Transcriptome analysis: a powerful tool to understand individual microbial behaviors and interactions in ecosystems.

Transcriptome analysis is a powerful tool for studying microbial ecology, especially individual microbial functions in an ecosystem and their interactions. With the development of high-throughput sequencing technology, great progress has been made in analytical methods for microbial communities in natural environments. 16S rRNA gene amplicon sequencing (ie microbial community structure analysis) and shotgun metagenome analysis have been widely used to determine the composition and potential metabolic capability of microorganisms in target environments without requiring culture. However, even if the types of microorganisms present and their genes are known, it is difficult to determine what they are doing in an ecosystem. Gene expression analysis (transcriptome analysis; RNA-seq) is a powerful tool to address these issues. The history and basic information of gene expression analysis, as well as examples of studies using this method to analyze microbial ecosystems, are presented.

Inferring microbial interactions with their environment from genomic and metagenomic data.

Microbial communities assemble through a complex set of interactions between microbes and their environment, and the resulting metabolic impact on the host ecosystem can be profound. Microbial activity is known to impact human health, plant growth, water quality, and soil carbon storage which has lead to the development of many approaches and products meant to manipulate the microbiome. In order to understand, predict, and improve microbial community engineering, genome-scale modeling techniques have been developed to translate genomic data into inferred microbial dynamics. However, these techniques rely heavily on simulation to draw conclusions which may vary with unknown parameters or initial conditions, rather than more robust qualitative analysis. To better understand microbial community dynamics using genome-scale modeling, we provide a tool to investigate the network of interactions between microbes and environmental metabolites over time. Using our previously developed algorithm for simulating microbial communities from genome-scale metabolic models (GSMs), we infer the set of microbe-metabolite interactions within a microbial community in a particular environment. Because these interactions depend on the available environmental metabolites, we refer to the networks that we infer as metabolically contextualized, and so name our tool MetConSIN: Metabolically Contextualized Species Interaction Networks.

Genotypic variations and interspecific interactions modify gene expression and biofilm formation of Xanthomonas retroflexus.

Multispecies biofilms are important models for studying the evolution of microbial interactions. Co-cultivation of Xanthomonas retroflexus (XR) and Paenibacillus amylolyticus (PA) systemically leads to the appearance of an XR wrinkled mutant (XRW), increasing biofilm production. The nature of this new interaction and the role of each partner remain unclear. We tested the involvement of secreted molecular cues in this interaction by exposing XR and XRW to PA or its supernatant and analysing the response using RNA-seq, colony-forming unit (CFU) estimates, biofilm quantification, and microscopy. Compared to wild type, the mutations in XRW altered its gene expression and increased its CFU number. These changes matched the reported effects for one of the mutated genes: a response regulator part of a two-component system involved in environmental sensing. When XRW was co-cultured with PA or its supernatant, the mutations effects on XRW gene expression were masked, except for genes involved in sedentary lifestyle, being consistent with the higher biofilm production. It appears that the higher biofilm production was the result of the interaction between the genetic context (mutations) and the biotic environment (PA signals). Regulatory genes involved in environmental sensing need to be considered to shed further light on microbial interactions.

Anti-CRISPR Discovery: Using Magnets to Find Needles in Haystacks.

CRISPR-Cas immune systems in bacteria and archaea protect against viral infection, which has spurred viruses to develop dedicated inhibitors of these systems called anti-CRISPRs (Acrs). Like most host-virus arms races, many diverse examples of these immune and counter-immune proteins are encoded by the genomes of bacteria, archaea, and their viruses. For the case of Acrs, it is almost certain that just a small minority of nature's true diversity has been described. In this review, I discuss the various approaches used to identify these Acrs and speculate on the future for Acr discovery. Because Acrs can determine infection outcomes in nature and regulate CRISPR-Cas activities in applied settings, they have a dual importance to both host-virus conflicts and emerging biotechnologies. Thus, revealing the largely hidden world of Acrs should provide important lessons in microbiology that have the potential to ripple far beyond the field.

Spatial-temporal dynamics of a microbial cooperative behavior resistant to cheating.

