Integrating microbial abundance time series with fermentation dynamics of the rumen microbiome via mathematical modelling.

The rumen represents a dynamic microbial ecosystem where fermentation metabolites and microbial concentrations change over time in response to dietary changes. The integration of microbial genomic knowledge and dynamic modelling can enhance our system-level understanding of rumen ecosystem's function. However, such an integration between dynamic models and rumen microbiota data is lacking. The objective of this work was to integrate rumen microbiota time series determined by 16S rRNA gene amplicon sequencing into a dynamic modelling framework to link microbial data to the dynamics of the volatile fatty acids (VFA) production during fermentation. For that, we used the theory of state observers to develop a model that estimates the dynamics of VFA from the data of microbial functional proxies associated with the specific production of each VFA. We determined the microbial proxies using CowPi to infer the functional potential of the rumen microbiota and extrapolate their functional modules from KEGG (Kyoto Encyclopedia of Genes and Genomes). The approach was challenged using data from an in vitro RUSITEC experiment and from an in vivo experiment with four cows. The model performance was evaluated by the coefficient of variation of the root mean square error (CRMSE). For the in vitro case study, the mean CVRMSE were 9.8% for acetate, 14% for butyrate and 14.5% for propionate. For the in vivo case study, the mean CVRMSE were 16.4% for acetate, 15.8% for butyrate and 19.8% for propionate. The mean CVRMSE for the VFA molar fractions were 3.1% for acetate, 3.8% for butyrate and 8.9% for propionate. Ours results show the promising application of state observers integrated with microbiota time series data for predicting rumen microbial metabolism.

Accessory genes define species-specific routes to antibiotic resistance.

A deeper understanding of the relationship between the antimicrobial resistance (AMR) gene carriage and phenotype is necessary to develop effective response strategies against this global burden. AMR phenotype is often a result of multi-gene interactions; therefore, we need approaches that go beyond current simple AMR gene identification tools. Machine-learning (ML) methods may meet this challenge and allow the development of rapid computational approaches for AMR phenotype classification. To examine this, we applied multiple ML techniques to 16,950 bacterial genomes across 28 genera, with corresponding MICs for 23 antibiotics with the aim of training models to accurately determine the AMR phenotype from sequenced genomes. This resulted in a >1.5-fold increase in AMR phenotype prediction accuracy over AMR gene identification alone. Furthermore, we revealed 528 unique (often species-specific) genomic routes to antibiotic resistance, including genes not previously linked to the AMR phenotype. Our study demonstrates the utility of ML in predicting AMR phenotypes across diverse clinically relevant organisms and antibiotics. This research proposes a rapid computational method to support laboratory-based identification of the AMR phenotype in pathogens.

Designing a synthetic microbial community through genome metabolic modeling to enhance plant-microbe interaction.

BACKGROUND: Manipulating the rhizosphere microbial community through beneficial microorganism inoculation has gained interest in improving crop productivity and stress resistance. Synthetic microbial communities, known as SynComs, mimic natural microbial compositions while reducing the number of components. However, achieving this goal requires a comprehensive understanding of natural microbial communities and carefully selecting compatible microorganisms with colonization traits, which still pose challenges. In this study, we employed multi-genome metabolic modeling of 270 previously described metagenome-assembled genomes from Campos rupestres to design a synthetic microbial community to improve the yield of important crop plants. RESULTS: We used a targeted approach to select a minimal community (MinCom) encompassing essential compounds for microbial metabolism and compounds relevant to plant interactions. This resulted in a reduction of the initial community size by approximately 4.5-fold. Notably, the MinCom retained crucial genes associated with essential plant growth-promoting traits, such as iron acquisition, exopolysaccharide production, potassium solubilization, nitrogen fixation, GABA production, and IAA-related tryptophan metabolism. Furthermore, our in-silico selection for the SymComs, based on a comprehensive understanding of microbe-microbe-plant interactions, yielded a set of six hub species that displayed notable taxonomic novelty, including members of the Eremiobacterota and Verrucomicrobiota phyla. CONCLUSION: Overall, the study contributes to the growing body of research on synthetic microbial communities and their potential to enhance agricultural practices. The insights gained from our in-silico approach and the selection of hub species pave the way for further investigations into the development of tailored microbial communities that can optimize crop productivity and improve stress resilience in agricultural systems.

