Rhometa: Population recombination rate estimation from metagenomic read datasets.

Prokaryotic evolution is influenced by the exchange of genetic information between species through a process referred to as recombination. The rate of recombination is a useful measure for the adaptive capacity of a prokaryotic population. We introduce Rhometa (https://github.com/sid-krish/Rhometa), a new software package to determine recombination rates from shotgun sequencing reads of metagenomes. It extends the composite likelihood approach for population recombination rate estimation and enables the analysis of modern short-read datasets. We evaluated Rhometa over a broad range of sequencing depths and complexities, using simulated and real experimental short-read data aligned to external reference genomes. Rhometa offers a comprehensive solution for determining population recombination rates from contemporary metagenomic read datasets. Rhometa extends the capabilities of conventional sequence-based composite likelihood population recombination rate estimators to include modern aligned metagenomic read datasets with diverse sequencing depths, thereby enabling the effective application of these techniques and their high accuracy rates to the field of metagenomics. Using simulated datasets, we show that our method performs well, with its accuracy improving with increasing numbers of genomes. Rhometa was validated on a real S. pneumoniae transformation experiment, where we show that it obtains plausible estimates of the rate of recombination. Finally, the program was also run on ocean surface water metagenomic datasets, through which we demonstrate that the program works on uncultured metagenomic datasets.

Critical Assessment of Metagenome Interpretation: the second round of challenges.

Evaluating metagenomic software is key for optimizing metagenome interpretation and focus of the Initiative for the Critical Assessment of Metagenome Interpretation (CAMI). The CAMI II challenge engaged the community to assess methods on realistic and complex datasets with long- and short-read sequences, created computationally from around 1,700 new and known genomes, as well as 600 new plasmids and viruses. Here we analyze 5,002 results by 76 program versions. Substantial improvements were seen in assembly, some due to long-read data. Related strains still were challenging for assembly and genome recovery through binning, as was assembly quality for the latter. Profilers markedly matured, with taxon profilers and binners excelling at higher bacterial ranks, but underperforming for viruses and Archaea. Clinical pathogen detection results revealed a need to improve reproducibility. Runtime and memory usage analyses identified efficient programs, including top performers with other metrics. The results identify challenges and guide researchers in selecting methods for analyses.

Xanthomonas rydalmerensis sp. nov., a non-pathogenic member of Group 1 Xanthomonas.

Five bacterial isolates were isolated from Fragaria × ananassa in 1976 in Rydalmere, Australia, during routine biosecurity surveillance. Initially, the results of biochemical characterisation indicated that these isolates represented members of the genus Xanthomonas. To determine their species, further analysis was conducted using both phenotypic and genotypic approaches. Phenotypic analysis involved using MALDI-TOF MS and BIOLOG GEN III microplates, which confirmed that the isolates represented members of the genus Xanthomonas but did not allow them to be classified with respect to species. Genome relatedness indices and the results of extensive phylogenetic analysis confirmed that the isolates were members of the genus Xanthomonas and represented a novel species. On the basis the minimal presence of virulence-associated factors typically found in genomes of members of the genus Xanthomonas, we suggest that these isolates are non-pathogenic. This conclusion was supported by the results of a pathogenicity assay. On the basis of these findings, we propose the name Xanthomonas rydalmerensis, with DAR 34855T = ICMP 24941 as the type strain.

qc3C: Reference-free quality control for Hi-C sequencing data.

Hi-C is a sample preparation method that enables high-throughput sequencing to capture genome-wide spatial interactions between DNA molecules. The technique has been successfully applied to solve challenging problems such as 3D structural analysis of chromatin, scaffolding of large genome assemblies and more recently the accurate resolution of metagenome-assembled genomes (MAGs). Despite continued refinements, however, preparing a Hi-C library remains a complex laboratory protocol. To avoid costly failures and maximise the odds of successful outcomes, diligent quality management is recommended. Current wet-lab methods provide only a crude assay of Hi-C library quality, while key post-sequencing quality indicators used have-thus far-relied upon reference-based read-mapping. When a reference is accessible, this reliance introduces a concern for quality, where an incomplete or inexact reference skews the resulting quality indicators. We propose a new, reference-free approach that infers the total fraction of read-pairs that are a product of proximity ligation. This quantification of Hi-C library quality requires only a modest amount of sequencing data and is independent of other application-specific criteria. The algorithm builds upon the observation that proximity ligation events are likely to create k-mers that would not naturally occur in the sample. Our software tool (qc3C) is to our knowledge the first to implement a reference-free Hi-C QC tool, and also provides reference-based QC, enabling Hi-C to be more easily applied to non-model organisms and environmental samples. We characterise the accuracy of the new algorithm on simulated and real datasets and compare it to reference-based methods.

