Distribution, Interaction and Functional Profiles of Epiphytic Bacterial Communities from the Rocky Intertidal Seaweeds, South Africa

Abstract

Interrelations between epiphytic bacteria and macroalgae are multifaceted and complicated, though little is known about the community structure, interaction and functions of those epiphytic bacteria. This study comprehensively characterized the epiphytic bacterial communities associated with eight different common seaweeds collected from a rocky intertidal zone on the Indian Ocean at Cape Vidal, South Africa. High-throughput sequencing analyses indicated that seaweed-associated bacterial communities were dominated by the phyla Proteobacteria, Bacteroidetes, Firmicutes, Cyanobacteria, Planctomycetes, Actinobacteria and Verrucomicrobia. Energy-dispersive X-ray (EDX) analysis showed the presence of elemental composition in the surface of examined seaweeds, in varying concentrations. Cluster analysis showed that bacterial communities of brown seaweeds (SW2 and SW4) were closely resembled those of green seaweeds (SW1) and red seaweeds (SW7) while those of brown seaweeds formed a separate branch. Predicted functional capabilities of epiphytic bacteria using PICRUSt analysis revealed abundance of genes related to metabolic and biosynthetic activities. Further important identified functional interactions included genes for bacterial chemotaxis, which could be responsible for the observed association and network of elemental-microbes interaction. The study concludes that the diversity of epiphytic bacteria on seaweed surfaces is greatly influenced by algal organic exudates as well as elemental deposits on their surfaces, which triggers chemotaxis responses from epiphytic bacteria with the requisite genes to metabolise those substrates.

Local Geomorphological Gradients and Land Use Patterns Play Key Role on the Soil Bacterial Community Diversity and Dynamics in the Highly Endemic Indigenous Afrotemperate Coastal Scarp Forest Biome

“…Cleaning of the resultant PCR product, index library preparation, pooling and sequencing on Illumina Miseq 250 ® to generate paired 300-bp high-quality reads of the V1-V3 region were performed as described by Selvarajan et al (2019). Briefly, amplified PCR product was initially cleaned using AMPure XP magnetic beads (Beckman Coulter, Massachusetts, United States) according to manufacturer's instructions.…”

“…Processing of the trimmed raw sequences (fastq files) was performed using next-generation sequencing short reads (ngsShoRT) trimmer algorithm (Chen et al, 2014) before being merged and analyzed on Mothur pipeline v.1.40.0 (Schloss et al, 2009), as described by Selvarajan et al (2019). During the analysis, sequence reads were quality filtered, chimeric sequences removed using UCHIME algorithm (Edgar et al, 2011), classifying quality reads using the Naïve Bayesian classifier algorithm (Wang et al, 2007) against the SILVA database version 132 (Quast et al, 2013).…”

Characterizing the cirri and gut microbiomes of the intertidal barnacle Semibalanus balanoides

“…However, the source of these pathways in the cirri may be due to algal associated bacteria. Both biotin and thiamine are cofactors often associated with algal-associated bacteria and with algal blooms [ 68 , 69 ]. The gut functions suggest that the microbiome may play a role in nutrition extraction within the barnacle.…”

“…On each island, our sampling was conducted in horizontal transects of ~ 20 m: LC sites were samples at vertical heights of ~ 0.10 m from sea level, HH sites were samples at vertical heights of ~ 1.8–2.0 m from sea level. In addition, we informed our collection based on tidal range information from the National Oceanic and Atmospheric Administration (NOAA) public tidal datasets [ 69 ]. We used data from the station 8416731 in Walpole, Damariscotta River, ME (Fig.…”

Distribution and comparison of bacterial communities in HVAC systems of two university buildings: Implications for indoor air quality and public health

“…Bacterial interaction networks on the predicted functional activities and taxa involved were generated using CoNet (Faust and Raes 2016), based on significant correlations among taxa determined by Spearman rank correlation, Bray-Curtis dissimilarity and Kullback-Leibler dissimilarity. The resultant p values were adjusted and merged using Benjamini-Hochberg multiple test correction (Benjamini and Hochberg 1995) and Brown's method as previously described by Selvarajan et al (2019). Cooccurrence network models generated were visualized and customised using Cytoscape v3.7.2 (Shannon et al 2003), to show the differential statistics such as the number of nodes (core OTUs), edges (associations), clustering coefficient, shortest paths, path length, density and heterogeneity between the two sampling sites.…”