Aims The rhizosphere microbiome substantially affects plant health, yet comparatively little is known regarding the foliar community dynamics. Here, we examine the relationship between the microbiota and their response to natural infection by pathogens. Methods We established an experimental system using a set of sorghum recombinant inbred lines (RILS). These RILS included four models denoted as resistant, moderately resistant, susceptible and highly susceptible. A combination of 16S rRNA and ITS gene amplicon approaches was used to assess bacteria and fungi, respectively, in foliar samples. Results We show that the foliar microbiome differs substantially in asymptomatic and symptomatic RILs subsequent to natural infection by pathogens. A significant association was found between plant health and microbial community structure. Our analyses revealed several distinct fungal and bacterial pathogens. These pathogens included Gibberella and Pantoea genera, which were associated with the highly susceptible group. In addition to these pathogens, we also found signatures for Ascochyta, a known plant pathogenic genus. Members of the bacterial genus Methylorubrum and the fungal genus Hannaella, both known to exhibit plant growth-promoting (PGP) traits, were associated with the resistant and moderately resistant groups. These data also reveal numerous highly diverse fungal and bacterial taxa in RILs that did not show symptoms. We also found taxonomic differences between the microbiota hosted by the symptomatic and asymptomatic RILs. Conclusions Together, these data suggest that pathogen infection may result in distinct microbiota. These results suggest that highly diverse microbiome may promote the plants ability to resist the effects of pathogens potentially contributing to plant health.

Mahewu is a fermented food product from maize, commonly consumed in Southern Africa. This study investigated the effect of optimizing fermentation (time and temperature) and boiling time of white maize (WM) and yellow maize (YM) mahewu, with the use of the Box–Behnken-response surface methodology (RSM). Fermentation time and temperature as well as boiling time were optimized and pH, total titratable acidity (TTA) and total soluble solids (TSS) determined. Results obtained showed that the processing conditions significantly (p ≤ 0.05) influenced the physicochemical properties. pH values of the mahewu samples ranged between 3.48–5.28 and 3.50–4.20 for YM mahewu and WM mahewu samples, respectively. Reduction in pH values after fermentation coincided with an increase in TTA as well as changes in the TSS values. Using the numerical multi-response optimisation of three investigated responses the optimal fermentation conditions were observed to be 25 °C for 54 h and a boiling time of 19 min for white maize mahewu and 29 °C for 72 h and a boiling time of 13 min for yellow maize mahewu. Thereafter white and yellow maize mahewu were prepared with the optimized conditions using different inocula (sorghum malt flour, wheat flour, millet malt flour or maize malt flour) and the pH, TTA and TSS of the derived mahewu samples determined. Additionally, amplicon sequencing of the 16S rRNA gene was used to characterise the relative abundance of bacterial genera in optimized mahewu samples, malted grains as well as flour samples. Major bacterial genera observed in the mahewu samples included Paenibacillus, Stenotrophomonas, Weissella, Pseudomonas, Lactococcus, Enterococcus, Lactobacillus, Bacillus, Massilia, Clostridium sensu stricto 1, Streptococcus, Staphylococcus, Sanguibacter, Roseococcus, Leuconostoc, Cutibacterium, Brevibacterium, Blastococcus, Sphingomonas and Pediococcus, with variations noted for YM mahewu and WM mahewu. As a result, the variations in physicochemical properties are due to differences in maize type and modification in processing conditions. This study also discovered the existence of variety of bacterial that can be isolated for controlled fermentation of mahewu.

As the global population is surging, the agricultural industry is required to meet the food demand while simultaneously providing eco-friendly sustainable crops that can withstand numerous abiotic and biotic stresses. The current era requires high-throughput biotechnology approaches to alleviate the current plant production and protection crisis. Omics approaches are regarded as a collection of high throughput technologies ending with “omics” such as genomics, proteomics, transcriptomics, metabolomics, phenomics and epigenomics. Furthermore, omics provide the best tactic to increase high quality crop production yield. A body of evidence has shown that microbial diversity, abundance, composition, functional gene patterns, and metabolic pathways at the genome level could also assist in understanding the contributions of the microbial community towards plant growth and protection. In addition, the link between plant genomes and phenotypes under physiological and environmental settings is highlighted by the integration of functional genomics with other omics. However, application of single omics technologies results in one disciplinary solution while raising multiple questions without answers. To address these challenges, we need to find new age solutions. For instance, omics technologies focusing on plant production and protection. Multi-layered information gathered from systems biology provides a comprehensive understanding of molecular regulator networks for improving plant growth and protection, which is supported by large-scale omics datasets. The conclusion drawn from the in-depth information is the holistic integration of multi-disciplinary omics approaches to pave the way towards eco-friendly, sustainable, agricultural productivity.

The aim of this present study was to optimize the fermentation conditions (time and temperature) of amasi (a Southern African fermented dairy product) using response surface methodology (RSM), and to determine the physicochemical properties, as well as the microbial composition, using next generation sequencing. Fermentation time and temperature were optimized to produce different amasi samples and different parameters, including pH, total soluble solids (TSS), total titratable acids (TTA), and consistency. All the variables studied were found to show significant (p ≤ 0.05) changes with increasing fermentation time and temperature. Numerical optimization was used to obtain the optimal fermentation conditions for amasi; based on RSM, it was 32 °C for 140 h, while with k-means clustering, it was 25 °C for 120 h. Under both conditions for the optimal samples, the pH reduced from 6.64 to 3.99, TTA increased from 0.02 to 0.11 (% lactic acid), TSS decreased from 9.47 to 6.67 °Brix, and the consistency decreased from 23 to 15.23 cm/min. Most of the identified bacteria were linked to lactic acid bacteria, with the family Lactobacillaceae being the most predominant in amasi, while in raw milk, Prevotellaceae was the most abundant. The fermentation conditions (time and temperature) had a significant influence on the parameters investigated in this study. Results of this study could provide information for the commercialization of quality amasi.