Genetic barriers more than environmental associations explain Serratia marcescens population structure.

Bacterial species often comprise well-separated lineages, likely emerged and maintained by genetic isolation and/or ecological divergence. How these two evolutionary actors interact in the shaping of bacterial population structure is currently not fully understood. In this study, we investigate the genetic and ecological drivers underlying the evolution of Serratia marcescens, an opportunistic pathogen with high genomic flexibility and able to colonise diverse environments. Comparative genomic analyses reveal a population structure composed of five deeply-demarcated genetic clusters with open pan-genome but limited inter-cluster gene flow, partially explained by Restriction-Modification (R-M) systems incompatibility. Furthermore, a large-scale research on hundred-thousands metagenomic datasets reveals only a partial habitat separation of the clusters. Globally, two clusters only show a separate gene composition coherent with ecological adaptations. These results suggest that genetic isolation has preceded ecological adaptations in the shaping of the species diversity, an evolutionary scenario coherent with the Evolutionary Extended Synthesis.

Optimising machine learning prediction of minimum inhibitory concentrations in Klebsiella pneumoniae.

Minimum Inhibitory Concentrations (MICs) are the gold standard for quantitatively measuring antibiotic resistance. However, lab-based MIC determination can be time-consuming and suffers from low reproducibility, and interpretation as sensitive or resistant relies on guidelines which change over time. Genome sequencing and machine learning promise to allow in silico MIC prediction as an alternative approach which overcomes some of these difficulties, albeit the interpretation of MIC is still needed. Nevertheless, precisely how we should handle MIC data when dealing with predictive models remains unclear, since they are measured semi-quantitatively, with varying resolution, and are typically also left- and right-censored within varying ranges. We therefore investigated genome-based prediction of MICs in the pathogen Klebsiella pneumoniae using 4367 genomes with both simulated semi-quantitative traits and real MICs. As we were focused on clinical interpretation, we used interpretable rather than black-box machine learning models, namely, Elastic Net, Random Forests, and linear mixed models. Simulated traits were generated accounting for oligogenic, polygenic, and homoplastic genetic effects with different levels of heritability. Then we assessed how model prediction accuracy was affected when MICs were framed as regression and classification. Our results showed that treating the MICs differently depending on the number of concentration levels of antibiotic available was the most promising learning strategy. Specifically, to optimise both prediction accuracy and inference of the correct causal variants, we recommend considering the MICs as continuous and framing the learning problem as a regression when the number of observed antibiotic concentration levels is large, whereas with a smaller number of concentration levels they should be treated as a categorical variable and the learning problem should be framed as a classification. Our findings also underline how predictive models can be improved when prior biological knowledge is taken into account, due to the varying genetic architecture of each antibiotic resistance trait. Finally, we emphasise that incrementing the population database is pivotal for the future clinical implementation of these models to support routine machine-learning based diagnostics.

High-throughput screening of pathogens in Ixodes ricinus removed from hosts in Lombardy, northern Italy.

Ticks are important vectors of many pathogens in Europe, where the most impactful species is Ixodes ricinus. Recently, the geographical distribution of this tick species has been expanding, resulting in an increased risk of human exposure to tick bites. With the present study, we aimed to screen 350 I. ricinus specimens collected from humans and wild animals (mainly ungulates), to have a broader understanding of the tick-borne pathogens circulating in the Lombardy region, in northern Italy. To do so, we took advantage of a high-throughput real-time microfluidic PCR approach to screen ticks in a cost-effective and time-saving manner. Molecular analysis of the dataset revealed the presence of four genera of bacteria and two genera of protozoa: in ungulates, 77 % of collected ticks carried Anaplasma phagocytophilum, while the most common pathogen species in ticks removed from humans were those belonging to Borrelia burgdorferi sensu lato group (7.6 %). We also detected other pathogenic microorganisms, such as Rickettisa monacensis, Rickettsia helvetica, Neoehrlichia mikurensis, Babesia venatorum, and Hepatozoon martis. Besides, we also reported the presence of the pathogenic agent Borrelia miyamotoi in the area (1.4 % overall). The most common dual co-infection detected in the same tick individual involved A. phagocytophilum and Rickettsia spp. Our study provided evidence of the circulation of different tick-borne pathogens in a densely populated region in Italy.