Much of our understanding of bacterial behavior stems from studies in liquid culture. In nature, however, bacteria frequently live in densely packed spatially-structured communities. How does spatial structure affect bacterial cooperative behaviors? In this work, we examine rhamnolipid production-a cooperative and virulent behavior of Pseudomonas aeruginosa. Here we show that, in striking contrast to well-mixed liquid culture, rhamnolipid gene expression in spatially-structured colonies is strongly associated with colony specific growth rate, and is impacted by perturbation with diffusible quorum signals. To interpret these findings, we construct a data-driven statistical inference model which captures a length-scale of bacterial interaction that develops over time. Finally, we find that perturbation of P. aeruginosa swarms with quorum signals preserves the cooperating genotype in competition, rather than creating opportunities for cheaters. Overall, our data demonstrate that the complex response to spatial localization is key to preserving bacterial cooperative behaviors.

Nutrient supply controls the linkage between species abundance and ecological interactions in marine bacterial communities.

Nutrient scarcity is pervasive for natural microbial communities, affecting species reproduction and co-existence. However, it remains unclear whether there are general rules of how microbial species abundances are shaped by biotic and abiotic factors. Here we show that the ribosomal RNA gene operon (rrn) copy number, a genomic trait related to bacterial growth rate and nutrient demand, decreases from the abundant to the rare biosphere in the nutrient-rich coastal sediment but exhibits the opposite pattern in the nutrient-scarce pelagic zone of the global ocean. Both patterns are underlain by positive correlations between community-level rrn copy number and nutrients. Furthermore, inter-species co-exclusion inferred by negative network associations is observed more in coastal sediment than in ocean water samples. Nutrient manipulation experiments yield effects of nutrient availability on rrn copy numbers and network associations that are consistent with our field observations. Based on these results, we propose a "hunger games" hypothesis to define microbial species abundance rules using the rrn copy number, ecological interaction, and nutrient availability.

Multi-kingdom microbiota analyses identify bacterial-fungal interactions and biomarkers of colorectal cancer across cohorts.

Despite recent progress in our understanding of the association between the gut microbiome and colorectal cancer (CRC), multi-kingdom gut microbiome dysbiosis in CRC across cohorts is unexplored. We investigated four-kingdom microbiota alterations using CRC metagenomic datasets of 1,368 samples from 8 distinct geographical cohorts. Integrated analysis identified 20 archaeal, 27 bacterial, 20 fungal and 21 viral species for each single-kingdom diagnostic model. However, our data revealed superior diagnostic accuracy for models constructed with multi-kingdom markers, in particular the addition of fungal species. Specifically, 16 multi-kingdom markers including 11 bacterial, 4 fungal and 1 archaeal feature, achieved good performance in diagnosing patients with CRC (area under the receiver operating characteristic curve (AUROC) = 0.83) and maintained accuracy across 3 independent cohorts. Coabundance analysis of the ecological network revealed associations between bacterial and fungal species, such as Talaromyces islandicus and Clostridium saccharobutylicum. Using metagenome shotgun sequencing data, the predictive power of the microbial functional potential was explored and elevated D-amino acid metabolism and butanoate metabolism were observed in CRC. Interestingly, the diagnostic model based on functional EggNOG genes achieved high accuracy (AUROC = 0.86). Collectively, our findings uncovered CRC-associated microbiota common across cohorts and demonstrate the applicability of multi-kingdom and functional markers as CRC diagnostic tools and, potentially, as therapeutic targets for the treatment of CRC.

The evolution of mechanisms to produce phenotypic heterogeneity in microorganisms.

In bacteria and other microorganisms, the cells within a population often show extreme phenotypic variation. Different species use different mechanisms to determine how distinct phenotypes are allocated between individuals, including coordinated, random, and genetic determination. However, it is not clear if this diversity in mechanisms is adaptive-arising because different mechanisms are favoured in different environments-or is merely the result of non-adaptive artifacts of evolution. We use theoretical models to analyse the relative advantages of the two dominant mechanisms to divide labour between reproductives and helpers in microorganisms. We show that coordinated specialisation is more likely to evolve over random specialisation in well-mixed groups when: (i) social groups are small; (ii) helping is more "essential"; and (iii) there is a low metabolic cost to coordination. We find analogous results when we allow for spatial structure with a more detailed model of cellular filaments. More generally, this work shows how diversity in the mechanisms to produce phenotypic heterogeneity could have arisen as adaptations to different environments.

