Biology subjects

Egli, A.

Publications and source records attributed to Egli, A..

32 records · Page 2Linked to original sources

bioRxiv2022

Improved MALDI-TOF MS based antimicrobial resistance prediction through hierarchical stratification

MotivationPredicting antimicrobial resistance using MALDI-TOF mass spectrometry based machine learning is a fast-growing field of research. Recent advances in machine learning methods specifically designed for MALDI-TOF mass spectra have outperformed established classification approaches. However, classification performance was observed to have a large standard deviation between different train-test splits. We hypothesise that this variance is caused by the underlying phylogenetic structure between microbial samples, which is implicitly reflected in their MALDI-TOF MS profiles, but not taken into account during the training of a model. ResultsIn this paper, we propose to infer this structure from the dataset--using agglomerative hierarchical clustering--and consider it during the dataset splitting between train and test. We show that incorporating such phylogenetic structure into the antimicrobial resistance prediction scenario leads to an improved classification performance. Average precision was increased from 42.3 to 47.1 for ciprofloxacin resistance prediction in Escherichia coli and from 44.6 to 50.8 for amoxicillin-clavulanic acid resistance prediction in Staphylococcus aureus using a Gaussian process classifier with a MALDI-TOF MS specific kernel. We envision that these results will support the quick and reliable identification of antimicrobial resistances, thus increasing patient well-being and reducing healthcare costs. AvailabilityAll data is available for download and code available as an easy-to-use Python package under https://github.com/BorgwardtLab/maldi_PIKE at branch maldi_stratification. Contactcaroline.weis@bsse.ethz.ch, karsten.borgwardt@bsse.ethz.ch Supplementary informationSupplementary information at the end of document.

bioinformatics↗

bioRxiv2022

Phage Paride hijacks bacterial stress responses to kill dormant, antibiotic-tolerant cells

Bacteriophages are fierce viral predators with no regard for pathogenicity or antibiotic resistance of their bacterial hosts1,2. Despite early recognition of their therapeutic potential and the current escalation of bacterial multidrug resistance, phages have so far failed to become a regular treatment option in clinical practice3-5. One reason is the occasional discrepancy between poor performance of selected phages in vivo despite high potency in vitro3,4,6,7. Similar resilience of supposedly drug-sensitive bacterial infections to antibiotic treatment has been linked to persistence of dormant cells inside patients8,9. Given the abundance of non-growing bacteria also in the environment10,11, we wondered whether some phages can infect and kill these antibiotic-tolerant cells. As shown previously12-16, most phages failed to replicate on dormant hosts and instead entered a state of hibernation or pseudolysogeny. However, we isolated a new Pseudomonas aeruginosa phage named Paride with the exciting ability to directly kill dormant, antibiotic-tolerant hosts by lytic replication, causing sterilization of deep-dormant cultures in synergy with the {beta}-lactam meropenem. Intriguingly, efficient replication of Paride on dormant hosts depends on the same bacterial stress responses that also drive antibiotic tolerance. We therefore suggest that Paride hijacks weak spots in the dormant physiology of antibiotic-tolerant bacteria that could be exploited as Achilles heels for the development of new treatments targeting resilient bacterial infections.

microbiology↗

bioRxiv2021

The effect of sequencing and assembly on the inference of horizontal gene transfer on chromosomal and plasmid phylogenies

The spread of antibiotic resistance genes on plasmids is a threat to human and animal health. Phylogenies of bacteria and their plasmids contain clues regarding the frequency of plasmid transfer events, as well as the co-evolution of plasmids and their hosts. However, whole genome sequencing data from diverse ecological or clinical bacterial samples is rarely used to study plasmid phylogenies and resistance gene transfer. This is partially due to the difficulty to extract plasmids from short-read sequencing data. Here, we use both short- and long-read sequencing data of 24 clinical extended-spectrum {beta}-lactamase producing Escherichia coli to estimate chromosomal and plasmid phylogenies. We compare the impact of different sequencing and assembly methodologies on these phylogenies and on the inference of horizontal gene transfer. We find chromosomal phylogenies can be estimated robustly with all methods, whereas plasmid phylogenies have more variable topology and branch lengths across the methods used. Specifically, hybrid methods that use long reads to resolve short-read assemblies (HybridSPAdes and Unicycler) perform better than those that started from long-reads during assembly graph generation (Canu). In contrast, the inference of plasmid and antibiotic resistance gene transfer using a parsimony-based criterion is mostly robust to the choice of sequencing and assembly method.

