The polymyxin antibiotic colistin has been used in decades for treatment and prevention of infectious diseases in livestock. Nowadays, it is even considered as last-line treatment option for severe human infections caused by multidrug-and carbapenem-resistant Gram-negative bacteria. Therefore, the discovery of plasmid-mediated mobile colistin resistance (mcr) genes raised major public health concern. The aim of our study was to analyze colistin-resistant Salmonella enterica strains from animals, food, feed and the environment collected at the National Reference Laboratory for Salmonella in Germany on the presence of mcr-1 to mcr-9 genes. Altogether 407 colistin-resistant (MIC >2 mg/L) Salmonella isolates received between 2011 and 2018 were selected and screened by PCR using a published mcr-1 to mcr-5 as well as a newly developed mcr-6 to mcr-9 multiplex PCR protocol. 254 of 407 (62.4%) isolates harbored either mcr-1 (n = 175), mcr-4 (n = 53), mcr-5 (n = 18) or mcr-1 and mcr-9 (n = 8). The number of mcr-positive isolates ranged from 19 (2017) to 64 (2012) per year. WGS revealed that none of our isolates harbored the mcr-9.1 gene. Instead, two novel mcr-9 variants were observed, which both were affected by frameshift mutations and are probably non-functional. The mcr-harboring isolates were mainly derived from animals (77.2%) or food (20.1%) and could be assigned to ten different Salmonella serovars. Many of the isolates were multidrug-resistant. Co-occurrence of mcr-1 and AmpC or ESBL genes was observed in eight isolates. Our findings suggest that mcr genes are widely spread among colistin-resistant Salmonella isolates from livestock and food in Germany. Potential transfer of mcr-harboring isolates along the food chain has to be considered critically.

Whole genome sequencing (WGS) of foodborne pathogens has become an effective method for investigating the information contained in the genome sequence of bacterial pathogens. In addition, its highly discriminative power enables the comparison of genetic relatedness between bacteria even on a sub-species level. For this reason, WGS is being implemented worldwide and across sectors (human, veterinary, food, and environment) for the investigation of disease outbreaks, source attribution, and improved risk characterization models. In order to extract relevant information from the large quantity and complex data produced by WGS, a host of bioinformatics tools has been developed, allowing users to analyze and interpret sequencing data, starting from simple gene-searches to complex phylogenetic studies. Depending on the research question, the complexity of the dataset and their bioinformatics skill set, users can choose between a great variety of tools for the analysis of WGS data. In this review, we describe the relevant approaches for phylogenomic studies for outbreak studies and give an overview of selected tools for the characterization of foodborne pathogens based on WGS data. Despite the efforts of the last years, harmonization and standardization of typing tools are still urgently needed to allow for an easy comparison of data between laboratories, moving towards a one health worldwide surveillance system for foodborne pathogens.

The reliable detection of novel bacterial pathogens from next-generation sequencing data is a key challenge for microbial diagnostics. Current computational tools usually rely on sequence similarity and often fail to detect novel species when closely related genomes are unavailable or missing from the reference database. Here we present the machine learning based approach PaPrBaG (Pathogenicity Prediction for Bacterial Genomes). PaPrBaG overcomes genetic divergence by training on a wide range of species with known pathogenicity phenotype. To that end we compiled a comprehensive list of pathogenic and non-pathogenic bacteria with human host, using various genome metadata in conjunction with a rule-based protocol. A detailed comparative study reveals that PaPrBaG has several advantages over sequence similarity approaches. Most importantly, it always provides a prediction whereas other approaches discard a large number of sequencing reads with low similarity to currently known reference genomes. Furthermore, PaPrBaG remains reliable even at very low genomic coverages. CombiningPaPrBaG with existing approaches further improves prediction results.The vast amount and diversity of bacteria on Earth, together with ever increasing human exposure 1 , suggests that we will be continuously confronted with novel bacterial pathogens, too. Encouragingly, next-generation sequencing (NGS) has emerged as a novel, powerful diagnostic tool in this regard. However, the direct NGS-based characterisation of novel pathogenic strains or even species is still problematic when closely related genomes are unavailable or missing from the respective reference database. Here we introduce a machine learning based approach, PaPrBaG, which overcomes genetic divergence in predicting bacterial pathogenicity by training on a wide range of species with known pathogenicity phenotype. Importantly, even if this is avoided for practical reasons at some points throughout this (and related) work, one may more cautiously speak of pathogenic potential than pathogenicity, given that the latter is ultimately governed by the complex interplay between host (state) and pathogen. Existing MethodsExisting approaches amenable to pathogenicity prediction broadly fall into two classes: protein content based and whole-genome based. Where assembled genomes are available, the presence/absence pattern of certain protein families can be expected to correlate with complex phenotypes, e.g. pathogenicity. This is primarily based on the presence of virulence factors (VFs) -often acquired through horizontal gene transfer 2 -or the absence of more common genes (functions) that become dispensable when e.g. host-specific pathogens evolve from commensal ancestors 3 . Three recent studies rely on these considerations.The BacFier method by Iraola et al. 4 was the first to apply the described approach on a large scale. The authors defined eight VF categories and obtained 814 related VF protein families from KEGG 5 . They further used a set of 848 human-pathogenic (HP) and generally n...

