Role of histone modifications in transcription regulation upon environmental stress in eukaryotes

Epigenetic modification serves as a crucial mechanism in regulating gene expression and facilitating cellular adaptation to environmental stress. This study aimed to identify histone residues and their associated post-translational modifications (PTMs) that contribute to stress response and adaptation in the eukaryotic model organism Saccharomyces cerevisiae. A comprehensive set of histone mutants was analyzed to understand the transcriptional and phenotypic effects of potential histone PTMs. Through rigorous selection criteria and extensive experimental validation, we refined our list of candidate histone residues for further study. Histone H3 lysine 64 (H3-K64) emerged as a key player, exhibiting significant transcriptional effects in a PTM-dependent manner. We postulated the PTM involved in this process and investigated the potential regulatory mechanisms of those PTMs involved in this process. Our findings suggest a potential association of the stress response modulating transcription factor, Msn2, and the Set domain-containing methyltransferase, Set1, in the regulation of H3-K64 methylation.La modificación epigenética desempeña un papel crucial en la regulación de la expresión génica y en la adaptación celular al estrés ambiental. Este estudio tuvo como objetivo identificar los residuos de histonas y sus modificaciones postraduccionales (PTMs) asociadas que contribuyen a la adaptación al estrés en el modelo eucariota S. cerevisiae. Se analizó un conjunto de mutantes de histonas para entender los efectos transcripcionales y fenotípicos de las PTMs de histonas. Aplicando criterios de selección rigurosos y una extensa validación experimental, refinamos nuestra lista de residuos candidatos para profundizar en su estudio. La histona H3 lisina 64 (H3-K64) exhibió defectos transcripcionales significativos de manera dependiente de sus PTMs. Postulamos posibles mecanismos regulatorios y propusimos las PTMs involucradas en este proceso. Sugerimos una asociación del factor de transcripción que modula la respuesta al estrés, Msn2, y la metiltransferasa con dominio Set, Set1, en la regulación de la metilación de H3-K64.Programa de Doctorat en Biomedicin

Plug-and-playable fluorescent cell imaging modular toolkits using the bacterial superglue, SpyTag/SpyCatcher

Simple plug-and-playable fluorescent cell imaging modular toolkits are established using the bacterial superglue SpyTag/SpyCatcher protein ligation system. A variety of affibody-fluorescent protein conjugates (AFPCs) are post-translationally generated via the isopeptide bond formation, and each AFPC effectively recognizes and binds to its targeting cells, visualizing them with selective colors on demand.close

Development of target-tunable P22 VLP-based delivery nanoplatforms using bacterial superglue

Protein cage nanoparticles are widely used as targeted delivery nanoplatforms, because they have well-defined symmetric architectures, high biocompatibility, and enough plasticity to be modified to produce a range of different functionalities. Targeting peptides and ligands are often incorporated on the surface of protein cage nanoparticles. In this research, we adopted the SpyTag/SpyCatcher protein ligation system to covalently display target-specific affibody molecules on the exterior surface of bacteriophage P22 virus-like particles (VLP) and evaluated their modularity and efficacy of targeted delivery. We genetically introduced the 13 amino acid SpyTag peptide into the C-terminus of the P22 capsid protein to construct a target-tunable nanoplatform. We constructed two different SpyCatcher-fused affibody molecules as targeting ligands, SC-EGFRAfb and SC-HER2Afb, which selectively bind to EGFR and HER2 surface markers, respectively. We produced target-specific P22 VLP-based delivery nanoplatforms for the target cell lines by selectively combining SpyTagged P22 VLP and SC-fused affibody molecules. We confirmed its target-switchable modularity through cell imaging and verified the target-specific drug delivery efficacy of the affibody molecules displaying P22 VLP using cell viability assays. The P22 VLP-based delivery nanoplatforms can be used as multifunctional delivery vehicles by ligating other functional proteins, as well as affibody molecules. The interior cavity of P22 VLP can be also used to load cargoes like enzymes and therapeutic proteins. We anticipate that the nanoplatforms will provide new opportunities for developing target-specific functional protein delivery systems

Neural networks and robotic microneedles enable autonomous extraction of plant metabolites

Plant metabolites comprise a wide range of extremely important chemicals. In many cases, like savory spices, they combine distinctive functional properties—deterrence against herbivory—with an unmistakable flavor. Others have remarkable therapeutic qualities, for instance, the malaria drug artemisinin, or mechanical properties, for example natural rubber. We present a breakthrough in plant metabolite extraction technology. Using a neural network, we teach a computer how to recognize metabolite-rich cells of the herbal plant rosemary (Rosmarinus officinalis) and automatically extract the chemicals using a microrobot while leaving the rest of the plant undisturbed. Our approach obviates the need for chemical and mechanical separation and enables the extraction of plant metabolites that currently lack proper methods for efficient biomass use. Computer code required to train the neural network, identify regions of interest, and control the micromanipulator is available as part of the Supplementary Material

Feature Selection Method Using Multi-Agent Reinforcement Learning Based on Guide Agents

In this study, we propose a method to automatically find features from a dataset that are effective for classification or prediction, using a new method called multi-agent reinforcement learning and a guide agent. Each feature of the dataset has one of the main and guide agents, and these agents decide whether to select a feature. Main agents select the optimal features, and guide agents present the criteria for judging the main agents’ actions. After obtaining the main and guide rewards for the features selected by the agents, the main agent that behaves differently from the guide agent updates their Q-values by calculating the learning reward delivered to the main agents. The behavior comparison helps the main agent decide whether its own behavior is correct, without using other algorithms. After performing this process for each episode, the features are finally selected. The feature selection method proposed in this study uses multiple agents, reducing the number of actions each agent can perform and finding optimal features effectively and quickly. Finally, comparative experimental results on multiple datasets show that the proposed method can select effective features for classification and increase classification accuracy