bioinformatics↗

bioRxiv2021

Real-time respiration changes as a viability indicator for rapid antibiotic susceptibility testing in a microfluidic chamber array

Rapid identification of a pathogen and the measurement of its antibiotic susceptibility are key elements in the diagnostic process of bacterial infections. Microfluidic technologies offer great control over handling and manipulation of low sample volumes with the possibility to study microbial cultures on the single-cell level. Downscaling the dimensions of cultivation systems directly results in a lower number of bacteria required for antibiotic susceptibility testing (AST) and thus in a reduction of the time to result. The developed platform presented in this work allows the reading of pathogen resistance profiles within 2-3 hours based on the changes of the dissolved oxygen levels during bacterial cultivation. The platform contains hundreds of individual growth chambers prefilled with a hydrogel containing oxygen-sensing nanoprobes and different concentrations of antibiotic compounds. The performance of the microfluidic platform is tested using quality control Escherichia coli strains (ATCC 25922 and ATCC 35218) in response to different clinically relevant antibiotics. The achieved results are in agreement with values given in clinical reference guides and independent measurements using a clinical AST protocol. Finally, the platform is successfully used for AST of an E. coli clinical isolate obtained from a patient blood culture.

microbiology↗

bioRxiv2020

Evolution of Antibiotic Tolerance Shapes Resistance Development in Chronic Pseudomonas aeruginosa Infections

The widespread use of antibiotics promotes the evolution and dissemination of resistance and tolerance mechanisms. To assess the relevance of tolerance and its implications for resistance development, we used in vitro evolution and analyzed inpatient microevolution of Pseudomonas aeruginosa, an important human pathogen causing acute and chronic infections. We show that the development of tolerance precedes and promotes the acquisition of resistance in vitro and we present evidence that similar processes shape antibiotic exposure in human patients. Our data suggest that during chronic infections, P. aeruginosa first acquires moderate drug tolerance before following distinct evolutionary trajectories that lead to high-level multi-drug tolerance or to antibiotic resistance. Our studies propose that the development of antibiotic tolerance predisposes bacteria for the acquisition of resistance at early stages of infection and that both mechanisms independently promote bacterial survival during antibiotic treatment at later stages of chronic infections.

microbiology↗

bioRxiv2020

Model-based inference of neutralizing antibody avidities against influenza virus

To assess the response to vaccination, quantity (concentration) and quality (avidity) of neutralizing antibodies are the most important parameters. Specifically, an increase in avidity indicates germinal center formation, which is required for establishing long-term protection. For influenza, the classical hemagglutination inhibition (HI) assay, however, quantifies a combination of both, and to separately determine avidity requires high experimental effort. We developed from first principles a biophysical model of hemagglutination inhibition to infer IgG antibody avidities from measured HI titers and IgG concentrations. The model accurately describes the relationship between neutralizing antibody concentration/avidity and HI titer, and explains quantitative aspects of the HI assay, such as robustness to pipetting errors and detection limit. We applied our model to infer avidities against the pandemic 2009 H1N1 influenza virus in vaccinated patients (n=45) after hematopoietic stem cell transplantation (HSCT) and validated our results with independent avidity measurements using an enzyme-linked immunosorbent assay with urea elution. Avidities inferred by the model correlated with experimentally determined avidities ({rho}=0.54, 95% CI=[0.31, 0.70], P<10-4). The model predicted that increases in IgG concentration mainly contribute to the observed HI titer increases in HSCT patients and that immunosuppressive treatment is associated with lower baseline avidities. Since our approach requires only easy-to-establish measurements as input, we anticipate that it will help to disentangle causes for poor vaccination outcomes also in larger patient populations. This study demonstrates that biophysical modelling can provide quantitative insights into agglutination assays and complement experimental measurements to refine antibody response analyses. Author SummaryInfluenza vaccines are assessed based on the induced antibody response, where antibody quantity (concentration) and antibody binding strength (avidity) determine the potency to neutralize the virus. In addition, an increase in avidity indicates a successful germinal center reaction, which is required for establishing long-term protection. However, the hemagglutination inhibition (HI) assay - traditionally used to assess influenza vaccines - measures a combination of both antibody concentration and avidity, and to separately determine avidity requires high experimental effort. We developed a biophysical model of the HI assay, which enables the inference of antibody avidities from measured HI titers and antibody concentrations. We applied our approach to a vaccinated population of immunocompromised patients after blood stem cell transplantation and validated our results experimentally. The model predicted that vaccination induced an increase in avidity in only a few patients and that patients under immunosuppressive treatment show lower baseline avidities. Since our approach requires only easily measurable data as input, it can facilitate the investigation of vaccine responses in larger populations. This study demonstrates that biophysical modelling can complement experimental data and provide additional details on agglutination experiments and antibody responses.

