The dynamic trophic architecture of open-ocean protist communities revealed through machine-guided metatranscriptomics

Predicting metabolic modules in incomplete bacterial genomes with MetaPathPredict

The reconstruction of complete microbial metabolic pathways using 'omics data from environmental samples remains challenging. Computational pipelines for pathway reconstruction that utilize machine learning methods to predict the presence or absence of KEGG modules in incomplete genomes are lacking. Here, we present MetaPathPredict, a software tool that incorporates machine learning models to predict the presence of complete KEGG modules within bacterial genomic datasets. Using gene annotation data and information from the KEGG module database, MetaPathPredict employs deep learning models to predict the presence of KEGG modules in a genome. MetaPathPredict can be used as a command line tool or as a Python module, and both options are designed to be run locally or on a compute cluster. Benchmarks show that MetaPathPredict makes robust predictions of KEGG module presence within highly incomplete genomes.

Prevalence and Preferred Niche of Small Eukaryotes with Mixotrophic Potentials in the Global Ocean

Unicellular eukaryotes that are capable of phago-mixotrophy in the ocean compete for inorganic nutrients and light with autotrophs, and for bacterial prey with heterotrophs. In this study, we ask what the overall prevalence of eukaryotic mixotrophs in the vast open ocean is, and how the availability of inorganic nutrients, light, and prey affects their relative success. We utilized the Tara Oceans eukaryotic 18S rRNA gene and environmental context variables dataset to conduct a large-scale field analysis. We also performed isolate-based culture experiments to verify growth and nutritional resource relationships for representative mixotrophic taxa. The field analysis suggested that the overall prevalence of mixotrophs were negatively correlated with nutrient concentrations and positively associated with light availability. Concentrations of heterotrophic bacteria as a single variable also presented a positive correlation with mixotrophic prevalence, but to a lesser extent. On the other hand, the culture experiments demonstrated a taxa-specific relationship between mixotrophic growth and nutrition resources, i.e., the growth of one group was significantly dependent on light availability, while the other group was less affected by light when they received sufficient prey. Both groups were capable of growing efficiently with low inorganic nutrients when receiving sufficient prey and light. Therefore, our field analysis and culture experiments both suggest that phago-mixotrophy for ocean eukaryotes is seemingly an efficient strategy to compensate for nutrient deficiency but unnecessary to compensate for light scarcity. This study collectively revealed a close relationship between abiotic and biotic nutritional resources and the prevalence of trophic strategies, shedding light on the importance of light and nutrients for determining the competitive success of mixotrophs versus autotrophic and heterotrophic eukaryotes in the ocean.

Metagenomics-Based Microbial Ecological Community Threshold and Indicators of Anthropogenic Disturbances in Estuarine Sediments

Assessing the impacts of cumulative anthropogenic disturbances on estuarine ecosystem health is challenging. Using spatially distributed sediments from the Pearl River Estuary (PRE) in southern China, which are significantly influenced by anthropogenic activities, we demonstrated that metagenomics-based surveillance of benthic microbial communities is a robust approach to assess anthropogenic impacts on estuarine benthic ecosystems. Correlational and threshold analyses between microbial compositions and environmental conditions indicated that anthropogenic disturbances in the PRE sediments drove the taxonomic and functional variations in the benthic microbial communities. An ecological community threshold of anthropogenic disturbances was identified, which delineated the PRE sediments into two groups (H and L) with distinct taxa and functional traits. Group H, located nearshore and subjected to a higher level of anthropogenic disturbances, was enriched with pollutant degraders, putative human pathogens, fecal pollution indicators, and functional traits related to stress tolerance. In contrast, Group L, located offshore and subjected to a lower level of anthropogenic disturbances, was enriched with halotolerant and oligotrophic taxa and functional traits related to growth and resource acquisition. The machine learning random forest model identified a number of taxonomic and functional indicators that could differentiate PRE sediments between Groups H and L. The identified ecological community threshold and microbial indicators highlight the utility of metagenomics-based microbial surveillance in assessing the adverse impacts of anthropogenic disturbances in estuarine sediments, which can assist environmental management to better protect ecosystem health.

