Dataset Information

Identifying Hidden Viable Bacterial Taxa in Tropical Forest Soils Using Amplicon Sequencing of Enrichment Cultures.

ABSTRACT: This study aims to estimate the proportion and diversity of soil bacteria derived from eDNA-based and culture-based methods. Specifically, we used Illumina Miseq to sequence and characterize the bacterial communities from (i) DNA extracted directly from forest soil and (ii) DNA extracted from a mixture of bacterial colonies obtained by enrichment cultures on agar plates of the same forest soil samples. The amplicon sequencing of enrichment cultures allowed us to rapidly screen a culturable community in an environmental sample. In comparison with an eDNA community (based on a 97% sequence similarity threshold), the fact that enrichment cultures could capture both rare and abundant bacterial taxa in forest soil samples was demonstrated. Enrichment culture and eDNA communities shared 2% of OTUs detected in total community, whereas 88% of enrichment cultures community (15% of total community) could not be detected by eDNA. The enrichment culture-based methods observed 17% of the bacteria in total community. FAPROTAX functional prediction showed that the rare and unique taxa, which were detected with the enrichment cultures, have potential to perform important functions in soil systems. We suggest that enrichment culture-based amplicon sequencing could be a beneficial approach to evaluate a cultured bacterial community. Combining this approach together with the eDNA method could provide more comprehensive information of a bacterial community. We expected that more unique cultured taxa could be detected if further studies used both selective and non-selective culture media to enrich bacteria at the first step.

SUBMITTER: Sansupa C

PROVIDER: S-EPMC8301126 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Forest soils provide a multitude of habitats for diverse communities of bacteria. In this study, we selected three tropical forests in Kenya to determine the diversity and community structure of soil bacteria inhabiting these regions. Kakamega and Irangi are rainforests, whereas Gazi Bay harbors mangrove forests. The three natural forests occupy different altitudinal zones and differ in their environmental characteristics. Soil samples were collected from a total of 12 sites and soil physicochemical parameters for each sampling site were analyzed. We used an amplicon-based Illumina high-throughput sequencing approach. Total community DNA was extracted from individual samples using the phenol-chloroform method. The 16S ribosomal RNA gene segment spanning the V4 region was amplified using the Illumina MiSeq platform. Diversity indices, rarefaction curves, hierarchical clustering, principal component analysis (PCA), and non-metric multidimensional scaling (NMDS) analyses were performed in R software. A total of 13,410 OTUs were observed at 97% sequence similarity. Bacterial communities were dominated by Proteobacteria, Bacteroidetes, Firmicutes, Actinobacteria, and Acidobacteria in both rainforest and mangrove sampling sites. Alpha diversity indices and species richness were higher in Kakamega and Irangi rainforests compared to mangroves in Gazi Bay. The composition of bacterial communities within and between the three forests was also significantly differentiated (R = 0.559, p = 0.007). Clustering in both PCA and NMDS plots showed that each sampling site had a distinct bacterial community profile. The NMDS analysis also indicated that soil EC, sodium, sulfur, magnesium, boron, and manganese contributed significantly to the observed variation in the bacterial community structure. Overall, this study demonstrated the presence of diverse taxa and heterogeneous community structures of soil bacteria inhabiting three tropical forests of Kenya. Our results also indicated that variation in soil chemical parameters was the major driver of the observed bacterial diversity and community structure in these forests.

Dataset Information

Identifying Hidden Viable Bacterial Taxa in Tropical Forest Soils Using Amplicon Sequencing of Enrichment Cultures.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets