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Research to identify appropriate biological agents for controlling pathogens might exploit experimental evolution to select for enhanced antagonism against pathogens. We developed an experimental regime facilitating this approach, featuring the bacterial broad‐spectrum predator Myxococcus xanthus and the fungal wheat pathogen Zymoseptoria tritici. We demonstrate that Z. tritici, a major cause of crop loss and fungicide use worldwide, both fuels growth of and is killed by M. xanthus on wheat straw, a probable source of seedling infection. M. xanthus was found capable of growth on moistened straw alone, unaided by Z. tritici, in a manner dependent on both M. xanthus initial density and on how long straw was moistened before M. xanthus inoculation. Such growth of non‐cellulolytic myxobacteria on plant detritus may have implications for understanding their roles in nutrient cycling. However, straw with Z. tritici was found to fuel greater M. xanthus growth than straw alone under many conditions. After shorter moistening periods, growth by low‐density M. xanthus populations was found to be fully dependent on Z. tritici. Such pathogen‐dependent M. xanthus populations could be sustained with population replacement over five 1‐week growth cycles, indicating the feasibility of conducting long‐term experimental evolution with this system. Furthermore, M. xanthus was found to kill majorities of Z. tritici populations on both buffered agar and straw. Our results suggest that myxobacteria may serve as effective biocontrol agents of Z. tritici and are amenable to long‐term experimental selection for enhanced killing of this pathogen, an approach broadly applicable to many potential biocontrol agents and target pathogens. [ABSTRACT FROM AUTHOR]

Septoria tritici blotch (STB) is one of the main causes of wheat yield loss in the world. Apart from good agrotechnical practice, disease‐resistant cultivars are required to prevent yield losses. Breeding of such cultivars is time‐consuming and laborious, mainly due to the quantitative character of such resistance, but can be shortened using precise phenotyping. We show that fluorescence parameters, collected in a high‐throughput system, can be used for estimating wheat STB resistance. Using machine learning methods (deep learning network, random forest), we demonstrate that disease resistance based on the percentage of necrotic leaf area can be estimated by alterations in fluorescence parameters 8 days after inoculation, and before disease symptoms become visible. Moreover, we applied a random forest classifier to assess the significance of fluorescence parameters in an informative way (classification accuracy = 64.84%, p value [Accuracy >No information rate] <0.001). Based on this, we observed extensive alterations in parameters of sensitive cultivars, reflecting worse photosynthetic performance. The most highly altered parameters were fluorescence intensity measurements and descriptors of energy flux through photosystem II (PSII) reaction centres towards PSI such as F2, F3 and F4 fluorescence intensities at 0.1, 0.270 and 2 ms; initial to maximal fluorescence ratio (F0/Fm); the time derivative of relative variable fluorescence (dV/dT0); maximum quantum yield of PSII in the dark‐adapted state (Fv/Fm); maximum electron transport flux at PSII (ET0/RC); maximum trapped excitonic energy flux per excited PSII cross section (CS) at time T0 (TR0/CS0); and quantum efficiency of the reduction of end acceptors dR0 [dR0/(1 − dR0)]. [ABSTRACT FROM AUTHOR]