Human milk oligosaccharide profiles and allergic disease up to 18 years.

BACKGROUND: Human milk oligosaccharides (HMO) are a diverse range of sugars secreted in breast milk that have direct and indirect effects on immunity. The profiles of HMOs produced differ between mothers. OBJECTIVE: We sought to determine the relationship between maternal HMO profiles and offspring allergic diseases up to age 18 years. METHODS: Colostrum and early lactation milk samples were collected from 285 mothers enrolled in a high-allergy-risk birth cohort, the Melbourne Atopy Cohort Study. Nineteen HMOs were measured. Profiles/patterns of maternal HMOs were determined using LCA. Details of allergic disease outcomes including sensitization, wheeze, asthma, and eczema were collected at multiple follow-ups up to age 18 years. Adjusted logistic regression analyses and generalized estimating equations were used to determine the relationship between HMO profiles and allergy. RESULTS: The levels of several HMOs were highly correlated with each other. LCA determined 7 distinct maternal milk profiles with memberships of 10% and 20%. Compared with offspring exposed to the neutral Lewis HMO profile, exposure to acidic Lewis HMOs was associated with a higher risk of allergic disease and asthma over childhood (odds ratio asthma at 18 years, 5.82; 95% CI, 1.59-21.23), whereas exposure to the acidic-predominant profile was associated with a reduced risk of food sensitization (OR at 12 years, 0.08; 95% CI, 0.01-0.67). CONCLUSIONS: In this high-allergy-risk birth cohort, some profiles of HMOs were associated with increased and some with decreased allergic disease risks over childhood. Further studies are needed to confirm these findings and realize the potential for intervention.

BEAGLE 3: Improved Performance, Scaling, and Usability for a High-Performance Computing Library for Statistical Phylogenetics.

BEAGLE is a high-performance likelihood-calculation library for phylogenetic inference. The BEAGLE library defines a simple, but flexible, application programming interface (API), and includes a collection of efficient implementations for calculation under a variety of evolutionary models on different hardware devices. The library has been integrated into recent versions of popular phylogenetics software packages including BEAST and MrBayes and has been widely used across a diverse range of evolutionary studies. Here, we present BEAGLE 3 with new parallel implementations, increased performance for challenging data sets, improved scalability, and better usability. We have added new OpenCL and central processing unit-threaded implementations to the library, allowing the effective utilization of a wider range of modern hardware. Further, we have extended the API and library to support concurrent computation of independent partial likelihood arrays, for increased performance of nucleotide-model analyses with greater flexibility of data partitioning. For better scalability and usability, we have improved how phylogenetic software packages use BEAGLE in multi-GPU (graphics processing unit) and cluster environments, and introduced an automated method to select the fastest device given the data set, evolutionary model, and hardware. For application developers who wish to integrate the library, we also have developed an online tutorial. To evaluate the effect of the improvements, we ran a variety of benchmarks on state-of-the-art hardware. For a partitioned exemplar analysis, we observe run-time performance improvements as high as 5.9-fold over our previous GPU implementation. BEAGLE 3 is free, open-source software licensed under the Lesser GPL and available at https://beagle-dev.github.io.

Identification of a novel lineage of plasmids within phylogenetically diverse subclades of IncHI2-ST1 plasmids.

