BackgroundAcinetobacter baumannii is an opportunistic pathogen, which has the ability to persist in the clinical environment, causing acute and chronic infections. A possible mechanism contributing to survival of A. baumannii is its ability to form a biofilm-like structure at the air/liquid interface, known as a pellicle. This study aimed to identify and characterise the molecular mechanisms required for pellicle formation in A. baumannii and to assess a broad range of clinical A. baumannii strains for their ability to form these multicellular structures.ResultsRandom transposon mutagenesis was undertaken on a previously identified hyper-motile variant of A. baumannii ATCC 17978 designated 17978hm. In total three genes critical for pellicle formation were identified; cpdA, a phosphodiesterase required for degradation of cyclic adenosine monophosphate (cAMP), and A1S_0112 and A1S_0115 which are involved in the production of a secondary metabolite. While motility of the A1S_0112::Tn and A1S_0115::Tn mutant strains was abolished, the cpdA::Tn mutant strain displayed a minor alteration in its motility pattern. Determination of cAMP levels in the cpdA::Tn strain revealed a ~24-fold increase in cellular cAMP, confirming the role CpdA plays in catabolising this secondary messenger molecule. Interestingly, transcriptional analysis of the cpdA::Tn strain showed significant down-regulation of the operon harboring the A1S_0112 and A1S_0115 genes, revealing a link between these three genes and pellicle formation. Examination of our collection of 54 clinical A. baumannii strains revealed that eight formed a measurable pellicle; all of these strains were motile.ConclusionsThis study shows that pellicle formation is a rare trait in A. baumannii and that a limited number of genes are essential for the expression of this phenotype. Additionally, an association between pellicle formation and motility was identified. The level of the signalling molecule cAMP was found to be controlled, in part, by the cpdA gene product, in addition to playing a critical role in pellicle formation, cellular hydrophobicity and motility. Furthermore, cAMP was identified as a novel regulator of the operon A1S_0112-0118.Electronic supplementary materialThe online version of this article (doi:10.1186/s12866-015-0440-6) contains supplementary material, which is available to authorized users.

The gut virome is an incredibly complex part of the gut ecosystem. Gut viruses play a role in many disease states, but it is unknown to what extent the gut virome impacts everyday human health. New experimental and bioinformatic approaches are required to address this knowledge gap. Gut virome colonization begins at birth and is considered unique and stable in adulthood. The stable virome is highly specific to each individual and is modulated by varying factors such as age, diet, disease state, and use of antibiotics. The gut virome primarily comprises bacteriophages, predominantly order Crassvirales, also referred to as crAss-like phages, in industrialized populations and other Caudoviricetes (formerly Caudovirales). The stability of the virome’s regular constituents is disrupted by disease. Transferring the fecal microbiome, including its viruses, from a healthy individual can restore the functionality of the gut. It can alleviate symptoms of chronic illnesses such as colitis caused by Clostridiodes difficile. Investigation of the virome is a relatively novel field, with new genetic sequences being published at an increasing rate. A large percentage of unknown sequences, termed ‘viral dark matter’, is one of the significant challenges facing virologists and bioinformaticians. To address this challenge, strategies include mining publicly available viral datasets, untargeted metagenomic approaches, and utilizing cutting-edge bioinformatic tools to quantify and classify viral species. Here, we review the literature surrounding the gut virome, its establishment, its impact on human health, the methods used to investigate it, and the viral dark matter veiling our understanding of the gut virome.

Background Most bacterial genomes contain integrated bacteriophages—prophages—in various states of decay. Many are active and able to excise from the genome and replicate, while others are cryptic prophages, remnants of their former selves. Over the last two decades, many computational tools have been developed to identify the prophage components of bacterial genomes, and it is a particularly active area for the application of machine learning approaches. However, progress is hindered and comparisons thwarted because there are no manually curated bacterial genomes that can be used to test new prophage prediction algorithms. Methods We present a library of gold-standard bacterial genome annotations that include manually curated prophage annotations, and a computational framework to compare the predictions from different algorithms. We use this suite to compare all extant stand-alone prophage prediction algorithms to identify their strengths and weaknesses. We provide a FAIR dataset for prophage identification, and demonstrate the accuracy, precision, recall, and f1 score from the analysis of seven different algorithms for the prediction of prophages. Results We identified different strengths and weaknesses between the prophage prediction tools. Several tools exhibit exceptional f1 scores, while others have better recall at the expense of more false positives. The tools vary greatly in runtime performance with few exhibiting all desirable qualities for large-scale analyses. Conclusions Our library of gold-standard prophage annotations and benchmarking framework provide a valuable resource for exploring strengths and weaknesses of current and future prophage annotation tools. We discuss caveats and concerns in this analysis, how those concerns may be mitigated, and avenues for future improvements. This framework will help developers identify opportunities for improvement and test updates. It will also help users in determining the tools that are best suited for their analysis.

Background Most bacterial genomes contain integrated bacteriophages—prophages—in various states of decay. Many are active and able to excise from the genome and replicate, while others are cryptic prophages, remnants of their former selves. Over the last two decades, many computational tools have been developed to identify the prophage components of bacterial genomes, and it is a particularly active area for the application of machine learning approaches. However, progress is hindered and comparisons thwarted because there are no manually curated bacterial genomes that can be used to test new prophage prediction algorithms. Methods We present a library of gold-standard bacterial genomes with manually curated prophage annotations, and a computational framework to compare the predictions from different algorithms. We use this suite to compare all extant stand-alone prophage prediction algorithms and identify their strengths and weaknesses. We provide a FAIR dataset for prophage identification, and demonstrate the accuracy, precision, recall, and f 1 score from the analysis of ten different algorithms for the prediction of prophages. Results We identified strengths and weaknesses between the prophage prediction tools. Several tools exhibit exceptional f 1 scores, while others have better recall at the expense of more false positives. The tools vary greatly in runtime performance with few exhibiting all desirable qualities for large-scale analyses. Conclusions Our library of gold-standard prophage annotations and benchmarking framework provide a valuable resource for exploring strengths and weaknesses of current and future prophage annotation tools. We discuss caveats and concerns in this analysis, how those concerns may be mitigated, and avenues for future improvements. This framework will help developers identify opportunities for improvement and test updates. It will also help users in determining the tools that are best suited for their analysis.

Most bacterial genomes contain integrated bacteriophages—prophages—in various states of decay. Many are active and able to excise from the genome and replicate, while others are cryptic prophages, remnants of their former selves. Over the last two decades, many computational tools have been developed to identify the prophage components of bacterial genomes, and it is a particularly active area for the application of machine learning approaches. However, progress is hindered and comparisons thwarted because there are no manually curated bacterial genomes that can be used to test new prophage prediction algorithms. Here, we present a library of gold-standard bacterial genome annotations that include manually curated prophage annotations, and a computational framework to compare the predictions from different algorithms. We use this suite to compare all extant stand-alone prophage prediction algorithms to identify their strengths and weaknesses. We provide a FAIR dataset for prophage identification, and demonstrate the accuracy, precision, recall, and f1 score from the analysis of seven different algorithms for the prediction of prophages. We discuss caveats and concerns in this analysis and how those concerns may be mitigated.