Jean-Pierre Flandrois

The Pectobacteriaceae family comprises plant pathogens able to provoke diverse diseases, including plant maceration due to the production of pectinases disrupting the plant cell wall. To better understand their natural diversity, a survey of pectinolytic bacteria was performed in lakes of the French region La Camargue near the Mediterranean Sea. Sixteen atypical pectinolytic isolates were obtained from brackish water of three lakes. The genome of six isolates was sequenced; their size is around 4.8 to 5.0 Mb, including a plasmid of 59 to 61 kb; their G+C values range from 49.1 to 49.3 mol%. Phylogenetic analyses indicated that the novel strains formed a new clade of Pectobacteriaceae , separate from previously described genera of this family. These analyses suggested also that Acerihabitans does not belong to Pectobacteriaceae and should be reclassified in the Bruguierivoracaceae family, while Symbiopectobacterium could be a true Pectobacteriaceae member. Based on phenotypic, genomic and phylogenetic characteristics, we propose the creation of a new genus with the name Prodigiosinella gen. nov. Both the phenotypic and phylogenetic analyses separated the strains into two distinct subgroups. However, the DNA–DNA relatedness values revealed a close relationship between the two groups, supporting their appurtenance to the same species. Thus, it is proposed to classify them as two subspecies of Prodigiosinella aquatilis sp. nov., for which we propose the name Prodigiosinella aquatilis subsp. aquatilis ssp. nov. (LS101 ^T = CFBP 8826 ^T = LMG 32072 ^T ) and Prodigiosinella aquatilis subsp. natabilis ssp. nov. (CE70 ^T = CFBP 9054 ^T = LMG 32867 ^T ).

Antimicrobial resistance (AMR) is a global threat. A better understanding of how antibiotic use and between-ward patient transfers (or connectivity) impact population-level AMR in hospital networks can help optimize antibiotic stewardship and infection control strategies. Here, we used a metapopulation framework to explain variations in the incidence of infections caused by seven major bacterial species and their drug-resistant variants in a network of 357 hospital wards. We found that ward-level antibiotic consumption volume had a stronger influence on the incidence of the more resistant pathogens, while connectivity had the most influence on hospital-endemic species and carbapenem-resistant pathogens. Piperacillin-tazobactam consumption was the strongest predictor of the cumulative incidence of infections resistant to empirical sepsis therapy. Our data provide evidence that both antibiotic use and connectivity measurably influence hospital AMR. Finally, we provide a ranking of key antibiotics by their estimated population-level impact on AMR that might help inform antimicrobial stewardship strategies.

The genus Dickeya is an important group of plant pathogens that currently comprises 10 recognized species. Although most Dickeya isolates originated from infected cultivated plants, they are also isolated from water. The genomic sequence of the Australian strain NCPPB 569^T clearly established its separation from the previously characterized Dickeya species. The average nucleotide identity and digital DNA–DNA hybridization values obtained by comparing strain NCPPB 569^T with strains of characterized Dickeya species were lower than 87 and 32 %, respectively, supporting the delineation of a new species. The name Dickeya poaceiphila sp. nov. is proposed for this taxon with the type strain NCPPB 569^T (=CFBP 8731^T). Two other strains isolated in Australia, CFBP 1537 and CFBP 2040, also belong to this species. Phenotypic and genomic comparisons enabled the identification of traits distinguishing D. poaceiphila isolates from strains of other Dickeya species.

Antimicrobial resistance (AMR) is a global threat. A better understanding of how antibiotic use and between ward patient transfers (or connectivity) impact hospital AMR can help optimize antibiotic stewardship and infection control strategies. Here, we used metapopulation ecology to explain variations in infection incidences of 17 ESKAPE pathogen variants in a network of 357 hospital wards. Multivariate models identified the strongest influence of ward-level antibiotic use on more resistant variants, and of connectivity on nosocomial species and carbapenem-resistant variants. Pairwise associations between infection incidence and the consumption of specific antibiotics were significantly stronger when such associations represented a priori AMR selection, suggesting that AMR evolves within the network. Piperacillin-tazobactam consumption was the strongest predictor of the cumulative incidence of infections resistant to empirical sepsis therapy. Our data establish that both antibiotic use and connectivity measurably influence hospital AMR and provide a ranking of key antibiotics by their impact on AMR.

Amplicon sequencing of 16S rRNA gene is commonly used for the identification of bacterial isolates in diagnostic laboratories, and mostly relies on the Sanger sequencing method. The latter, however, suffers from a number of limitations with the most significant being the inability to resolve mixed amplicons when closely related species are co-amplified from a mixed culture. This often leads to either increased turnover time or absence of usable sequence data. Short-read NGS technologies could address the mixed amplicon issue, but would lack both cost efficiency at low throughput and fast turnaround times. Nanopore sequencing developed by Oxford Nanopore Technologies (ONT) could solve those issues by enabling flexible number of samples per run and adjustable sequencing time. Here we report on the development of a standardized laboratory workflow combined with a fully automated analysis pipeline LORCAN (Long Read Consensus ANalysis), which together provide a sample-to-report solution for amplicon sequencing and taxonomic identification of the resulting consensus sequences. Validation of the approach was conducted on a panel of reference strains and on clinical samples consisting of single or mixed rRNA amplicons associated with various bacterial genera by direct comparison to the corresponding Sanger sequences. Additionally, artificial read mixtures of closely related species were used to assess LORCAN ’s behaviour when dealing with samples with known cross-contamination level. We demonstrate that by combining ONT amplicon sequencing results with LORCAN , the accuracy of Sanger sequencing can be closely matched (>99.6% sequence identity) and that mixed samples can be resolved at the single base resolution level. The presented approach has the potential to significantly improve the flexibility, reliability and availability of amplicon sequencing in diagnostic settings.