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Background The rnpB gene encodes for an essential catalytic RNA (RNase P). Like other essential RNAs, RNase P’s sequence is highly variable. However, unlike other essential RNAs (i.e. tRNA, 16 S, 6 S,...) its structure is also variable with at least 5 distinct structure types observed in prokaryotes. This structural variability makes it labor intensive and challenging to create and maintain covariance models for the detection of RNase P RNA in genomic and metagenomic sequences. The lack of a facile and rapid annotation algorithm has led to the rnpB gene being the most grossly under annotated essential gene in completed prokaryotic genomes with only a 24% annotation rate. Here we describe the coupling of the largest RNase P RNA database with the local alignment scoring algorithm to create the most sensitive and rapid prokaryote rnpB gene identification and annotation algorithm to date. Results Of the 2772 completed microbial genomes downloaded from GenBank only 665 genomes had an annotated rnpB gene. We applied P Finder to these genomes and were able to identify 2733 or nearly 99% of the 2772 microbial genomes examined. From these results four new rnpB genes that encode the minimal T-type P RNase P RNAs were identified computationally for the first time. In addition, only the second C-type RNase P RNA was identified in Sphaerobacter thermophilus. Of special note, no RNase P RNAs were detected in several obligate endosymbionts of sap sucking insects suggesting a novel evolutionary adaptation. Conclusions The coupling of the largest RNase P RNA database and associated structure class identification with the P Finder algorithm is both sensitive and rapid, yielding high quality results to aid researchers annotating either genomic or metagenomic data. It is the only algorithm to date that can identify challenging RNAse P classes such as C-type and the minimal T-type RNase P RNAs. P Finder is written in C# and has a user-friendly GUI that can run on multiple 64-bit windows platforms (Windows Vista/7/8/10). P Finder is free available for download at https://github.com/JChristopherEllis/P-Finder as well as a small sample RNase P RNA file for testing.

Abstract Two apparently irreconcilable models dominate research into the origin of eukaryotes. In one model, amitochondrial proto-eukaryotes emerged autogenously from the last universal common ancestor of all cells. Proto-eukaryotes subsequently acquired mitochondrial progenitors by the phagocytic capture of bacteria. In the second model, two prokaryotes, probably an archaeon and a bacterial cell, engaged in prokaryotic endosymbiosis, with the species resident within the host becoming the mitochondrial progenitor. Both models have limitations. A search was therefore undertaken for alternative routes towards the origin of eukaryotic cells. The question was addressed by considering classes of potential pathways from prokaryotic to eukaryotic cells based on considerations of cellular topology. Among the solutions identified, one, called here the “third-space model”, has not been widely explored. A version is presented in which an extracellular space (the third-space), serves as a proxy cytoplasm for mixed populations of archaea and bacteria to “merge” as a transitionary complex without obligatory endosymbiosis or phagocytosis and to form a precursor cell. Incipient nuclei and mitochondria diverge by division of labour. The third-space model can accommodate the reorganization of prokaryote-like genomes to a more eukaryote-like genome structure. Nuclei with multiple chromosomes and mitosis emerge as a natural feature of the model. The model is compatible with the loss of archaeal lipid biochemistry while retaining archaeal genes and provides a route for the development of membranous organelles such as the Golgi apparatus and endoplasmic reticulum. Advantages, limitations and variations of the “third-space” models are discussed. Reviewers This article was reviewed by Damien Devos, Buzz Baum and Michael Gray.

The intricate details of how proteins bind to proteins, DNA, and RNA are crucial for the understanding of almost all biological processes. Disease-causing sequence variants often affect binding residues. Here, we described a new, comprehensive system of in silico methods that take only protein sequence as input to predict binding of protein to DNA, RNA, and other proteins. Firstly, we needed to develop several new methods to predict whether or not proteins bind (per-protein prediction). Secondly, we developed independent methods that predict which residues bind (per-residue). Not requiring three-dimensional information, the system can predict the actual binding residue. The system combined homology-based inference with machine learning and motif-based profile-kernel approaches with word-based (ProtVec) solutions to machine learning protein level predictions. This achieved an overall non-exclusive three-state accuracy of 77% ± 1% (±one standard error) corresponding to a 1.8 fold improvement over random (best classification for protein–protein with F1 = 91 ± 0.8%). Standard neural networks for per-residue binding residue predictions appeared best for DNA-binding (Q2 = 81 ± 0.9%) followed by RNA-binding (Q2 = 80 ± 1%) and worst for protein–protein binding (Q2 = 69 ± 0.8%). The new method, dubbed ProNA2020, is available as code through github ( https://github.com/Rostlab/ProNA2020.git ) and through PredictProtein ( www.predictprotein.org ).

The discovery of the CRISPR systems has enriched the application of gene therapy and biotechnology. As a type of robust and simple toolbox, the CRISPR system has greatly promoted the development of cellular signal sensors at the genomic level. Although CRISPR systems have demonstrated that they can be used in eukaryotic and even mammalian cells after extraction from prokaryotic cells, controlling their gene-editing activity remains a challenge. Here we summarize the advantages and disadvantages of building a CRIRPR-based signal sensor through sgRNA reconstruction, as well as possible ways to reprogram the signal network of cells. We also propose how to further improve the design of the current signal sensors based on sgRNA-riboswitch. We believe that the development of these technologies and the construction of platforms can further promote the development of environment detection, disease diagnosis, and gene therapy by means of synthetic biology.

The formation and use of the scientific names of prokaryotes is governed by the International Code of Nomenclature of Prokaryotes. Originally deriving from the 1935 revision of the International Code of Botanical Nomenclature, it retains the treatment of scientific names as Latin words. Above the rank of genus the rank is generally denoted by a single, standardised suffix. This has great advantage in text mining and database infrastructure where the identification of the standardised suffix can automatically be linked to the rank at which the scientific name is being used. The only exception at present are names at the rank of class where, although a standardised suffix has been proposed (-ia) it does not allow one to unambiguously identify the rank of the scientific name, since it is also a suffix used at the rank of genus. In addition, due to the fact that the suffix at the rank of class was not regulated in earlier versions of the International Code of Nomenclature of Bacteria, there are names that do not follow the standardised suffix. Uniformity would be an advantage. The problem and a proposed solution are discussed.

