Ensemble of Multiple Classifiers for Multilabel Classification of Plant Protein Subcellular Localization

PlasmidEC and gplas2: an optimized short-read approach to predict and reconstruct antibiotic resistance plasmids in Escherichia coli

Accurate reconstruction of Escherichia coli antibiotic resistance gene (ARG) plasmids from Illumina sequencing data has proven to be a challenge with current bioinformatic tools. In this work, we present an improved method to reconstruct E. coli plasmids using short reads. We developed plasmidEC, an ensemble classifier that identifies plasmid-derived contigs by combining the output of three different binary classification tools. We showed that plasmidEC is especially suited to classify contigs derived from ARG plasmids with a high recall of 0.941. Additionally, we optimized gplas, a graph-based tool that bins plasmid-predicted contigs into distinct plasmid predictions. Gplas2 is more effective at recovering plasmids with large sequencing coverage variations and can be combined with the output of any binary classifier. The combination of plasmidEC with gplas2 showed a high completeness (median=0.818) and F1-Score (median=0.812) when reconstructing ARG plasmids and exceeded the binning capacity of the reference-based method MOB-suite. In the absence of long-read data, our method offers an excellent alternative to reconstruct ARG plasmids in E. coli.

Cucumis sativus CsbZIP90 suppresses Podosphaera xanthii resistance by modulating reactive oxygen species

Resistance to disease in plants requires the coordinated action of multiple functionally related genes, as it is difficult to improve disease resistance with a single functional gene. Therefore, the use of transcription factors to regulate the expression of multiple resistance genes to improve disease resistance has become a recent focus in the field of gene research. The basic leucine zipper (bZIP) transcription factor family plays vital regulatory roles in processes, such as plant growth and development and the stress response. In our previous study, CsbZIP90 (Cucsa.134370) was involved in the defense response of cucumber to Podosphaera xanthii, but the relationship between cucumber and resistance to powdery mildew remained unclear. Herein, we detected the function of CsbZIP90 in response to P. xanthii. CsbZIP90 was localized to the cytoplasm and nucleus, and its expression was significantly induced during P. xanthii attack. Transient overexpression of CsbZIP90 in cucumber cotyledons resulted in decreased resistance to P. xanthii, while silencing CsbZIP90 increased resistance to P. xanthii. CsbZIP90 negatively regulated the expression of reactive oxygen species (ROS)-related genes and activities of ROS-related kinases. Taken together, our results show that CsbZIP90 suppresses P. xanthi resistance by modulating ROS. This study will provide target genes for breeding cucumbers resistant to P. xanthii.

Genome-Wide Identification of Strawberry C2H2-ZFP C1-2i Subclass and the Potential Function of FaZAT10 in Abiotic Stress

C2H2-type zinc finger proteins (C2H2-ZFPs) play a key role in various plant biological processes and responses to environmental stresses. In Arabidopsisthaliana, C2H2-ZFP members with two zinc finger domains have been well-characterized in response to abiotic stresses. To date, the functions of these genes in strawberries are still uncharacterized. Here, 126 C2H2-ZFPs in cultivated strawberry were firstly identified using the recently sequenced Fragaria × ananassa genome. Among these C2H2-ZFPs, 46 members containing two zinc finger domains in cultivated strawberry were further identified as the C1-2i subclass. These genes were unevenly distributed on 21 chromosomes and classified into five groups according to the phylogenetic relationship, with similar physicochemical properties and motif compositions in the same group. Analyses of conserved domains and gene structures indicated the evolutionary conservation of the C1-2i subclass. A Ka/Ks analysis indicated that the C1-2i members were subjected to purifying selection during evolution. Furthermore, FaZAT10, a typical C2H2-ZFP, was isolated. FaZAT10 was expressed the highest in roots, and it was induced by drought, salt, low-temperature, ABA, and MeJA treatments. It was localized in the nucleus and showed no transactivation activity in yeast cells. Overall, these results provide useful information for enriching the analysis of the ZFPs gene family in strawberry, and they provide support for revealing the mechanism of FaZAT10 in the regulatory network of abiotic stress.