Whole-Genome Sequencing of Corallococcus sp. Strain EGB Reveals the Genetic Determinants Linking Taxonomy and Predatory Behavior.

Corallococcus sp. strain EGB is a Gram-negative myxobacteria isolated from saline soil, and has considerable potential for the biocontrol of phytopathogenic fungi. However, the detailed mechanisms related to development and predatory behavior are unclear. To obtain a comprehensive overview of genetic features, the genome of strain EGB was sequenced, annotated, and compared with 10 other Corallococcus species. The strain EGB genome was assembled as a single circular chromosome of 9.4 Mb with 7916 coding genes. Phylogenomics analysis showed that strain EGB was most closely related to Corallococcus interemptor AB047A, and it was inferred to be a novel species within the Corallococcus genus. Comparative genomic analysis revealed that the pan-genome of Corallococcus genus was large and open. Only a small proportion of genes were specific to strain EGB, and most of them were annotated as hypothetical proteins. Subsequent analyses showed that strain EGB produced abundant extracellular enzymes such as chitinases and ß-(1,3)-glucanases, and proteases to degrade the cell-wall components of phytopathogenic fungi. In addition, 35 biosynthetic gene clusters potentially coding for antimicrobial compounds were identified in the strain EGB, and the majority of them were present in the dispensable pan-genome with unexplored metabolites. Other genes related to secretion and regulation were also explored for strain EGB. This study opens new perspectives in the greater understanding of the predatory behavior of strain EGB, and facilitates a potential application in the biocontrol of fungal plant diseases in the future.

OrtSuite: from genomes to prediction of microbial interactions within targeted ecosystem processes.

The high complexity found in microbial communities makes the identification of microbial interactions challenging. To address this challenge, we present OrtSuite, a flexible workflow to predict putative microbial interactions based on genomic content of microbial communities and targeted to specific ecosystem processes. The pipeline is composed of three user-friendly bash commands. OrtSuite combines ortholog clustering with genome annotation strategies limited to user-defined sets of functions allowing for hypothesis-driven data analysis such as assessing microbial interactions in specific ecosystems. OrtSuite matched, on average, 96% of experimentally verified KEGG orthologs involved in benzoate degradation in a known group of benzoate degraders. We evaluated the identification of putative synergistic species interactions using the sequenced genomes of an independent study that had previously proposed potential species interactions in benzoate degradation. OrtSuite is an easy-to-use workflow that allows for rapid functional annotation based on a user-curated database and can easily be extended to ecosystem processes where connections between genes and reactions are known. OrtSuite is an open-source software available at https://github.com/mdsufz/OrtSuite.

The mutated Bacillus amyloliquefaciens strain shows high resistance to Aeromonas hydrophila and Aeromonas veronii in grass carp.

Bacillus amyloliquefaciens X030 (BaX030) has broad-spectrum antibacterial activity against the fish pathogens Aeromonas hydrophila and Aeromonas veronii. To improve its antibacterial effect, BaX030 was subjected to compound mutagenesis of atmospheric and room temperature plasma (ARTP) and nitrosoguanidine (NTG). The results showed that, compared with the original strain, the production of macrolactin A and oxydifficidin in mutated strain N-11 increased to 39 % and 268 %, respectively. The re-sequencing analysis suggested that there were SNPs and InDels in the gene clusters focused on the sucrose utilization pathway, glycolysis pathway and fatty acid synthesis pathway. Scanning electron microscopy revealed that strain N-11 became thin and long. The qRT-PCR results indicated that the expression of immune factors in the liver or kidney tissue of grass carp increased after feeding with N-11. H&E staining and protection experiments also showed that the mortality and surface symptoms of grass carp infected by the two pathogens were significantly reduced. The study identified a probiotic strain with potential application value in aquaculture production and provided a new strategy for the discovery of new strains with higher antibacterial biological activity.