StORF-Reporter: finding genes between genes.

Large regions of prokaryotic genomes are currently without any annotation, in part due to well-established limitations of annotation tools. For example, it is routine for genes using alternative start codons to be misreported or completely omitted. Therefore, we present StORF-Reporter, a tool that takes an annotated genome and returns regions that may contain missing CDS genes from unannotated regions. StORF-Reporter consists of two parts. The first begins with the extraction of unannotated regions from an annotated genome. Next, Stop-ORFs (StORFs) are identified in these unannotated regions. StORFs are open reading frames that are delimited by stop codons and thus can capture those genes most often missing in genome annotations. We show this methodology recovers genes missing from canonical genome annotations. We inspect the results of the genomes of model organisms, the pangenome of Escherichia coli, and a set of 5109 prokaryotic genomes of 247 genera from the Ensembl Bacteria database. StORF-Reporter extended the core, soft-core and accessory gene collections, identified novel gene families and extended families into additional genera. The high levels of sequence conservation observed between genera suggest that many of these StORFs are likely to be functional genes that should now be considered for inclusion in canonical annotations.

Beehives possess their own distinct microbiomes.

BACKGROUND: Honeybees use plant material to manufacture their own food. These insect pollinators visit flowers repeatedly to collect nectar and pollen, which are shared with other hive bees to produce honey and beebread. While producing these products, beehives accumulate a considerable number of microbes, including bacteria that derive from plants and different parts of the honeybees' body. Whether bacteria form similar communities amongst beehives, even if located in close proximity, is an ecologically important question that has been addressed in this study. Specific ecological factors such as the surrounding environment and the beekeeping methods used can shape the microbiome of the beehive as a whole, and eventually influence the health of the honeybees and their ecosystem. RESULTS: We conducted 16S rRNA meta-taxonomic analysis on honey and beebread samples that were collected from 15 apiaries in the southeast of England to quantify the bacteria associated with different beehives. We observed that honeybee products carry a significant variety of bacterial groups that comprise bee commensals, environmental bacteria and symbionts and pathogens of plants and animals. Remarkably, this bacterial diversity differs not only amongst apiaries, but also between the beehives of the same apiary. In particular, the levels of the bee commensals varied significantly, and their fluctuations correlated with the presence of different environmental bacteria and various apiculture practices. CONCLUSIONS: Our results show that every hive possesses their own distinct microbiome and that this very defined fingerprint is affected by multiple factors such as the nectar and pollen gathered from local plants, the management of the apiaries and the bacterial communities living around the beehives. Based on our findings, we suggest that the microbiome of beehives could be used as a valuable biosensor informing of the health of the honeybees and their surrounding environment.

Improving Orthologous Signal and Model Fit in Datasets Addressing the Root of the Animal Phylogeny.

There is conflicting evidence as to whether Porifera (sponges) or Ctenophora (comb jellies) comprise the root of the animal phylogeny. Support for either a Porifera-sister or Ctenophore-sister tree has been extensively examined in the context of model selection, taxon sampling, and outgroup selection. The influence of dataset construction is comparatively understudied. We re-examine five animal phylogeny datasets that have supported either root hypothesis using an approach designed to enrich orthologous signal in phylogenomic datasets. We find that many component orthogroups in animal datasets fail to recover major lineages as monophyletic with the exception of Ctenophora, regardless of the supported root. Enriching these datasets to retain orthogroups recovering ≥3 major lineages reduces dataset size by up to 50% while retaining underlying phylogenetic information and taxon sampling. Site-heterogeneous phylogenomic analysis of these enriched datasets recovers both Porifera-sister and Ctenophora-sister positions, even with additional constraints on outgroup sampling. Two datasets which previously supported Ctenophora-sister support Porifera-sister upon enrichment. All enriched datasets display improved model fitness under posterior predictive analysis. While not conclusively rooting animals at either Porifera or Ctenophora, we do see an increase in signal for Porifera-sister and a decrease in signal for Ctenophore-sister when data are filtered for orthologous signal. Our results indicate that dataset size and construction as well as model fit influence animal root inference.

