Crops in simplified, low-diversity agroecosystems assimilate only a fraction of the inorganic nitrogen (N) fertilizer inputs. Much of this N fertilizer is lost to the environment as N oxides, which degrade water quality and contribute to climate change and loss of biodiversity.

Walnut bacterial blight caused by Xanthomonas arboricola pv. juglandis (Xaj) has serious repercussions for walnut production around the world. Between 2015 and 2017, disease samples were collected from six counties (Danjiangkou, Baokang, Suizhou, Shennongjia, Zigui, and Xingshan) in Hubei province, China. Fifty-nine Xaj strains were identified by morphology and specific PCR primers from 206 isolates. The genetic diversity of 60 Xaj strains (59 from Hubei plus one from Beijing) was evaluated by Multilocus Sequence Analysis (MLST), and their resistance to copper ion (Cu2+) treatment was determined. A Neighbor Joining phylogenetic dendrogram was constructed based on four sequences of housekeeping genes (atpD-dnaK-glnA-gyrB). Two groups of strains were identified whose clustering was consistent with that of glnA. The minimal inhibitory concentration of copper ion on representative Xaj strain DW3F3 (the first genome sequenced Xaj from China) was 115 μg/ml. Setting the copper resistant threshold value to 125 μg/ml, 47 and 13 strains were considered sensitive and resistant to Cu2+, respectively. Furthermore, five strains showed Cu2+ resistance at 270 μg/ml. Compared to the copB from sensitive strains, the copB gene in resistant strains had a 15-bp insertion and eight scattered single nucleotide polymorphisms. Interestingly, the clustering based on MLSA was distinct between Xaj copper ion resistant and sensitive strains.

Walnut blight caused by Xanthomonas arboricola pv. juglandis (Xaj) is an important bacterial disease for walnut production worldwide. The objective of the present study was to characterize one endophytic bacterium, namely OFE17 from Osmanthus fragrans leaves, evaluate its potential biocontrol efficiency against the disease, and identify the probable underlying mechanisms of its function. Based on morphology, biochemical and physiological characteristics, 16S-rDNA and gyrB sequences, and antibiotic production genes, the endophyte OFE17 was tentatively identified as Bacillus sp. A disease control efficiency of up to 68.69% was observed through a biocontrol test on detached immature walnut fruits under controlled conditions. OFE17 can produce protease, cellulase, amylase, siderophores, and demonstrates phosphate dissolving ability.However, OFE17 is unable to produce extracellular lipase, IAA (indoleacetic acid), and has no nitrogen fixation capability. The active compounds of OFE17 were composed primarily non-protein compounds, and the optimum organic extraction solvent was chloroform. Through specific PCR detection, we discovered it contains the genes ituA and ituD which play a key role in active compound synthesis of iturin A synthetase. This study added a promising biocontrol agent candidate for the disease control and laid a foundation for further exploration.

Accurately modeling nitrification and understanding the role specific ammonia- or nitrite-oxidizing taxa play in it are of great interest and importance to microbial ecologists. In this study, we applied machine learning to 16S rRNA sequence and nitrification potential data from an experiment examining interactions between cropping systems and rhizosphere on microbial community assembly and nitrogen cycling processes. Given the high dimensionality of microbiome datasets, we only included nitrifers since only a few taxa are capable of ammonia and nitrite oxidation. We compared the performance of linear and nonlinear algorithms with and without qPCR measures of bacterial and archaea ammonia monooxygenase subunit A (amoA) gene abundance. Our feature selection process facilitated the identification of taxons that are most predictive of nitrification and to compare habitats. We found that Nitrosomonas and Nitrospirae were more frequently identified as important predictors of nitrification in conventional systems, whereas Thaumarchaeota were more important predictors in diversified systems. Our results suggest that model performance was not substantively improved by incorporating additional time-consuming and expensive qPCR data on amoA gene abundance. We also identified several clades of nitrifiers important for nitrification in different cropping systems, though we were unable to detect system- or rhizosphere-specific patterns in OTU-level biomarkers for nitrification. Finally, our results highlight the inherent risk of combining data from disparate habitats with the goal of increasing sample size to avoid overfitting models. This study represents a step toward developing machine learning approaches for microbiome research to identify nitrifier ecotypes that may be important for distinguishing ecotypes with defining roles in different habitats.