(Optochemical) Control of Synthetic Microbial Coculture Interactions on a Microcolony Level.

Synthetic microbial cocultures carry enormous potential for applied biotechnology and are increasingly the subject of fundamental research. So far, most cocultures have been designed and characterized based on bulk cultivations without considering the potentially highly heterogeneous and diverse single-cell behavior. However, an in-depth understanding of cocultures including their interacting single cells is indispensable for the development of novel cultivation approaches and control of cocultures. We present the development, validation, and experimental characterization of an optochemically controllable bacterial coculture on a microcolony level consisting of two Corynebacterium glutamicum strains. Our coculture combines an l-lysine auxotrophic strain together with a l-lysine-producing variant carrying the genetically IPTG-mediated induction of l-lysine production. We implemented two control approaches utilizing IPTG as inducer molecule. First, unmodified IPTG was supplemented to the culture enabling a medium-based control of the production of l-lysine, which serves as the main interacting component. Second, optochemical control was successfully performed by utilizing photocaged IPTG activated by appropriate illumination. Both control strategies were validated studying cellular growth on a microcolony level. The novel microfluidic single-cell cultivation strategies applied in this work can serve as a blueprint to validate cellular control strategies of synthetic mono- and cocultures with single-cell resolution at defined environmental conditions.

How does feedback from phage infections influence the evolution of phase variation in Campylobacter?

Campylobacter jejuni (C. jejuni) causes gastroenteritis following the consumption of contaminated poultry meat, resulting in a large health and economic burden worldwide. Phage therapy is a promising technique for eradicating C. jejuni from poultry flocks and chicken carcasses. However, C. jejuni can resist infections by some phages through stochastic, phase-variable ON/OFF switching of the phage receptors mediated by simple sequence repeats (SSR). While selection strength and exposure time influence the evolution of SSR-mediated phase variation (PV), phages offer a more complex evolutionary environment as phage replication depends on having a permissive host organism. Here, we build and explore several continuous culture bacteria-phage computational models, each analysing different phase-variable scenarios calibrated to the experimental SSR rates of C. jejuni loci and replication parameters for the F336 phage. We simulate the evolution of PV rates via the adaptive dynamics framework for varying levels of selective pressures that act on the phage-resistant state. Our results indicate that growth reducing counter-selection on a single PV locus results in the stable maintenance of the phage, while compensatory selection between bacterial states affects the evolutionary stable mutation rates (i.e. very high and very low mutation rates are evolutionarily disadvantageous), whereas, in the absence of either selective pressure the evolution of PV rates results in mutation rates below the basal values. Contrastingly, a biologically-relevant model with two phase-variable loci resulted in phage extinction and locking of the bacteria into a phage-resistant state suggesting that another counter-selective pressure is required, instance, the use of a distinct phage whose receptor is an F336-phage-resistant state. We conclude that a delicate balance between counter-selection and phage-attack can result in both the evolution of phase-variable phage receptors and persistence of PV-receptor-specific phage.

Pseudomonas aeruginosa T6SS-mediated molybdate transport contributes to bacterial competition during anaerobiosis.

Type VI secretion system (T6SS) is widely distributed in Gram-negative bacteria and functions as a versatile protein export machinery that translocates effectors into eukaryotic or prokaryotic target cells. Growing evidence indicates that T6SS can deliver several effectors to promote bacterial survival in harmful environments through metal ion acquisition. Here, we report that the Pseudomonas aeruginosa H2-T6SS mediates molybdate (MoO42-) acquisition by secretion of a molybdate-binding protein, ModA. The expression of H2-T6SS genes is activated by the master regulator Anr and anaerobiosis. We also identified a ModA-binding protein, IcmP, an insulin-cleaving metalloproteinase outer membrane protein. The T6SS-ModA-IcmP system provides P. aeruginosa with a growth advantage in bacterial competition under anaerobic conditions and plays an important role in bacterial virulence. Overall, this study clarifies the role of T6SS in secretion of an anion-binding protein, emphasizing the fundamental importance of this bacterium using T6SS-mediated molybdate uptake to adapt to complex environmental conditions.