The origin of eukaryotes remains unclear1–4. Current data suggest that eukaryotes may have emerged from an archaeal lineage known as ‘Asgard’ archaea5,6. Despite the eukaryote-like genomic features that are found in these archaea, the evolutionary transition from archaea to eukaryotes remains unclear, owing to the lack of cultured representatives and corresponding physiological insights. Here we report the decade-long isolation of an Asgard archaeon related to Lokiarchaeota from deep marine sediment. The archaeon—‘Candidatus Prometheoarchaeum syntrophicum’ strain MK-D1—is an anaerobic, extremely slow-growing, small coccus (around 550 nm in diameter) that degrades amino acids through syntrophy. Although eukaryote-like intracellular complexes have been proposed for Asgard archaea6, the isolate has no visible organelle-like structure. Instead, Ca. P. syntrophicum is morphologically complex and has unique protrusions that are long and often branching. On the basis of the available data obtained from cultivation and genomics, and reasoned interpretations of the existing literature, we propose a hypothetical model for eukaryogenesis, termed the entangle–engulf–endogenize (also known as E3) model., Isolation and characterization of an archaeon that is most closely related to eukaryotes reveals insights into how eukaryotes may have evolved from prokaryotes.

One of the goals of the International Code of Nomenclature of Prokaryotes is not only to make nomenclature transparent and predictable, but to also make sure that the biological material on which it is based is available to either verify previous work or to allow further work to be undertaken. The key elements in ensuring the latter two aspects are nomenclatural types (type strains) at the rank of species and subspecies. With increasing regulations controlling access to genetic resources, the limitations put on access are not always evident at the time novel species or subspecies are proposed and corresponding nomenclatural types (type strains) designated. In a number of cases, limitations put on access have been discovered after the fact.

Ferritins are multimers comprised of 4 α-helical bundle monomers that co-assemble to form protein shells surrounding an approximately spherical internal cavity. The assembled multimers acquire Fe2+ from their surroundings by utilising channels that penetrate the protein for the transportation of iron to diiron catalytic centres buried within the monomeric units. Here oxidation of the substrate to Fe3+ is coupled to the reduction of O2 and/or peroxide to yield the precursor to a ferric oxy hydroxide mineral that is stored within the internal cavity. The rhombic dodecahedral quaternary structure results in channels of 4-fold and 3-fold symmetry, located at the vertices, which are common to all 24mer-ferritins. Ferritins isolated from higher eukaryotes have been demonstrated to take up Fe2+ via the 3-fold channels. One of the defining features of ferritins isolated from prokaryotes is the presence of a further 24 channels, the B-channels, and these are thought to play an important role in Fe2+ uptake in this sub-family. SynFtn is an unusual ferritin isolated from the marine cyanobacterium Synechococcus CC9311. The reported structure of SynFtn derived from Fe2+ soaked crystals revealed the presence of a fully hydrated Fe2+ associated with three aspartate residues (Asp137 from each of the three symmetry related subunits) within each three-fold channel, suggesting that it might be the route for Fe2+ entry. Here, we present structural and spectro-kinetic data on two variants of SynFtn, D137A and E62A, designed to assess this possibility. Glu62 is equivalent to residues demonstrated to be important in the transfer of iron from the inner exit of the 3-fold channel to the catalytic centre in animal ferritins. As expected replacing Asp137 with a non-coordinating residue eliminated rapid iron oxidation by SynFtn. In contrast the rate of mineral core formation was severely impaired whilst the rate of iron transit into the catalytic centre was largely unaffected upon introducing a non-coordinating residue in place of Glu62 suggesting a role for this residue in release of the oxidised product. The identification of these two residues in SynFtn maps out major routes for Fe2+ entry to, and exit from, the catalytic ferroxidase centres.

Objectives Cell-culture studies reported that prokaryotic RNA molecules among the various microbe-associated molecular patterns (MAMPs) were uniquely present in live bacteria and were categorized as viability-associated MAMPs. They also reported that specific nucleotide modifications are instrumental in the discrimination between self and nonself RNAs. The aim of this study was to characterize the in vivo immune induction potential of prokaryotic and eukaryotic ribosomal RNAs (rRNAs) using zebrafish embryos as novel whole animal model system. Additionally, we aimed to test the possible role of rRNA modifications in immune recognition. Results We used three immune markers to evaluate the induction potential of prokaryotic rRNA derived from Escherichia coli and eukaryotic rRNAs from chicken (nonself) and zebrafish (self). Lipopolysaccharide (LPS) of Pseudomonas aeruginosa served as a positive control. E. coli rRNA had an induction potential equivalent to that of LPS. The zebrafish innate immune system could discriminate between self and nonself rRNAs. Between the nonself rRNAs, E. coli rRNA was more immunogenic than chicken rRNA. The in vitro transcript of zebrafish 18S rRNA gene without the nucleotide modifications was not recognized by its own immune system. Our data suggested that prokaryotic rRNA is immunostimulatory in vivo and could be useful as an adjuvant.

A classic example of an all-protein natural nano-bioreactor, the bacterial microcompartment is a prokaryotic organelle that confines enzymes in a small volume enveloped by an outer protein shell. These protein compartments metabolize specific organic molecules, allowing bacteria to survive in restricted nutrient environments. In this work, 1,2-propanediol utilization microcompartment (PduMCP) was used as a model to study the effect of molecular confinement on the stability and catalytic activity of native enzymes in the microcompartment. A combination of enzyme assays, spectroscopic techniques, binding assays, and computational analysis were used to evaluate the impact of the major shell protein PduBB' on the stability and activity of PduMCP's signature enzyme, dioldehydratase PduCDE. While free PduCDE shows ∼45 % reduction in its optimum activity (activity at 37 °C) when exposed to a temperature of 45 °C, it retains similar activity up to 50 °C when encapsulated within PduMCP. PduBB', a major component of the outer shell of PduMCP, preserves the catalytic efficiency of PduCDE under thermal stress and prevents temperature-induced unfolding and aggregation of PduCDE in vitro. We observed that while both PduB and PduB' interact with the enzyme with micromolar affinity, only the PduBB' combination influences its activity and stability, highlighting the importance of the unique PduBB' combination in the functioning of PduMCP.