P-DOR, an easy-to-use pipeline to reconstruct bacterial outbreaks using genomics.

SUMMARY: Bacterial Healthcare-Associated Infections (HAIs) are a major threat worldwide, which can be counteracted by establishing effective infection control measures, guided by constant surveillance and timely epidemiological investigations. Genomics is crucial in modern epidemiology but lacks standard methods and user-friendly software, accessible to users without a strong bioinformatics proficiency. To overcome these issues we developed P-DOR, a novel tool for rapid bacterial outbreak characterization. P-DOR accepts genome assemblies as input, it automatically selects a background of publicly available genomes using k-mer distances and adds it to the analysis dataset before inferring a Single-Nucleotide Polymorphism (SNP)-based phylogeny. Epidemiological clusters are identified considering the phylogenetic tree topology and SNP distances. By analyzing the SNP-distance distribution, the user can gauge the correct threshold. Patient metadata can be inputted as well, to provide a spatio-temporal representation of the outbreak. The entire pipeline is fast and scalable and can be also run on low-end computers. AVAILABILITY AND IMPLEMENTATION: P-DOR is implemented in Python3 and R and can be installed using conda environments. It is available from GitHub https://github.com/SteMIDIfactory/P-DOR under the GPL-3.0 license.

The evolution of intramitochondriality in Midichloria bacteria.

Midichloria spp. are intracellular bacterial symbionts of ticks. Representatives of this genus colonise mitochondria in the cells of their hosts. To shed light on this unique interaction we evaluated the presence of an intramitochondrial localization for three Midichloria in the respective tick host species and generated eight high-quality draft genomes and one closed genome, showing that this trait is non-monophyletic, either due to losses or multiple acquisitions. Comparative genomics supports the first hypothesis, as the genomes of non-mitochondrial symbionts are reduced subsets of those capable of colonising the organelles. We detect genomic signatures of mitochondrial tropism, including the differential presence of type IV secretion system and flagellum, which could allow the secretion of unique effectors and/or direct interaction with mitochondria. Other genes, including adhesion molecules, proteins involved in actin polymerisation, cell wall and outer membrane proteins, are only present in mitochondrial symbionts. The bacteria could use these to manipulate host structures, including mitochondrial membranes, to fuse with the organelles or manipulate the mitochondrial network.

Antigen discovery by bioinformatics analysis and peptide microarray for the diagnosis of cystic echinococcosis.

BACKGROUND: Cystic echinococcosis (CE), caused by Echinococcus granulosus sensu lato, is a neglected zoonosis. Its diagnosis relies on imaging, supported by serology, while only imaging is useful for staging and follow-up. Since diagnostic tools and expertise are not widely available, new accurate and easily implementable assays for the diagnosis and follow-up of CE are highly needed. METHODOLOGY/PRINCIPAL FINDINGS: We aimed to identify new E. granulosus antigens through a bioinformatics selection applied to the parasite genome, followed by peptide microarray screening and validation in ELISA, using independent panels of sera from patients with hepatic CE and clinically relevant controls. From 950 proteins selected in silico, 2,379 peptides were evaluated by microarray for IgG reactivity and eight candidates selected for validation. Reactivity to one peptide was significantly higher in the CE group (p = 0.044), but had suboptimal diagnostic accuracy. CONCLUSIONS/SIGNIFICANCE: Here we performed bioinformatics analysis and peptide microarray for antigen discovery, useful for the diagnosis of CE. Eight candidates were selected and validated. Reactivity to one peptide associated to CE but had suboptimal diagnostic accuracy. Importantly, the database developed in this study may be used to identify other antigenic candidates for CE diagnosis and follow-up.

Comparative analysis of SARS-CoV-2 quasispecies in the upper and lower respiratory tract shows an ongoing evolution in the spike cleavage site.