IncHI2-ST1 plasmids play an important role in co-mobilizing genes conferring resistance to critically important antibiotics and heavy metals. Here we present the identification and analysis of IncHI2-ST1 plasmid pSPRC-Echo1, isolated from an Enterobacter hormaechei strain from a Sydney hospital, which predates other multi-drug resistant IncHI2-ST1 plasmids reported from Australia. Our time-resolved phylogeny analysis indicates pSPRC-Echo1 represents a new lineage of IncHI2-ST1 plasmids and show how their diversification relates to the era of antibiotics.

Online Bayesian Phylogenetic Inference: Theoretical Foundations via Sequential Monte Carlo.

Phylogenetics, the inference of evolutionary trees from molecular sequence data such as DNA, is an enterprise that yields valuable evolutionary understanding of many biological systems. Bayesian phylogenetic algorithms, which approximate a posterior distribution on trees, have become a popular if computationally expensive means of doing phylogenetics. Modern data collection technologies are quickly adding new sequences to already substantial databases. With all current techniques for Bayesian phylogenetics, computation must start anew each time a sequence becomes available, making it costly to maintain an up-to-date estimate of a phylogenetic posterior. These considerations highlight the need for an online Bayesian phylogenetic method which can update an existing posterior with new sequences. Here, we provide theoretical results on the consistency and stability of methods for online Bayesian phylogenetic inference based on Sequential Monte Carlo (SMC) and Markov chain Monte Carlo. We first show a consistency result, demonstrating that the method samples from the correct distribution in the limit of a large number of particles. Next, we derive the first reported set of bounds on how phylogenetic likelihood surfaces change when new sequences are added. These bounds enable us to characterize the theoretical performance of sampling algorithms by bounding the effective sample size (ESS) with a given number of particles from below. We show that the ESS is guaranteed to grow linearly as the number of particles in an SMC sampler grows. Surprisingly, this result holds even though the dimensions of the phylogenetic model grow with each new added sequence.

Effective Online Bayesian Phylogenetics via Sequential Monte Carlo with Guided Proposals.

Modern infectious disease outbreak surveillance produces continuous streams of sequence data which require phylogenetic analysis as data arrives. Current software packages for Bayesian phylogenetic inference are unable to quickly incorporate new sequences as they become available, making them less useful for dynamically unfolding evolutionary stories. This limitation can be addressed by applying a class of Bayesian statistical inference algorithms called sequential Monte Carlo (SMC) to conduct online inference, wherein new data can be continuously incorporated to update the estimate of the posterior probability distribution. In this article, we describe and evaluate several different online phylogenetic sequential Monte Carlo (OPSMC) algorithms. We show that proposing new phylogenies with a density similar to the Bayesian prior suffers from poor performance, and we develop "guided" proposals that better match the proposal density to the posterior. Furthermore, we show that the simplest guided proposals can exhibit pathological behavior in some situations, leading to poor results, and that the situation can be resolved by heating the proposal density. The results demonstrate that relative to the widely used MCMC-based algorithm implemented in MrBayes, the total time required to compute a series of phylogenetic posteriors as sequences arrive can be significantly reduced by the use of OPSMC, without incurring a significant loss in accuracy.

Analysis of Theileria orientalis draft genome sequences reveals potential species-level divergence of the Ikeda, Chitose and Buffeli genotypes.

BACKGROUND: Theileria orientalis (Apicomplexa: Piroplasmida) has caused clinical disease in cattle of Eastern Asia for many years and its recent rapid spread throughout Australian and New Zealand herds has caused substantial economic losses to production through cattle deaths, late term abortion and morbidity. Disease outbreaks have been linked to the detection of a pathogenic genotype of T. orientalis, genotype Ikeda, which is also responsible for disease outbreaks in Asia. Here, we sequenced and compared the draft genomes of one pathogenic (Ikeda) and two apathogenic (Chitose, Buffeli) isolates of T. orientalis sourced from Australian herds. RESULTS: Using de novo assembled sequences and a single nucleotide variant (SNV) analysis pipeline, we found extensive genetic divergence between the T. orientalis genotypes. A genome-wide phylogeny reconstructed to address continued confusion over nomenclature of this species displayed concordance with prior phylogenetic studies based on the major piroplasm surface protein (MPSP) gene. However, average nucleotide identity (ANI) values revealed that the divergence between isolates is comparable to that observed between other theilerias which represent distinct species. Analysis of SNVs revealed putative recombination between the Chitose and Buffeli genotypes and also between Australian and Japanese Ikeda isolates. Finally, to inform future vaccine studies, dN/dS ratios and surface location predictions were analysed. Six predicted surface protein targets were confirmed to be expressed during the piroplasm phase of the parasite by mass spectrometry. CONCLUSIONS: We used whole genome sequencing to demonstrate that the T. orientalis Ikeda, Chitose and Buffeli variants show substantial genetic divergence. Our data indicates that future researchers could potentially consider disease-associated Ikeda and closely related genotypes as a separate species from non-pathogenic Chitose and Buffeli.