Most microorganisms exist in biofilms, which comprise aggregates of cells surrounded by an extracellular matrix that provides protection from external stresses. Based on the conditions under which they form, biofilm structures vary in significant ways. For instance, biofilms that develop when microbes are incubated under static conditions differ from those formed when microbes encounter the shear forces of a flowing liquid. Moreover, biofilms develop dynamically over time. Here, we describe a cost-effective coverslip holder, printed with a three-dimensional (3D) printer, that facilitates surface adhesion assays under a broad range of standing and shaking culture conditions. This multipanel adhesion (mPAD) mount further allows cultures to be sampled at multiple time points, ensuring consistency and comparability between samples and enabling analyses of the dynamics of biofilm formation. As a proof of principle, using the mPAD mount for shaking, oxic cultures, we confirm previous flow chamber experiments showing that the Pseudomonas aeruginosa wild-type strain and a phenazine deletion mutant (Δphz) strain form biofilms with similar structure but reduced density in the mutant strain. Extending this analysis to anoxic conditions, we reveal that microcolony formation and biofilm formation can only be observed under shaking conditions and are decreased in the Δphz mutant compared to wild-type cultures, indicating that phenazines are crucial for the formation of biofilms if oxygen as an electron acceptor is unavailable. Furthermore, while the model archaeon Haloferax volcanii does not require archaella for surface attachment under static conditions, we demonstrate that an H. volcanii mutant that lacks archaella is impaired in early stages of biofilm formation under shaking conditions. IMPORTANCE Due to the versatility of the mPAD mount, we anticipate that it will aid the analysis of biofilm formation in a broad range of bacteria and archaea. Thereby, it contributes to answering critical biological questions about the regulatory and structural components of biofilm formation and understanding this process in a wide array of environmental, biotechnological, and medical contexts.

Motivation The biases in CoDing Sequence (CDS) prediction tools, which have been based on historic genomic annotations from model organisms, impact our understanding of novel genomes and metagenomes. This hinders the discovery of new genomic information as it results in predictions being biased towards existing knowledge. To date, users have lacked a systematic and replicable approach to identify the strengths and weaknesses of any CDS prediction tool and allow them to choose the right tool for their analysis. Results We present an evaluation framework (ORForise) based on a comprehensive set of 12 primary and 60 secondary metrics that facilitate the assessment of the performance of CDS prediction tools. This makes it possible to identify which performs better for specific use-cases. We use this to assess 15 ab initio- and model-based tools representing those most widely used (historically and currently) to generate the knowledge in genomic databases. We find that the performance of any tool is dependent on the genome being analysed, and no individual tool ranked as the most accurate across all genomes or metrics analysed. Even the top-ranked tools produced conflicting gene collections, which could not be resolved by aggregation. The ORForise evaluation framework provides users with a replicable, data-led approach to make informed tool choices for novel genome annotations and for refining historical annotations. Availability and implementation Code and datasets for reproduction and customisation are available at https://github.com/NickJD/ORForise. Supplementary information Supplementary data are available at Bioinformatics online.

Though being fundamental to global diversity distribution, little is known about the geographic pattern of soil microorganisms across different biotas on a large scale. Here, we investigated soil prokaryotic communities from Chinese northern grasslands on a scale up to 4000 km in both alpine and temperate biotas. Prokaryotic similarities increased over geographic distance after tipping points of 1760–1920 km, generating a significant U-shape pattern. Such pattern was likely due to decreased disparities in environmental heterogeneity over geographic distance when across biotas, supported by three lines of evidences: (1) prokaryotic similarities still decreased with the environmental distance, (2) environmental selection dominated prokaryotic assembly, and (3) short-term environmental heterogeneity followed the U-shape pattern spatially, especially attributed to dissolved nutrients. In sum, these results demonstrate that environmental selection overwhelmed the geographic ‘distance’ effect when across biotas, overturning the previously well-accepted geographic pattern for microbes on a large scale.

Most prokaryotes are not available as pure cultures and therefore ineligible for naming under the rules and recommendations of the International Code of Nomenclature of Prokaryotes (ICNP). Here we summarize the development of the SeqCode, a code of nomenclature under which genome sequences serve as nomenclatural types. This code enables valid publication of names of prokaryotes based upon isolate genome, metagenome-assembled genome or single-amplified genome sequences. Otherwise, it is similar to the ICNP with regard to the formation of names and rules of priority. It operates through the SeqCode Registry ( https://seqco.de/ ), a registration portal through which names and nomenclatural types are registered, validated and linked to metadata. We describe the two paths currently available within SeqCode to register and validate names, including Candidatus names, and provide examples for both. Recommendations on minimal standards for DNA sequences are provided. Thus, the SeqCode provides a reproducible and objective framework for the nomenclature of all prokaryotes regardless of cultivability and facilitates communication across microbiological disciplines.

Promoters are crucial regulatory DNA regions for gene transcriptional activation. Rapid advances in next-generation sequencing technologies have accelerated the accumulation of genome sequences, providing increased training data to inform computational approaches for both prokaryotic and eukaryotic promoter prediction. However, it remains a significant challenge to accurately identify species-specific promoter sequences using computational approaches. To advance computational support for promoter prediction, in this study, we curated 58 comprehensive, up-to-date, benchmark datasets for 7 different species (i.e. Escherichia coli, Bacillus subtilis, Homo sapiens, Mus musculus, Arabidopsis thaliana, Zea mays and Drosophila melanogaster) to assist the research community to assess the relative functionality of alternative approaches and support future research on both prokaryotic and eukaryotic promoters. We revisited 106 predictors published since 2000 for promoter identification (40 for prokaryotic promoter, 61 for eukaryotic promoter, and 5 for both). We systematically evaluated their training datasets, computational methodologies, calculated features, performance and software usability. On the basis of these benchmark datasets, we benchmarked 19 predictors with functioning webservers/local tools and assessed their prediction performance. We found that deep learning and traditional machine learning–based approaches generally outperformed scoring function–based approaches. Taken together, the curated benchmark dataset repository and the benchmarking analysis in this study serve to inform the design and implementation of computational approaches for promoter prediction and facilitate more rigorous comparison of new techniques in the future.

