16S rRNA Metabarcoding Inference Reveals Adaptive Bacterial Communities and Predicted Copper-Lead Resistance in Tanjung Emas Harbor Sediments

Abstract

Bacteria employ diverse mechanisms to cope with environmental stress, yet how sediment-associated microbial communities adjust to contamination—particularly heavy metals—in tropical port ecosystems remains insufficiently characterized. This study characterized the bacterial community profile based on amplicon sequencing and predicted the functional potential for heavy-metal resistance in surface sediments from Tanjung Emas Port, Semarang (TEPS), an area chronically exposed to wastewater pollutants and metal contamination from intensive anthropogenic activities. Amplicon-based metabarcoding targeting the 16S rRNA V3–V4 region was applied. Sequence processing using Cutadapt and DADA2 yielded 38,742–42,064 high-quality Amplicon Sequence Variants (ASVs) per sample. Functional potentials were inferred using PICRUSt2, and downstream analyses were conducted in RStudio. The community was dominated by the phylum Pseudomonadota (31–39%), class Gammaproteobacteria (27–35%), order Steroidobacterales (8–10%), family Woeseiaceae (8–10%), and genus Woeseia (13–16%), reflecting adaptation to organic-rich, pollutant-impacted conditions including Cu and Pb contamination. Alpha and beta-diversity analyses revealed significant differences among sampling locations (p<0.05). Predictive functional profiling indicated a high potential for putative heavy-metal resistance genes (HMRGs) associated with Cu and Pb resistance, highlighting the ecological resilience and bioremediation potential of the indigenous bacterial community.