Filtering artifactual signal increases support for Xenacoelomorpha and Ambulacraria sister relationship in the animal tree of life.

Conflicting studies place a group of bilaterian invertebrates containing xenoturbellids and acoelomorphs, the Xenacoelomorpha, as either the primary emerging bilaterian phylum1,2,3,4,5,6 or within Deuterostomia, sister to Ambulacraria.7,8,9,10,11 Although their placement as sister to the rest of Bilateria supports relatively simple morphology in the ancestral bilaterian, their alternative placement within Deuterostomia suggests a morphologically complex ancestral bilaterian along with extensive loss of major phenotypic traits in the Xenacoelomorpha. Recent studies have questioned whether Deuterostomia should be considered monophyletic at all.10,12,13 Hidden paralogy and poor phylogenetic signal present a major challenge for reconstructing species phylogenies.14,15,16,17,18 Here, we assess whether these issues have contributed to the conflict over the placement of Xenacoelomorpha. We reanalyzed published datasets, enriching for orthogroups whose gene trees support well-resolved clans elsewhere in the animal tree.16 We find that most genes in previously published datasets violate incontestable clans, suggesting that hidden paralogy and low phylogenetic signal affect the ability to reconstruct branching patterns at deep nodes in the animal tree. We demonstrate that removing orthogroups that cannot recapitulate incontestable relationships alters the final topology that is inferred, while simultaneously improving the fit of the model to the data. We discover increased, but ultimately not conclusive, support for the existence of Xenambulacraria in our set of filtered orthogroups. At a time when we are progressing toward sequencing all life on the planet, we argue that long-standing contentious issues in the tree of life will be resolved using smaller amounts of better quality data that can be modeled adequately.19.

Microbiome-derived antimicrobial peptides offer therapeutic solutions for the treatment of Pseudomonas aeruginosa infections.

Microbiomes are rife for biotechnological exploitation, particularly the rumen microbiome, due to their complexicity and diversity. In this study, antimicrobial peptides (AMPs) from the rumen microbiome (Lynronne 1, 2, 3 and P15s) were assessed for their therapeutic potential against seven clinical strains of Pseudomonas aeruginosa. All AMPs exhibited antimicrobial activity against all strains, with minimum inhibitory concentrations (MICs) ranging from 4-512 µg/mL. Time-kill kinetics of all AMPs at 3× MIC values against strains PAO1 and LES431 showed complete kill within 10 min to 4 h, although P15s was not bactericidal against PAO1. All AMPs significantly inhibited biofilm formation by strains PAO1 and LES431, and induction of resistance assays showed no decrease in activity against these strains. AMP cytotoxicity against human lung cells was also minimal. In terms of mechanism of action, the AMPs showed affinity towards PAO1 and LES431 bacterial membrane lipids, efficiently permeabilising the P. aeruginosa membrane. Transcriptome and metabolome analysis revealed increased catalytic activity at the cell membrane and promotion of ß-oxidation of fatty acids. Finally, tests performed with the Galleria mellonella infection model showed that Lynronne 1 and 2 were efficacious in vivo, with a 100% survival rate following treatment at 32 mg/kg and 128 mg/kg, respectively. This study illustrates the therapeutic potential of microbiome-derived AMPs against P. aeruginosa infections.