Experimental studies on mRNA stability have established several, qualitatively distinct decay patterns for the amount of mRNA within the living cell. Furthermore, a variety of different and complex biochemical pathways for mRNA degradation have been identified. The central aim of this paper is to bring together both the experimental evidence about the decay patterns and the biochemical knowledge about the multi-step nature of mRNA degradation in a coherent mathematical theory. We first introduce a mathematical relationship between the mRNA decay pattern and the lifetime distribution of individual mRNA molecules. This relationship reveals that the mRNA decay patterns at steady state expression level must obey a general convexity condition, which applies to any degradation mechanism. Next, we develop a theory, formulated as a Markov chain model, that recapitulates some aspects of the multi-step nature of mRNA degradation. We apply our theory to experimental data for yeast and explicitly derive the lifetime distribution of the corresponding mRNAs. Thereby, we show how to extract single-molecule properties of an mRNA, such as the age-dependent decay rate and the residual lifetime. Finally, we analyze the decay patterns of the whole translatome of yeast cells and show that yeast mRNAs can be grouped into three broad classes that exhibit three distinct decay patterns. This paper provides both a method to accurately analyze non-exponential mRNA decay patterns and a tool to validate different models of degradation using decay data.

We compared the performance of four open-source in silico Salmonella typing tools (SeqSero, SeqSero2, Salmonella In Silico Typing Resource [SISTR], and Metric Oriented Sequence Typer [MOST]) to assess their potential for replacing laboratory serological testing with serovar predictions from whole-genome sequencing data. We conducted a retrospective analysis of 1,624 Salmonella isolates of 72 serovars submitted to the German National Salmonella Reference Laboratory between 1999 and 2019. All isolates are derived from animal and foodstuff origins. We conducted Illumina short-read sequencing and compared the in silico serovar prediction results with the results of routine laboratory serotyping. We found the best-performing in silico serovar prediction tool to be SISTR, with 94% correctly typed isolates, followed by SeqSero2 (87%), SeqSero (81%), and MOST (79%). Furthermore, we found that mapping-based tools like SeqSero and SeqSero2 (allele mode) were more reliable for the prediction of monophasic variants, while sequence type and cluster-based methods like MOST and SISTR (core-genome multilocus sequence type [cgMLST]), showed greater resilience when confronted with GC-biased sequencing data. We showed that the choice of library preparation kit could substantially affect O antigen detection, due to the low GC content of the wzx and wzy genes. Although the accuracy of computational serovar predictions is still not quite on par with traditional serotyping by Salmonella reference laboratories, the command-line tools investigated in this study perform a rapid, efficient, inexpensive, and reproducible analysis, which can be integrated into in-house characterization pipelines. Based on our results, we find SISTR most suitable for automated, routine serotyping for public health surveillance of Salmonella. IMPORTANCE Salmonella spp. are important foodborne pathogens. To reduce the number of infected patients, it is essential to understand which subtypes of the bacteria cause disease outbreaks. Traditionally, characterization of Salmonella requires serological testing, a laboratory method by which Salmonella isolates can be classified into over 2,600 distinct subtypes, called serovars. Due to recent advances in whole-genome sequencing, many tools have been developed to replace traditional testing methods with computational analysis of genome sequences. It is crucial to validate that these tools, many already in use for routine surveillance, deliver accurate and reliable serovar information. In this study, we set out to compare which of the currently available open-source command-line tools is most suitable to replace serological testing. A thorough evaluation of the differing computational approaches is highly important to ensure the backward compatibility of serotyping data and to maintain comparability between laboratories.