What drives study‐dependent differences in distance–decay relationships of microbial communities?

Clark, Dave R.; Underwood, Graham J. C.; McGenity, Terry J.; Dumbrell, Alex J.

doi:10.1111/geb.13266

Cited by 72 publications

(57 citation statements)

References 88 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Geographical distance was generated using the ‘geosphere’ package ( https://cran.r-project.org/web/packages/geosphere/index.html ). The relationship between Bray-Curtis dissimilarity of microbial communities [ 94 ] involved in DMSP/DMS cycling and geographic distance or water depth were analysed using the Mantel test. Alpha-diversity analysis was performed on polar microbiota involved in DMS/DMSP cycling.…”

Section: Methodsmentioning

confidence: 99%

Biogeographic traits of dimethyl sulfide and dimethylsulfoniopropionate cycling in polar oceans

et al. 2021

View full text Add to dashboard Cite

Background Dimethyl sulfide (DMS) is the dominant volatile organic sulfur in global oceans. The predominant source of oceanic DMS is the cleavage of dimethylsulfoniopropionate (DMSP), which can be produced by marine bacteria and phytoplankton. Polar oceans, which represent about one fifth of Earth’s surface, contribute significantly to the global oceanic DMS sea-air flux. However, a global overview of DMS and DMSP cycling in polar oceans is still lacking and the key genes and the microbial assemblages involved in DMSP/DMS transformation remain to be fully unveiled. Results Here, we systematically investigated the biogeographic traits of 16 key microbial enzymes involved in DMS/DMSP cycling in 60 metagenomic samples from polar waters, together with 174 metagenome and 151 metatranscriptomes from non-polar Tara Ocean dataset. Our analyses suggest that intense DMS/DMSP cycling occurs in the polar oceans. DMSP demethylase (DmdA), DMSP lyases (DddD, DddP, and DddK), and trimethylamine monooxygenase (Tmm, which oxidizes DMS to dimethylsulfoxide) were the most prevalent bacterial genes involved in global DMS/DMSP cycling. Alphaproteobacteria (Pelagibacterales) and Gammaproteobacteria appear to play prominent roles in DMS/DMSP cycling in polar oceans. The phenomenon that multiple DMS/DMSP cycling genes co-occurred in the same bacterial genome was also observed in metagenome assembled genomes (MAGs) from polar oceans. The microbial assemblages from the polar oceans were significantly correlated with water depth rather than geographic distance, suggesting the differences of habitats between surface and deep waters rather than dispersal limitation are the key factors shaping microbial assemblages involved in DMS/DMSP cycling in polar oceans. Conclusions Overall, this study provides a global overview of the biogeographic traits of known bacterial genes involved in DMS/DMSP cycling from the Arctic and Antarctic oceans, laying a solid foundation for further studies of DMS/DMSP cycling in polar ocean microbiome at the enzymatic, metabolic, and processual levels.

show abstract

Section: Methodsmentioning

confidence: 99%

Biogeographic traits of dimethyl sulfide and dimethylsulfoniopropionate cycling in polar oceans

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Determining co-assemblies using metagenomic distances In (13), the authors studied the abundance of diazotrophic bacteria in oceanic surface metagenomes and showed that nitrogen fixation is an important feature of the prokaryotic communities living in ocean surface. As microbial genetic distances often co-vary with geographic distances in several habitats (27), co-assemblies were performed based on the geographic coordinates of the metagenomes, i.e. metagenomes belonging to the same oceanic region were co-assembled.…”

