Fig 1.
Workflow of metagenomic DNA extraction and sequencing.
Fig 2.
Workflow for raw data analysis and biosynthetic gene cluster mining in Sof Umer Cave microbiomes.
Table 1.
The sequence quality metrics of the Sof Umer Cave rock sample.
Table 2.
Statistics for scaftigs (≥500 bp).
Fig 3.
(A) The genus-level taxonomic abundance in Sof Umer Cave, analyzed using Kaiju program. (B) The species-level taxonomic abundance in Sof Umer Cave, analyzed using Kaiju program.
Fig 4.
Genomic regions annotated and predicted the distribution of various BGC types using antiSMASH.
RiPPs with an LAP group (23.91%), RiPPs with a lanthipeptide group (23.91%), terpenes (19.57%), NRPSs (13.04%), hybrid clusters (2.18%), and other newly annotated compounds (10.87%).
Fig 5.
Putative regions containing gene clusters associated with biosynthesis.
The alignment, conducted via Cluster-BLAST, revealed gene clusters that are related to the queried gene cluster. Specifically, the top ten matches to the setomimycin and kinamycin antibiotic KPS gene cluster from the Stenotrophomonas indicatrix strain EYE 224 are shown. Genes with significant homology (BLAST; highest sequence identity of 100%; BLAST alignment covering 100% of the sequence) are color-coded identically.
Fig 6.
Putative region contains the biosynthesis-related gene cluster, K127_195235-Region 09-posttranslationally modified peptides.
The alignment, which was conducted via Cluster-Blast, revealed the gene clusters with the highest similarity to the queried gene cluster.
Table 3.
MIBiG comparison analysis of distinct protoclusters in relation to reference regions and their respective properties.
Fig 7.
Analysis of major secondary metabolites predicted by NAPDOS based on ketosynthase (KS) domains.
Yersiniabactin was identified as a siderophore (iron-chelating agent), syringomycin was identified as an antifungal agent, and the remaining were antibacterial agents.