Spatial Physicochemical and Metagenomic Analysis of Desert Environment

Investigating bacterial diversity and its metabolic capabilities is crucial for interpreting the ecological patterns in a desert environment and assessing the presence of exploitable microbial resources. In this study, we evaluated the spatial heterogeneity of physicochemical parameters, soil bacterial diversity and metabolic adaptation at meter scale. Soil samples were collected from two quadrats of a desert (Thar Desert, India) with a hot, arid climate, very little rainfall and extreme temperatures. Analysis of physico-chemical parameters and subsequent variance analysis (p-values less than 0.05) revealed that sulfate, potassium and magnesium ions were the most variable between the quadrats. Microbial diversity of the two quadrats was studied using Illumina bar-coded sequencing by targeting V3-V4 regions of 16S rDNA. As for the results, 702504 high-quality sequence reads, assigned to 173 operational taxonomic units (OTUs) at species level, were examined. The most abundant phyla in both quadrats were Actinobacteria (38.72%), Proteobacteria (32.94%), and Acidobacteria (9.24%). At genus level, Gaiella represented highest prevalence, followed by Streptomyces, Solirubrobacter, Aciditerrimonas, Geminicoccus, Geodermatophilus, Microvirga, and Rubrobacter. Between the quadrats, significant difference (p-values less than 0.05) was found in the abundance of Aciditerrimonas, Geodermatophilus, Geminicoccus, Ilumatobacter, Marmoricola, Nakamurella, and Solirubrobacter. Metabolic functional mapping revealed diverse biological activities, and was significantly correlated with physicochemical parameters. The results revealed spatial variation of ions, microbial abundance and functional attributes in the studied quadrats, and patchy nature in local scale. Interestingly, abundance of the biotechnologically important phylum Actinobacteria, with large proposition of unclassified species in the desert, suggested that this arid environment is a promising site for bioprospection.