The ecology of microbes frequently involves the mixing of entire communities (community coalescence), for example, flooding events, host excretion, and soil tillage [1, 2], yet the consequences of this process for community structure and function are poorly understood [3-7]. Recent theory suggests that a community, due to coevolution between constituent species, may act as a partially cohesive unit [8-11], resulting in one community dominating after community coalescence. This dominant community is predicted to be the one that uses resources most efficiently when grown in isolation [11]. We experimentally tested these predictions using methanogenic communities, for which efficient resource use, quantified by methane production, requires coevolved cross-feeding interactions between species [12]. After propagation in laboratory-scale anaerobic digesters, community composition (determined from 16S rRNA sequencing) and methane production of mixtures of communities closely resembled that of the single most productive community grown in isolation. Analysis of each community's contribution toward the final mixture suggests that certain combinations of taxa within a community might be co-selected as a result of coevolved interactions. As a corollary of these findings, we also show that methane production increased with the number of inoculated communities. These findings are relevant to the understanding of the ecological dynamics of natural microbial communities, as well as demonstrating a simple method of predictably enhancing microbial community function in biotechnology, health, and agriculture [13].

Background Seminal studies of vertebrate protein evolution speculated that gene regulatory changes can drive anatomical innovations. However, very little is known about gene regulatory network (GRN) evolution associated with phenotypic effect across ecologically diverse species. Here we use a novel approach for comparative GRN analysis in vertebrate species to study GRN evolution in representative species of the most striking examples of adaptive radiations, the East African cichlids. We previously demonstrated how the explosive phenotypic diversification of East African cichlids can be attributed to diverse molecular mechanisms, including accelerated regulatory sequence evolution and gene expression divergence. Results To investigate these mechanisms across species at a genome-wide scale, we develop a novel computational pipeline that predicts regulators for co-extant and ancestral co-expression modules along a phylogeny, and candidate regulatory regions associated with traits under selection in cichlids. As a case study, we apply our approach to a well-studied adaptive trait—the visual system—for which we report striking cases of network rewiring for visual opsin genes, identify discrete regulatory variants, and investigate their association with cichlid visual system evolution. In regulatory regions of visual opsin genes, in vitro assays confirm that transcription factor binding site mutations disrupt regulatory edges across species and segregate according to lake species phylogeny and ecology, suggesting GRN rewiring in radiating cichlids. Conclusions Our approach reveals numerous novel potential candidate regulators and regulatory regions across cichlid genomes, including some novel and some previously reported associations to known adaptive evolutionary traits.

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Cell differentiation is affected by complex networks of transcription factors that co-ordinate re-organisation of the chromatin landscape. The hierarchies of these relationships can be difficult to dissect. During in vitro differentiation of normal human uro-epithelial cells, formaldehyde-assisted isolation of regulatory elements (FAIRE-seq) and RNA-seq was used to identify alterations in chromatin accessibility and gene expression changes following activation of the nuclear receptor peroxisome proliferator-activated receptor gamma (PPARγ) as a differentiation-initiating event. Regions of chromatin identified by FAIRE-seq, as having altered accessibility during differentiation, were found to be enriched with sequence-specific binding motifs for transcription factors predicted to be involved in driving basal and differentiated urothelial cell phenotypes, including forkhead box A1 (FOXA1), P63, GRHL2, CTCF and GATA-binding protein 3 (GATA3). In addition, co-occurrence of GATA3 motifs was observed within subsets of differentiation-specific peaks containing P63 or FOXA1. Changes in abundance of GRHL2, GATA3 and P63 were observed in immunoblots of chromatin-enriched extracts. Transient siRNA knockdown of P63 revealed that P63 favoured a basal-like phenotype by inhibiting differentiation and promoting expression of basal marker genes. GATA3 siRNA prevented differentiation-associated downregulation of P63 protein and transcript, and demonstrated positive feedback of GATA3 on PPARG transcript, but showed no effect on FOXA1 transcript or protein expression. This approach indicates that as a transcriptionally regulated programme, urothelial differentiation operates as a heterarchy, wherein GATA3 is able to co-operate with FOXA1 to drive expression of luminal marker genes, but that P63 has potential to transrepress expression of the same genes.

Supertrees are a commonly used tool in phylogenetics to summarize collections of partial phylogenetic trees. As a generalization of supertrees, phylogenetic supernetworks allow, in addition, the visual representation of conflict between the trees that is not possible to observe with a single tree. Here, we introduce SuperQ, a new method for constructing such supernetworks (SuperQ is freely available at >www.uea.ac.uk/computing/superq.). It works by first breaking the input trees into quartet trees, and then stitching these together to form a special kind of phylogenetic network, called a split network. This stitching process is performed using an adaptation of the QNet method for split network reconstruction employing a novel approach to use the branch lengths from the input trees to estimate the branch lengths in the resulting network. Compared with previous supernetwork methods, SuperQ has the advantage of producing a planar network. We compare the performance of SuperQ to the Z-closure and Q-imputation supernetwork methods, and also present an analysis of some published data sets as an illustration of its applicability.