Section: Resultsmentioning

confidence: 99%

MAGNETO: an automated workflow for genome-resolved metagenomics

Churcheward¹,

Millet²,

Bihouée

et al. 2022

Preprint

View full text Add to dashboard Cite

Metagenome-Assembled Genomes (MAGs) represent individual genomes recovered from metagenomic data. MAGs are extremely useful to analyse uncultured microbial genomic diversity, as well as to characterize associated functional and metabolic potential in natural environments. Recent computational developments have considerably improved MAGs reconstruction but also emphasized several limitations, such as the non-binning of sequence regions with repetitions or distinct nucleotidic composition. Different assembly and binning strategies are often used, however, it still remains unclear which assembly strategy in combination with which binning approach, offers the best performance for MAGs recovery. Several workflows have been proposed in order to reconstruct MAGs, but users are usually limited to single-metagenome assembly or need to manually define sets of metagenomes to co-assemble prior to genome binning. Here, we present MAGNETO, an automated workflow dedicated to MAGs reconstruction, which includes a fully-automated co-assembly step informed by optimal clustering of metagenomic distances, and implements complementary genome binning strategies, for improving MAGs recovery. MAGNETO is implemented as a Snakemake workflow and is available at: https://gitlab.univ-nantes.fr/bird_pipeline_registry/magneto.IMPORTANCEGenome-resolved metagenomics has led to the discovery of previously untapped biodiversity within the microbial world. As the development of computational methods for the recovery of genomes from metagenomes continues, existing strategies need to be evaluated and compared to eventually lead to standardized computational workflows. In this study, we compared commonly used assembly and binning strategies and assessed their performance using both simulated and real metagenomic datasets. We propose a novel approach to automate co-assembly, avoiding the requirement for a priori knowledge to combine metagenomic information. The comparison against a previous co-assembly approach demonstrates a strong impact of this step on genome binning results, but also the benefits of informing co-assembly for improving the quality of recovered genomes. MAGNETO integrates complementary assembly-binning strategies to optimize genome reconstruction and provides a complete reads-to-genomes workflow for the growing microbiome research community.

show abstract

“…Concerning microorganisms, a large proportion of studies that investigate the effect of connectivity on microorganisms focus on habitat isolation, the inverse of structural connectivity, using the Euclidean distance, assuming that the landscape matrix does not matter. These studies generally report the well‐known distance‐decay pattern predicted by the biogeography theory (see Clark et al 2021 for a meta‐analysis). For instance, Peay et al (2010) used two types of metrics to test the effects of connectivity on fungal species richness on a single tree species, including distance to the mainland and distance to the nearest individual.…”

Section: Modeling Connectivity In Microorganisms: From Biogeography T...mentioning

confidence: 89%

Landscape connectivity for the invisibles

et al. 2022

View full text Add to dashboard Cite

Because of land use changes, a worldwide decrease in biodiversity is underway, mostly driven by habitat degradation and fragmentation. Increasing landscape connectivity (i.e. the degree to which the landscape facilitates movement between habitat patches) has been proposed as a key landscape-level strategy to counterbalance the negative effects of habitat fragmentation. A robust theoretical and methodological framework has been developed for the concept of connectivity, and an increasing body of empirical evidence supports the relevance of connectivity for biodiversity. However, the framework was built ignoring species that represent the dominant proportion of biodiversity on earth: microorganisms. The extent to which the existing conceptual and methodological frameworks on connectivity can be applied to microorganisms remain unknown. We reviewed existing evidence and analyzed methods to test the influence of connectivity on microorganisms. We included all types of microorganisms, from symbiotic to pathogenic and free-living microorganisms, across all ecosystems. We describe the effect of connectivity on microorganism populations and communities, and identify the limitations and large gaps in current knowledge. Microorganisms can differ from macroorganisms in their response to connectivity due to short (distance less than a meter) dispersal distance of some groups, longer time lag of microorganisms response (possibly accompanied by evolutionary processes) and host association. The latter relies on tight interactions and feedback effects that drive microbial-landscape relationships and lead to possible coadaptation processes. Incorporating the connectivity concept in microbial community assembly rules to preserve the diversity of microbial communities and the ecosystem services they provide could be a crucial step forward in the face of pressing global changes.

show abstract

What drives study‐dependent differences in distance–decay relationships of microbial communities?

Cited by 72 publications

References 88 publications

Biogeographic traits of dimethyl sulfide and dimethylsulfoniopropionate cycling in polar oceans

Biogeographic traits of dimethyl sulfide and dimethylsulfoniopropionate cycling in polar oceans

MAGNETO: an automated workflow for genome-resolved metagenomics

Landscape connectivity for the invisibles

Contact Info

Product

Resources

About