In silico identification of two peptides with antibacterial activity against multidrug-resistant Staphylococcus aureus.

Here we report two antimicrobial peptides (AMPs), HG2 and HG4 identified from a rumen microbiome metagenomic dataset, with activity against multidrug-resistant (MDR) bacteria, especially methicillin-resistant Staphylococcus aureus (MRSA) strains, a major hospital and community-acquired pathogen. We employed the classifier model design to analyse, visualise, and interpret AMP activities. This approach allowed in silico discrimination of promising lead AMP candidates for experimental evaluation. The lead AMPs, HG2 and HG4, are fast-acting and show anti-biofilm and anti-inflammatory activities in vitro and demonstrated little toxicity to human primary cell lines. The peptides were effective in vivo within a Galleria mellonella model of MRSA USA300 infection. In terms of mechanism of action, HG2 and HG4 appear to interact with the cytoplasmic membrane of target cells and may inhibit other cellular processes, whilst preferentially binding to bacterial lipids over human cell lipids. Therefore, these AMPs may offer additional therapeutic templates for MDR bacterial infections.

Using the forces of hydrodynamic countercurrent chromatography for the study of bacteriophages.

Bacteriophages (phages) are viruses that target bacteria, with the ability to lyse and kill host bacterial cells. Due to this, they have been of some interest as a therapeutic since their discovery in the early 1900s, but with the recent increase in antibiotic resistance, phages have seen a resurgence in attention. Current methods of isolation and purification of phages can be long and tedious, with caesium chloride concentration gradients the gold standard for purifying a phage fraction. Isolation of novel phages requires centrifugation and ultrafiltration of mixed samples, such as water sources, effluent or faecal samples etc, to prepare phage filtrates for further testing. We propose countercurrent chromatography as a novel and alternative approach to use when studying phages, as a scalable and high-yield method for obtaining phage fractions. However, the full extent of the usefulness and resolution of separation with this technique has not been researched; it requires optimization and ample testing before this can be revealed. Here we present an initial study to determine survivability of two phages, T4 and ϕX174, using only water as a mobile phase in a Spectrum Series 20 HPCCC. Both phages were found to remain active once eluted from the column. Phages do not fully elute from the column and sodium hydroxide is necessary to flush the column between runs to deactivate remaining phages.

Forage grass growth under future climate change scenarios affects fermentation and ruminant efficiency.

With an increasing human population access to ruminant products is an important factor in global food supply. While ruminants contribute to climate change, climate change could also affect ruminant production. Here we investigated how the plant response to climate change affects forage quality and subsequent rumen fermentation. Models of near future climate change (2050) predict increases in temperature, CO2, precipitation and altered weather systems which will produce stress responses in field crops. We hypothesised that pre-exposure to altered climate conditions causes compositional changes and also primes plant cells such that their post-ingestion metabolic response to the rumen is altered. This "stress memory" effect was investigated by screening ten forage grass varieties in five differing climate scenarios, including current climate (2020), future climate (2050), or future climate plus flooding, drought or heat shock. While varietal differences in fermentation were detected in terms of gas production, there was little effect of elevated temperature or CO2 compared with controls (2020). All varieties consistently showed decreased digestibility linked to decreased methane production as a result of drought or an acute flood treatment. These results indicate that efforts to breed future forage varieties should target tolerance of acute stress rather than long term climate.

Whole-Genome Sequencing and Comparative Genomic Analysis of Antimicrobial Producing Streptococcus lutetiensis from the Rumen.