The impact of migratory flyways on the spread of avian influenza virus in North America.

BACKGROUND: Wild birds are the major reservoir hosts for influenza A viruses (AIVs) and have been implicated in the emergence of pandemic events in livestock and human populations. Understanding how AIVs spread within and across continents is therefore critical to the development of successful strategies to manage and reduce the impact of influenza outbreaks. In North America many bird species undergo seasonal migratory movements along a North-South axis, thereby providing opportunities for viruses to spread over long distances. However, the role played by such avian flyways in shaping the genetic structure of AIV populations remains uncertain. RESULTS: To assess the relative contribution of bird migration along flyways to the genetic structure of AIV we performed a large-scale phylogeographic study of viruses sampled in the USA and Canada, involving the analysis of 3805 to 4505 sequences from 36 to 38 geographic localities depending on the gene segment data set. To assist in this we developed a maximum likelihood-based genetic algorithm to explore a wide range of complex spatial models, depicting a more complete picture of the migration network than determined previously. CONCLUSIONS: Based on phylogenies estimated from nucleotide sequence data sets, our results show that AIV migration rates are significantly higher within than between flyways, indicating that the migratory patterns of birds play a key role in viral dispersal. These findings provide valuable insights into the evolution, maintenance and transmission of AIVs, in turn allowing the development of improved programs for surveillance and risk assessment.

Alignathon: a competitive assessment of whole-genome alignment methods.

Multiple sequence alignments (MSAs) are a prerequisite for a wide variety of evolutionary analyses. Published assessments and benchmark data sets for protein and, to a lesser extent, global nucleotide MSAs are available, but less effort has been made to establish benchmarks in the more general problem of whole-genome alignment (WGA). Using the same model as the successful Assemblathon competitions, we organized a competitive evaluation in which teams submitted their alignments and then assessments were performed collectively after all the submissions were received. Three data sets were used: Two were simulated and based on primate and mammalian phylogenies, and one was comprised of 20 real fly genomes. In total, 35 submissions were assessed, submitted by 10 teams using 12 different alignment pipelines. We found agreement between independent simulation-based and statistical assessments, indicating that there are substantial accuracy differences between contemporary alignment tools. We saw considerable differences in the alignment quality of differently annotated regions and found that few tools aligned the duplications analyzed. We found that many tools worked well at shorter evolutionary distances, but fewer performed competitively at longer distances. We provide all data sets, submissions, and assessment programs for further study and provide, as a resource for future benchmarking, a convenient repository of code and data for reproducing the simulation assessments.

Phylogenetically driven sequencing of extremely halophilic archaea reveals strategies for static and dynamic osmo-response.

