Description: The South Pacific Gyre (SPG) covers 10% of the ocean’s surface and is often regarded as a marine biological desert. To gain an on-site overview of the remote, ultraoligotrophic microbial community of the SPG, we developed a novel onboard analysis pipeline, which combines next-generation sequencing with fluorescence in situ hybridization and automated cell enumeration. We tested the pipeline during the SO-245 “UltraPac” cruise from Chile to New Zealand and found that the overall microbial community of the SPG was highly similar to those of other oceanic gyres. The SPG was dominated by 20 major bacterial clades, including SAR11, SAR116, the AEGEAN-169 marine group, SAR86, Prochlorococcus, SAR324, SAR406, and SAR202. Most of the bacterial clades showed a strong vertical (20 m to 5,000 m), but only a weak longitudinal (80°W to 160°W), distribution pattern. Surprisingly, in the central gyre, Prochlorococcus, the dominant photosynthetic organism, had only low cellular abundances in the upper waters (20 to 80 m) and was more frequent around the 1% irradiance zone (100 to 150 m). Instead, the surface waters of the central gyre were dominated by the SAR11, SAR86, and SAR116 clades known to harbor light-driven proton pumps. The alphaproteobacterial AEGEAN-169 marine group was particularly abundant in the surface waters of the central gyre, indicating a potentially interesting adaptation to ultraoligotrophic waters and high solar irradiance. In the future, the newly developed community analysis pipeline will allow for on-site insights into a microbial community within 35 h of sampling, which will permit more targeted sampling efforts and hypothesis-driven research.

Description: BACKGROUND: Several fungi-specific primers target the 18S rRNA gene sequence, one of the prominent markers for fungal classification. The design of most primers goes back to the last decades. Since then, the number of sequences in public databases increased leading to the discovery of new fungal groups and changes in fungal taxonomy. However, no reevaluation of primers was carried out and relevant information on most primers is missing. With this study, we aimed to develop an 18S rRNA gene sequence primer toolkit allowing an easy selection of the best primer pair appropriate for different sequencing platforms, research aims (biodiversity assessment versus isolate classification) and target groups. RESULTS: We performed an intensive literature research, reshuffled existing primers into new pairs, designed new Illumina-primers, and annealing blocking oligonucleotides. A final number of 439 primer pairs were subjected to in silico PCRs. Best primer pairs were selected and experimentally tested. The most promising primer pair with a small amplicon size, nu-SSU-1333-5'/nu-SSU-1647-3' (FF390/FR-1), was successful in describing fungal communities by Illumina sequencing. Results were confirmed by a simultaneous metagenomics and eukaryote-specific primer approach. Co-amplification occurred in all sample types but was effectively reduced by blocking oligonucleotides. CONCLUSIONS: The compiled data revealed the presence of an enormous diversity of fungal 18S rRNA gene primer pairs in terms of fungal coverage, phylum spectrum and co-amplification. Therefore, the primer pair has to be carefully selected to fulfill the requirements of the individual research projects. The presented primer toolkit offers comprehensive lists of 164 primers, 439 primer combinations, 4 blocking oligonucleotides, and top primer pairs holding all relevant information including primer's characteristics and performance to facilitate primer pair selection.

Description: BACKGROUND: Metagenomics caused a quantum leap in microbial ecology. However, the inherent size and complexity of metagenomic data limit its interpretation. The quantification of metagenomic traits in metagenomic analysis workflows has the potential to improve the exploitation of metagenomic data. Metagenomic traits are organisms' characteristics linked to their performance. They are measured at the genomic level taking a random sample of individuals in a community. As such, these traits provide valuable information to uncover microorganisms' ecological patterns. The Average Genome Size (AGS) and the 16S rRNA gene Average Copy Number (ACN) are two highly informative metagenomic traits that reflect microorganisms' ecological strategies as well as the environmental conditions they inhabit. RESULTS: Here, we present the ags.sh and acn.sh tools, which analytically derive the AGS and ACN metagenomic traits. These tools represent an advance on previous approaches to compute the AGS and ACN traits. Benchmarking shows that ags.sh is up to 11 times faster than state-of-the-art tools dedicated to the estimation AGS. Both ags.sh and acn.sh show comparable or higher accuracy than existing tools used to estimate these traits. To exemplify the applicability of both tools, we analyzed the 139 prokaryotic metagenomes of TARA Oceans and revealed the ecological strategies associated with different water layers. CONCLUSION: We took advantage of recent advances in gene annotation to develop the ags.sh and acn.sh tools to combine easy tool usage with fast and accurate performance. Our tools compute the AGS and ACN metagenomic traits on unassembled metagenomes and allow researchers to improve their metagenomic data analysis to gain deeper insights into microorganisms' ecology. The ags.sh and acn.sh tools are publicly available using Docker container technology at https://github.com/pereiramemo/AGS-and-ACN-tools .