Expresslon of the tobacco mosalc virus 30-kD movement protein (TMV MP) gene in tobacco plants increases the plasmodesmatal size exclusion limlt (SEL) 10-fold between mesophyll cells in mature leaves. In the present study, we examined the structure of plasmodesmata as a functlon of leaf development. In young leaves of 30-kD TMV MP transgenic (line 274) and vector control ( l h e 306) plants, almost all plasmodesmata were primary in nature. In both plant lines, secondary plasmodesmata were formed, in a basipetal pattern, as the leaves underwent expansion growth. Ultrastructural and immunolabellng studies demonstrated that ln line 274 the TMV MP accumulated predominantly in secondary plasmodesmata of nonvascular tissues and was associated with a filamentous material. A developmental progression was detected in terms of the presence of TMV MP; all secondary plasmodesmata in the tip of the fourth leaf contained TMV MP in association with the filamentous material. Dye-coupling experiments demonstrated that the TMV MP-induced increase in plasmodesmatal SEL could be routinely detected in the tip of the fourth leaf, but was restricted to mesophyll and bundle sheath cells. These findings are dlscussed with respect to the structure and function of plasmodesmata, particularly those aspects related to virus movement.

Expression of the tobacco mosaic virus 30-kD movement protein (TMV MP) gene in tobacco plants increases the plasmodesmatal size exclusion limit (SEL) 10-fold between mesophyll cells in mature leaves. In the present study, we examined the structure of plasmodesmata as a function of leaf development. In young leaves of 30-kD TMV MP transgenic (line 274) and vector control (line 306) plants, almost all plasmodesmata were primary in nature. In both plant lines, secondary plasmodesmata were formed, in a basipetal pattern, as the leaves underwent expansion growth. Ultrastructural and immunolabeling studies demonstrated that in line 274 the TMV MP accumulated predominantly in secondary plasmodesmata of nonvascular tissues and was associated with a filamentous material. A developmental progression was detected in terms of the presence of TMV MP; all secondary plasmodesmata in the tip of the fourth leaf contained TMV MP in association with the filamentous material. Dye-coupling experiments demonstrated that the TMV MP-induced increase in plasmodesmatal SEL could be routinely detected in the tip of the fourth leaf, but was restricted to mesophyll and bundle sheath cells. These findings are discussed with respect to the structure and function of plasmodesmata, particularly those aspects related to virus movement.

Areas within an agricultural field in the same season often differ in crop productivity despite having the same cropping history, crop genotype, and management practices. One hypothesis is that abiotic or biotic factors in the soils differ between areas resulting in these productivity differences. In this study, bulk soil samples collected from a high and a low productivity area from within six agronomic fields in Illinois were quantified for abiotic and biotic characteristics. Extracted DNA from these bulk soil samples were shotgun sequenced. While logistic regression analyses resulted in no significant association between crop productivity and the 26 soil characteristics, principal coordinate analysis and constrained correspondence analysis showed crop productivity explained a major proportion of the taxa variance in the bulk soil microbiome. Metagenome-wide association studies (MWAS) identified more Bradyrhizodium and Gammaproteobacteria in higher productivity areas and more Actinobacteria, Ascomycota, Planctomycetales, and Streptophyta in lower productivity areas. Machine learning using a random forest method successfully predicted productivity based on the microbiome composition with the best accuracy of 0.79 at the order level. Our study showed that crop productivity differences were associated with bulk soil microbiome composition and highlighted several nitrogen utility-related taxa. We demonstrated the merit of MWAS and machine learning for the first time in a plant-microbiome study.