Antimicrobial peptides (AMPs) can efficiently control different microbial pathogens and show the potential to be applied in clinical practice and livestock production. In this work, the aim was to isolate AMP-producing ruminal streptococci and to characterize their genetic features through whole-genome sequencing. We cultured 463 bacterial isolates from the rumen of Nelore bulls, 81 of which were phenotypically classified as being Streptococcaceae. Five isolates with broad-range activity were genome sequenced and confirmed as being Streptococcus lutetiensis. The genetic features linked to their antimicrobial activity or adaptation to the rumen environment were characterized through comparative genomics. The genome of S. lutetiensis UFV80 harbored a putative CRISPR-Cas9 system (Type IIA). Computational tools were used to discover novel biosynthetic clusters linked to the production of bacteriocins. All bacterial genomes harbored genetic clusters related to the biosynthesis of class I and class II bacteriocins. SDS-PAGE confirmed the results obtained in silico and demonstrated that the class II bacteriocins predicted in the genomes of three S. lutetiensis strains had identical molecular mass (5197 Da). These results demonstrate that ruminal bacteria of the Streptococcus bovis/equinus complex represent a promising source of novel antimicrobial peptides.

Adult schistosomes have an epithelial bacterial population distinct from the surrounding mammalian host blood.

BACKGROUND: Schistosomiasis is a neglected tropical parasitic and chronic disease affecting hundreds of millions of people. Adult schistosomes reside in the blood stream of the definitive mammalian host. These helminth parasites possess two epithelial surfaces, the tegument and the gastrodermis, both of which interact with the host during immune evasion and in nutrient uptake. METHODS: Female ARC Swiss mice (4-6 weeks old) were infected percutaneously with Schistosoma japonicum cercariae freshly shed from Oncomelania hupensis quadrasi snails (Philippines strain). Fluorescent in situ hybridisation (FISH) was performed by using fresh adult S. japonicum perfused from those infected mice. Adult S. japonicum worms were processed to isolate the tegument from the carcass containing the gastrodermis; blood and bile were collected individually from infected and uninfected mice. Total DNA extracted from all those samples were used for microbiome profiling. RESULTS: FISH and microbiome profiling showed the presence of bacterial populations on two epithelial surfaces of adult worms, suggesting they were distinct not only from the host blood but also from each other. Whereas microbial diversity was reduced overall in the parasite epithelial tissues when compared with that of host blood, specific bacterial taxa, including Anoxybacillus and Escherichia, were elevated on the tegument. Minimal differences were evident in the microbiome of host blood during an active infection, compared with that of control uninfected blood. However, sampling of bile from infected animals identified some differences compared with controls, including elevated levels of Limnohabitans, Clostridium and Curvibacter. CONCLUSIONS: Using FISH and microbial profiling, we were able to demonstrate, for the first time, that bacteria are presented on the epithelial surfaces of adult schistosomes. These schistosome surface-associated bacteria, which are distinct from the host blood microenvironment, should be considered as a new and important component of the host-schistosome interaction. The importance of individual bacterial species in relation to schistosome parasitism needs further elucidation.

No one tool to rule them all: prokaryotic gene prediction tool annotations are highly dependent on the organism of study.

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.

Characterization of an Ex Vivo Equine Endometrial Tissue Culture Model Using Next-Generation RNA-Sequencing Technology.

Persistent mating-induced endometritis is a major cause of poor fertility rates in the mare. Endometritis can be investigated using an ex vivo equine endometrial explant system which measures uterine inflammation using prostaglandin F2α as a biomarker. However, this model has yet to undergo a wide-ranging assessment through transcriptomics. In this study, we assessed the transcriptomes of cultured endometrial explants and the optimal temporal window for their use. Endometrium harvested immediately post-mortem from native pony mares (n = 8) were sampled (0 h) and tissue explants were cultured for 24, 48 and 72 h. Tissues were stored in RNALater, total RNA was extracted and sequenced. Differentially expressed genes (DEGs) were defined using DESeq2 (R/Bioconductor). Principal component analysis indicated that the greatest changes in expression occurred in the first 24 h of culture when compared to autologous biopsies at 0 h. Fewer DEGs were seen between 24 and 48 h of culture suggesting the system was more stable than during the first 24 h. No genes were differentially expressed between 48 and 72 h but the low number of background gene expression suggested that explant viability was compromised after 48 h. ESR1, MMP9, PTGS2, PMAIP1, TNF, GADD45B and SELE genes were used as biomarkers of endometrial function, cell death and inflammation across tissue culture timepoints. STRING assessments of gene ontology suggested that DEGs between 24 and 48 h were linked to inflammation, immune system, cellular processes, environmental information processing and signal transduction, with an upregulation of most biomarker genes at 24 h. Taken together our observations indicated that 24-48 h is the optimal temporal window when the explant model can be used, as explants restore microcirculation, perform wound healing and tackle inflammation during this period. This key observation will facilitate the appropriate use of this as a model for further research into the equine endometrium and potentially the progression of mating-induced endometritis to persistent inflammation between 24 and 48 h.