Integrative microbiomics in bronchiectasis exacerbations.

Bronchiectasis, a progressive chronic airway disease, is characterized by microbial colonization and infection. We present an approach to the multi-biome that integrates bacterial, viral and fungal communities in bronchiectasis through weighted similarity network fusion ( https://integrative-microbiomics.ntu.edu.sg ). Patients at greatest risk of exacerbation have less complex microbial co-occurrence networks, reduced diversity and a higher degree of antagonistic interactions in their airway microbiome. Furthermore, longitudinal interactome dynamics reveals microbial antagonism during exacerbation, which resolves following treatment in an otherwise stable multi-biome. Assessment of the Pseudomonas interactome shows that interaction networks, rather than abundance alone, are associated with exacerbation risk, and that incorporation of microbial interaction data improves clinical prediction models. Shotgun metagenomic sequencing of an independent cohort validated the multi-biome interactions detected in targeted analysis and confirmed the association with exacerbation. Integrative microbiomics captures microbial interactions to determine exacerbation risk, which cannot be appreciated by the study of a single microbial group. Antibiotic strategies probably target the interaction networks rather than individual microbes, providing a fresh approach to the understanding of respiratory infection.

In Vivo Competitions between Fibrobacter succinogenes, Ruminococcus flavefaciens, and Ruminoccus albus in a Gnotobiotic Sheep Model Revealed by Multi-Omic Analyses.

Fibrobacter succinogenes, Ruminococcus albus, and Ruminococcus flavefaciens are the three predominant cellulolytic bacterial species found in the rumen. In vitro studies have shown that these species compete for adherence to, and growth upon, cellulosic biomass. Yet their molecular interactions in vivo have not heretofore been examined. Gnotobiotically raised lambs harboring a 17-h-old immature microbiota devoid of culturable cellulolytic bacteria and methanogens were inoculated first with F. succinogenes S85 and Methanobrevibacter sp. strain 87.7, and 5 months later, the lambs were inoculated with R. albus 8 and R. flavefaciens FD-1. Longitudinal samples were collected and profiled for population dynamics, gene expression, fibrolytic enzyme activity, in sacco fibrolysis, and metabolite profiling. Quantitative PCR, metagenome and metatranscriptome data show that F. succinogenes establishes at high levels initially but is gradually outcompeted following the introduction of the ruminococci. This shift resulted in an increase in carboxymethyl cellulase (CMCase) and xylanase activities but not in greater fibrolysis, suggesting that F. succinogenes and ruminococci deploy different but equally effective means to degrade plant cell walls. Expression profiles showed that F. succinogenes relied upon outer membrane vesicles and a diverse repertoire of CAZymes, while R. albus and R. flavefaciens preferred type IV pili and either CBM37-harboring or cellulosomal carbohydrate-active enzymes (CAZymes), respectively. The changes in cellulolytics also affected the rumen metabolome, including an increase in acetate and butyrate at the expense of propionate. In conclusion, this study provides the first demonstration of in vivo competition between the three predominant cellulolytic bacteria and provides insight on the influence of these ecological interactions on rumen fibrolytic function and metabolomic response.IMPORTANCE Ruminant animals, including cattle and sheep, depend on their rumen microbiota to digest plant biomass and convert it into absorbable energy. Considering that the extent of meat and milk production depends on the efficiency of the microbiota to deconstruct plant cell walls, the functionality of predominant rumen cellulolytic bacteria, Fibrobacter succinogenes, Ruminococcus albus, and Ruminococcus flavefaciens, has been extensively studied in vitro to obtain a better knowledge of how they operate to hydrolyze polysaccharides and ultimately find ways to enhance animal production. This study provides the first evidence of in vivo competitions between F. succinogenes and the two Ruminococcus species. It shows that a simple disequilibrium within the cellulolytic community has repercussions on the rumen metabolome and fermentation end products. This finding will have to be considered in the future when determining strategies aiming at directing rumen fermentations for animal production.

Roles of bacteriophages, plasmids and CRISPR immunity in microbial community dynamics revealed using time-series integrated meta-omics.

