The Fe-S cluster biosynthesis in Enterococcus faecium is essential for anaerobic growth and gastrointestinal colonization.

The facultative anaerobic Gram-positive bacterium Enterococcus faecium is a ubiquitous member of the human gut microbiota. However, it has gradually evolved into a pathogenic and multidrug resistant lineage that causes nosocomial infections. The establishment of high-level intestinal colonization by enterococci represents a critical step of infection. The majority of current research on Enterococcus has been conducted under aerobic conditions, while limited attention has been given to its physiological characteristics in anaerobic environments, which reflects its natural colonization niche in the gut. In this study, a high-density transposon mutant library containing 26,620 distinct insertion sites was constructed. Tn-seq analysis identified six genes that significantly contribute to growth under anaerobic conditions. Under anaerobic conditions, deletion of sufB (encoding Fe-S cluster assembly protein B) results in more extensive and significant impairments on carbohydrate metabolism compared to aerobic conditions. Consistently, the pathways involved in this utilization-restricted carbohydrates were mostly expressed at significantly lower levels in mutant compared to wild-type under anaerobic conditions. Moreover, deletion of sufB or pflA (encoding pyruvate formate lyase-activating protein A) led to failure of gastrointestinal colonization in mice. These findings contribute to our understanding of the mechanisms by which E. faecium maintains proliferation under anaerobic conditions and establishes colonization in the gut.

Deep learning model accurately classifies metastatic tumors from primary tumors based on mutational signatures.

Metastatic propagation is the leading cause of death for most cancers. Prediction and elucidation of metastatic process is crucial for the treatment of cancer. Even though somatic mutations have been linked to tumorigenesis and metastasis, it is less explored whether metastatic events can be identified through genomic mutational signatures, which are concise descriptions of the mutational processes. Here, we developed MetaWise, a Deep Neural Network (DNN) model, by applying mutational signatures as input features calculated from Whole-Exome Sequencing (WES) data of TCGA and other metastatic cohorts. This model can accurately classify metastatic tumors from primary tumors and outperform traditional machine learning (ML) models and a deep learning (DL) model, DiaDeL. Signatures of non-coding mutations also have a major impact on the model's performance. SHapley Additive exPlanations (SHAP) and Local Surrogate (LIME) analyses identify several mutational signatures which are directly correlated to metastatic spread in cancers, including APOBEC-mutagenesis, UV-induced signatures, and DNA damage response deficiency signatures.

TMSCNet: A three-stage multi-branch self-correcting trait estimation network for RGB and depth images of lettuce.

Growth traits, such as fresh weight, diameter, and leaf area, are pivotal indicators of growth status and the basis for the quality evaluation of lettuce. The time-consuming, laborious and inefficient method of manually measuring the traits of lettuce is still the mainstream. In this study, a three-stage multi-branch self-correcting trait estimation network (TMSCNet) for RGB and depth images of lettuce was proposed. The TMSCNet consisted of five models, of which two master models were used to preliminarily estimate the fresh weight (FW), dry weight (DW), height (H), diameter (D), and leaf area (LA) of lettuce, and three auxiliary models realized the automatic correction of the preliminary estimation results. To compare the performance, typical convolutional neural networks (CNNs) widely adopted in botany research were used. The results showed that the estimated values of the TMSCNet fitted the measurements well, with coefficient of determination (R 2) values of 0.9514, 0.9696, 0.9129, 0.8481, and 0.9495, normalized root mean square error (NRMSE) values of 15.63, 11.80, 11.40, 10.18, and 14.65% and normalized mean squared error (NMSE) value of 0.0826, which was superior to compared methods. Compared with previous studies on the estimation of lettuce traits, the performance of the TMSCNet was still better. The proposed method not only fully considered the correlation between different traits and designed a novel self-correcting structure based on this but also studied more lettuce traits than previous studies. The results indicated that the TMSCNet is an effective method to estimate the lettuce traits and will be extended to the high-throughput situation. Code is available at https://github.com/lxsfight/TMSCNet.git.