Assessment of Carbon Substrate Catabolism Pattern and Functional Metabolic Pathway for Microbiota of Limestone Caves

Abstract

Microbially induced calcium carbonate precipitation (MICP), a widespread biochemical process involving heterotopic bacterial communities, generally occurs in organic matter-rich environments. Limestone caves, whose oligotrophic conditions result from the absence of sunlight, are considered an extreme environment. In such environments, bacteria have the potential to form calcium carbonate. In this study, the microbial community diversity and taxonomical structure outside and inside a limestone cave was investigated with their community-level carbon source by fingerprinting and functional metabolic pathway prediction using 16S rRNA amplicon sequencing analysis. The Biolog EcoPlate™ assay revealed that microbes from outside the cave were metabolically highly active, resulting in a rising carbon source utilization rate curve. Conversely, the microbial community within the cave was not very active in consuming the carbon substrates of Biolog EcoPlate™. Although major carbon sources were found to be used by microbial communities both inside and outside the cave, the microbial utilization rate of carbon bacteria inside was much lower than for bacteria outside the cave. The taxonomic classification of microbial diversity using 16S rRNA metagenomic analysis revealed eight predominant bacterial phyla associated with both sampling areas: Proteobacteria, Acidobacteria, Actinobacteria, Planctomycetes, Nitrospirae, Chloroflexi, Gemmatimonadetes, and Cyanobacteria. Among these, Planctomycetes, Proteobacteria, Cyanobacteria, and Nitrospirae were predominantly associated with external cave samples, whereas Acidobacteria, Actinobacteria, Chloroflexi, and Gemmatimonadetes were associated with internal cave samples. Functional prediction analysis showed that bacterial communities both inside and outside the cave were functionally involved in the metabolism of carbohydrates, amino acids, other amino acid, lipids, xenobiotic compounds, energy metabolism, and environmental information processing. However, the amino acid and carbohydrate metabolic pathways were predominantly linked to the external cave samples, while xenobiotic compounds, lipids, other amino acids, and energy metabolism were associated with internal cave samples. Overall, a positive correlation was observed between Biolog EcoPlate™ assay carbon utilization and metagenomically observed metabolic function.

Species- and site-specific circulating bacterial DNA in Subantarctic sentinel mussels Aulacomya atra and Mytilus platensis

“…www.nature.com/scientificreports/ Functional analysis of the cmDNA. Predictive functional analysis from 16 s rRNA profiling is becoming a common tool to link the abundance of specific taxa with metabolic profiles [45][46][47] . To further explore the distinctive traits that distinguish the circulating microbiome of mussels at different sites, we studied its functional content predicted from the KEGG database using Piphillin tool.…”

Evaluation of soil microbial activity after application of Paenibacillus polymyxa-based biofertilizer in wheat field experiment

“…Usually, if there are no inhibitory factors that could suppress the growth and metabolic activity of bacteria the curve of AWCD keeps a typical sigmoid shape (Stefanowicz, 2006, Lima et al, 2015. However, either depending on the analysed samples and the participating species a longer lag phase (Koner et al, 2021) or specific differences in the trend can also be observed (Li et al, 2012). The Biolog EcoPlate substrates are organized in three sets that can be summarized in six guilds (amino acids, carbohydrates, carboxylic acids, amines, polymers, and phenolic compounds) and calculation of AWCD separately per each guild provided a more detailed information about utilisation of specific compounds.…”

Skin and Blood Microbial Signatures of Sedentary and Migratory Trout (Salmo trutta) of Kerguelen Islands

“…Predictive functional analysis from 16S rRNA profiling of cmDNA is routinely used to link the abundance of specific taxa with metabolic profiles [52][53][54]. To further explore the distinctive traits between sedentary and migratory S. trutta, we studied the functional content of the blood microbiome predicted from the KEGG database using the Piphillin tool (http://secondgenome.com/Piphillin) [53].…”