Viruses and plasmids (invasive mobile genetic elements (iMGEs)) have important roles in shaping microbial communities, but their dynamic interactions with CRISPR-based immunity remain unresolved. We analysed generation-resolved iMGE-host dynamics spanning one and a half years in a microbial consortium from a biological wastewater treatment plant using integrated meta-omics. We identified 31 bacterial metagenome-assembled genomes encoding complete CRISPR-Cas systems and their corresponding iMGEs. CRISPR-targeted plasmids outnumbered their bacteriophage counterparts by at least fivefold, highlighting the importance of CRISPR-mediated defence against plasmids. Linear modelling of our time-series data revealed that the variation in plasmid abundance over time explained more of the observed community dynamics than phages. Community-scale CRISPR-based plasmid-host and phage-host interaction networks revealed an increase in CRISPR-mediated interactions coinciding with a decrease in the dominant 'Candidatus Microthrix parvicella' population. Protospacers were enriched in sequences targeting genes involved in the transmission of iMGEs. Understanding the factors shaping the fitness of specific populations is necessary to devise control strategies for undesirable species and to predict or explain community-wide phenotypes.

A Deep Learning-Based Method for Identification of Bacteriophage-Host Interaction.

Multi-drug resistance (MDR) has become one of the greatest threats to human health worldwide, and novel treatment methods of infections caused by MDR bacteria are urgently needed. Phage therapy is a promising alternative to solve this problem, to which the key is correctly matching target pathogenic bacteria with the corresponding therapeutic phage. Deep learning is powerful for mining complex patterns to generate accurate predictions. In this study, we develop PredPHI (Predicting Phage-Host Interactions), a deep learning-based tool capable of predicting the host of phages from sequence data. We collect >3000 phage-host pairs along with their protein sequences from PhagesDB and GenBank databases and extract a set of features. Then we select high-quality negative samples based on the K-Means clustering method and construct a balanced training set. Finally, we employ a deep convolutional neural network to build the predictive model. The results indicate that PredPHI can achieve a predictive performance of 81 percent in terms of the area under the receiver operating characteristic curve on the test set, and the clustering-based method is significantly more robust than that based on randomly selecting negative samples. These results highlight that PredPHI is a useful and accurate tool for identifying phage-host interactions from sequence data.

Genome Sequence of the Biocontrol Agent Coniothyrium minitans Conio (IMI 134523).

Coniothyrium minitans (synonym, Paraphaeosphaeria minitans) is a highly specific mycoparasite of the wide host range crop pathogen Sclerotinia sclerotiorum. The capability of C. minitans to destroy the sclerotia of S. sclerotiorum has been well recognized and it is available as a widely used biocontrol product Contans WG. We present the draft genome sequence of C. minitans Conio (IMI 134523), which has previously been used in extensive studies that formed part of a registration package of the commercial product. This work provides a distinctive resource for further research into the molecular basis of mycoparasitism to harness the biocontrol potential of C. minitans.[Formula: see text] Copyright © 2021 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Seasonal and diel patterns of abundance and activity of viruses in the Red Sea.

Virus-microbe interactions have been studied in great molecular details for many years in cultured model systems, yielding a plethora of knowledge on how viruses use and manipulate host machinery. Since the advent of molecular techniques and high-throughput sequencing, methods such as cooccurrence, nucleotide composition, and other statistical frameworks have been widely used to infer virus-microbe interactions, overcoming the limitations of culturing methods. However, their accuracy and relevance is still debatable as cooccurrence does not necessarily mean interaction. Here we introduce an ecological perspective of marine viral communities and potential interaction with their hosts, using analyses that make no prior assumptions on specific virus-host pairs. By size fractionating water samples into free viruses and microbes (i.e., also viruses inside or attached to their hosts) and looking at how viral group abundance changes over time along both fractions, we show that the viral community is undergoing a change in rank abundance across seasons, suggesting a seasonal succession of viruses in the Red Sea. We use abundance patterns in the different size fractions to classify viral clusters, indicating potential diverse interactions with their hosts and potential differences in life history traits between major viral groups. Finally, we show hourly resolved variations of intracellular abundance of similar viral groups, which might indicate differences in their infection cycles or metabolic capacities.