High Prevalence of Human-Associated Escherichia coli in Wetlands Located in Eastern France.

Escherichia coli that are present in the rivers are mostly brought by human and animal feces. Contamination occurs mostly through wastewater treatment plant (WWTP) outflows and field amendment with sewage sludge or manure. However, the survival of these isolates in river-associated wetlands remains unknown. Here, we assessed E. coli population structure in low-anthropized wetlands located along three floodplains to identify the major source of contamination of wetlands, whose functioning is different from the rivers. We retrieved 179 E. coli in water samples collected monthly from 19 sites located in eastern France over 1 year. Phylogroups B1 and B2 were dominant in the E. coli population, while phylogroup A was dominant in isolates resistant to third-generation cephalosporins, which harbored the extended-spectrum ß-lactamase (ESBL) encoding genes bla CTX-M-15 and bla CTX-M-27 in half of the cases. The high proportion of isolates from human source can be attributed to WWTP outflows and the spread of sewage sludge. We analyzed the distribution of the isolates belonging to the most human-associated phylogroups (B2 and D) on a phylogenetic tree of the whole species and compared it with that of isolates retrieved from patients and from WWTP outflows. The distribution of the three E. coli populations was similar, suggesting the absence of a specific population in the environment. Our results suggest that a high proportion of E. coli isolates that reach and survive in low-anthropized environments such as wetlands are from human source. To the best of our knowledge, this is the first study assessing E. coli contamination and resistance genes in natural freshwater wetlands.

Economic evaluations of cancer immunotherapy: a systematic review and quality evaluation.

OBJECTIVES: Scientific advances in the last decade have highlighted the use of immunotherapy, especially immune checkpoint inhibitors, to be an effective strategy in cancer therapy. However, these immunotherapeutic agents are expensive, and their use must take into account economic criteria. Thus, the objective of the present study was to systematically identify and review published EE related to the use of ipilimumab, nivolumab or pembrolizumab in melanoma, lung cancer, head and neck cancer or renal cell carcinoma, and to assess their quality. METHODS: The systematic literature research was conducted on Medline via PubMed and the Cochrane Central Register of Controlled Trials to identify economic evaluations published before July 2018. The quality of each selected economic evaluation was assessed by two independent reviewers using the Drummond checklist. RESULTS: Our systematic review was based on 32 economic evaluations using different methodological approaches, different perspectives and different time horizons. Three-quarters of the economic evaluations are full (n = 24) with a Drummond score ≥ 7, synonymous of "high quality". Among them, 66% reported a strategy that was cost-effective. The most assessed immunotherapeutic agent was nivolumab. In patients with renal cell carcinoma or head and neck cancer, it was less likely to be cost-effective than in patients with melanoma or lung cancer. CONCLUSIONS: Whether or not these findings will be confirmed remains to be seen when market approval to cover more indications is extended and new effective immunotherapeutic agents become available.

Deciphering the role of insertion sequences in the evolution of bacterial epidemic pathogens with panISa software.

Next-generation sequencing (NGS) is now widely used in microbiology to explore genome evolution and the structure of pathogen outbreaks. Bioinformatics pipelines readily detect single-nucleotide polymorphisms or short indels. However, bacterial genomes also evolve through the action of small transposable elements called insertion sequences (ISs), which are difficult to detect due to their short length and multiple repetitions throughout the genome. We designed panISa software for the ab initio detection of IS insertions in the genomes of prokaryotes. PanISa has been released as open source software (GPL3) available from https://github.com/bvalot/panISa. In this study, we assessed the utility of this software for evolutionary studies, by reanalysing five published datasets for outbreaks of human major pathogens in which ISs had not been specifically investigated. We reanalysed the raw data from each study, by aligning the reads against reference genomes and running panISa on the alignments. Each hit was automatically curated and IS-related events were validated on the basis of nucleotide sequence similarity, by comparison with the ISFinder database. In Acinetobacter baumannii, the panISa pipeline identified ISAba1 or ISAba125 upstream from the ampC gene, which encodes a cephalosporinase in all third-generation cephalosporin-resistant isolates. In the genomes of Vibrio cholerae isolates, we found that early Haitian isolates had the same ISs as Nepalese isolates, confirming the inferred history of the contamination of this island. In Enterococcus faecalis, panISa identified regions of high plasticity, including a pathogenicity island enriched in IS-related events. The overall distribution of ISs deduced with panISa was consistent with SNP-based phylogenic trees, for all species considered. The role of ISs in pathogen evolution has probably been underestimated due to difficulties detecting these transposable elements. We show here that panISa is a useful addition to the bioinformatics toolbox for analyses of the evolution of bacterial genomes. PanISa will facilitate explorations of the functional impact of ISs and improve our understanding of prokaryote evolution.

panISa: ab initio detection of insertion sequences in bacterial genomes from short read sequence data.

Motivation: The advent of next-generation sequencing has boosted the analysis of bacterial genome evolution. Insertion sequence (IS) elements play a key role in prokaryotic genome organization and evolution, but their repetitions in genomes complicate their detection from short-read data. Results: PanISa is a software pipeline that identifies IS insertions ab initio in bacterial genomes from short-read data. It is a highly sensitive and precise tool based on the detection of read-mapping patterns at the insertion site. PanISa performs better than existing IS detection systems as it is based on a database-free approach. We applied it to a high-risk clone lineage of the pathogenic species Pseudomonas aeruginosa, and report 43 insertions of five different ISs (among which three are new) and a burst of ISPa1635 in a hypermutator isolate. Availability and implementation: PanISa is implemented in Python and released as an open source software (GPL3) at https://github.com/bvalot/panISa. Supplementary information: Supplementary data are available at Bioinformatics online.