Microbiomes attached to fresh perennial ryegrass are temporally resilient and adapt to changing ecological niches.

BACKGROUND: Gut microbiomes, such as the rumen, greatly influence host nutrition due to their feed energy-harvesting capacity. We investigated temporal ecological interactions facilitating energy harvesting at the fresh perennial ryegrass (PRG)-biofilm interface in the rumen using an in sacco approach and prokaryotic metatranscriptomic profiling. RESULTS: Network analysis identified two distinct sub-microbiomes primarily representing primary (≤ 4 h) and secondary (≥ 4 h) colonisation phases and the most transcriptionally active bacterial families (i.e Fibrobacteriaceae, Selemondaceae and Methanobacteriaceae) did not interact with either sub-microbiome, indicating non-cooperative behaviour. Conversely, Prevotellaceae had most transcriptional activity within the primary sub-microbiome (focussed on protein metabolism) and Lachnospiraceae within the secondary sub-microbiome (focussed on carbohydrate degradation). Putative keystone taxa, with low transcriptional activity, were identified within both sub-microbiomes, highlighting the important synergistic role of minor bacterial families; however, we hypothesise that they may be 'cheating' in order to capitalise on the energy-harvesting capacity of other microbes. In terms of chemical cues underlying transition from primary to secondary colonisation phases, we suggest that AI-2-based quorum sensing plays a role, based on LuxS gene expression data, coupled with changes in PRG chemistry. CONCLUSIONS: In summary, we show that fresh PRG-attached prokaryotes are resilient and adapt quickly to changing niches. This study provides the first major insight into the complex temporal ecological interactions occurring at the plant-biofilm interface within the rumen. The study also provides valuable insights into potential plant breeding strategies for development of the utopian plant, allowing optimal sustainable production of ruminants. Video Abstract.

Long-Term Effects of Dietary Supplementation with Olive Oil and Hydrogenated Vegetable Oil on the Rumen Microbiome of Dairy Cows.

Dietary lipids increase energy density in dairy cow diets and in some cases can increase beneficial fatty acids (FA) in milk and dairy products. However, the degree of FA saturation may affect the rumen microbiome. The objective of this study was to determine the long-term effects of feeding saturated (hydrogenated vegetable oil; HVO) or unsaturated (olive oil; OO) fatty acid (FA) sources on the rumen microbiome of dairy cows. For 63 days, 15 mid-lactating cows were fed with either a basal diet (no fat supplement), or the basal diet supplemented with 3% dry matter (DM), either HVO or OO. Rumen contents were collected on days 21, 42 and 63 for 16S rRNA gene sequencing using the Illumina MiSeq platform. The results reveal dominance of the phyla Firmicutes (71.5%) and Bacteroidetes (26.2%), and their respective prevalent genera Succiniclasticum (19.4%) and Prevotella (16.6%). Succiniclasticum increased with both treatments at all time points. Prevotella was reduced on day 42 in both diets. Bacterial diversity alpha or beta were not affected by diets. Predicted bacterial functions by CowPI showed changes in energy and protein metabolism. Overall, 3% DM of lipid supplementation over 63 days can be used in dairy cow diets without major impacts on global bacterial community structure.