The knowledge that natural products (NPs) are potent and selective modulators of important biomacromolecules (e.g., DNA and proteins) has inspired some of the world’s most successful pharmaceuticals and agrochemicals. Notwithstanding these successes and despite a growing number of reports on naturally occurring pairs of enantiomers, this area of NP science still remains largely unexplored, consistent with the adage “If you don’t seek, you don’t find”. Statistically, a rapidly growing number of enantiomeric NPs have been reported in the last several years. The current review provides a comprehensive overview of recent records on natural enantiomers, with the aim of advancing awareness and providing a better understanding of the chemical diversity and biogenetic context, as well as the biological properties and therapeutic (drug discovery) potential, of enantiomeric NPs.

The transcriptional regulatory network in prokaryotes controls global gene expression mostly through transcription factors (TFs), which are DNA-binding proteins. Chromatin immunoprecipitation (ChIP) with DNA sequencing methods can identify TF binding sites across the genome, providing a bottom-up, mechanistic understanding of how gene expression is regulated. ChIP provides indispensable evidence toward the goal of acquiring a comprehensive understanding of cellular adaptation and regulation, including condition-specificity. ChIP-derived data's importance and labor-intensiveness motivate its broad dissemination and reuse, which is currently an unmet need in the prokaryotic domain. To fill this gap, we present proChIPdb (prochipdb.org), an information-rich, interactive web database. This website collects public ChIP-seq/-exo data across several prokaryotes and presents them in dashboards that include curated binding sites, nucleotide-resolution genome viewers, and summary plots such as motif enrichment sequence logos. Users can search for TFs of interest or their target genes, download all data, dashboards, and visuals, and follow external links to understand regulons through biological databases and the literature. This initial release of proChIPdb covers diverse organisms, including most major TFs of Escherichia coli, and can be expanded to support regulon discovery across the prokaryotic domain.

Metabarcoding approaches are exponentially increasing our understanding of soil biodiversity, with a major focus on the bacterial part of the microbiome. Part of the soil diversity are also eukaryotes that include fungi, algae, protists and Metazoa. Nowadays, soil eukaryotes are targeted with the same approaches developed for bacteria and archaea (prokaryotes). However, fundamental differences exist between domains. After providing a short historical overview of the developments of metabarcoding applied to environmental microbiology, we compile the most important differences between domains that prevent direct method transfers between prokaryotic and eukaryotic soil metabarcoding approaches, currently dominated by short-read sequencing. These include the existence of divergent diversity concepts and the variations in eukaryotic morphology that affect sampling and DNA extraction. Furthermore, eukaryotes experienced much more variable evolutionary rates than prokaryotes, which prevent capturing the entire eukaryotic diversity in a soil with a single amplification protocol fit for short-read sequencing. In the final part we focus on future potentials for optimization of eukaryotic metabarcoding that include superior possibility of functionally characterizing eukaryotes and to extend the current information obtained, such as by adding a real quantitative component. This review should optimize future metabarcoding approaches targeting soil eukaryotes and kickstart this promising research direction.

Nature microbiology 7(11), 1857 - 1869 (2022). doi:10.1038/s41564-022-01239-0, Argonaute (Ago) proteins are found in all three domains of life. The so-called long Agos are composed of four major domains (N, PAZ, MID and PIWI) and contribute to RNA silencing in eukaryotes (eAgos) or defence against invading mobile genetic elements in prokaryotes (pAgos). The majority (~60%) of pAgos identified bioinformatically are shorter (comprising only MID and PIWI domains) and are typically associated with Sir2, Mrr or TIR domain-containing proteins. The cellular function and mechanism of short pAgos remain enigmatic. Here we show that Geobacter sulfurreducens short pAgo and the NAD$^+$-bound Sir2 protein form a stable heterodimeric complex. The GsSir2/Ago complex presumably recognizes invading plasmid or phage DNA and activates the Sir2 subunit, which triggers endogenous NAD$^+$ depletion and cell death, and prevents the propagation of invading DNA. We reconstituted NAD$^+$ depletion activity in vitro and showed that activated GsSir2/Ago complex functions as a NADase that hydrolyses NAD$^+$ to ADPR. Thus, short Sir2-associated pAgos provide defence against phages and plasmids, underscoring the diversity of mechanisms of prokaryotic Agos., Published by Nature Publishing Group, London

Marine sponges (phylum Porifera) are leading organisms for the discovery of bioactive compounds from nature. Their often rich and species-specific microbiota is hypothesised to be producing many of these compounds. Yet, environmental influences on the sponge-associated microbiota and bioactive compound production remain elusive. Here, we investigated the changes of microbiota and metabolomes in sponges along a depth range of 1232 m. Using 16S rRNA gene amplicon sequencing and untargeted metabolomics, we assessed prokaryotic and chemical diversities in three deep-sea sponge species: Geodia barretti, Stryphnus fortis, and Weberella bursa. Both prokaryotic communities and metabolome varied significantly with depth, which we hypothesized to be the effect of different water masses. Up to 35.5 % of microbial ASVs (amplicon sequence variants) showed significant changes with depth while phylum-level composition of host microbiome remained unchanged. The metabolome varied with depth, with relative quantities of known bioactive compounds increasing or decreasing strongly. Other metabolites varying with depth were compatible solutes regulating osmolarity of the cells. Correlations between prokaryotic community and the bioactive compounds in G. barretti suggested members of Acidobacteria, Proteobacteria, Chloroflexi, or an unclassified prokaryote as potential producers. Title in dissertation list of papers: Go with the flow: oceanographic setting influences the prokaryotic community and metabolome in deep-sea sponges De två första författarna delar förstaförfattarskapet.De två sista författarna delar sistaförfattarskapet.

