Identification of Microbial Profiles in Heavy-Metal-Contaminated Soil from Full-Length 16S rRNA Reads Sequenced by a PacBio System

Abstract

Heavy metal pollution is a serious environmental problem as it adversely affects crop production and human activity. In addition, the microbial community structure and composition are altered in heavy-metal-contaminated soils. In this study, using full-length 16S rRNA gene sequences obtained by a PacBio RS II system, we determined the microbial diversity and community structure in heavy-metal-contaminated soil. Furthermore, we investigated the microbial distribution, inferred their putative functional traits, and analyzed the environmental effects on the microbial compositions. The soil samples selected in this study were heavily and continuously contaminated with various heavy metals due to closed mines. We found that certain microorganisms (e.g., sulfur or iron oxidizers) play an important role in the biogeochemical cycle. Using phylogenetic investigation of communities by reconstruction of unobserved states (PICRUSt) analysis, we predicted Kyoto Encyclopedia of Genes and Genomes (KEGG) functional categories from abundances of microbial communities and revealed a high proportion belonging to transport, energy metabolism, and xenobiotic degradation in the studied sites. In addition, through full-length analysis, Conexibacter-like sequences, commonly identified by environmental metagenomics among the rare biosphere, were detected. In addition to microbial composition, we confirmed that environmental factors, including heavy metals, affect the microbial communities. Unexpectedly, among these environmental parameters, electrical conductivity (EC) might have more importance than other factors in a community description analysis.

Microbial diversity in intensively farmed lake sediment contaminated by heavy metals and identification of microbial taxa bioindicators of environmental quality

“…The main contributions in community differentiation were made by the classes Alphaproteobacteria, Cyanophyceae, Betaproteobacteria, Actinobacteria and Deltaproteobacteria. These findings are similar to other studies that report these classes as the predominant classes in sediments of freshwater environments 48 . However, it differs with some studies regarding Alphaproteobacteria which report that they are generally not very abundant in freshwater environments 45 , 49 .…”

The Third-Generation Sequencing Challenge: Novel Insights for the Omic Sciences

“…Another comparative analysis revealed that the low sequencing accuracy of PacBio may impair its taxonomic resolution in the study of human gut microbiota [88]. Nevertheless, PacBio sequencing has been successfully used in several metagenomic studies and was able, for example, to identify the microbiome profiles of heavy-metal-contaminated soils [89], analyze extrachromosomal genetic elements (mainly plasmids) in the human gut [90], characterize the airway microbiome of chronic obstructive pulmonary disease patients [91], identify low-biomass human gut phageomes [92], and highlight the association between the indoor microbiome and nasal/oral humans microbiome [93]. A very recent paper by Eisenhofer and colleagues compared shotgun metagenomics carried out with both NGS and PacBio technologies and found that even if PacBio ameliorates the quality of bacterial genome assembly, it is more expensive and needs a higher sequencing coverage with respect to short reads, thus suggesting that the most optimal strategy depends on the aims of each specific project [94].…”

16S-ITGDB: An Integrated Database for Improving Species Classification of Prokaryotic 16S Ribosomal RNA Sequences

“…The single-molecule real-time (SMRT) and circular consensus sequencing (CCS) technologies developed by PacBio could provide high quality 16S full-length sequencing ( Korlach, 2013 ). During the past 5 years, a growing number of studies took the advantage of long read sequencing technology to attain more comprehensive microbial composition of the environments ( Hur and Park, 2019 ; Tremblay and Yergeau, 2019 ; Lam et al, 2020 ; Wade and Prosdocimi, 2020 ; Mahmud et al, 2021 ; Pootakham et al, 2021 ). However, although there were several widely used 16S analytical pipelines for NGS data analysis, such as QIIME2 ( Bolyen et al, 2019 ), Mothur ( Schloss, 2020 ), and UPARSE ( Edgar, 2013 ), there still lacks comprehensive and convenient 16S tools for TGS data analysis.…”