Environmental Microbiome最新文献

Background: Recent endeavours in metagenomics, exemplified by projects such as the human microbiome project and TARA Oceans, have illuminated the complexities of microbial biomes. A robust bioinformatic pipeline and meticulous evaluation of their methodology have contributed to the success of these projects. The soil environment, however, with its unique challenges, requires a specialized methodological exploration to maximize microbial insights. A notable limitation in soil microbiome studies is the dearth of soil-specific reference databases available to classifiers that emulate the complexity of soil communities. There is also a lack of in-vitro mock communities derived from soil strains that can be assessed for taxonomic classification accuracy.

Results: In this study, we generated a custom in-silico mock community containing microbial genomes commonly observed in the soil microbiome. Using this mock community, we simulated shotgun sequencing data to evaluate the performance of three leading metagenomic classifiers: Kraken2 (supplemented with Bracken, using a custom database derived from GTDB-TK genomes along with its own default database), Kaiju, and MetaPhlAn, utilizing their respective default databases for a robust analysis. Our results highlight the importance of optimizing taxonomic classification parameters, database selection, as well as analysing trimmed reads and contigs. Our study showed that classifiers tailored to the specific taxa present in our samples led to fewer errors compared to broader databases including microbial eukaryotes, protozoa, or human genomes, highlighting the effectiveness of targeted taxonomic classification. Notably, an optimal classifier performance was achieved when applying a relative abundance threshold of 0.001% or 0.005%. The Kraken2 supplemented with bracken, with a custom database demonstrated superior precision, sensitivity, F1 score, and overall sequence classification. Using a custom database, this classifier classified 99% of in-silico reads and 58% of real-world soil shotgun reads, with the latter identifying previously overlooked phyla using a custom database.

Conclusion: This study underscores the potential advantages of in-silico methodological optimization in metagenomic analyses, especially when deciphering the complexities of soil microbiomes. We demonstrate that the choice of classifier and database significantly impacts microbial taxonomic profiling. Our findings suggest that employing Kraken2 with Bracken, coupled with a custom database of GTDB-TK genomes and fungal genomes at a relative abundance threshold of 0.001% provides optimal accuracy in soil shotgun metagenome analysis.

Background: Anthropogenic activities significantly contribute to the dissemination of antibiotic resistance genes (ARGs), posing a substantial threat to humankind. The development of methods that allow robust ARG surveillance is a long-standing challenge. Here, we use city-scale monitoring of ARGs by using two of the most promising cutting-edge technologies, digital PCR (dPCR) and metagenomics.

Methods: ARG hot-spots were sampled from the urban water and wastewater distribution systems. Metagenomics was used to provide a broad view of ARG relative abundance and richness in the prokaryotic and viral fractions. From the city-core ARGs in all samples, the worldwide dispersed sul2 and tetW conferring resistance to sulfonamide and tetracycline, respectively, were monitored by dPCR and metagenomics.

Results: The largest relative overall ARG abundance and richness were detected in the hospital wastewater and the WWTP inlet (up to ≈6,000 ARGs/Gb metagenome) with a large fraction of unclassified resistant bacteria. The abundance of ARGs in DNA and RNA contigs classified as viruses was notably lower, demonstrating a reduction of up to three orders of magnitude compared to contigs associated to prokaryotes. By metagenomics and dPCR, a similar abundance tendency of sul2 and tetW was obtained, with higher abundances in hospital wastewater and WWTP input (≈125-225 ARGs/Gb metagenome). dPCR absolute abundances were between 6,000 and 18,600 copies per ng of sewage DNA (≈10^5-7 copies/mL) and 6.8 copies/mL in seawater near the WWTP discharging point.

Conclusions: dPCR was more sensitive and accurate, while metagenomics provided broader coverage of ARG detection. While desirable, a reliable correlation of dPCR absolute abundance units into metagenomic relative abundance units was not obtained here (r² < 0.4) suggesting methodological factors that introduce variability. Evolutionary pressure does not significantly select the targeted ARGs in natural aquatic environments.

Background: The complex and co-evolved interplay between plants and their microbiota is crucial for the health and fitness of the plant holobiont. However, the microbiota of the seeds is still relatively unexplored and no studies have been conducted with olive trees so far. In this study, we aimed to characterize the bacterial, fungal and archaeal communities present in seeds of ten olive genotypes growing in the same orchard through amplicon sequencing to test whether the olive genotype is a major driver in shaping the seed microbial community, and to identify the origin of the latter. Therefore, we have developed a methodology for obtaining samples from the olive seed's endosphere under sterile conditions.

Results: A diverse microbiota was uncovered in olive seeds, the plant genotype being an important factor influencing the structure and composition of the microbial communities. The most abundant bacterial phylum was Actinobacteria, accounting for an average relative abundance of 41%. At genus level, Streptomyces stood out because of its potential influence on community structure. Within the fungal community, Basidiomycota and Ascomycota were the most abundant phyla, including the genera Malassezia, Cladosporium, and Mycosphaerella. The shared microbiome was composed of four bacterial (Stenotrophomonas, Streptomyces, Promicromonospora and Acidipropionibacterium) and three fungal (Malassezia, Cladosporium and Mycosphaerella) genera. Furthermore, a comparison between findings obtained here and earlier results from the root endosphere of the same trees indicated that genera such as Streptomyces and Malassezia were present in both olive compartments.

Conclusions: This study provides the first insights into the composition of the olive seed microbiota. The highly abundant fungal genus Malassezia and the bacterial genus Streptomyces reflect a unique signature of the olive seed microbiota. The genotype clearly shaped the composition of the seed's microbial community, although a shared microbiome was found. We identified genera that may translocate from the roots to the seeds, as they were present in both organs of the same trees. These findings set the stage for future research into potential vertical transmission of olive endophytes and the role of specific microbial taxa in seed germination, development, and seedling survival.