Studies are needed to better understand the genomic evolution of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This study aimed to describe viral quasispecies population of upper and lower respiratory tract by next-generation sequencing in patients admitted to intensive care unit. A deep sequencing of the S gene of SARS-CoV-2 from 109 clinical specimens, sampled from the upper respiratory tract (URT) and lower respiratory tract (LRT) of 77 patients was performed. A higher incidence of non-synonymous mutations and indels was observed in the LRT among minority variants. This might be explained by the ability of the virus to invade cells without interacting with ACE2 (e.g. exploiting macrophage phagocytosis). Minority variants are highly concentrated around the gene portion encoding for the Spike cleavage site, with a higher incidence in the URT; four mutations are highly recurring among samples and were found associated with the URT. Interestingly, 55.8% of minority variants detected in this locus were T>G and G>T transversions. Results from this study evidenced the presence of selective pressure and suggest that an evolutionary process is still ongoing in one of the crucial sites of spike protein associated with the spillover to humans.

Spread of multiple SARS-CoV-2 lineages April-August 2020 anticipated the second pandemic wave in Lombardy (Italy).

During the early phase of the pandemic (20 February-4 April 2020), we have investigated the temporal and geographical evolution of the virus in Lombardy showing the circulation of at least seven lineages distributed differently in the Region. In the present study, the molecular epidemiology of SARS-CoV-2 was monitored in a period between two pandemic waves in order to track the circulation of new variants (April-August 2020). A great majority of SARS-CoV-2 strains (70.8%) belonged to lineages B, B.1, B.1.1 and B.1.1.1, and five strains belonging to four lineages were already reported in Italy (B.1.1.148, B.1.1.162, B.1.1.71, and B.1.425). In addition, 21 SARS-CoV-2 strains belonged to six lineages not previously observed in Italy were detected. No variants of concern were observed. A total of 152/1274 (11.3%) amino acid changes were observed among spike gene sequences and only 26/152 (17.1%) occurred in the receptor-binding domain region of the spike protein. Results of this study are indicative of ongoing transmission throughout the lockdown period, rather than re-introduction of novel lineages past lockdown. The use of molecular epidemiology in Italy should be promoted in order to provide additional understanding of the transmission of the disease and to have major effect on controlling the spread of disease.

Genome of Superficieibacter maynardsmithii, a novel, antibiotic susceptible representative of Enterobacteriaceae.

During a citywide microbiological screening project in Pavia (Italy) a bacterial strain isolated from the surface of an Automated Teller Machine was classified as a Klebsiella sp. by MALDI-TOF spectrometry, and shown to be susceptible to the most antimicrobial classes by phenotypic testing. After Illumina genome sequencing and subsequent assembly, a high-quality draft genome was obtained (size = 5,051,593 bp, N50 = 615,571 bp, largest contig = 1,328,029 bp, N_contig = 17, GC content = 51.58%, coverage = 141.42), absence of antimicrobial resistance genes was confirmed, but the strain resulted to be highly divergent from all Klebsiella, and more related to other Enterobacteriaceae. The higher values of 16S rRNA identity were with members of the genera Citrobacter, Salmonella, and "Superficieibacter." An ortholog-based phylogenomic analysis indicated a sister group relationship with "Superficieibacter electus," in a distinct clade from other members of the Enterobacteriaceae family. In order to evaluate whether the novel genome represents a new species of "Superficiebacter," average nucleotide identity (ANI) and Hadamard analysis were performed on a dataset of 78 Enterobacteriaceae. The novel genome showed an ANI of 87.51% with S. electus, which compared on identity values between other members of the family, clearly indicates that the genome represents a new species within the genus "Superficieibacter." We propose for the new species the name "Superficieibacter maynardsmithii."

MeltingPlot, a user-friendly online tool for epidemiological investigation using High Resolution Melting data.

