College of Veterinary Medicine, Jilin University, Changchun, China.
Department of Urology, The First Hospital of Jilin University, Changchun, Jilin, China.
mSphere. 2024 Jul 30;9(7):e0034624. doi: 10.1128/msphere.00346-24. Epub 2024 Jul 12.
In the process of oxygen reduction, reactive oxygen species (ROS) are generated as intermediates, including superoxide anion (O), hydrogen peroxide (HO), and hydroxyl radicals (OH). ROS can be destructive, and an imbalance between oxidants and antioxidants in the body can lead to pathological inflammation. Inappropriate ROS production can cause oxidative damage, disrupting the balance in the body and potentially leading to DNA damage in intestinal epithelial cells and beneficial bacteria. Microorganisms have evolved various enzymes to mitigate the harmful effects of ROS. Accurately predicting the types of ROS-scavenging enzymes (ROSes) is crucial for understanding the oxidative stress mechanisms and formulating strategies to combat diseases related to the "gut-organ axis." Currently, there are no available ROSes databases (DBs). In this study, we propose a systematic workflow comprising three modules and employ a hierarchical multi-task deep learning approach to collect, expand, and explore ROSes-related entries. Based on this, we have developed the human gut microbiota ROSes DB (http://39.101.72.186/), which includes 7,689 entries. This DB provides user-friendly browsing and search features to support various applications. With the assistance of ROSes DB, various communication-based microbial interactions can be explored, further enabling the construction and analysis of the evolutionary and complex networks of ROSes DB in human gut microbiota species.IMPORTANCEReactive oxygen species (ROS) is generated during the process of oxygen reduction, including superoxide anion, hydrogen peroxide, and hydroxyl radicals. ROS can potentially cause damage to cells and DNA, leading to pathological inflammation within the body. Microorganisms have evolved various enzymes to mitigate the harmful effects of ROS, thereby maintaining a balance of microorganisms within the host. The study highlights the current absence of a ROSes DB, emphasizing the crucial importance of accurately predicting the types of ROSes for understanding oxidative stress mechanisms and developing strategies for diseases related to the "gut-organ axis." This research proposes a systematic workflow and employs a multi-task deep learning approach to establish the human gut microbiota ROSes DB. This DB comprises 7,689 entries and serves as a valuable tool for researchers to delve into the role of ROSes in the human gut microbiota.
在氧还原过程中，会产生活性氧物种（ROS）作为中间体，包括超氧阴离子（O）、过氧化氢（HO）和羟基自由基（OH）。ROS 可能具有破坏性，体内氧化剂和抗氧化剂之间的不平衡可能导致病理性炎症。ROS 的产生不当会导致氧化损伤，破坏体内平衡，并可能导致肠道上皮细胞和有益细菌的 DNA 损伤。微生物已经进化出各种酶来减轻 ROS 的有害影响。准确预测 ROS 清除酶（ROSes）的类型对于了解氧化应激机制和制定与“肠道-器官轴”相关疾病的治疗策略至关重要。目前，还没有可用的 ROSes 数据库（DB）。在这项研究中，我们提出了一个包含三个模块的系统工作流程，并采用分层多任务深度学习方法来收集、扩展和探索 ROSes 相关条目。在此基础上，我们开发了人类肠道微生物群 ROSes DB（http://39.101.72.186/），其中包含 7689 条条目。该数据库提供了用户友好的浏览和搜索功能，支持各种应用。在 ROSes DB 的帮助下，可以探索各种基于通信的微生物相互作用，进一步支持构建和分析人类肠道微生物群物种中 ROSes DB 的进化和复杂网络。
活性氧物种（ROS）在氧还原过程中产生，包括超氧阴离子、过氧化氢和羟基自由基。ROS 可能对细胞和 DNA 造成损伤，导致体内病理性炎症。微生物已经进化出各种酶来减轻 ROS 的有害影响，从而维持宿主内微生物的平衡。该研究强调了目前缺乏 ROSes DB 的问题，强调了准确预测 ROSes 类型对于了解氧化应激机制和开发与“肠道-器官轴”相关疾病的策略至关重要。本研究提出了一个系统的工作流程，并采用多任务深度学习方法建立了人类肠道微生物群 ROSes DB。该数据库包含 7689 个条目，是研究人员深入研究 ROSes 在人类肠道微生物群中的作用的有价值工具。