Protistan community composition and metabolism in the North Pacific Subtropical Gyre: Influences of mesoscale eddies and depth

Marine protists and their metabolic activities are intricately tied to the cycling of nutrients and the flow of energy through microbial food webs. Physiochemical changes in the environment, such as those that result from mesoscale eddies, may impact protistan communities, but the effects that such changes have on protists are poorly known. A metatranscriptomic study was conducted to investigate how eddies affected protists at adjacent cyclonic and anticyclonic eddy sites in the oligotrophic ocean at four depths from 25 to 250 m. Eddy polarity impacted protists at all depths sampled, although the effects of eddy polarity were secondary to the impact of depth across the depth range. Eddy-induced vertical shifts in the water column yielded differences in the cyclonic and anticyclonic eddy protistan communities, and these differences were the most pronounced at and just below the deep chlorophyll maximum. An analysis of transcripts associated with protistan nutritional physiology at 150 m revealed that cyclonic eddies may support a more heterotrophic community, while anticyclonic eddies promote a more phototrophic community. The results of this study indicate that eddies alter the metabolism of protists particularly in the lower euphotic zone and may therefore impact carbon export from the euphotic zone.

High amino acid osmotrophic incorporation by marine eukaryotic phytoplankton revealed by click chemistry

The osmotrophic uptake of dissolved organic compounds in the ocean is considered to be dominated by heterotrophic prokaryotes, whereas the role of planktonic eukaryotes is still unclear. We explored the capacity of natural eukaryotic plankton communities to incorporate the synthetic amino acid L-homopropargylglycine (HPG, analogue of methionine) using biorthogonal noncanonical amino acid tagging (BONCAT), and we compared it with prokaryotic HPG use throughout a 9-day survey in the NW Mediterranean. BONCAT allows to fluorescently identify translationally active cells, but it has never been applied to natural eukaryotic communities. We found a large diversity of photosynthetic and heterotrophic eukaryotes incorporating HPG into proteins, with dinoflagellates and diatoms showing the highest percentages of BONCAT-labelled cells (49 ± 25% and 52 ± 15%, respectively). Among them, pennate diatoms exhibited higher HPG incorporation in the afternoon than in the morning, whereas small (≤5 μm) photosynthetic eukaryotes and heterotrophic nanoeukaryotes showed the opposite pattern. Centric diatoms (e.g. Chaetoceros, Thalassiosira, and Lauderia spp.) dominated the eukaryotic HPG incorporation due to their high abundances and large sizes, accounting for up to 86% of the eukaryotic BONCAT signal and strongly correlating with bulk 3H-leucine uptake rates. When including prokaryotes, eukaryotes were estimated to account for 19-31% of the bulk BONCAT signal. Our results evidence a large complexity in the osmotrophic uptake of HPG, which varies over time within and across eukaryotic groups and highlights the potential of BONCAT to quantify osmotrophy and protein synthesis in complex eukaryotic communities.

MarFERReT, an open-source, version-controlled reference library of marine microbial eukaryote functional genes

Metatranscriptomics generates large volumes of sequence data about transcribed genes in natural environments. Taxonomic annotation of these datasets depends on availability of curated reference sequences. For marine microbial eukaryotes, current reference libraries are limited by gaps in sequenced organism diversity and barriers to updating libraries with new sequence data, resulting in taxonomic annotation of about half of eukaryotic environmental transcripts. Here, we introduce Marine Functional EukaRyotic Reference Taxa (MarFERReT), a marine microbial eukaryotic sequence library designed for use with taxonomic annotation of eukaryotic metatranscriptomes. We gathered 902 publicly accessible marine eukaryote genomes and transcriptomes and assessed their sequence quality and cross-contamination issues, selecting 800 validated entries for inclusion in MarFERReT. Version 1.1 of MarFERReT contains reference sequences from 800 marine eukaryotic genomes and transcriptomes, covering 453 species- and strain-level taxa, totaling nearly 28 million protein sequences with associated NCBI and PR2 Taxonomy identifiers and Pfam functional annotations. The MarFERReT project repository hosts containerized build scripts, documentation on installation and use case examples, and information on new versions of MarFERReT.

Eukaryotic genomes from a global metagenomic data set illuminate trophic modes and biogeography of ocean plankton

