The Relationship between rhizosphere microbial community structure and function with wheat (Triticum aestivum) yield

The rhizosphere accommodates an enormous amount of soil microorganisms and, due to its proximity to plant roots, this particular soil microbiome is considered to be a significant contributor to plant growth and health. Microorganisms colonising the rhizosphere have been noted to provide a number of benefits, ranging from assisting plant nutrient uptake to increasing biocontrol against soil-borne pathogens. These properties are of great interest in the wake of current issues regarding food security and the ever-increasing demand for sustainable agriculture production/management practices. However, the true scope of the rhizosphere’s functionality towards plant fitness and yield is still poorly understood, a gap predominantly caused by limited knowledge of microbial interactions and investigations of natural and/or agricultural soil microbial consortiums. This study tests the hypothesis that there exist microbial properties within the rhizosphere that correlate with higher/lower wheat (Triticum aestivum) yield in crops cultivated in an agricultural field. The elucidation of the rhizosphere’s composition, diversity and dynamics was achieved by metagenetic and metatranscriptomic analyses (simultaneous measurement of DNA and mRNA content) of the microbiome within the rhizosphere of wheat. Wheat yield was represented as the total protein content, determined by the measurement of total nitrogen by the Kjeldahl method. A custom bioinformatics pipeline was devised to process the sequence data and machine learning implemented to identify taxonomic and functional features of the wheat rhizosphere that correlate with crop yields. Several taxonomic and functional features of the microbial communities were shown to be associated with yield values, although none appeared to have a large effect. Nevertheless, the study’s focus on a microbiome that is critical to plant development and expansion on its taxonomic and functional features is an important step towards exploring natural ecosystems for the benefit of human health and environmental protection. Outcomes of the work include a workflow designed to simultaneously assess the structure and function of the rhizosphere microbiome using high throughput technologies, a machine/statistical learning framework for identifying microbiome features associated with crop phenotypic traits, and the identification of several metabolic pathways/phyla that can be considered for future analysis