Transposon Insertion Sequencing in a Clinical Isolate of Legionella pneumophila Identifies Essential Genes and Determinants of Natural Transformation.

Legionella pneumophila is a Gram-negative bacterium ubiquitous in freshwater environments which, if inhaled, can cause a severe pneumonia in humans. The emergence of L. pneumophila is linked to several traits selected in the environment, the acquisition of some of which involved intra- and interkingdom horizontal gene transfer events. Transposon insertion sequencing (TIS) is a powerful method to identify the genetic basis of selectable traits as well as to identify fitness determinants and essential genes, which are possible antibiotic targets. TIS has not yet been used to its full power in L. pneumophila, possibly because of the difficulty of obtaining a high-saturation transposon insertion library. Indeed, we found that isolates of sequence type 1 (ST1), which includes the commonly used laboratory strains, are poorly permissive to saturating mutagenesis by conjugation-mediated transposon delivery. In contrast, we obtained high-saturation libraries in non-ST1 clinical isolates, offering the prospect of using TIS on unaltered L. pneumophila strains. Focusing on one of them, we then used TIS to identify essential genes in L. pneumophila We also revealed that TIS could be used to identify genes controlling vertical transmission of mobile genetic elements. We then applied TIS to identify all the genes required for L. pneumophila to develop competence and undergo natural transformation, defining the set of major and minor type IV pilins that are engaged in DNA uptake. This work paves the way for the functional exploration of the L. pneumophila genome by TIS and the identification of the genetic basis of other life traits of this species.IMPORTANCELegionella pneumophila is the etiologic agent of a severe form of nosocomial and community-acquired pneumonia in humans. The environmental life traits of L. pneumophila are essential to its ability to accidentally infect humans. A comprehensive identification of their genetic basis could be obtained through the use of transposon insertion sequencing. However, this powerful approach had not been fully implemented in L. pneumophila Here, we describe the successful implementation of the transposon-sequencing approach in a clinical isolate of L. pneumophila We identify essential genes, potential drug targets, and genes required for horizontal gene transfer by natural transformation. This work represents an important step toward identifying the genetic basis of the many life traits of this environmental and pathogenic species.

A multi-centric evaluation of self-learning GAN based pseudo-CT generation software for low field pelvic magnetic resonance imaging.

Purpose/objectives: An artificial intelligence-based pseudo-CT from low-field MR images is proposed and clinically evaluated to unlock the full potential of MRI-guided adaptive radiotherapy for pelvic cancer care. Materials and method: In collaboration with TheraPanacea (TheraPanacea, Paris, France) a pseudo-CT AI-model was generated using end-to-end ensembled self-supervised GANs endowed with cycle consistency using data from 350 pairs of weakly aligned data of pelvis planning CTs and TrueFisp-(0.35T)MRIs. The image accuracy of the generated pCT were evaluated using a retrospective cohort involving 20 test cases coming from eight different institutions (US: 2, EU: 5, AS: 1) and different CT vendors. Reconstruction performance was assessed using the organs at risk used for treatment. Concerning the dosimetric evaluation, twenty-nine prostate cancer patients treated on the low field MR-Linac (ViewRay) at Montpellier Cancer Institute were selected. Planning CTs were non-rigidly registered to the MRIs for each patient. Treatment plans were optimized on the planning CT with a clinical TPS fulfilling all clinical criteria and recalculated on the warped CT (wCT) and the pCT. Three different algorithms were used: AAA, AcurosXB and MonteCarlo. Dose distributions were compared using the global gamma passing rates and dose metrics. Results: The observed average scaled (between maximum and minimum HU values of the CT) difference between the pCT and the planning CT was 33.20 with significant discrepancies across organs. Femoral heads were the most reliably reconstructed (4.51 and 4.77) while anal canal and rectum were the less precise ones (63.08 and 53.13). Mean gamma passing rates for 1%1mm, 2%/2mm, and 3%/3mm tolerance criteria and 10% threshold were greater than 96%, 99% and 99%, respectively, regardless the algorithm used. Dose metrics analysis showed a good agreement between the pCT and the wCT. The mean relative difference were within 1% for the target volumes (CTV and PTV) and 2% for the OARs. Conclusion: This study demonstrated the feasibility of generating clinically acceptable an artificial intelligence-based pseudo CT for low field MR in pelvis with consistent image accuracy and dosimetric results.

Querying Legionella Genomes Using Transposition-Sequencing.

Transposition-sequencing (Tn-seq) has recently emerged as a powerful technique to query bacterial genomes. Tn-seq can be used to query the bacterial genome with unprecedented resolution, allowing the identification of small genes (e.g., noncoding RNA) that may be missed in conventional screening approaches. Tn-seq can be used to predict genes essential for in vitro growth and to directly identify genetic requirements for survival under multiple conditions. For instance, Tn-seq can be applied to determine the genes, and cellular processes, required to resist an antibacterial treatment or to acquire new resistance genes, to adapt to intracellular life or to compete with other bacteria. Virtually any assay that involves a selection pressure can be used to identify the associated genetic determinants. So far, genome-wide Tn-seq has not been applied to Legionella species. Here, we provide a protocol covering all the different steps to conduct a Tn-seq analysis in L. pneumophila. This includes generating a high-density library of insertional mutants, setting up a selection screen, sequencing the libraries, mapping the insertion sites, and analyzing the data to obtain the list of genes involved in surviving the applied selection.