Contrary to epipelagic waters, where biogeochemical processes closely follow the light and dark periods, little is known about diel cycles in the ocean's mesopelagic realm. Here, we monitored the dynamics of dissolved organic matter (DOM) and planktonic heterotrophic prokaryotes every 2 h for one day at 0 and 550 m (a depth occupied by vertically migrating fishes during light hours) in oligotrophic waters of the central Red Sea. We additionally performed predator-free seawater incubations of samples collected from the same site both at midnight and at noon. Comparable in situ variability in microbial biomass and dissolved organic carbon concentration suggests a diel supply of fresh DOM in both layers. The presence of fishes in the mesopelagic zone during daytime likely promoted a sustained, longer growth of larger prokaryotic cells. The specific growth rates were consistently higher in the noon experiments from both depths (surface: 0.34 vs. 0.18 d-1, mesopelagic: 0.16 vs. 0.09 d-1). Heterotrophic prokaryotes in the mesopelagic layer were also more efficient at converting extant DOM into new biomass. These results suggest that the ocean's twilight zone receives a consistent diurnal supply of labile DOM from the diel vertical migration of fishes, enabling an unexpectedly active community of heterotrophic prokaryotes.

Operon prediction in prokaryotes is critical not only for understanding the regulation of endogenous gene expression, but also for exogenous targeting of genes using newly developed tools such as CRISPR-based gene modulation. A number of methods have used transcriptomics data to predict operons, based on the premise that contiguous genes in an operon will be expressed at similar levels. While promising results have been observed using these methods, most of them do not address uncertainty caused by technical variability between experiments, which is especially relevant when the amount of data available is small. In addition, many existing methods do not provide the flexibility to determine the stringency with which genes should be evaluated for being in an operon pair. We present OperonSEQer, a set of machine learning algorithms that uses the statistic and p-value from a non-parametric analysis of variance test (Kruskal-Wallis) to determine the likelihood that two adjacent genes are expressed from the same RNA molecule. We implement a voting system to allow users to choose the stringency of operon calls depending on whether your priority is high recall or high specificity. In addition, we provide the code so that users can retrain the algorithm and re-establish hyperparameters based on any data they choose, allowing for this method to be expanded as additional data is generated. We show that our approach detects operon pairs that are missed by current methods by comparing our predictions to publicly available long-read sequencing data. OperonSEQer therefore improves on existing methods in terms of accuracy, flexibility, and adaptability., Author summary Bacteria and archaea, single-cell organisms collectively known as prokaryotes, live in all imaginable environments and comprise the majority of living organisms on this planet. Prokaryotes play a critical role in the homeostasis of multicellular organisms (such as animals and plants) and ecosystems. In addition, bacteria can be pathogenic and cause a variety of diseases in these same hosts and ecosystems. In short, understanding the biology and molecular functions of bacteria and archaea and devising mechanisms to engineer and optimize their properties are critical scientific endeavors with significant implications in healthcare, agriculture, manufacturing, and climate science among others. One major molecular difference between unicellular and multicellular organisms is the way they express genes–multicellular organisms make individual RNA molecules for each gene while, prokaryotes express operons (i.e., a group of genes coding functionally related proteins) in contiguous polycistronic RNA molecules. Understanding which genes exist within operons is critical for elucidating basic biology and for engineering organisms. In this work, we use a combination of statistical and machine learning-based methods to use next-generation sequencing data to predict operon structure across a range of prokaryotes. Our method provides an easily implemented, robust, accurate, and flexible way to determine operon structure in an organism-agnostic manner using readily available data.

Background Microbial pan-genomes are shaped by a complex combination of stochastic and deterministic forces. Even closely related genomes exhibit extensive variation in their gene content. Understanding what drives this variation requires exploring the interactions of gene products with each other and with the organism’s external environment. However, to date, conceptual models of pan-genome dynamics often represent genes as independent units and provide limited information about their mechanistic interactions. Results We simulated the stochastic process of gene-loss using the pooled genome-scale metabolic reaction networks of 46 taxonomically diverse bacterial and archaeal families as proxies for their pan-genomes. The frequency by which reactions are retained in functional networks when stochastic gene loss is simulated in diverse environments allowed us to disentangle the metabolic reactions whose presence depends on the metabolite composition of the external environment (constrained by “nutrition”) from those that are independent of the environment (constrained by “nature”). By comparing the frequency of reactions from the first group with their observed frequencies in bacterial and archaeal families, we predicted the metabolic niches that shaped the genomic composition of these lineages. Moreover, we found that the lineages that were shaped by a more diverse metabolic niche also occur in more diverse biomes as assessed by global environmental sequencing datasets. Conclusion We introduce a computational framework for analyzing and interpreting pan-reactomes that provides novel insights into the ecological and evolutionary drivers of pan-genome dynamics.

Extracellular vesicles (EVs) originate from eukaryotic and prokaryotic cells and play a crucial role in intercellular communications. They are found in the environment of cells and tissues, and contribute to the complexity of different biological media, in particular biofluids. Due to their high diversity of cell origin, size range, concentration and composition, EVs offer some of the most important challenges in (pre-)analytical fields. To tackle these challenges, many works deal with the development and implementation of a wide variety of approaches, technologies and methodologies to enrich, isolate, quantify and characterize EVs and their subsets. Nevertheless, other components such as lipoproteins or viruses in complex samples, can interfere with EVs qualification, and make difficult, even today, to standardize biochemical and physical approaches for this purpose. The present chapter presents EVs and the mostly used technics for their isolation and characterization. Performances of methods in terms of resolution, discrimination, throughput and also ability to be or not applied in clinics, are also discussed.

