Background: The ocean microbiota modulates global biogeochemical cycles and changes in its configuration may have large-scale consequences. Yet, the underlying ecological mechanisms structuring it are unclear. Here, we investigate how fundamental ecological mechanisms (selection,dispersalandecological drift) shape the smallest members of the tropical and subtropical surface-ocean microbiota: prokaryotes and minute eukaryotes (picoeukaryotes). Furthermore, we investigate the agents exerting abiotic selection on this assemblage as well as the spatial patterns emerging from the action of ecological mechanisms. To explore this, we analysed the composition of surface-ocean prokaryotic and picoeukaryotic communities using DNA-sequence data (16S- and 18S-rRNA genes) collected during the circumglobal expeditionsMalaspina-2010andTARA-Oceans.Results: We found that the two main components of the tropical and subtropical surface-ocean microbiota, prokaryotes and picoeukaryotes, appear to be structured by different ecological mechanisms. Picoeukaryotic communities were predominantly structured by dispersal-limitation, while prokaryotic counterparts appeared to be shaped by the combined action of dispersal-limitation, selection and drift. Temperature-driven selection appeared as a major factor, out of a few selected factors, influencing species co-occurrence networks in prokaryotes but not in picoeukaryotes, indicating that association patterns may contribute to understand ocean microbiota structure and response to selection. Other measured abiotic variables seemed to have limited selective effects on community structure in the tropical and subtropical ocean. Picoeukaryotes displayed a higher spatial differentiation between communities and a higher distance decay when compared to prokaryotes, consistent with a scenario of higher dispersal limitation in the former after considering environmental heterogeneity. Lastly, random dynamics ordriftseemed to have a more important role in structuring prokaryotic communities than picoeukaryotic counterparts.Conclusions: The differential action of ecological mechanisms seems to cause contrasting biogeography, in the tropical and subtropical ocean, among the smallest surface plankton, prokaryotes and picoeukaryotes. This suggests that the idiosyncrasy of the main constituents of the ocean microbiota should be considered in order to understand its current and future configuration, which is especially relevant in a context of global change, where the reaction of surface ocean plankton to temperature increase is still unclear.

Microbial interactions are crucial for Earth ecosystem function, but our knowledge about them is limited and has so far mainly existed as scattered records. Here, we have surveyed the literature involving planktonic protist interactions and gathered the information in a manually curated Protist Interaction DAtabase (PIDA). In total, we have registered ~2500 ecological interactions from ~500 publications, spanning the last 150 years. All major protistan lineages were involved in interactions as hosts, symbionts (mutualists and commensalists), parasites, predators, and/or prey. Predation was the most common interaction (39% of all records), followed by symbiosis (29%), parasitism (18%), and ‘unresolved interactions’ (14%, where it is uncertain whether the interaction is beneficial or antagonistic). Using bipartite networks, we found that protist predators seem to be ‘multivorous’ while parasite–host and symbiont–host interactions appear to have moderate degrees of specialization. The SAR supergroup (i.e., Stramenopiles, Alveolata, and Rhizaria) heavily dominated PIDA, and comparisons against a global-ocean molecular survey (TARA Oceans) indicated that several SAR lineages, which are abundant and diverse in the marine realm, were underrepresented among the recorded interactions. Despite historical biases, our work not only unveils large-scale eco-evolutionary trends in the protist interactome, but it also constitutes an expandable resource to investigate protist interactions and to test hypotheses deriving from omics tools.

