Database and primer selections affect nematode community composition under different vegetations of Changbai Mountain

High-throughput sequencing technology is increasingly used in the study of nematode biodiversity.However,the annotation difference of commonly used primers and reference databases on nematode community is still unclear.We compared two pairs of primers(3NDf/C_1132rmod,NF1F/18Sr2bR)and three databases(NT_V20200604,SILVA138/18s Eukaryota and PR2_v4.5 databases)on the determination of nematode community from four different vegetation types in Changbai Mountain,including mixed broadleaf-conifer forest,dark coniferous forest,betula ermanii Cham and alpine tundra.Our results showed that the selection of different primers and databases influenced the annotation of nematode taxa,but the diversity of nematode community showed consistent pattern among different vegetation types.Our findings emphasize that it is necessary to select appropriate primer and database according to the target taxonomic level.The difference in primers will affect the result of nematode taxa at different classification levels,so sequencing analysis cannot be used for comparison with studies using different primers.In terms of annotation effect in this study,3NDf/C_1132rmod primers with NT_v20200604 database could provide more information than other combinations at the genus or species levels.

Rhizosphere immunity: targeting the underground for sustainable plant health management

Managing plant health is a great challenge formodern food production and is further complicated by thelack of common ground between the many disciplinesinvolved in disease control. Here we present the concept ofrhizosphere immunity, in which plant health is consideredas an ecosystem level property emerging from networks ofinteractions between plants, microbiota and the surround-ing soil matrix. These interactions can potentially extendthe innate plant immune system to a point where therhizosphere immunity can fulfil all four core functions ofafull immune system: pathogen prevention, recognition,response and homeostasis. We suggest that consideringplant health from a meta-organism perspective will help indeveloping multidisciplinary pathogen management stra-tegies that focus on steering the whole plant-microbe-soilnetworks instead of individual components. This might beachieved by bringing together the latest discoveries inphytopathology, microbiome research, soil science andagronomy to pave the way toward more sustainable andproductive agriculture.

The relative importance of soil moisture in predicting bacterial wilt disease occurrence

Soil-borne plant diseases cause major economic losses globally.This is partly because their epidemiology is difficult to predict in agricultural fields,where multiple environmental factors could determine disease outcomes.Here we used a combination of field sampling and direct experimentation to identify key abiotic and biotic soil properties that can predict the occurrence of bacterial wilt caused by pathogenic Ralstonia solanacearum.By analyzing 139 tomato rhizosphere soils samples isolated from six provinces in China,we first show a clear link between soil properties,pathogen density and plant health.Specifically,disease outcomes were positively associated with soil moisture,bacterial abundance and bacterial community composition.Based on soil properties alone,random forest machine learning algorithm could predict disease outcomes correctly in 75%of cases with soil moisture being the most significant predictor.The importance of soil moisture was validated causally in a controlled greenhouse experiment,where the highest disease incidence was observed at 60%of maximum water holding capacity.Together,our results show that local soil properties can predict disease occurrence across a wider agricultural landscape,and that management of soil moisture could potentially offer a straightforward method for reducing crop losses to R.solanacearum.