Comparison of coastal phytoplankton composition estimated from the V4 and V9 regions of the 18S rRNA gene with a focus on photosynthetic groups and especially Chlorophyta

Tragin, Margot; Zingone, Adriana; Vaulot, Daniel

doi:10.1111/1462-2920.13952

Cited by 95 publications

(89 citation statements)

References 56 publications

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“…These discrepancies limit comparability of bacterial and fungal diversity using rRNA gene sequences. For example, in a simpler application involving only eukaryotes, Tragin et al (2018) found that V4 and V9 regions of 18S rRNA genes in phytoplankton samples had similar levels of a-diversity, but the V9 region identified 20% more OTUs. To address these issues, we constrained fungal and bacterial comparisons in four ways.…”

Section: Discussionmentioning

confidence: 99%

Spatiotemporal patterns of microbial composition and diversity in precipitation

Aho

Weber

Christner

et al. 2019

Ecological Monographs

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Microbes in the atmosphere have broad ecological impacts, including the potential to trigger precipitation through species and strains that act as ice nucleation particles. To characterize spatiotemporal trends of microbial assemblages in precipitation we sequenced 16S (bacterial) and 18S (fungal) rRNA gene amplicon libraries collected from 72 precipitation events in three U.S. states (Idaho, Louisiana, and Virginia) over four seasons. We considered these data from the perspective of a novel metacommunity framework. In agreement with our heuristic, we found evidence for distinct mechanisms underlying the composition and diversity of bacterial and fungal assemblages in precipitation. Specifically, we determined that (1) bacterial operational taxonomic unit (OTU) composition of precipitation was strongly associated with macroscale drivers including season and high-altitude characteristics of storms; (2) fungal OTU composition was strongly correlated with mesoscale drivers including particular spatial locations; (3) b-diversity (heterogeneity of taxa among samples) for both bacteria and fungi was largely maintained by turnover of taxa; however, (4) bacterial assemblages had higher contributions to total b-diversity from nestedness (i.e., lower richness assemblages were largely taxonomic subsets of richer assemblages), due to losses of taxa during dispersal, particularly among potential ice nucleation active bacteria; and (5) fungal assemblages had higher contributions to total b-diversity from turnover due to OTU replacement. Spatiotemporal trends in precipitation-borne metacommunities allowed delineation of a large number of statistically significant indicator taxa for particular sites and seasons, including trends for bacteria that are potentially ice nucleation active. Our findings advance understanding regarding the dispersion of aerosolized microbes via wet deposition, and the development of theory concerning potential assembly rules for bioaerosol assemblages.

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Section: Discussionmentioning

confidence: 99%

Spatiotemporal patterns of microbial composition and diversity in precipitation

Aho

Weber

Christner

et al. 2019

Ecological Monographs

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“…The European Ocean Sampling Day project (OSD) sampled the world coastal waters in 2014 around the summer solstice (21 June) with the aim of analyzing the diversity and distribution of marine microorganisms 7 especially in coastal waters using 18S rRNA metabarcodes (V4 and V9 hyper-variable regions) 8 . This dataset allowed to determine the distribution of fourteen classes of Chlorophyta 9 .…”

Section: Introductionmentioning

confidence: 99%

Novel diversity within marine Mamiellophyceae (Chlorophyta) unveiled by metabarcoding

