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Microbe-enhanced phytoremediation has been considered as a promising measure for the remediation of metal-contaminated soils. In this study, two bacterial strains JYX7 and JYX10 were isolated from rhizosphere soils of Polygonum pubescens grown in metal-polluted soil and identified as of Enterobacter sp. and Klebsiella sp. based on 16S rDNA sequences, respectively. JYX7 and JYX10 showed high Cd, Pb and Zn tolerance and increased water-soluble Cd, Pb and Zn concentrations in culture solution and metal-added soils. Two isolates produced plant growth-promoting substances such as indole acetic acid, siderophore, 1-aminocyclopropane-1-carboxylic deaminase, and solubilized inorganic phosphate. Based upon their ability in metal tolerance and solubilization, two isolates were further studied for their effects on growth and accumulation of Cd, Pb, and Zn in Brassica napus (rape) by pot experiments. Rapes inoculated with JYX7 and JYX10 had significantly higher dry weights, concentrations and uptakes of Cd, Pb, Zn in both above-ground and root tissues than those without inoculation grown in soils amended with Cd (25 mg kg(-1)), Pb (200 mg kg(-1)) or Zn (200 mg kg(-1)). The present results demonstrated that JYX7 and JYX10 are valuable microorganism, which can improve the efficiency of phytoremediation in soils polluted by Cd, Pb, and Zn.

To evaluate the interactions among endophytes, plants and heavy metal/arsenic contamination, root endophytic bacteria of Prosopis laevigata (Humb and Bonpl. ex Willd) and Sphaeralcea angustifolia grown in a heavy metal(loid)-contaminated zone in San Luis Potosi, Mexico, were isolated and characterized. Greater abundance and species richness were found in Prosopis than in Sphaeralcea and in the nutrient Pb-Zn-rich hill than in the poor nutrient and As-Cu-rich mine tailing. The 25 species identified among the 60 isolates formed three groups in the correspondence analysis, relating to Prosopis/hill (11 species), Prosopis/mine tailing (4 species) and Sphaeralcea/hill (4 species), with six species ungrouped. Most of the isolates showed high or extremely high resistance to arsenic, such as ≥100 mM for As(V) and ≥20 mM for As(III), in mineral medium. These results demonstrated that the abundance and community composition of root endophytic bacteria were strongly affected by the concentration and type of the heavy metals and metalloids (arsenic), as well as the plant species.

T-lymphocyte (T-cell) is a very important component in human immune system. T-cell epitopes can be used for the accurately monitoring the immune responses which activation by major histocompatibility complex (MHC), and rationally designing vaccines. Therefore, accurate prediction of T-cell epitopes is crucial for vaccine development and clinical immunology. In current study, two types peptide features, i.e., amino acid properties and chemical molecular features were used for the T-cell epitopes peptide representation. Based on these features, random forest (RF) algorithm, a powerful machine learning algorithm, was used to classify T-cell epitopes and non-T-cell epitopes. The classification accuracy, sensitivity, specificity, Matthews correlation coefficient (MCC), and area under the curve (AUC) values for proposed method are 97.54%, 97.22%, 97.60%, 0.9193, and 0.9868, respectively. These results indicate that current method based on the combined features and RF is effective for T-cell epitopes prediction.

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