Metagenomics is a powerful method for interpreting the ecological roles and physiological capabilities of mixed microbial communities. Yet, many tools for processing metagenomic data are neither designed to consider eukaryotes nor are they built for an increasing amount of sequence data. EukHeist is an automated pipeline to retrieve eukaryotic and prokaryotic metagenome-assembled genomes (MAGs) from large-scale metagenomic sequence data sets. We developed the EukHeist workflow to specifically process large amounts of both metagenomic and/or metatranscriptomic sequence data in an automated and reproducible fashion. Here, we applied EukHeist to the large-size fraction data (0.8-2,000 µm) from Tara Oceans to recover both eukaryotic and prokaryotic MAGs, which we refer to as TOPAZ (Tara Oceans Particle-Associated MAGs). The TOPAZ MAGs consisted of >900 environmentally relevant eukaryotic MAGs and >4,000 bacterial and archaeal MAGs. The bacterial and archaeal TOPAZ MAGs expand upon the phylogenetic diversity of likely particle- and host-associated taxa. We use these MAGs to demonstrate an approach to infer the putative trophic mode of the recovered eukaryotic MAGs. We also identify ecological cohorts of co-occurring MAGs, which are driven by specific environmental factors and putative host-microbe associations. These data together add to a number of growing resources of environmentally relevant eukaryotic genomic information. Complementary and expanded databases of MAGs, such as those provided through scalable pipelines like EukHeist, stand to advance our understanding of eukaryotic diversity through increased coverage of genomic representatives across the tree of life.IMPORTANCESingle-celled eukaryotes play ecologically significant roles in the marine environment, yet fundamental questions about their biodiversity, ecological function, and interactions remain. Environmental sequencing enables researchers to document naturally occurring protistan communities, without culturing bias, yet metagenomic and metatranscriptomic sequencing approaches cannot separate individual species from communities. To more completely capture the genomic content of mixed protistan populations, we can create bins of sequences that represent the same organism (metagenome-assembled genomes [MAGs]). We developed the EukHeist pipeline, which automates the binning of population-level eukaryotic and prokaryotic genomes from metagenomic reads. We show exciting insight into what protistan communities are present and their trophic roles in the ocean. Scalable computational tools, like EukHeist, may accelerate the identification of meaningful genetic signatures from large data sets and complement researchers' efforts to leverage MAG databases for addressing ecological questions, resolving evolutionary relationships, and discovering potentially novel biodiversity.

PLM-ARG: antibiotic resistance gene identification using a pretrained protein language model

MOTIVATION

Antibiotic resistance presents a formidable global challenge to public health and the environment. While considerable endeavors have been dedicated to identify antibiotic resistance genes (ARGs) for assessing the threat of antibiotic resistance, recent extensive investigations using metagenomic and metatranscriptomic approaches have unveiled a noteworthy concern. A significant fraction of proteins defies annotation through conventional sequence similarity-based methods, an issue that extends to ARGs, potentially leading to their under-recognition due to dissimilarities at the sequence level.

RESULTS

Herein, we proposed an Artificial Intelligence-powered ARG identification framework using a pretrained large protein language model, enabling ARG identification and resistance category classification simultaneously. The proposed PLM-ARG was developed based on the most comprehensive ARG and related resistance category information (>28K ARGs and associated 29 resistance categories), yielding Matthew's correlation coefficients (MCCs) of 0.983 ± 0.001 by using a 5-fold cross-validation strategy. Furthermore, the PLM-ARG model was verified using an independent validation set and achieved an MCC of 0.838, outperforming other publicly available ARG prediction tools with an improvement range of 51.8%-107.9%. Moreover, the utility of the proposed PLM-ARG model was demonstrated by annotating resistance in the UniProt database and evaluating the impact of ARGs on the Earth's environmental microbiota.

AVAILABILITY AND IMPLEMENTATION

PLM-ARG is available for academic purposes at https://github.com/Junwu302/PLM-ARG, and a user-friendly webserver (http://www.unimd.org/PLM-ARG) is also provided.

Movement of protistan trophic groups in soil-plant continuums

Protists, functionally divided into consumers, phototrophs, and parasites act as integral components and vital regulators of microbiomes in soil-plant continuums. However, the drivers of community structure, assembly mechanisms, co-occurrence patterns, and the associations with human pathogens and different protistan trophic groups remain unknown. Here, we characterized the phyllosphere and soil protistan communities associated with three vegetables under different fertilization treatments (none and organic fertilization) at five growth stages. In this study, consumers were the most diverse soil protist group, had the role of inter-kingdom connector, and were the primary biomarker for rhizosphere soils which were subjected to decreasing deterministic processes during plant growth. In contrast, phototrophs had the greatest niche breadth and formed soil protistan hubs, and were the primary biomarkers for both bulk soils and the phyllosphere. Parasites had minimal input to microbial co-occurrence networks. Organic fertilization increased the relative abundance (RA) of pathogenic protists and the number of pathogen-consumer connections in rhizosphere soils but decreased protistan richness and the number of internal protistan links. This study advances our understanding of the ecological roles and potential links between human pathogens and protistan trophic groups associated with soil-plant continuums, which is fundamental to the regulation of soil-plant microbiomes and maintenance of environmental and human health.