Organisms across the tree of life use a variety of mechanisms to respond to stress-inducing fluctuations in osmotic conditions. Cellular response mechanisms and phenotypes associated with osmoadaptation also play important roles in bacterial virulence, human health, agricultural production and many other biological systems. To improve understanding of osmoadaptive strategies, we have generated 59 high-quality draft genomes for the haloarchaea (a euryarchaeal clade whose members thrive in hypersaline environments and routinely experience drastic changes in environmental salinity) and analyzed these new genomes in combination with those from 21 previously sequenced haloarchaeal isolates. We propose a generalized model for haloarchaeal management of cytoplasmic osmolarity in response to osmotic shifts, where potassium accumulation and sodium expulsion during osmotic upshock are accomplished via secondary transport using the proton gradient as an energy source, and potassium loss during downshock is via a combination of secondary transport and non-specific ion loss through mechanosensitive channels. We also propose new mechanisms for magnesium and chloride accumulation. We describe the expansion and differentiation of haloarchaeal general transcription factor families, including two novel expansions of the TATA-binding protein family, and discuss their potential for enabling rapid adaptation to environmental fluxes. We challenge a recent high-profile proposal regarding the evolutionary origins of the haloarchaea by showing that inclusion of additional genomes significantly reduces support for a proposed large-scale horizontal gene transfer into the ancestral haloarchaeon from the bacterial domain. The combination of broad (17 genera) and deep (≥5 species in four genera) sampling of a phenotypically unified clade has enabled us to uncover both highly conserved and specialized features of osmoadaptation. Finally, we demonstrate the broad utility of such datasets, for metagenomics, improvements to automated gene annotation and investigations of evolutionary processes.

A5-miseq: an updated pipeline to assemble microbial genomes from Illumina MiSeq data.

MOTIVATION: Open-source bacterial genome assembly remains inaccessible to many biologists because of its complexity. Few software solutions exist that are capable of automating all steps in the process of de novo genome assembly from Illumina data. RESULTS: A5-miseq can produce high-quality microbial genome assemblies on a laptop computer without any parameter tuning. A5-miseq does this by automating the process of adapter trimming, quality filtering, error correction, contig and scaffold generation and detection of misassemblies. Unlike the original A5 pipeline, A5-miseq can use long reads from the Illumina MiSeq, use read pairing information during contig generation and includes several improvements to read trimming. Together, these changes result in substantially improved assemblies that recover a more complete set of reference genes than previous methods. AVAILABILITY: A5-miseq is licensed under the GPL open-source license. Source code and precompiled binaries for Mac OS X 10.6+ and Linux 2.6.15+ are available from http://sourceforge.net/projects/ngopt CONTACT: aaron.darling@uts.edu.au SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Comparative genomic analysis of toxin-negative strains of Clostridium difficile from humans and animals with symptoms of gastrointestinal disease.

BACKGROUND: Clostridium difficile infections (CDI) are a significant health problem to humans and food animals. Clostridial toxins ToxA and ToxB encoded by genes tcdA and tcdB are located on a pathogenicity locus known as the PaLoc and are the major virulence factors of C. difficile. While toxin-negative strains of C. difficile are often isolated from faeces of animals and patients suffering from CDI, they are not considered to play a role in disease. Toxin-negative strains of C. difficile have been used successfully to treat recurring CDI but their propensity to acquire the PaLoc via lateral gene transfer and express clinically relevant levels of toxins has reinforced the need to characterise them genetically. In addition, further studies that examine the pathogenic potential of toxin-negative strains of C. difficile and the frequency by which toxin-negative strains may acquire the PaLoc are needed. RESULTS: We undertook a comparative genomic analysis of five Australian toxin-negative isolates of C. difficile that lack tcdA, tcdB and both binary toxin genes cdtA and cdtB that were recovered from humans and farm animals with symptoms of gastrointestinal disease. Our analyses show that the five C. difficile isolates cluster closely with virulent toxigenic strains of C. difficile belonging to the same sequence type (ST) and have virulence gene profiles akin to those in toxigenic strains. Furthermore, phage acquisition appears to have played a key role in the evolution of C. difficile. CONCLUSIONS: Our results are consistent with the C. difficile global population structure comprising six clades each containing both toxin-positive and toxin-negative strains. Our data also suggests that toxin-negative strains of C. difficile encode a repertoire of putative virulence factors that are similar to those found in toxigenic strains of C. difficile, raising the possibility that acquisition of PaLoc by toxin-negative strains poses a threat to human health. Studies in appropriate animal models are needed to examine the pathogenic potential of toxin-negative strains of C. difficile and to determine the frequency by which toxin-negative strains may acquire the PaLoc.

Comparative genomic analysis of a multiple antimicrobial resistant enterotoxigenic E. coli O157 lineage from Australian pigs.

