Resurrected microorganisms: a plethora of resting bacteria underway for human interaction

Glaciers, which form due to the accumulation of snow, play a crucial role in providing freshwater resources, supporting river systems, and maintaining ecosystem stability. Pakistan is habitat to over 5000 glaciers, primarily located in the Hindukash, Himalaya, and Karakoram mountain ranges. Understanding the microbial communities thriving in these extreme environments becomes of utmost importance. These glaciers offer a unique perspective on extremophile adaptation, as they harbor microorganisms that are capable of surviving and thriving under harsh conditions. Glacial melting poses a significant threat to ancient microbiomes, potentially leading to the resurgence of epidemics and exposure of life to paleomicrobiota. Mostly glacial microbiome is evenly distributed and shows similar diversity. With the threat of resurrection of ages old microbiome and its incorporation into the waters have raised a major concern for revival of epidemics and exposure of life with paleanmicrobiota again. This has led the scientist to deeply observe the bacterial flora embedded in the cryonite holes of glaciers. This study aims to investigate the bacterial diversity within various glaciers of Pakistan using metagenomic techniques. Kamri, Burzil, Siachin, Baltoro, Shigar Basin, Biafo and Panama Glaciers designated from G1 to G7 respectively were chosen from Pakistan. Through rigorous physicochemical analyses, distinct characteristics among glaciers are revealed, including variations in temperature, depth, electrical conductivity, pH levels, and nutrient concentrations. The exploration of alpha diversity, employing metrics such as Chao1, Shannon, Simpson, and Inverse Simpson indices, offers valuable insights into the richness, evenness, and dominance of species within different samples. Beta diversity was calculated by using R software. The vegan package was used for NMSD, cluster and PCoA analysis based on Bray–Curtis distance. PCA analysis was done by using prcomp package from R software. Based on OTU abundance and environmental factor data, DCA analysis was done to determine the linear model from the gradient value (RDA) and the unimodal model (CCA). results were compiled by drawing cluster dendrogram which predicts the patterns of similarity and dissimilarity between different samples. Notably, phyla Proteobacteria emerge as the dominant phylum, accompanied by Actinobacteria , Firmicutes , and Bacteroidetes . The dendrogram shows five clusters, with close similarity between