Extreme environments offer an unprecedented opportunity to understand microbial eukaryotic ecology, evolution, and genome biology

Research in extreme environments has substantially expanded our understanding of the ecology and evolution of life on Earth, but a major group of organisms has been largely overlooked: microbial eukaryotes (i.e., protists). In this Perspective, we summarize data from over 80 studies of protists in extreme environments and identify focal lineages that are of significant interest for further study, including clades within Echinamoebida, Heterolobosea, Radiolaria, Haptophyta, Oomycota, and Cryptophyta. We argue that extreme environments are prime sampling targets to fill gaps in the eukaryotic tree of life and to increase our understanding of the ecology, metabolism, genome architecture, and evolution of eukaryotic life.

Assembly processes and functional diversity of marine protists and their rare biosphere

BACKGROUND

The mechanisms shaping the rare microbial biosphere and its role in ecosystems remain unclear. We developed an approach to study ecological patterns in the rare biosphere and use it on a vast collection of marine microbiomes, sampled in coastal ecosystems at a regional scale. We study the assembly processes, and the ecological strategies constituting the rare protistan biosphere. Using the phylogeny and morpho-trophic traits of these protists, we also explore their functional potential.

RESULTS

Taxonomic community composition remained stable along rank abundance curves. Conditionally rare taxa, driven by selection processes, and transiently rare taxa, with stochastic distributions, were evidenced along the rank abundance curves of all size-fractions. Specific taxa within the divisions Sagenista, Picozoa, Telonemia, and Choanoflagellida were rare across time and space. The distribution of traits along rank abundance curves outlined a high functional redundancy between rare and abundant protists. Nevertheless, trophic traits illustrated an interplay between the trophic groups of different size-fractions.

CONCLUSIONS

Our results suggest that rare and abundant protists are evolutionary closely related, most notably due to the high microdiversity found in the rare biosphere. We evidenced a succession of assembly processes and strategies of rarity along rank abundance curves that we hypothesize to be common to most microbiomes at the regional scale. Despite high functional redundancy in the rare protistan biosphere, permanently rare protists were evidenced, and they could play critical functions as bacterivores and decomposers from within the rare biosphere. Finally, changes in the composition of the rare protistan biosphere could be influenced by the trophic regime of aquatic ecosystems. Our work contributes to understanding the role of rare protists in microbiomes.

Mixoplankton and mixotrophy: future research priorities

Phago-mixotrophy, the combination of photoautotrophy and phagotrophy in mixoplankton, organisms that can combine both trophic strategies, have gained increasing attention over the past decade. It is now recognized that a substantial number of protistan plankton species engage in phago-mixotrophy to obtain nutrients for growth and reproduction under a range of environmental conditions. Unfortunately, our current understanding of mixoplankton in aquatic systems significantly lags behind our understanding of zooplankton and phytoplankton, limiting our ability to fully comprehend the role of mixoplankton (and phago-mixotrophy) in the plankton food web and biogeochemical cycling. Here, we put forward five research directions that we believe will lead to major advancement in the field: (i) evolution: understanding mixotrophy in the context of the evolutionary transition from phagotrophy to photoautotrophy; (ii) traits and trade-offs: identifying the key traits and trade-offs constraining mixotrophic metabolisms; (iii) biogeography: large-scale patterns of mixoplankton distribution; (iv) biogeochemistry and trophic transfer: understanding mixoplankton as conduits of nutrients and energy; and (v) in situ methods: improving the identification of in situ mixoplankton and their phago-mixotrophic activity.

Gene expression dynamics of natural assemblages of heterotrophic flagellates during bacterivory

BACKGROUND

Marine heterotrophic flagellates (HF) are dominant bacterivores in the ocean, where they represent the trophic link between bacteria and higher trophic levels and participate in the recycling of inorganic nutrients for regenerated primary production. Studying their activity and function in the ecosystem is challenging since most of the HFs in the ocean are still uncultured. In the present work, we investigated gene expression of natural HF communities during bacterivory in four unamended seawater incubations.

RESULTS

The most abundant species growing in our incubations belonged to the taxonomic groups MAST-4, MAST-7, Chrysophyceae, and Telonemia. Gene expression dynamics were similar between incubations and could be divided into three states based on microbial counts, each state displaying distinct expression patterns. The analysis of samples where HF growth was highest revealed some highly expressed genes that could be related to bacterivory. Using available genomic and transcriptomic references, we identified 25 species growing in our incubations and used those to compare the expression levels of these specific genes. Video Abstract CONCLUSIONS: Our results indicate that several peptidases, together with some glycoside hydrolases and glycosyltransferases, are more expressed in phagotrophic than in phototrophic species, and thus could be used to infer the process of bacterivory in natural assemblages.