Bacteria need to survive in a wide range of environments. Currently, there is an incomplete understanding of the genetic basis for mechanisms underpinning survival in stressful conditions, such as the presence of anti-microbials. Transposon directed insertion-site sequencing (TraDIS) is a powerful tool to identify genes and networks which are involved in survival and fitness under a given condition by simultaneously assaying the fitness of millions of mutants, thereby relating genotype to phenotype and contributing to an understanding of bacterial cell biology. A recent refinement of this approach allows the roles of essential genes in conditional stress survival to be inferred by altering their expression. These advancements combined with the rapidly falling costs of sequencing now allows comparisons between multiple experiments to identify commonalities in stress responses to different conditions. This capacity however poses a new challenge for analysis of multiple data sets in conjunction. To address this analysis need, we have developed 'AlbaTraDIS'; a software application for rapid large-scale comparative analysis of TraDIS experiments that predicts the impact of transposon insertions on nearby genes. AlbaTraDIS can identify genes which are up or down regulated, or inactivated, between multiple conditions, producing a filtered list of genes for further experimental validation as well as several accompanying data visualisations. We demonstrate the utility of our new approach by applying it to identify genes used by Escherichia coli to survive in a wide range of different concentrations of the biocide Triclosan. AlbaTraDIS identified all well characterised Triclosan resistance genes, including the primary target, fabI. A number of new loci were also implicated in Triclosan resistance and the predicted phenotypes for a selection of these were validated experimentally with results being consistent with predictions. AlbaTraDIS provides a simple and rapid method to analyse multiple transposon mutagenesis data sets allowing this technology to be used at large scale. To our knowledge this is the only tool currently available that can perform these tasks. AlbaTraDIS is written in Python 3 and is available under the open source licence GNU GPL 3 from https://github.com/quadram-institute-bioscience/albatradis.

Background Fosfomycin is an antibiotic that has seen a revival in use due to its unique mechanism of action and efficacy against isolates resistant to many other antibiotics. In Escherichia coli, fosfomycin often selects for loss-of-function mutations within the genes encoding the sugar importers, GlpT and UhpT. There has, however, not been a genome-wide analysis of the basis for fosfomycin susceptibility reported to date. Methods Here we used TraDIS-Xpress, a high-density transposon mutagenesis approach, to assay the role of all genes in E. coli involved in fosfomycin susceptibility. Results The data confirmed known fosfomycin susceptibility mechanisms and identified new ones. The assay was able to identify domains within proteins of importance and revealed essential genes with roles in fosfomycin susceptibility based on expression changes. Novel mechanisms of fosfomycin susceptibility that were identified included those involved in glucose metabolism and phosphonate catabolism (phnC-M), and the phosphate importer, PstSACB. The impact of these genes on fosfomycin susceptibility was validated by measuring the susceptibility of defined inactivation mutants. Conclusions This work reveals a wider set of genes that contribute to fosfomycin susceptibility, including core sugar metabolism genes and two systems involved in phosphate uptake and metabolism previously unrecognized as having a role in fosfomycin susceptibility.

The mechanisms by which antimicrobials exert inhibitory effects against bacterial cells and by which bacteria display resistance vary under different conditions. Our understanding of the full complement of genes which can influence sensitivity to many antimicrobials is limited and often informed by experiments completed in a small set of exposure conditions. Capturing a broader suite of genes which contribute to survival under antimicrobial stress will improve our understanding of how antimicrobials work and how resistance can evolve. Here, we apply a new version of 'TraDIS' (Transposon Directed Insert Sequencing); a massively parallel transposon mutagenesis approach to identify different responses to the common biocide triclosan across a 125-fold range of concentrations. We have developed a new bioinformatic tool 'AlbaTraDIS' allowing both predictions of the impacts of individual transposon inserts on gene function to be made and comparisons across multiple TraDIS data sets. This new TraDIS approach allows essential genes as well as non-essential genes to be assayed for their contribution to bacterial survival and growth by modulating their expression. Our results demonstrate that different sets of genes are involved in survival following exposure to triclosan under a wide range of concentrations spanning bacteriostatic to bactericidal. The identified genes include those previously reported to have a role in triclosan resistance as well as a new set of genes not previously implicated in triclosan sensitivity. Amongst these novel genes are those involved in barrier function, small molecule uptake and integrity of transcription and translation. These data provide new insights into potential routes of triclosan entry and bactericidal mechanisms of action. Our data also helps to put recent work which has demonstrated the ubiquitous nature of triclosan in people and the built environment into context in terms of how different triclosan exposures may influence evolution of bacteria. We anticipate the approach we show here that allows comparisons across multiple experimental conditions of TraDIS data will be a starting point for future work examining how different drug conditions impact bacterial survival mechanisms.