immunology↗

bioRxiv2020

Direct Antimicrobial Resistance Prediction from MALDI-TOF mass spectra profile in clinical isolates through Machine Learning

Early administration of effective antimicrobial treatments is critical for the outcome of infections. Antimicrobial resistance testing enables the selection of optimal antibiotic treatments, but current culture-based techniques take up to 72 hours. We have developed a novel machine learning approach to predict antimicrobial resistance directly from MALDI-TOF mass spectra profiles of clinical samples. We trained calibrated classifiers on a newly-created publicly available database of mass spectra profiles from clinically most relevant isolates with linked antimicrobial susceptibility phenotypes. The dataset combines more than 300,000 mass spectra with more than 750,000 antimicrobial resistance phenotypes from four medical institutions. Validation against a panel of clinically important pathogens, including Staphylococcus aureus, Escherichia coli, and Klebsiella pneumoniae, resulting in AUROC values of 0.8, 0.74, and 0.74 respectively, demonstrated the potential of using machine learning to substantially accelerate antimicrobial resistance determination and change of clinical management. Furthermore, a retrospective clinical case study found that implementation of this approach would have resulted in a beneficial change in the clinical treatment in 88% (8/9) of cases. MALDI-TOF mass spectra based machine learning may thus be an important new tool for antibiotic stewardship.

microbiology↗

bioRxiv2020

Characterising the epidemic spread of Influenza A/H3N2 within a city through phylogenetics

Infecting large portions of the global population, seasonal influenza is a major burden on societies around the globe. While the global source sink dynamics of the different seasonal influenza viruses have been studied intensively, its local spread remains less clear. In order to improve our understanding of how influenza is transmitted on a city scale, we collected an extremely densely sampled set of influenza sequences alongside patient metadata. To do so, we sequenced influenza viruses isolated from patients of two different hospitals, as well as private practitioners in Basel, Switzerland during the 2016/2017 influenza season. The genetic sequences reveal that repeated introductions into the city drove the influenza season. We then reconstruct how the effective reproduction number changed over the course of the season. We find trends in transmission dynamics correlated positively with trends in temperature, but not relative humidity nor school holidays. Alongside the genetic sequence data that allows us to see how individual cases are connected, we gathered patient information, such as the age or household status. Zooming into the local transmission outbreaks suggests that the elderly were to a large extent infected within their own transmission network, while school children likely drove the spread within the remaining transmission network. These patterns will be valuable to plan interventions combating the spread of respiratory diseases within cities given that similar patterns are observed for other influenza seasons and cities. Author summaryAs shown with the current SARS-CoV-2 pandemic, respiratory diseases can quickly spread around the globe. While it can be hugely important to understand how diseases spread around the globe, local spread is most often the main driver of novel infections of respiratory diseases such as SARS-CoV-2 or influenza. We here use genetic sequence data alongside patient information to better understand what the drives the local spread of influenza by looking at the 2016/2017 influenza season in Basel, Switzerland as an example. The genetic sequence data allows us to reconstruct the how the transmission dynamics changed over the course of the season, which we correlate to changes, but not humidity or school holidays. Additionally, the genetic sequence data allows us to see how individual cases are connected. Using patient information, such as age and household status our analyses suggest that the elderly mainly transmit within their own transmission network. Additionally, they suggest that school aged children, but not pre-school aged children are important drivers of the local spread of influenza.