BACKGROUND: The rapid identification of pathogen clones is pivotal for effective epidemiological control strategies in hospital settings. High Resolution Melting (HRM) is a molecular biology technique suitable for fast and inexpensive pathogen typing protocols. Unfortunately, the mathematical/informatics skills required to analyse HRM data for pathogen typing likely limit the application of this promising technique in hospital settings. RESULTS: MeltingPlot is the first tool specifically designed for epidemiological investigations using HRM data, easing the application of HRM typing to large real-time surveillance and rapid outbreak reconstructions. MeltingPlot implements a graph-based algorithm designed to discriminate pathogen clones on the basis of HRM data, producing portable typing results. The tool also merges typing information with isolates and patients metadata to create graphical and tabular outputs useful in epidemiological investigations and it runs in a few seconds even with hundreds of isolates. AVAILABILITY: https://skynet.unimi.it/index.php/tools/meltingplot/ . CONCLUSIONS: The analysis and result interpretation of HRM typing protocols can be not trivial and this likely limited its application in hospital settings. MeltingPlot is a web tool designed to help the user to reconstruct epidemiological events by combining HRM-based clustering methods and the isolate/patient metadata. The tool can be used for the implementation of HRM based real time large scale surveillance programs in hospital settings.

Epidemiological Characterization of Listeria monocytogenes Infections in Pavia Province in 2017 Reveals the Presence of Multiple Concurrently Circulating Strains.

Consumption of raw food, especially smoked fish, meat, soft cheeses, and vegetables, contaminated with Listeria monocytogenes can cause listeriosis, which can be invasive in pregnant women, elderly, and immunocompromised and diabetic patients. Through June to November of 2017, 11 patients developed invasive listeriosis in a small area of northern Italy. In the same period, 15 food samples (ready-to-eat seafood, raw vegetables, cheese samples, and salami) collected during the routine screening programs in the same area were found to be contaminated with L. monocytogenes. We characterized the isolates to determine the relatedness of L. monocytogenes strains isolated from patients and isolates from food samples and food-processing plants. Whole genome sequencing analysis showed that multiple L. monocytogenes strains were circulating in the area and no association was found between clinical and food isolates.

Comparative Analysis of the Two Acinetobacter baumannii Multilocus Sequence Typing (MLST) Schemes.

Acinetobacter species assigned to the Acinetobacter calcoaceticus-baumannii (Acb) complex, are Gram-negative bacteria responsible for a large number of human infections. The population structure of Acb has been studied using two 7-gene MLST schemes, introduced by Bartual and coworkers (Oxford scheme) and by Diancourt and coworkers (Pasteur scheme). The schemes have three genes in common but underlie two coexisting nomenclatures of sequence types and clonal complexes, which complicates communication on A. baumannii genotypes. The aim of this study was to compare the characteristics of the two schemes to make a recommendation about their usage. Using genome sequences of 730 strains of the Acb complex, we evaluated the phylogenetic congruence of MLST schemes, the correspondence between sequence types, their discriminative power and genotyping reliability from genomic sequences. In silico ST re-assignments highlighted the presence of a second copy of the Oxford gdhB locus, present in 553/730 genomes that has led to the creation of artefactual profiles and STs. The reliability of the two MLST schemes was tested statistically comparing MLST-based phylogenies to two reference phylogenies (core-genome genes and genome-wide SNPs) using topology-based and likelihood-based tests. Additionally, each MLST gene fragment was evaluated by correlating the pairwise nucleotide distances between each pair of genomes calculated on the core-genome and on each single gene fragment. The Pasteur scheme appears to be less discriminant among closely related isolates, but less affected by homologous recombination and more appropriate for precise strain classification in clonal groups, which within this scheme are more often correctly monophyletic. Statistical tests evaluate the tree deriving from the Oxford scheme as more similar to the reference genome trees. Our results, together with previous work, indicate that the Oxford scheme has important issues: gdhB paralogy, recombination, primers sequences, position of the genes on the genome. While there is no complete agreement in all analyses, when considered as a whole the above results indicate that the Pasteur scheme is more appropriate for population biology and epidemiological studies of A. baumannii and related species and we propose that it should be the scheme of choice during the transition toward, and in parallel with, core genome MLST.