BACKGROUND: Enterotoxigenic Escherichia coli (ETEC) are a major economic threat to pig production globally, with serogroups O8, O9, O45, O101, O138, O139, O141, O149 and O157 implicated as the leading diarrhoeal pathogens affecting pigs below four weeks of age. A multiple antimicrobial resistant ETEC O157 (O157 SvETEC) representative of O157 isolates from a pig farm in New South Wales, Australia that experienced repeated bouts of pre- and post-weaning diarrhoea resulting in multiple fatalities was characterized here. Enterohaemorrhagic E. coli (EHEC) O157:H7 cause both sporadic and widespread outbreaks of foodborne disease, predominantly have a ruminant origin and belong to the ST11 clonal complex. Here, for the first time, we conducted comparative genomic analyses of two epidemiologically-unrelated porcine, disease-causing ETEC O157; E. coli O157 SvETEC and E. coli O157:K88 734/3, and examined their phylogenetic relationship with EHEC O157:H7. RESULTS: O157 SvETEC and O157:K88 734/3 belong to a novel sequence type (ST4245) that comprises part of the ST23 complex and are genetically distinct from EHEC O157. Comparative phylogenetic analysis using PhyloSift shows that E. coli O157 SvETEC and E. coli O157:K88 734/3 group into a single clade and are most similar to the extraintestinal avian pathogenic Escherichia coli (APEC) isolate O78 that clusters within the ST23 complex. Genome content was highly similar between E. coli O157 SvETEC, O157:K88 734/3 and APEC O78, with variability predominantly limited to laterally acquired elements, including prophages, plasmids and antimicrobial resistance gene loci. Putative ETEC virulence factors, including the toxins STb and LT and the K88 (F4) adhesin, were conserved between O157 SvETEC and O157:K88 734/3. The O157 SvETEC isolate also encoded the heat stable enterotoxin STa and a second allele of STb, whilst a prophage within O157:K88 734/3 encoded the serum survival gene bor. Both isolates harbor a large repertoire of antibiotic resistance genes but their association with mobile elements remains undetermined. CONCLUSIONS: We present an analysis of the first draft genome sequences of two epidemiologically-unrelated, pathogenic ETEC O157. E. coli O157 SvETEC and E. coli O157:K88 734/3 belong to the ST23 complex and are phylogenetically distinct to EHEC O157 lineages that reside within the ST11 complex.

Progressive genome-wide introgression in agricultural Campylobacter coli.

Hybridization between distantly related organisms can facilitate rapid adaptation to novel environments, but is potentially constrained by epistatic fitness interactions among cell components. The zoonotic pathogens Campylobacter coli and C. jejuni differ from each other by around 15% at the nucleotide level, corresponding to an average of nearly 40 amino acids per protein-coding gene. Using whole genome sequencing, we show that a single C. coli lineage, which has successfully colonized an agricultural niche, has been progressively accumulating C. jejuni DNA. Members of this lineage belong to two groups, the ST-828 and ST-1150 clonal complexes. The ST-1150 complex is less frequently isolated and has undergone a substantially greater amount of introgression leading to replacement of up to 23% of the C. coli core genome as well as import of novel DNA. By contrast, the more commonly isolated ST-828 complex bacteria have 10-11% introgressed DNA, and C. jejuni and nonagricultural C. coli lineages each have <2%. Thus, the C. coli that colonize agriculture, and consequently cause most human disease, have hybrid origin, but this cross-species exchange has so far not had a substantial impact on the gene pools of either C. jejuni or nonagricultural C. coli. These findings also indicate remarkable interchangeability of basic cellular machinery after a prolonged period of independent evolution.

Evaluation of recombination detection methods for viral sequencing.

