Draft genome sequence of potato crop bacterial isolates and nanoparticles-intervention for the induction of secondary metabolites biosynthesis.

Introduction: Insights about the effects of gold nanoparticles (AuNPs) on the biosynthetic manipulation of unknown microbe secondary metabolites could be a promising technique for prospective research on nano-biotechnology. Aim: In this research, we aimed to isolate a fresh, non-domesticated unknown bacterium strain from a common scab of potato crop located in Saudi Arabia and study the metabolic profile. Methodology: This was achieved through genomic DNA (gDNA) sequencing using Oxford Nanopore Technology. The genomic data were subjected to several bioinformatics tools, including canu-1.9 software, Prokka, DFAST, Geneious Prime, and AntiSMASH. We exposed the culture of the bacterial isolate with different concentrations of AuNPs and investigated the effects of AuNPs on secondary metabolites biosynthesis using several analytical techniques. Furthermore, Tandem-mass spectrometric (MS/MS) technique was optimized for the characterization of several significant sub-classes. Results: The genomic draft sequence assembly, alignment, and annotation have verified the bacterial isolate as Priestia megaterium. This bacterium has secondary metabolites related to different biosynthetic gene clusters. AuNPs intervention showed an increase in the production of compounds with the molecular weights of 254 and 270 Da in a direct-dependent manner with the increase of the AuNPs concentrations. Conclusion: The increase in the yields of compound 1 and 2 concomitantly with the increase in the concentration of the added AuNPs provide evidences about the effects of nanoparticles on the biosynthesis of the secondary metabolites. It contributes to the discovery of genes involved in different biosynthetic gene clusters (BGCs) and prediction of the structures of the natural products.

Optimal Deployment of WSN Nodes for Crop Monitoring Based on Geostatistical Interpolations.

This paper proposes an integrated method for the estimation of soil moisture in potato crops that uses a low-cost wireless sensor network (WSN). Soil moisture estimation maps were created by applying the Kriging technique over a WSN composed of 11×11 nodes. Our goal is to estimate the soil moisture of the crop with a small-scale WSN. Using a perfect mesh approach on a potato crop, experimental results demonstrated that 25 WSN nodes were optimal and sufficient for soil moisture characterization, achieving estimations errors <2%. We provide a strategy to select the number of nodes to use in a WSN, to characterize the moisture behavior for spatio-temporal analysis of soil moisture in the crop. Finally, the implementation cost of this strategy is shown, considering the number of nodes and the corresponding margin of error.

Toward Decentralized Agrigenomic Surveillance? A Polymerase Chain Reaction-Restriction Fragment Length Polymorphism Approach for Adaptable and Rapid Detection of User-Defined Fungal Pathogens in Potato Crops.

Agrigenomics is one of the emerging focus areas for omics sciences. Yet, agrigenomics differs from medical omics applications such as pharmacogenomics and precision medicine, by virtue of vastly distributed geography of applications at the intersection of agriculture, nutrition, and genomics research streams. Crucially, agrigenomics can address diagnostics and safety surveillance needs in remote and rural farming communities or decentralized food, crop, and environmental monitoring programs for prompt, selective, and differential identification of pathogens. A case in point is the potato crop that serves as a fundamental nutritional source worldwide. Decentralized potato crop and plant protection facilities are pivotal to minimize unnecessary, preemptive use of broad-spectrum fungicides, thus helping to curtail the costs, environmental burden, and the development of resistance in opportunistic human pathogenic fungi. We report here a polymerase chain reaction-restriction fragment length polymorphism approach that is sensitive and adaptable in detection and broad identification of fungal pathogens in potato crops, with a view to future decentralized agrigenomic surveillance programs. Notably, the fingerprinting patterns obtained by the method fully differentiated 12 fungal species examined in silico, with 10 of them also tested in vitro. The method can be scaled up through improvements in electrophoresis and enzyme panel for adaption to other crops and/or pathogens. We suggest that decentralized and integrated agrosurveillance programs and translational agrigenomic programs can inform future innovations in multidomain biosecurity, particularly across omics applications from agriculture and nutrition to clinical medicine and environmental biosafety.