PDF(Pubmed)
Abstract：
In the process of oxygen reduction, reactive oxygen species (ROS) are generated as intermediates, including superoxide anion (O2-), hydrogen peroxide (H2O2), 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.
摘要：
在氧还原过程中,活性氧（ROS）作为中间体产生，包括超氧阴离子（O2-），过氧化氢(H2O2),和羟基自由基(OH-)。ROS可能是破坏性的，体内氧化剂和抗氧化剂之间的失衡会导致病理性炎症。ROS产生不当会引起氧化损伤，破坏体内平衡，并可能导致肠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）的类型对于理解氧化应激机制和制定与肠道器官轴相关的疾病的策略至关重要。\"目前,没有可用的ROS数据库(DB)。在这项研究中,我们提出了一个由三个模块组成的系统工作流程，并采用分层多任务深度学习方法来收集，展开,并探索与ROS相关的条目。基于此,我们开发了人类肠道微生物群ROSesDB（http://39.101.72.186/），其中包括7,689个条目。此DB提供用户友好的浏览和搜索功能，以支持各种应用程序。在ROSesDB的帮助下，可以探索各种基于交流的微生物相互作用，进一步构建和分析人类肠道微生物群物种中ROSesDB的进化和复杂网络。氧还原过程中会产生活性氧（ROS），包括超氧阴离子,过氧化氢,和羟基自由基。ROS可能会对细胞和DNA造成损害,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。这项研究强调了目前缺乏ROSesDB，强调准确预测ROSes类型对于理解氧化应激机制和制定与肠道-器官轴相关疾病的策略至关重要。“这项研究提出了一个系统的工作流程，并采用了多任务深度学习方法来建立人类肠道微生物群ROSesDB。该数据库包含7,689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的工具。
