Vibrio natriegens genome-scale modeling reveals insights into halophilic adaptations and resource allocation.

Vibrio natriegens is a Gram-negative bacterium with an exceptional growth rate that has the potential to become a standard biotechnological host for laboratory and industrial bioproduction. Despite this burgeoning interest, the current lack of organism-specific qualitative and quantitative computational tools has hampered the community's ability to rationally engineer this bacterium. In this study, we present the first genome-scale metabolic model (GSMM) of V. natriegens. The GSMM (iLC858) was developed using an automated draft assembly and extensive manual curation and was validated by comparing predicted yields, central metabolic fluxes, viable carbon substrates, and essential genes with empirical data. Mass spectrometry-based proteomics data confirmed the translation of at least 76% of the enzyme-encoding genes predicted to be expressed by the model during aerobic growth in a minimal medium. iLC858 was subsequently used to carry out a metabolic comparison between the model organism Escherichia coli and V. natriegens, leading to an analysis of the model architecture of V. natriegens' respiratory and ATP-generating system and the discovery of a role for a sodium-dependent oxaloacetate decarboxylase pump. The proteomics data were further used to investigate additional halophilic adaptations of V. natriegens. Finally, iLC858 was utilized to create a Resource Balance Analysis model to study the allocation of carbon resources. Taken together, the models presented provide useful computational tools to guide metabolic engineering efforts in V. natriegens.

Introducing differential RNA-seq mapping to track the early infection phase for Pseudomonas phage ɸKZ.

As part of the ongoing renaissance of phage biology, more phage genomes are becoming available through DNA sequencing. However, our understanding of the transcriptome architecture that allows these genomes to be expressed during host infection is generally poor. Transcription start sites (TSSs) and operons have been mapped for very few phages, and an annotated global RNA map of a phage - alone or together with its infected host - is not available at all. Here, we applied differential RNA-seq (dRNA-seq) to study the early, host takeover phase of infection by assessing the transcriptome structure of Pseudomonas aeruginosa jumbo phage ɸKZ, a model phage for viral genetics and structural research. This map substantially expands the number of early expressed viral genes, defining TSSs that are active ten minutes after ɸKZ infection. Simultaneously, we record gene expression changes in the host transcriptome during this critical metabolism conversion. In addition to previously reported upregulation of genes associated with amino acid metabolism, we observe strong activation of genes with functions in biofilm formation (cdrAB) and iron storage (bfrB), as well as an activation of the antitoxin ParD. Conversely, ɸKZ infection rapidly down-regulates complexes IV and V of oxidative phosphorylation (atpCDGHF and cyoABCDE). Taken together, our data provide new insights into the transcriptional organization and infection process of the giant bacteriophage ɸKZ and adds a framework for the genome-wide transcriptomic analysis of phage-host interactions.

Differential transcription profiling of the phage LUZ19 infection process in different growth media.

RNA sequencing of phage-infected bacterial cultures offers a snapshot of transcriptional events occurring during the infection process, providing insights into the phage transcriptional organization as well as the bacterial response. To better mimic real environmental contexts, we performed RNA-seq of Pseudomonas aeruginosa PAO1 cultures infected with phage LUZ19 in a mammalian cell culture medium to better simulate a phage therapy event and the data were compared to lysogeny broth medium. Regardless of the media, phage LUZ19 induces significant transcriptional changes in the bacterial host over time, particularly during early infection (t = 5 min) and gradually shuts down bacterial transcription. In a common response in both media, 56 P. aeruginosa PAO1 genes are differentially transcribed and clustered into several functional categories such as metabolism, translation and transcription. Our data allowed us to tease apart a medium-specific response during infection from the identified infection-associated responses. This reinforces the concept that phages overtake bacterial transcriptome in a strict manner to gain control of the bacterial machinery and reallocate resources for infection, in this case overcoming the nutritional limitations of the mammalian cell culture medium. From a phage therapy perspective, this study contributes towards a better understanding of phage-host interaction in human physiological conditions and demonstrates the versatility of phage LUZ19 to adapt to different environments.

SAPPHIRE: a neural network based classifier for σ70 promoter prediction in Pseudomonas.

BACKGROUND: In silico promoter prediction represents an important challenge in bioinformatics as it provides a first-line approach to identifying regulatory elements to support wet-lab experiments. Historically, available promoter prediction software have focused on sigma factor-associated promoters in the model organism E. coli. As a consequence, traditional promoter predictors yield suboptimal predictions when applied to other prokaryotic genera, such as Pseudomonas, a Gram-negative bacterium of crucial medical and biotechnological importance. RESULTS: We developed SAPPHIRE, a promoter predictor for σ70 promoters in Pseudomonas. This promoter prediction relies on an artificial neural network that evaluates sequences on their similarity to the - 35 and - 10 boxes of σ70 promoters found experimentally in P. aeruginosa and P. putida. SAPPHIRE currently outperforms established predictive software when classifying Pseudomonas σ70 promoters and was built to allow further expansion in the future. CONCLUSIONS: SAPPHIRE is the first predictive tool for bacterial σ70 promoters in Pseudomonas. SAPPHIRE is free, publicly available and can be accessed online at www.biosapphire.com . Alternatively, users can download the tool as a Python 3 script for local application from this site.

SAPPHIRE.CNN: Implementation of dRNA-seq-driven, species-specific promoter prediction using convolutional neural networks.

Data availability is a consistent bottleneck for the development of bacterial species-specific promoter prediction software. In this work we leverage genome-wide promoter datasets generated with dRNA-seq in the Gram-negative bacteria Pseudomonas aeruginosa and Salmonella enterica for promoter prediction. Convolutional neural networks are presented as an optimal architecture for model training and are further modified and tailored for promoter prediction. The resulting predictors reach high binary accuracies (95% and 94.9%) on test sets and outperform each other when predicting promoters in their associated species. SAPPHIRE.CNN is available online and can also be downloaded to run locally. Our results indicate a dependency of binary promoter classification on an organism's GC content and a decreased performance of our classifiers on genera they were not trained for, further supporting the need for dedicated, species-specific promoter classification tools.