Tragin

Vaulot

2018

Preprint

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The Ocean Sampling Day (OSD) project provided metabarcoding data for the V4 hyper-variable regions of the 18S rRNA gene from 157 samples collected at 143 mostly coastal stations. In this paper we focus on the class Mamiellophyceae, which was found at nearly all OSD stations and represented 55 % of the green microalgae (Chlorophyta) reads in the 2014 OSD dataset. We performed phylogenetic analyses of unique OSD metabarcodes (ASV, amplicon single variants) and reference GenBank sequences from cultures and from the environment, focusing on the four most represented genera: Ostreococcus (45 % of the Mamiellophyceae reads), Micromonas (34 %), Bathycoccus (10 %) and Mantoniella (8.7 %).These analyses uncovered novel diversity within each genus except Bathycoccus. In Ostreococcus, a new clade (E) with 2 very clear base pair differences compared to the oceanic clade B in the V4 region was the second most represented clade after the coastal Ostreococcus "lucimarinus". Within Micromonas, ten clades were found exceeding the 4 species and 2 candidate species already described. Finally, we found 2 new environmental clades of Mantoniella. Each Mamiellophyceae clade had a specific distribution in the OSD dataset suggesting that they are adapted to different ecological niches. Monomastigales 10 . Monomastigales are confined to freshwater environments while Dolichomasti-20 gales, although quite diversified in marine waters 12, 13 , are a minor component of Mamiel-21 lophyceae in coastal waters. In contrast, Mamiellales and Bathycoccales host some of the most 22 common Chlorophyta microalgae such as the ubiquitous Micromonas, the smallest known eu-23 karyote Ostreococcus or the coccoid Bathycoccus 10 . Within Mamiellales, Micromonas pusilla 14 24 was recently split into four species: Micromonas bravo (previously clade B.E.3), Micromonas com-25 moda (previous clade A.ABC.1-2), Micromonas polaris (previously clade B arctic), Micromonas 26 pusilla (previously clade C.D.5) and two clades mentioned as candidate species 1 (clade B. .4) 27 2/34 and candidate species 2 (clade B warm) 15 . Within the genus Mantoniella, only two species 28 have been described: the ubiquitous Mantoniella squamata 3, 16 , first described as Micromonas 29 squamata 17 , and Mantoniella antarctica 18 . Within Bathycoccales, four Ostreococcus clades have 30 been delineated 19 : Ostreococcus tauri 20 , Ostreococcus mediterraneus 21 , both of which have 31 been formerly described, Ostreococcus "lucimarinus" (clade A) and clade B, which both lack 32 formal taxonomic description. Analyses of pigment content and response to light levels allowed 33 to distinguish two broad ecotypes: strains adapted to high (O. tauri,O. mediterraneus, O. "luci-34 marinus" ) and low (Ostreococcus clade B) light 22 . The second genus within Bathycoccales hosts 35 a single species, Bathycoccus prasinos 23 . No clades can be delineated inside this species based 36 on 18S rRNA gene sequences from cultures and the environment. However, divergence in ITS 37 sequences suggest that Bathycoccus p...

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“…The short and hyper-variable V4 and V9 fragments only provide partial phylogenetic signal of the full SSU-rRNA locus , which is in-turn, only an approximation of the real protistan phylogenetic relatedness as assessed with whole genome sequencing (Burki, 2014). Besides, the genetic distances estimated between these two hyper-variable regions can be the same or drastically different depending on which taxa are being compared (Dunthorn, Klier, Bunge, & Stoeck, 2012;Hu et al, 2015;Tragin, Zingone, & Vaulot, 2018). The congruent results for protistan co-occurrences and co-exclusions derived from both pairwise sequence distances and phylogenetic distances shows that both type of distances can be used to infer phylogenetic relatedness.…”

Section: Discussionmentioning

confidence: 94%

Relating network analyses to phylogenetic relatedness to infer protistan co-occurrences and co-exclusions in marine and terrestrial environments

Lentendu

Dunthorn

2020

Preprint

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We used two large-scale metabarcoding datasets to evaluate phylogenetic signals at global marine and regional terrestrial scales using co-occurrence and co-exclusion networks. Phylogenetic relatedness was estimated using either global pairwise sequence distance or phylogenetic distance and the significance of observed patterns relating networks and phylogenies were evaluated against two null models. In all datasets, we found that phylogenetically close OTUs significantly cooccurred more often, and OTUs with intermediate phylogenetic relatedness co-occurred less often, than expected by chance. Phylogenetically close OTUs co-excluded less often than expected by chance in the marine datasets only. Simultaneous excess of co-occurrences and co-exclusions were observed in the inversion zone between close and intermediate phylogenetic distance classes in marine surface. Similar patterns were observed by using either pairwise sequence or phylogenetic distances, and by using both null models. These results suggest that environmental filtering and dispersal limitation are the preponderant forces driving co-occurrence of protists in both environments, while signal of competitive exclusion was only detected in the marine surface environment. The discrepancy in the co-exclusion pattern is potentially linked to the individual 1 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 environments: water bodies are more homogeneous while tropical forest soils contain a myriad of nutrient rich micro-environment reducing the strength of mutual exclusion.

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Comparison of coastal phytoplankton composition estimated from the V4 and V9 regions of the 18S rRNA gene with a focus on photosynthetic groups and especially Chlorophyta

Cited by 95 publications

References 56 publications

Spatiotemporal patterns of microbial composition and diversity in precipitation

Spatiotemporal patterns of microbial composition and diversity in precipitation

Novel diversity within marine Mamiellophyceae (Chlorophyta) unveiled by metabarcoding

Relating network analyses to phylogenetic relatedness to infer protistan co-occurrences and co-exclusions in marine and terrestrial environments

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