge between beast and byte realms, this review serves as a resource for envisioning novel AI applications in zoological research that have not yet been explored.