Radiolarians are marine planktonic protists that belong to the eukaryote supergroup Rhizaria together with Foraminifera and Cercozoa. Radiolaria has traditionally been divided into four main groups based on morphological characters; i.e. Polycystina, Acantharia, Nassellaria and Phaeodaria. But recent 18S rDNA phylogenies have shown that Phaeodaria belongs within Cerocozoa, and that the previously heliozoan group Taxopodida should be included in Radiolaria. 18S rDNA phylogenies have not yet resolved the sister relationship between the main Radiolaria groups, but nevertheless suggests that Spumellaria, and thereby also Polycystina, are polyphyletic. Very few sequences other than 18S rDNA have so far been generated from radiolarian cells, mostly due to the fact that Radiolaria has been impossible to cultivate and single cell PCR has been hampered by low success rate. Here we have therefore investigated the mutual evolutionary relationship of the main radiolarian groups by using the novel approach of combining single cell whole genome amplification with targeted PCR amplification of the 18S and 28S rDNA genes. Combined 18S and 28S phylogeny of sequences obtained from single cells shows that Radiolaria is divided into two main lineages: Polycystina (Spumellaria+Nassellaria) and Spasmaria (Acantharia+Taxopodida). Further we show with high support that Foraminifera groups within Radiolaria supporting the Retaria hypothesis.

An understanding of land plant evolution is a prerequisite for in-depth knowledge of plant biology. Here we extract and explore information hidden in the increasing number of sequenced plant genomes, from bryophytes to angiosperms, to elucidate a specific biological question—how peptide signaling evolved. To conquer land and cope with changing environmental conditions, plants have gone through transformations that must have required innovations in cell-to-cell communication. We discuss peptides mediating endogenous and exogenous changes by interaction with receptors activating intracellular molecular signaling. Signaling peptides were discovered in angiosperms and operate in tissues and organs such as flowers, seeds, vasculature, and 3D meristems that are not universally conserved across land plants. Nevertheless, orthologs of angiosperm peptides and receptors have been identified in nonangiosperms. These discoveries provoke questions regarding coevolution of ligands and their receptors, and whether de novo interactions in peptide signaling pathways may have contributed to generate novel traits in land plants. The answers to such questions will have profound implications for the understanding of the evolution of cell-to-cell communication and the wealth of diversified terrestrial plants. Under this perspective, we have generated, analyzed, and reviewed phylogenetic, genomic, structural, and functional data to elucidate the evolution of peptide signaling.

Background: Ecological interactions among microorganisms are fundamental for ecosystem function, yet they are mostly unknown or poorly understood. High-throughput-omics can indicate microbial interactions through associations across time and space, which can be represented as association networks. Associations could result from either ecological interactions between microorganisms, or from environmental selection, where the association is environmentally driven. Therefore, before downstream analysis and interpretation, we need to distinguish the nature of the association, particularly if it is due to environmental selection or not. Results: We present EnDED (environmentally driven edge detection), an implementation of four approaches as well as their combination to predict which links between microorganisms in an association network are environmentally driven. The four approaches are sign pattern, overlap, interaction information, and data processing inequality. We tested EnDED on networks from simulated data of 50 microorganisms. The networks contained on average 50 nodes and 1087 edges, of which 60 were true interactions but 1026 false associations (i.e., environmentally driven or due to chance). Applying each method individually, we detected a moderate to high number of environmentally driven edges—87% sign pattern and overlap, 67% interaction information, and 44% data processing inequality. Combining these methods in an intersection approach resulted in retaining more interactions, both true and false (32% of environmentally driven associations). After validation with the simulated datasets, we applied EnDED on a marine microbial network inferred from 10 years of monthly observations of microbial-plankton abundance. The intersection combination predicted that 8.3% of the associations were environmentally driven, while individual methods predicted 24.8% (data processing inequality), 25.7% (interaction information), and up to 84.6% (sign pattern as well as overlap). The fraction of environmentally driven edges among negative microbial associations in the real network increased rapidly with the number of environmental factors. Conclusions: To reach accurate hypotheses about ecological interactions, it is important to determine, quantify, and remove environmentally driven associations in marine microbial association networks. For that, EnDED offers up to four individual methods as well as their combination. However, especially for the intersection combination, we suggest using EnDED with other strategies to reduce the number of false associations and consequently the number of potential interaction hypotheses.