evolutionary biology↗

bioRxiv2020

High-resolution influenza mapping of a city reveals socioeconomic determinants of transmission within and between urban quarters

With two-thirds of the global population projected to be living in urban areas by 2050, understanding the transmission patterns of viral pathogens within cities is crucial for effective prevention strategies. Here, in unprecedented spatial resolution, we analysed the socioeconomic determinants of influenza transmission in a European city. We combined geographical and epidemiological data with whole genome sequencing of influenza viruses at the scale of urban quarters and statistical blocks, the smallest geographic subdivisions within a city. We observed annually re-occurring geographic clusters of influenza incidences, mainly associated with net income, and independent of population density and living space. Vaccination against influenza was also mainly associated with household income and was linked to the likelihood of influenza-like illness within an urban quarter. Transmissions patterns within and between quarters were complex. High-resolution city-level epidemiological studies combined with social science surveys such as this will be essential for understanding seasonal and pandemic transmission chains and delivering tailored public health information and vaccination programs at the municipal level.

microbiology↗

bioRxiv2020

Evolution, geographic spreading, and demographic distribution of Enterovirus D68

BackgroundWorldwide outbreaks of enterovirus D68 (EV-D68) in 2014 and 2016 have caused serious respiratory and neurological disease. MethodsWe collected samples from several European countries during the 2018 out-break and determined 53 near full-length genome ( whole genome) sequences. These sequences were combined with 718 whole genome and 1,987 VP1-gene publicly available sequences. FindingsIn 2018, circulating strains clustered into multiple subgroups in the B3 and A2 subclades, with different phylogenetic origins. Clusters in subclade B3 emerged from strains circulating primarily in the US and Europe in 2016, though some had deeper roots linking to Asian strains, while clusters in A2 traced back to strains detected in East Asia in 2015-2016. In 2018, all sequences from the USA formed a distinct subgroup, containing only three non-US samples. Alongside the varied origins of seasonal strains, we found that diversification of these variants begins up to 18 months prior to the first diagnostic detection during a EV-D68 season. EV-D68 displays strong signs of continuous antigenic evolution and all 2018 A2 strains had novel patterns in the putative neutralizing epitopes in the BC- and DE-loops. The pattern in the BC-loop of the USA B3 subgroup had not been detected on that continent before. Patients with EV-D68 in subclade A2 were significantly older than patients with a B3 subclade virus. In contrast to other subclades, the age distribution of A2 is distinctly bimodal and was found primarily among children and in the elderly. InterpretationWe hypothesize that EV-D68s rapid evolution of surface proteins, extensive diversity, and high rate of geographic mixing could be explained by substantial reinfection of adults. FundingUniversity of Basel and Swedish Foundation for Research and Development in Medical Microbiology

microbiology↗

bioRxiv2019

Impact of different amino acid substitutions in penicillin-binding protein 3 on beta-lactam susceptibility in Haemophilus influenzae

PurposeBeta-lactam antibiotics in combination with a beta-lactamase inhibitor are the first-line treatment option for Haemophilus influenzae infections. However, beta-lactamase-independent resistance to beta-lactams is increasing. This resistance mechanism has been linked to amino acid substitutions in the penicillin-binding protein 3 (PBP3), but how these substitutions lead to decreased binding affinities to certain beta-lactam antimicrobials remains unknown. MethodsWe investigated beta-lactam resistance and amino acid substitutions in PBP3 from fifty-three clinical isolates of H. influenzae collected in Switzerland from January to April 2016. Identification of key polymorphisms and classification of strains into PBP3 amino acid substitution groups I, II, and M was done as previously described. Based on published PBP3 crystal structures, we investigated how the group-specific amino acid substitutions impact the beta-lactam binding site. ResultsWe found that both group I and group II substitutions disrupt the Asn526-Arg517-Glu324 interaction, which might affect the configuration of the beta-lactam binding site. Amino acid substitutions in group M strains are distant from the active site and have most likely no impact on beta-lactam binding. In accordance with this observation, all group M strains showed minimal inhibitory concentrations (MICs) within the susceptible range for all tested antimicrobials and were not significantly different to the wild type (beta-lactamase producers excluded), while group I and group II strains showed significantly higher MICs for beta-lactam antimicrobials. ConclusionGroup M strains are phenotypically equal to the wild type, while amino acid substitutions of group I and group II might affect the beta-lactam binding through a common mechanism by disrupting the Asn526-Arg517-Glu324 interaction.

