The Expansion of Life

The story of life tells of relentless expansion from obscure beginnings to smother the earth in organized biochemistry. First came the prokaryotes, Bacteria and Archaea, followed some two billion years later by eukaryotic microbes. The latter pattern of organization underpins the rise of multicellular organisms, and their spectacular proliferation over the past 600 million years. There have been no fundamentally new kinds of organisms since, but the rise of mind culminating in humanity may signal a new phase in life’s history. Life has expanded in both quantity and quality, a gyre of mounting size, complexity, and functional capacity; in some elusive sense evolution is progressive. Multicellularity, the key invention, is not singular but happened multiple times in several eukaryotic lineages. The proliferation of higher organisms was probably enabled by increased energy flow, and dependent on the increase in atmospheric oxygen. It is studded with innovations in structure, physiology, and behavior, whose origin is a recurrent theme in evolutionary biology. Novelty is rooted in mutational events at the gene level, supplemented by the acquisition of genes from the outside by both gene transfer and symbiosis, and possibly by other avenues. Chance events were scrutinized and culled by natural selection. There appears to be no intrinsic progressive drive, but natural selection generally favors the more functional and better organized.

The long non-coding RNA landscape of Candida yeast pathogens

Long non-coding RNAs (lncRNAs) constitute a poorly studied class of transcripts with emerging roles in key cellular processes. Despite efforts to characterize lncRNAs across a wide range of species, these molecules remain largely unexplored in most eukaryotic microbes, including yeast pathogens of the Candida clade. Here, we analyze thousands of publicly available sequencing datasets to infer and characterize the lncRNA repertoires of five major Candida pathogens: Candida albicans, Candida tropicalis, Candida parapsilosis, Candida auris and Candida glabrata. Our results indicate that genomes of these species encode hundreds of lncRNAs that show levels of evolutionary constraint intermediate between those of intergenic genomic regions and protein-coding genes. Despite their low sequence conservation across the studied species, some lncRNAs are syntenic and are enriched in shared sequence motifs. We find co-expression of lncRNAs with certain protein-coding transcripts, hinting at potential functional associations. Finally, we identify lncRNAs that are differentially expressed during infection of human epithelial cells for four of the studied species. Our comprehensive bioinformatic analyses of Candida lncRNAs pave the way for future functional characterization of these transcripts.

Sesquiterpenoids Specially Produced by Fungi: Structures, Biological Activities, Chemical and Biosynthesis (2015–2020)

Fungi are widely distributed in the terrestrial environment, freshwater, and marine habitat. Only approximately 100,000 of these have been classified although there are about 5.1 million characteristic fungi all over the world. These eukaryotic microbes produce specialized metabolites and participate in a variety of ecological functions, such as quorum detection, chemical defense, allelopathy, and maintenance of symbiosis. Fungi therefore remain an important resource for the screening and discovery of biologically active natural products. Sesquiterpenoids are arguably the richest natural products from plants and micro-organisms. The rearrangement of the 15 high-ductility carbons gave rise to a large number of different skeletons. At the same time, abundant structural variations lead to a diversification of biological activity. This review examines the isolation, structural determination, bioactivities, and synthesis of sesquiterpenoids that were specially produced by fungi over the past five years (2015‒2020).

Revealing the composition of the eukaryotic microbiome of oyster spat by CRISPR-Cas Selective Amplicon Sequencing (CCSAS)