Lignin biosynthesis regulated by CsCSE1 is required for Cucumis sativus defence to Podosphaera xanthii

Lignin is a complex phenolic compound that can enhance the stiffness, hydrophobicity, and antioxidant capacity of the cell wall; it thus provides a critical barrier against pathogen and insect invaders. Caffeoyl shikimate esterase (CSE) is a key novel enzyme involved in lignin biosynthesis that is associated with genetic improvements in lignocellulosic biomass; however, no research thus far have revealed the role of CSE in resistance to pathogenic stress. CsCSE1 (Cucsa.134370) has previously been shown to highly associated with the response of cucumber to attack by Podosphaera xanthii through RNA sequencing. Here, we detected the exactly role of CsCSE1 in the defence of cucumber to P. xanthii infection. Homologous sequence alignment revealed that CsCSE1 contains two highly conserved lyase domains (GXSXG), suggesting that CsCSE1 possesses CSE activity. Subcellular localization analysis manifested that CsCSE1 was localized to the plasma membrane and endoplasmic reticulum (ER). Functional analysis demonstrated that the transient silencing of CsCSE1 in cucumber dramatically attenuated resistance to P. xanthii, whereas overexpression of CsCSE1 in cucumber markedly increased resistance to P. xanthii. Further investigation of the abundance of lignin in transient transgenic plants revealed that CsCSE1 might actively mediate the disease resistance of cucumber by promoting lignin biosynthesis. CsCSE1 also affects the expression of its downstream lignin biosynthesis-related genes, like CsLAC, CsCOMT, CsCCR, and CsCAD. The results of this study provide targets for the genetic breeding of tolerant cucumber cultivars as well as new insights that could aid the control of plant diseases.

Recent Advances in the Prediction of Subcellular Localization of Proteins and Related Topics

Prediction of subcellular localization of proteins from their amino acid sequences has a long history in bioinformatics and is still actively developing, incorporating the latest advances in machine learning and proteomics. Notably, deep learning-based methods for natural language processing have made great contributions. Here, we review recent advances in the field as well as its related fields, such as subcellular proteomics and the prediction/recognition of subcellular localization from image data.

iAtbP-Hyb-EnC: Prediction of antitubercular peptides via heterogeneous feature representation and genetic algorithm based ensemble learning model

Tuberculosis (TB) is a worldwide illness caused by the bacteria Mycobacterium tuberculosis. Owing to the high prevalence of multidrug-resistant tuberculosis, numerous traditional strategies for developing novel alternative therapies have been presented. The effectiveness and dependability of these procedures are not always consistent. Peptide-based therapy has recently been regarded as a preferable alternative due to its excellent selectivity in targeting specific cells without affecting the normal cells. However, due to the rapid growth of the peptide samples, predicting TB accurately has become a challenging task. To effectively identify antitubercular peptides, an intelligent and reliable prediction model is indispensable. An ensemble learning approach was used in this study to improve expected results by compensating for the shortcomings of individual classification algorithms. Initially, three distinct representation approaches were used to formulate the training samples: k-space amino acid composition, composite physiochemical properties, and one-hot encoding. The feature vectors of the applied feature extraction methods are then combined to generate a heterogeneous vector. Finally, utilizing individual and heterogeneous vectors, five distinct nature classification models were used to evaluate prediction rates. In addition, a genetic algorithm-based ensemble model was used to improve the suggested model's prediction and training capabilities. Using Training and independent datasets, the proposed ensemble model achieved an accuracy of 94.47% and 92.68%, respectively. It was observed that our proposed "iAtbP-Hyb-EnC" model outperformed and reported ~10% highest training accuracy than existing predictors. The "iAtbP-Hyb-EnC" model is suggested to be a reliable tool for scientists and might play a valuable role in academic research and drug discovery. The source code and all datasets are publicly available at https://github.com/Farman335/iAtbP-Hyb-EnC.