Secondary metabolite biosynthetic gene clusters and microbial diversity as predicted by shotgun metagenomic sequencing approach in hospitals and pharmaceutical industry wastes

Abstract

Hospital and pharmaceutical industry wastes harbor a diverse microbial community influenced by their complex chemical composition, including antibiotic-rich compounds originating from soil microorganisms. This study employs a shotgun metagenomic approach to comprehensively characterize the microbial composition of hospital and Pharmaceutical industry wastes for the first time in Ethiopia. Metagenomic DNA was extracted and used to construct a whole-genome shotgun library, which was subsequently sequenced using the Illumina HiSeq1500 platform. Taxonomic analysis revealed that bacteria were the predominant domain across all samples, followed by eukaryotes and archaea. Among the bacterial phyla, Pseudomonadota was the most prevalent in both hospital and pharmaceutical waste samples. At the genus level, Pseudomonas and Pedobacter were the most abundant taxa, followed by Flavobacterium and Streptomyces. Notably, Streptomyces exhibited higher-than-expected abundance in the waste metagenome, suggesting a potential adaptive response to environmental stressors. Across all samples, Pedobacter and Pseudomonas were consistently the most dominant bacterial genera. Functional analysis using gene ontology (GO) annotations highlighted the predominance of metabolic and biosynthetic processes. Further investigation revealed an enrichment of ATP-binding cassette (ABC) transporter protein families and winged-helix protein domains, both of which are linked to antibiotic resistance, metabolite translocation, and antibiotic biosynthesis regulation. KEGG pathway analysis identified key biosynthetic pathways, including terpenoid and polyketide biosynthesis, as well as beta-lactam antibiotic production. Additionally, antiSMASH analysis detected multiple biosynthetic gene clusters (BGCs), including those encoding terpenes, bacteriocins, and non-ribosomal peptide synthetases. Overall, this study underscores the adaptive potential of microorganisms inhabiting industrial waste environments, highlighting their genomic capacity to produce bioactive secondary metabolites in response to toxic compounds. These findings provide valuable insights into microbial resilience and the potential for biotechnological applications in bioremediation and drug discovery.

Keywords: Biosynthesis gene clusters; Hospital wastes; Metagenomics; Microbial diversity; Pharmaceutical wastes; Shotgun sequencing; Unculturable bacteria.

Fig. 1

Geographical location of the hospital (shaded in red) and pharmaceutical industry (shaded in blue) sampling sites located in Addis Ababa, Ethiopia. A location map of the study area was created in ArcGIS Desktop software Version 10.4 (https://www.esri.com/en-us/home).

Fig. 2

The taxonomy composition of microbial communities at the phylum level illustrates relative abundances across samples of two hospitals and two pharmaceutical industry wastes.

Fig. 3

Relative abundance of bacteria at the genus level in samples of hospital and pharmaceutical industry wastes based on small subunit rRNA.

Fig. 4

Venn diagram showing the numbers of common and unique Phylum (left) and Genus (right) among hospital and pharmaceutical industry wastes based on SSU rRNA. Eight and two taxon were common across samples in the phylum and genus, respectively.

Fig. 5

Relative abundance of bacteria at the species level in samples of hospital and pharmaceutical industry wastes based on SSU rRNA.

Fig. 6

The top 10 gene ontology (GO) biological process (BP) terms found in hospital and pharmaceutical Samples with metabolic and biosynthesis processes show higher match. The horizontal axis was the GO BP terms, and the vertical axis was the number of genes.

Fig. 7

A summary of InterPro entry matches with descriptions a(AH), b(CP), c(EP) and d(TB) for hospital and pharmaceutical Samples. The abundant protein domains are winged helix DNA-binding proteins, Alpha/beta hydrolase fold and Aldolase-type TIM.

Fig. 8

The secondary metabolite biosynthetic gene clusters (BGCs) predicted by antiSMASH in Hospital and Pharmaceutical samples. The length of each vertical bar corresponds with the number of BGCs. Terpenes were the most abundant BGCs across samples.