DNA chemical modifications, including methylation, are widespread and play important roles in prokaryotes and viruses. However, current knowledge of these modification systems is severely biased towards a limited number of culturable prokaryotes, despite the fact that a vast majority of microorganisms have not yet been cultured. Here, using single-molecule real-time sequencing, we conducted culture-independent ‘metaepigenomic’ analyses (an integrated analysis of metagenomics and epigenomics) of marine microbial communities. A total of 233 and 163 metagenomic-assembled genomes (MAGs) were constructed from diverse prokaryotes and viruses, respectively, and 220 modified motifs and 276 DNA methyltransferases (MTases) were identified. Most of the MTase genes were not genetically linked with the endonuclease genes predicted to be involved in defense mechanisms against extracellular DNA. The MTase-motif correspondence found in the MAGs revealed 10 novel pairs, 5 of which showed novel specificities and experimentally confirmed the catalytic specificities of the MTases. We revealed novel alternative specificities in MTases that are highly conserved in Alphaproteobacteria, which may enhance our understanding of the co-evolutionary history of the methylation systems and the genomes. Our findings highlight diverse unexplored DNA modifications that potentially affect the ecology and evolution of prokaryotes and viruses in nature.

Argonaute (Ago) proteins are widespread nucleic-acid-guided enzymes that recognize targets through complementary base pairing. Although, in eukaryotes, Agos are involved in RNA silencing, the functions of prokaryotic Agos (pAgos) remain largely unknown. In particular, a clade of truncated and catalytically inactive pAgos (short pAgos) lacks characterization. Here, we reveal that a short pAgo protein in the archaeon Sulfolobus islandicus, together with its two genetically associated proteins, Aga1 and Aga2, provide robust antiviral protection via abortive infection. Aga2 is a toxic transmembrane effector that binds anionic phospholipids via a basic pocket, resulting in membrane depolarization and cell killing. Ago and Aga1 form a stable complex that exhibits nucleic-acid-directed nucleic-acid-recognition ability and directly interacts with Aga2, pointing to an immune sensing mechanism. Together, our results highlight the cooperation between pAgos and their widespread associated proteins, suggesting an uncharted diversity of pAgo-derived immune systems.

The mechanisms and impact of horizontal gene transfer processes to distribute gene functions with potential adaptive benefit among prokaryotes have been well documented. In contrast, little is known about the life-style of mobile elements mediating horizontal gene transfer, whereas this is the ultimate determinant for their transfer fitness. Here, we investigate the life-style of an integrative and conjugative element (ICE) within the genus Pseudomonas that is a model for a widespread family transmitting genes for xenobiotic compound metabolism and antibiotic resistances. Previous work showed bimodal ICE activation, but by using single cell time-lapse microscopy coupled to combinations of chromosomally integrated single copy ICE promoter-driven fluorescence reporters, RNA sequencing and mutant analysis, we now describe the complete regulon leading to the arisal of differentiated dedicated transfer competent cells. The regulon encompasses at least three regulatory nodes and five (possibly six) further conserved gene clusters on the ICE that all become expressed under stationary phase conditions. Time-lapse microscopy indicated expression of two regulatory nodes (i.e., bisR and alpA-bisDC) to precede that of the other clusters. Notably, expression of all clusters except of bisR was confined to the same cell subpopulation, and was dependent on the same key ICE regulatory factors. The ICE thus only transfers from a small fraction of cells in a population, with an estimated proportion of between 1.7-4%, which express various components of a dedicated transfer competence program imposed by the ICE, and form the centerpiece of ICE conjugation. The components mediating transfer competence are widely conserved, underscoring their selected fitness for efficient transfer of this class of mobile elements.

Helitrons are the only group of rolling-circle transposons that encode a transposase with a helicase domain (Hel), which belongs to the Pif1 family. Because Pif1 helicases are important components of eukaryotic genomes, it has been suggested that Hel domains probably originated after a host eukaryotic Pif1 gene was captured by a Helitron ancestor. However, the few analyses exploring the evolution of Helitron transposases (RepHel) have focused on its Rep domain, which is also present in other mobile genetic elements. Here, we used phylogenetic and nonmetric multidimensional scaling analyses to investigate the relationship between Hel domains and Pif1-like helicases from a variety of organisms. Our results reveal that Hel domains are only distantly related to genomic helicases from eukaryotes and prokaryotes, and thus are unlikely to have originated from a captured Pif1 gene. Based on this evidence, and on recent studies indicating that Rep domains are more closely related to rolling-circle plasmids and phages, we suggest that Helitrons are descendants of a RepHel-encoding prokaryotic plasmid element that invaded eukaryotic genomes before the radiation of its major groups. We discuss how a Pif1-like helicase domain might have favored the transposition of Helitrons in eukaryotes beyond simply unwinding DNA intermediates. Finally, we demonstrate that some examples in the literature describing genomic helicases from eukaryotes actually consist of Hel domains from Helitrons, a finding that underscores how transposons can hamper the analysis of eukaryotic genes. This investigation also revealed that two groups of land plants appear to have lost genomic Pif1 helicases independently.

Carbon budgets of hydrothermal plumes result from the balance between carbon sinks through plume chemoautotrophic processes and carbon release via microbial respiration. However, the lack of comprehensive analysis of the metabolic processes and biomass production rates hinders an accurate estimate of their contribution to the deep ocean carbon cycle. Here, we use a biogeochemical model to estimate the autotrophic and heterotrophic production rates of microbial communities in hydrothermal plumes and validate it with in situ data. We show how substrate limitation might prevent net chemolithoautotrophic production in hydrothermal plumes. Elevated prokaryotic heterotrophic production rates (up to 0.9 gCm−2y−1) compared to the surrounding seawater could lead to 0.05 GtCy−1 of C-biomass produced through chemoorganotrophy within hydrothermal plumes, similar to the Particulate Organic Carbon (POC) export fluxes reported in the deep ocean. We conclude that hydrothermal plumes must be accounted for as significant deep sources of POC in ocean carbon budgets., Hydrothermal vents are biogeochemically important, but their contribution to the carbon cycle is poorly constrained. Here the authors build a biogeochemical model that estimates autotrophic and heterotrophic production rates of microbial communities within hydrothermal plumes along mid-ocean ridges.