Recombination is a key evolutionary driver in shaping novel viral populations and lineages. When unaccounted for, recombination can impact evolutionary estimations or complicate their interpretation. Therefore, identifying signals for recombination in sequencing data is a key prerequisite to further analyses. A repertoire of recombination detection methods (RDMs) have been developed over the past two decades; however, the prevalence of pandemic-scale viral sequencing data poses a computational challenge for existing methods. Here, we assessed eight RDMs: PhiPack (Profile), 3SEQ, GENECONV, recombination detection program (RDP) (OpenRDP), MaxChi (OpenRDP), Chimaera (OpenRDP), UCHIME (VSEARCH), and gmos; to determine if any are suitable for the analysis of bulk sequencing data. To test the performance and scalability of these methods, we analysed simulated viral sequencing data across a range of sequence diversities, recombination frequencies, and sample sizes. Furthermore, we provide a practical example for the analysis and validation of empirical data. We find that RDMs need to be scalable, use an analytical approach and resolution that is suitable for the intended research application, and are accurate for the properties of a given dataset (e.g. sequence diversity and estimated recombination frequency). Analysis of simulated and empirical data revealed that the assessed methods exhibited considerable trade-offs between these criteria. Overall, we provide general guidelines for the validation of recombination detection results, the benefits and shortcomings of each assessed method, and future considerations for recombination detection methods for the assessment of large-scale viral sequencing data.

Expanding the range of the respiratory infectome in Australian feedlot cattle with and without respiratory disease using metatranscriptomics.

BACKGROUND: Bovine respiratory disease (BRD) is one of the most common diseases in intensively managed cattle, often resulting in high morbidity and mortality. Although several pathogens have been isolated and extensively studied, the complete infectome of the respiratory complex consists of a more extensive range unrecognised species. Here, we used total RNA sequencing (i.e., metatranscriptomics) of nasal and nasopharyngeal swabs collected from animals with and without BRD from two cattle feedlots in Australia. RESULTS: A high abundance of bovine nidovirus, influenza D, bovine rhinitis A and bovine coronavirus was found in the samples. Additionally, we obtained the complete or near-complete genome of bovine rhinitis B, enterovirus E1, bovine viral diarrhea virus (sub-genotypes 1a and 1c) and bovine respiratory syncytial virus, and partial sequences of other viruses. A new species of paramyxovirus was also identified. Overall, the most abundant RNA virus, was the bovine nidovirus. Characterisation of bacterial species from the transcriptome revealed a high abundance and diversity of Mollicutes in BRD cases and unaffected control animals. Of the non-Mollicutes species, Histophilus somni was detected, whereas there was a low abundance of Mannheimia haemolytica. CONCLUSION: This study highlights the use of untargeted sequencing approaches to study the unrecognised range of microorganisms present in healthy or diseased animals and the need to study previously uncultured viral species that may have an important role in cattle respiratory disease. Video Abstract.

Impact of homologous and non-homologous recombination in the genomic evolution of Escherichia coli.

BACKGROUND: Escherichia coli is an important species of bacteria that can live as a harmless inhabitant of the guts of many animals, as a pathogen causing life-threatening conditions or freely in the non-host environment. This diversity of lifestyles has made it a particular focus of interest for studies of genetic variation, mainly with the aim to understand how a commensal can become a deadly pathogen. Many whole genomes of E. coli have been fully sequenced in the past few years, which offer helpful data to help understand how this important species evolved. RESULTS: We compared 27 whole genomes encompassing four phylogroups of Escherichia coli (A, B1, B2 and E). From the core-genome we established the clonal relationships between the isolates as well as the role played by homologous recombination during their evolution from a common ancestor. We found strong evidence for sexual isolation between three lineages (A+B1, B2, E), which could be explained by the ecological structuring of E. coli and may represent on-going speciation. We identified three hotspots of homologous recombination, one of which had not been previously described and contains the aroC gene, involved in the essential shikimate metabolic pathway. We also described the role played by non-homologous recombination in the pan-genome, and showed that this process was highly heterogeneous. Our analyses revealed in particular that the genomes of three enterohaemorrhagic (EHEC) strains within phylogroup B1 have converged from originally separate backgrounds as a result of both homologous and non-homologous recombination. CONCLUSIONS: Recombination is an important force shaping the genomic evolution and diversification of E. coli, both by replacing fragments of genes with an homologous sequence and also by introducing new genes. In this study, several non-random patterns of these events were identified which correlated with important changes in the lifestyle of the bacteria, and therefore provide additional evidence to explain the relationship between genomic variation and ecological adaptation.