Background The microbiome affects the health of plants and animals, including humans, and has many biological, ecological, and evolutionary consequences. Microbiome studies typically rely on sequencing ribosomal 16S RNA gene fragments, which serve as taxonomic markers for prokaryotic communities; however, for eukaryotic microbes this approach is compromised, because 18S rRNA gene sequences from microbial eukaryotes are swamped by contaminating host rRNA gene sequences. Results To overcome this problem, we developed CRISPR-Cas Selective Amplicon Sequencing (CCSAS), a high-resolution and efficient approach for characterizing eukaryotic microbiomes. CCSAS uses taxon-specific single-guide RNA (sgRNA) to direct Cas9 to cut 18S rRNA gene sequences of the host, while leaving protistan and fungal sequences intact. We validated the specificity of the sgRNA on ten model organisms and an artificially constructed (mock) community of nine protistan and fungal pathogens. The results showed that > 96.5% of host rRNA gene amplicons were cleaved, while 18S rRNA gene sequences from protists and fungi were unaffected. When used to assess the eukaryotic microbiome of oyster spat from a hatchery, CCSAS revealed a diverse community of eukaryotic microbes, typically with much less contamination from oyster 18S rRNA gene sequences than other methods using non-metazoan or blocking primers. However, each method revealed taxonomic groups that were not detected using the other methods, showing that a single approach is unlikely to uncover the entire eukaryotic microbiome in complex communities. To facilitate the application of CCSAS, we designed taxon-specific sgRNA for ~16,000 metazoan and plant taxa, making CCSAS widely available for characterizing eukaryotic microbiomes that have largely been neglected. Conclusion CCSAS provides a high-through-put and cost-effective approach for resolving the eukaryotic microbiome of metazoa and plants with minimal contamination from host 18S rRNA gene sequences.

Interactions with Arsenic: Mechanisms of Toxicity and Cellular Resistance in Eukaryotic Microorganisms

Arsenic (As) is quite an abundant metalloid, with ancient origin and ubiquitous distribution, which represents a severe environmental risk and a global problem for public health. Microbial exposure to As compounds in the environment has happened since the beginning of time. Selective pressure has induced the evolution of various genetic systems conferring useful capacities in many microorganisms to detoxify and even use arsenic, as an energy source. This review summarizes the microbial impact of the As biogeochemical cycle. Moreover, the poorly known adverse effects of this element on eukaryotic microbes, as well as the As uptake and detoxification mechanisms developed by yeast and protists, are discussed. Finally, an outlook of As microbial remediation makes evident the knowledge gaps and the necessity of new approaches to mitigate this environmental challenge.

Whokaryote: distinguishing eukaryotic and prokaryotic contigs in metagenomes based on gene structure

Metagenomics has become a prominent technology to study the functional potential of all organisms in a microbial community. Most studies focus on the bacterial content of these communities, while ignoring eukaryotic microbes. Indeed, many metagenomics analysis pipelines silently assume that all contigs in a metagenome are prokaryotic. However, because of marked differences in gene structure, prokaryotic gene prediction tools fail to accurately predict eukaryotic genes. Here, we developed a classifier that distinguishes eukaryotic from prokaryotic contigs based on foundational differences between these taxa in gene structure. We first developed a random forest classifier that uses intergenic distance, gene density and gene length as the most important features. We show that, with an estimated accuracy of 97%, this classifier with principled features grounded in biology can perform almost as well as the classifiers EukRep and Tiara, which use k-mer frequencies as features. By re-training our classifier with Tiara predictions as additional feature, weaknesses of both types of classifiers are compensated; the result is an enhanced classifier that outperforms all individual classifiers, with an F1-score of 1.00 on precision, recall and accuracy for both eukaryotes and prokaryotes, while still being fast. In a reanalysis of metagenome data from a disease-suppressive plant endosphere microbial community, we show how using Whokaryote to select contigs for eukaryotic gene prediction facilitates the discovery of several biosynthetic gene clusters that were missed in the original study. Our enhanced classifier, which we call ′Whokaryote′, is wrapped in an easily installable package and is freely available from https://git.wageningenur.nl/lotte.pronk/whokaryote.

Estimating the maximal growth rates of eukaryotic microbes from cultures and metagenomes via codon usage patterns

Microbial eukaryotes are ubiquitous in the environment and play important roles in key ecosystem processes, including accounting for a significant portion of global primary production. Yet, our tools for assessing the functional capabilities of eukaryotic microbes in the environment are quite limited because many microbes have yet to be grown in culture. Maximum growth rate is a fundamental parameter of microbial lifestyle that reveals important information about an organism's functional role in a community. We developed and validated a genomic estimator of maximum growth rate for eukaryotic microbes, enabling the assessment of growth potential for both cultivated and yet-to-be-cultivated organisms. We produced a database of over 700 growth predictions from genomes, transcriptomes, and metagenome-assembled genomes, and found that closely related and/or functionally similar organisms tended to have similar maximal growth rates. By comparing the maximal growth rates of existing culture collections with environmentally-derived genomes we found that, unlike for prokaryotes, culture collections of microbial eukaryotes are only minimally biased in terms of growth potential. We then extended our tool to make community-wide estimates of growth potential from over 500 marine metagenomes, mapping growth potential across the global oceans. We found that prokaryotic and eukaryotic communities have highly correlated growth potentials near the ocean surface, but that this relationship disappears deeper in the water column. This suggests that fast growing eukaryotes and prokaryotes thrive under similar conditions at the ocean surface, but that there is a decoupling of these communities as resources become scarce deeper in the water column.