Background The field of viromics has greatly benefited from recent developments in metagenomics, with significant efforts focusing on viral discovery. However, functional annotation of the increasing number of viral genomes is lagging behind. This is highlighted by the degree of annotation of the protein clusters in the prokaryotic Virus Orthologous Groups (pVOGs) database, with 83% of its current 9518 pVOGs having an unknown function. Results In this study we describe a machine learning approach to explore potential functional associations between pVOGs. We measure seven genomic features and use them as input to a Random Forest classifier to predict protein–protein interactions between pairs of pVOGs. After systematic evaluation of the model’s performance on 10 different datasets, we obtained a predictor with a mean accuracy of 0.77 and Area Under Receiving Operation Characteristic (AUROC) score of 0.83. Its application to a set of 2,133,027 pVOG-pVOG interactions allowed us to predict 267,265 putative interactions with a reported probability greater than 0.65. At an expected false discovery rate of 0.27, we placed 95.6% of the previously unannotated pVOGs in a functional context, by predicting their interaction with a pVOG that is functionally annotated. Conclusions We believe that this proof-of-concept methodology, wrapped in a reproducible and automated workflow, can represent a significant step towards obtaining a more complete picture of bacteriophage biology.

Eukaryotes and prokaryotes have distinct genome architectures, with marked differences in genome size, the ratio of coding/non-coding DNA, and the abundance of transposable elements (TEs). As TEs replicate independently of their hosts, the proliferation of TEs is thought to have driven genome expansion in eukaryotes. However, prokaryotes also have TEs in intergenic spaces, so why do prokaryotes have small, streamlined genomes? Using an in silico model describing the genomes of single-celled asexual organisms that coevolve with TEs, we show that TEs acquired from the environment by horizontal gene transfer can promote the evolution of genome streamlining. The process depends on local interactions and is underpinned by rock–paper–scissors dynamics in which populations of cells with streamlined genomes beat TEs, which beat non-streamlined genomes, which beat streamlined genomes, in continuous and repeating cycles. Streamlining is maladaptive to individual cells, but improves lineage viability by hindering the proliferation of TEs. Streamlining does not evolve in sexually reproducing populations because recombination partially frees TEs from the deleterious effects they cause. This article is part of the theme issue ‘The secret lives of microbial mobile genetic elements’.

Prokaryote pangenomes are influenced heavily by environmental factors and the opportunity for gene gain and loss events. As the field of pangenome analysis has expanded, so has the need to fully understand the complexity of how eco-evolutionary dynamics shape pangenomes. Here, we describe current models of pangenome evolution and discuss their suitability and accuracy. We suggest that pangenomes are dynamic entities under constant flux, highlighting the influence of two-way interactions between pangenome and environment. New classifications of core and accessory genes are also considered, underscoring the need for continuous evaluation of nomenclature in a fast-moving field. We conclude that future models of pangenome evolution should incorporate eco-evolutionary dynamics to fully encompass their dynamic, changeable nature.

Horizontal gene transfer (HGT) or lateral gene transfer (LGT), the exchange of genetic materials among organisms by means of other than parent-to-offspring (vertical) inheritance, plays a major role in prokaryotic genome evolution, facilitating adaptation of prokaryotes to changes in the environment. Phylogenetic methods have been frequently invoked to catalog horizontally acquired genes; however, these methods are often constrained by the paucity of sequenced genomes of close relatives (and even distant relatives) for a robust analysis and reliable inference. In this chapter, we describe a HGT quantification protocol that exploits the complementary strengths of the integrative segmentation and clustering method and the comparative genomics approach to identify foreign genes. Users can use this pipeline in combination with phylogenetic tree reconstruction to identify foreign genes that are supported by multiple lines of evidence, that is, atypical composition, atypical distribution in close relatives, and aberrant phylogenetic pattern.

The invention of a scanning electron microscopy (SEM) pushed the imaging methods and allowed for the observation of cell details with a high resolution. Currently, SEM appears as an extremely useful tool to analyse the morphology of biological samples. The aim of this paper is to provide a set of guidelines for using SEM to analyse morphology of prokaryotic and eukaryotic cells, taking as model cases Escherichia coli bacteria and B-35 rat neuroblastoma cells. Herein, we discuss the necessity of a careful sample preparation and provide an optimised protocol that allows to observe the details of cell ultrastructure (≥ 50 nm) with a minimum processing effort. Highlighting the versatility of morphometric descriptors, we present the most informative parameters and couple them with molecular processes. In this way, we indicate the wide range of information that can be collected through SEM imaging of biological materials that makes SEM a convenient screening method to detect cell pathology.

In the last decade, the high-throughput and relatively low cost of short-read sequencing technologies have revolutionized prokaryotic genomics. This has led to an exponential increase in the number of bacterial and archaeal genome sequences available, as well as corresponding increase of genome assembly and annotation tools developed. Together, these hardware and software technologies have given scientists unprecedented options to study their chosen microbial systems without the need for large teams of bioinformaticists or supercomputing facilities. While these analysis tools largely fall into only a few categories, each may have different requirements, caveats and file formats, and some may be rarely updated or even abandoned. And so, despite the apparent ease in sequencing and analyzing a prokaryotic genome, it is no wonder that the budding genomicist may quickly find oneself overwhelmed. Here, we aim to provide the reader with an overview of genome annotation and its most important considerations, as well as an easy-to-follow protocol to get started with annotating a prokaryotic genome.

The principles of organisation and functioning of complex biological objects have inspired the development of new materials and systems in engineering, electronics, design, chemistry and other fields. Two major areas of biological applications of biomimetic systems are delivery of therapeutic and imaging agents and tissue engineering, both of which are described below. Biomimetic drug delivery systems can imitate the structure and functions of eukaryotic and prokaryotic cells or viruses by reproducing their morphology, mechanical properties and characteristic molecular traits. In tissue engineering, the biomimetic approach is essentially indispensable for reproducing natural tissues and organs in order to restore the organism functionality lost as a result of either trauma or disease.

Computational tool composites alternative way to identify essential genes and it is low-cost and time-efficient. Based on experimental essentiality sets deposited in the databases DEG and OGEE as reference, we developed an automatically computational tool named Geptop to select essential genes from the set of protein-coding genes in a prokaryotic genome, which utilizes the strategy of reciprocally best hit for homology search and evolutionary distance for weight assigning. The latest version of Geptop is 2.0 ( http://guolab.whu.edu.cn/geptop ), which can predict gene essentiality with the mean AUC 0f 0.84 in prokaryotes and is more stable. The chapter is to briefly introduce the tool and tell how to use it.