microbiology↗

bioRxiv2019

Rationally designed oral vaccines can set an evolutionary trap for Salmonella Typhimurium

Introductory paragraphThe ability of gut bacterial pathogens to escape immunity by antigenic variation, particularly via changes to surface-exposed antigens, is a major barrier to immune clearance1. However, not all variants are equally fit in all environments2, 3. It should therefore be possible to exploit such immune escape mechanisms to direct an evolutionary trade-off. Here we demonstrated this phenomenon using Salmonella enterica subspecies enterica serovar Typhimurium (S.Tm). A dominant surface antigen of S.Tm is its O-antigen: A long, repetitive glycan that can be rapidly varied by mutations in biosynthetic pathways or by phase-variation4, 5. We quantified the selective advantage of O-antigen variants in the presence and absence of O-antigen specific IgA and identified a set of evolutionary trajectories allowing immune escape without an associated fitness cost in naive mice. Through the use of oral vaccines, we rationally induced IgA responses blocking all of these trajectories, which selected for Salmonella mutants carrying deletions of the O-antigen polymerase wzyB. Due to their short O-antigen, these evolved mutants were more susceptible to environmental stressors (detergents, complement), predation (bacteriophages), and were impaired in gut colonization and virulence in mice. Therefore, a rationally induced cocktail of intestinal antibodies can direct an evolutionary trade-off in S.Tm. This lays the foundations for the exploration of mucosal vaccines capable of setting evolutionary traps as a prophylactic strategy.

immunology↗

bioRxiv2019

Extended-spectrum beta-lactamase antibiotic resistance plasmids have diverse transfer rates and can be spread in the absence of selection

Horizontal gene transfer, mediated by conjugative plasmids, is a major driver of the global spread of antibiotic resistance. However, the relative contributions of factors that underlie the spread of clinically relevant plasmids are unclear. Here, we quantified conjugative transfer dynamics of Extended Spectrum Beta-Lactamase (ESBL) producing plasmids in the absence of antibiotics. We showed that clinical Escherichia coli strains natively associated with ESBL-plasmids conjugate efficiently with three distinct E. coli strains and one Salmonella enterica serovar Typhimurium strain, reaching final transconjugant frequencies of up to 1% within 24 hours in vitro. The variation of final transconjugant frequencies varied among plasmids, donors and recipients and was better explained by variation in conjugative transfer efficiency than by variable clonal expansion. We identified plasmid-specific genetic factors, specifically the presence/absence of transfer genes, that influenced final transconjugant frequencies. Finally, we investigated plasmid spread within the mouse intestine, demonstrating qualitative agreement between plasmid spread in vitro and in vivo. This suggests a potential for the prediction of plasmid spread in the gut of animals and humans, based on in vitro testing. Altogether, this may allow the identification of resistance plasmids with high spreading potential and help to devise appropriate measures to restrict their spread.

microbiology↗

bioRxiv2018

Resolving structural diversity of Carbapenemase-producing gram-negative bacteria using single molecule sequencing

Carbapenemase-producing bacteria are resistant against almost all commonly used betalactam and cephalosporin antibiotics and represent a growing public health crisis. Carbapenemases reside predominantly in mobile genetic elements and rapidly spread across genetic backgrounds and species boundaries. Here, we report more than one hundred finished, high quality genomes of carbapenemase producing enterobacteriaceae, P. aeruginosa and A. baumannii sequenced with Oxford Nanopore and Illumina technologies. We developed a number of high-throughput criteria to assess the quality of fully assembled genomes for which curated references do not exist. Using this diverse collection of closed genomes and plasmids, we demonstrate rapid movement of carbapenemase between genomic neighborhoods, sequence types, and across species boundaries with distinct patterns for different carbapenemases. Lastly, we present evidence of multiple ancestral recombination events between different Enterobacteriaceae MLSTs. Taken together, our samples suggest a hierarchical picture of genomic variation produced by the evolution of carbapenemase producing bacteria that will require new models to adequately understand and track.

microbiology↗