Phylogenetic and Phenogenetic Diversity of Synechococcus along a Yellow Sea Section Reveal Its Environmental Dependent Distribution and Co-Occurrence Microbial Pattern

Synechococcus is a dominant genus of the coastal phytoplankton with an effective contribution to primary productivity. Here, the phylogenetic and phenogenetic composition of Synechococcus in the coastal Yellow Sea was addressed by sequencing marker gene methods. Meanwhile, its co-occurrence pattern with bacterial and eukaryotic microbes was further investigated based on the construction of networks. The result revealed that Synechococcus abundance ranged from 9.8 × 102 cells mL−1 to 1.6 × 105 cells mL−1, which was significantly correlated to sampling depth and nutrient contents of nitrite, ammonia, and dissolved silicon. A total of eight Synechococcus phylogenetic lineages were detected, of which clade III was dominant in most of the samples. Meanwhile, clade I increased along the water column and even reached a maximum value of 76.13% at 20 m of station B. Phenogenetically, Synechococcus PT3 was always the predominant pigment type across the whole study zone. Only salinity was significantly correlated to the phenogenetic constitution. The networks revealed that Synechococcus co-occurred with 159 prokaryotes, as well as 102 eukaryotes including such possible grazers as Gymnodinium clades and Alveolata. Potential function prediction further showed that microbes co-occurring with Synechococcus were associated with diverse element cycles, but the exact mechanism needed further experimentation to verify. This research promotes exploring regularity in the genomic composition and niche position of Synechococcus in the coastal ecosystem and is significant to further discuss its potential participation in materials circulation and bottom-up effects in microbial food webs.

Proteomic traits vary across taxa in a coastal Antarctic phytoplankton bloom

Production and use of proteins is under strong selection in microbes, but it is unclear how proteome-level traits relate to ecological strategies. We identified and quantified proteomic traits of eukaryotic microbes and bacteria through an Antarctic phytoplankton bloom using in situ metaproteomics. Different taxa, rather than different environmental conditions, formed distinct clusters based on their ribosomal and photosynthetic proteomic proportions, and we propose that these characteristics relate to ecological differences. We defined and used a proteomic proxy for regulatory cost, which showed that SAR11 had the lowest regulatory cost of any taxa we observed at our summertime Southern Ocean study site. Haptophytes had lower regulatory cost than diatoms, which may underpin haptophyte-to-diatom bloom progression in the Ross Sea. We were able to make these proteomic trait inferences by assessing various sources of bias in metaproteomics, providing practical recommendations for researchers in the field. We have quantified several proteomic traits (ribosomal and photosynthetic proteomic proportions, regulatory cost) in eukaryotic and bacterial taxa, which can then be incorporated into trait-based models of microbial communities that reflect resource allocation strategies.

Draft genome assemblies for tree pathogens Phytophthora pseudosyringae and Phytophthora boehmeriae

Species of Phytophthora, plant pathogenic eukaryotic microbes, can cause disease on many tree species. Genome sequencing of species from this genus has helped to determine components of their pathogenicity arsenal. Here we sequenced genomes for two widely distributed species, P. pseudosyringae and P. boehmeriae, yielding genome assemblies of 49 Mb and 40 Mb, respectively. We identified more than 280 candidate disease promoting RXLR effector coding genes for each species, and hundreds of genes encoding candidate plant cell wall degrading carbohydrate active enzymes (CAZymes). These data boost genome sequence representation across the Phytophthora genus, and form resources for further study of Phytophthora pathogenesis.