Trophic strategies explain the ocean niches of small eukaryotic phytoplankton

A large fraction of marine primary production is performed by diverse small protists, and many of these phytoplankton are phagotrophic mixotrophs that vary widely in their capacity to consume bacterial prey. Prior analyses suggest that mixotrophic protists as a group vary in importance across ocean environments, but the mechanisms leading to broad functional diversity among mixotrophs, and the biogeochemical consequences of this, are less clear. Here we use isolates from seven major taxa to demonstrate a tradeoff between phototrophic performance (growth in the absence of prey) and phagotrophic performance (clearance rate when consuming Prochlorococcus). We then show that trophic strategy along the autotrophy-mixotrophy spectrum correlates strongly with global niche differences, across depths and across gradients of stratification and chlorophyll a. A model of competition shows that community shifts can be explained by greater fitness of faster-grazing mixotrophs when nutrients are scarce and light is plentiful. Our results illustrate how basic physiological constraints and principles of resource competition can organize complexity in the surface ocean ecosystem.

The hitchhikers' guide to RNA sequencing and functional analysis

DNA and RNA sequencing technologies have revolutionized biology and biomedical sciences, sequencing full genomes and transcriptomes at very high speeds and reasonably low costs. RNA sequencing (RNA-Seq) enables transcript identification and quantification, but once sequencing has concluded researchers can be easily overwhelmed with questions such as how to go from raw data to differential expression (DE), pathway analysis and interpretation. Several pipelines and procedures have been developed to this effect. Even though there is no unique way to perform RNA-Seq analysis, it usually follows these steps: 1) raw reads quality check, 2) alignment of reads to a reference genome, 3) aligned reads' summarization according to an annotation file, 4) DE analysis and 5) gene set analysis and/or functional enrichment analysis. Each step requires researchers to make decisions, and the wide variety of options and resulting large volumes of data often lead to interpretation challenges. There also seems to be insufficient guidance on how best to obtain relevant information and derive actionable knowledge from transcription experiments. In this paper, we explain RNA-Seq steps in detail and outline differences and similarities of different popular options, as well as advantages and disadvantages. We also discuss non-coding RNA analysis, multi-omics, meta-transcriptomics and the use of artificial intelligence methods complementing the arsenal of tools available to researchers. Lastly, we perform a complete analysis from raw reads to DE and functional enrichment analysis, visually illustrating how results are not absolute truths and how algorithmic decisions can greatly impact results and interpretation.

Strain-specific transcriptional responses overshadow salinity effects in a marine diatom sampled along the Baltic Sea salinity cline

The salinity gradient separating marine and freshwater environments represents a major ecological divide for microbiota, yet the mechanisms by which marine microbes have adapted to and ultimately diversified in freshwater environments are poorly understood. Here, we take advantage of a natural evolutionary experiment: the colonization of the brackish Baltic Sea by the ancestrally marine diatom Skeletonema marinoi. To understand how diatoms respond to low salinity, we characterized transcriptomic responses of acclimated S. marinoi grown in a common garden. Our experiment included eight strains from source populations spanning the Baltic Sea salinity cline. Gene expression analysis revealed that low salinities induced changes in the cellular metabolism of S. marinoi, including upregulation of photosynthesis and storage compound biosynthesis, increased nutrient demand, and a complex response to oxidative stress. However, the strain effect overshadowed the salinity effect, as strains differed significantly in their response, both regarding the strength and the strategy (direction of gene expression) of their response. The high degree of intraspecific variation in gene expression observed here highlights an important but often overlooked source of biological variation associated with how diatoms respond to environmental change.

Interfacing Machine Learning and Microbial Omics: A Promising Means to Address Environmental Challenges

Microbial communities are ubiquitous and carry an exceptionally broad metabolic capability. Upon environmental perturbation, microbes are also amongst the first natural responsive elements with perturbation-specific cues and markers. These communities are thereby uniquely positioned to inform on the status of environmental conditions. The advent of microbial omics has led to an unprecedented volume of complex microbiological data sets. Importantly, these data sets are rich in biological information with potential for predictive environmental classification and forecasting. However, the patterns in this information are often hidden amongst the inherent complexity of the data. There has been a continued rise in the development and adoption of machine learning (ML) and deep learning architectures for solving research challenges of this sort. Indeed, the interface between molecular microbial ecology and artificial intelligence (AI) appears to show considerable potential for significantly advancing environmental monitoring and management practices through their application. Here, we provide a primer for ML, highlight the notion of retaining biological sample information for supervised ML, discuss workflow considerations, and review the state of the art of the exciting, yet nascent, interdisciplinary field of ML-driven microbial ecology. Current limitations in this sphere of research are also addressed to frame a forward-looking perspective toward the realization of what we anticipate will become a pivotal toolkit for addressing environmental monitoring and management challenges in the years ahead.