Understanding the evolutionary forces shaping prokaryotic pangenome structure is a major goal of microbial evolution research. Recent work has highlighted that a substantial proportion of accessory genes appear to confer niche-specific adaptations. This work has primarily focused on selection acting at the level of individual cells. Herein, we discuss a lower level of selection that also contributes to pangenome variation: genic selection. This refers to cases where genetic elements, rather than individual cells, are the entities under selection. The clearest examples of this form of selection are selfish mobile genetic elements, which are those that have either a neutral or a deleterious effect on host fitness. We review the major classes of these and other mobile elements and discuss the characteristic features of such elements that could be under genic selection. We also discuss how genetic elements that are beneficial to hosts can also be under genic selection, a scenario that may be more prevalent but not widely appreciated, because disentangling the effects of selection at different levels (i.e., organisms vs. genes) is challenging. Nonetheless, an appreciation for the potential action and implications of genic selection is important to better understand the evolution of prokaryotic pangenomes.

The plastid outer envelope (OE) is a mixture of components inherited from their prokaryotic ancestor like galactolipids, carotenoids and porin type ion channels supplemented with eukaryotic inventions to make the endosymbiotic process successful as well as to control plastid biogenesis and differentiation. In this review we wanted to highlight the importance of the OE proteins and its evolutionary origin. For a long time, the OE was thought to be a diffusion barrier only, but with the recent discoveries of all kinds of different proteins in the OE it has been shown that the OE can modulate various functions within the cell. The phenotypic changes show that channels like the outer envelope proteins OEP40, OEP16 or JASSY have a pronounced ion selectivity that cannot be replaced by other ion channels present in the OE. Eukaryotic additions, like the GTPase receptors Toc33 and Toc159 or the ubiquitin proteasome system for chloroplast protein quality control, round up the profile of the OE.

Multidrug and toxic compound extrusion (MATE) transporters are widespread in all domains of life. Bacterial MATE transporters confer multidrug resistance by utilizing an electrochemical gradient of H(+) or Na(+) to export xenobiotics across the membrane. Despite the availability of X-ray structures of several MATE transporters, a detailed understanding of the transport mechanism has remained elusive. Here we report the crystal structure of a MATE transporter from Aquifex aeolicus at 2.0-Å resolution. In light of its phylogenetic placement outside of the diversity of hitherto-described MATE transporters and the lack of conserved acidic residues, this protein may represent a subfamily of prokaryotic MATE transporters, which was proven by phylogenetic analysis. Furthermore, the crystal structure and substrate docking results indicate that the substrate binding site is located in the N bundle. The importance of residues surrounding this binding site was demonstrated by structure-based site-directed mutagenesis. We suggest that Aq_128 is functionally similar but structurally diverse from DinF subfamily transporters. Our results provide structural insights into the MATE transporter, which further advances our global understanding of this important transporter family.

Synthetic biology has the potential to transform cell- and gene-based therapies for a variety of diseases. Sophisticated tools are now available for both eukaryotic and prokaryotic cells to engineer cells to selectively achieve therapeutic effects in response to one or more disease-related signals, thus sparing healthy tissue from potentially cytotoxic effects. This report summarizes the Keystone eSymposium "Synthetic Biology: At the Crossroads of Genetic Engineering and Human Therapeutics," which took place on May 3 and 4, 2021. Given that several therapies engineered using synthetic biology have entered clinical trials, there was a clear need for a synthetic biology symposium that emphasizes the therapeutic applications of synthetic biology as opposed to the technical aspects. Presenters discussed the use of synthetic biology to improve T cell, gene, and viral therapies, to engineer probiotics, and to expand upon existing modalities and functions of cell-based therapies.

Bacterial genomes often reflect a bias in the usage of codons. These biases are often most notable within highly expressed genes. While deviations in codon usage can be attributed to selection or mutational biases, they can also be functional, for example controlling gene expression or guiding protein structure. Several different metrics have been developed to identify biases in codon usage. Previously we released a database, CBDB: The Codon Bias Database, in which users could retrieve precalculated codon bias data for bacterial RefSeq genomes. With the increase of bacterial genome sequence data since its release a new tool was needed. Here we present the Dynamic Codon Biaser (DCB) tool, a web application that dynamically calculates the codon usage bias statistics of prokaryotic genomes. DCB bases these calculations on 40 different highly expressed genes (HEGs) that are highly conserved across different prokaryotic species. A user can either specify an NCBI accession number or upload their own sequence. DCB returns both the bias statistics and the genome’s HEG sequences. These calculations have several downstream applications, such as evolutionary studies and phage–host predictions. The source code is freely available, and the website is hosted at www.cbdb.info.

The growth of complexity in evolution is a most intriguing phenomenon. Using gene phylostratigraphy, we showed this growth (as reflected in regulatory mechanisms) in the human genome, tracing the path from prokaryotes to hominids. Generally, the different regulatory gene families expanded at different times, yet only up to the Euteleostomi (bony vertebrates). The only exception was the expansion of transcription factors (TF) in placentals, however, we argue that this was not related to increase in general complexity. Surprisingly, although TF originated in the Prokaryota while chromatin appeared only in the Eukaryota, the expansion of epigenetic factors predated the expansion of TF. Signaling receptors, tumor suppressors, oncogenes, and aging- and disease-associated genes (indicating vulnerabilities in terms of complex organization and strongly enrichment in regulatory genes) also expanded only up to the Euteleostomi. The complexity-related gene properties (protein size, number of alternative splicing mRNA, length of untranslated mRNA, number of biological processes per gene, number of disordered regions in a protein, and density of TF–TF interactions) rose in multicellular organisms and declined after the Euteleostomi, and possibly earlier. At the same time, the speed of protein sequence evolution sharply increased in the genes that originated after the Euteleostomi. Thus, several lines of evidence indicate that molecular mechanisms of complexity growth were changing with time, and in the phyletic lineage leading to humans, the most salient shift occurred after the basic vertebrate body plan was fixed with bony skeleton. The obtained results can be useful for evolutionary medicine.