It is well established that plasmids play an important role in the dissemination of antimicrobial resistance (AMR) genes; however, little is known about the role of the underlying interactions between different plasmid categories and other mobile genetic elements (MGEs) in shaping the promiscuous spread of AMR genes. Here, we developed a tool designed for plasmid classification, AMR gene annotation, and plasmid visualization and found that most plasmid-borne AMR genes, including those localized on class 1 integrons, are enriched in conjugative plasmids. Notably, we report the discovery and characterization of a massive insertion sequence (IS)-associated AMR gene transfer network (245 combinations covering 59 AMR gene subtypes and 53 ISs) linking conjugative plasmids and phylogenetically distant pathogens, suggesting a general evolutionary mechanism for the horizontal transfer of AMR genes mediated by the interaction between conjugative plasmids and ISs. Moreover, our experimental results confirmed the importance of the observed interactions in aiding the horizontal transfer and expanding the genetic range of AMR genes within complex microbial communities.

Surveillance of drug-resistant bacteria is essential for healthcare providers to deliver effective empirical antibiotic therapy. However, traditional molecular epidemiology does not typically occur on a timescale that could affect patient treatment and outcomes. Here, we present a method called 'genomic neighbour typing' for inferring the phenotype of a bacterial sample by identifying its closest relatives in a database of genomes with metadata. We show that this technique can infer antibiotic susceptibility and resistance for both Streptococcus pneumoniae and Neisseria gonorrhoeae. We implemented this with rapid k-mer matching, which, when used on Oxford Nanopore MinION data, can run in real time. This resulted in the determination of resistance within 10 min (91% sensitivity and 100% specificity for S. pneumoniae and 81% sensitivity and 100% specificity for N. gonorrhoeae from isolates with a representative database) of starting sequencing, and within 4 h of sample collection (75% sensitivity and 100% specificity for S. pneumoniae) for clinical metagenomic sputum samples. This flexible approach has wide application for pathogen surveillance and may be used to greatly accelerate appropriate empirical antibiotic treatment.

gregory.kucherov@univ-mlv.fr.

MotivationDe Bruijn graphs play an essential role in computational biology, facilitating rapid alignment-free comparison of genomic datasets as well as reconstruction of underlying genomic sequences. Subsequently, an important question is how to efficiently represent, compress, and transmit de Bruijn graphs of the most common types of genomic data sets, such as sequencing reads, genomes, and pan-genomes. ResultsWe introduce simplitigs, an effective representation of de Bruijn graphs for alignment-free applications. Simplitigs are a generalization of unitigs and correspond to spellings of vertex-disjoint paths in a de Bruijn graph. We present an easy-to-plug-in greedy heuristic for their computation and provide a reference implementation in a program called ProphAsm. We use ProphAsm to compare the scaling of simplitigs and unitigs on a range of genomic datasets. We demonstrate that simplitigs are superior to unitigs in terms of the cumulative sequence length as well as of the number of sequences, and that they are sufficiently close to the theoretical bounds for practical applications. Finally, we demonstrate that, when combined with standard full-text indexes, simplitigs provide a scalable solution for k-mer search in pan-genomes. AvailabilityProphAsm is written in C++ and is available under the MIT license from De Bruijn graphs belong to the most popular graph representations of genomic datasets. They are defined as directed graphs where V is the set of all k-mers (i.e., subwords of a fixed length k) occurring in the V , ) G = ( E dataset with edges connecting a vertex v to a vertex w if there is a long prefix-suffix overlap between these v k − 1 and w. As follows from the definition, we can associate a de Bruijn graph with the underlying k-mer set and edges can be defined implicitly (unlike the edge-centric definition where k-mer sets are associated with edges [5] ). In this paper, we consider only vertex-centric graphs.De Bruijn graphs feature remarkable properties. First, their computation from data is easy and deterministic.Algorithms for enumerating and counting k-mers have been extensively studied and many programs are available [6][7][8][9] . If the datasets contain sequencing errors, the computation may also involve graph cleaning. This aims at removing those k-mers that are the result of sequencing errors and are due to their supposed randomness expected to be rare. Second, if k is chosen appropriately, de Bruijn graphs can capture substantial information about the entire molecules under sequencing as these correspond to (some of the) walks in the graphs, provided that sequencing was sufficiently deep. Third, de Bruijn graphs can be handled easily, which simplifies software development as well as dataset analysis and interpretation. These properties have led to a large variety of applications of de Bruijn graphs.De Bruijn graphs have been widely studied in the context of sequence assembly [10][11][12] . Here, their construction is typically the first step to the reconstruction of the genomes and transcr...

Abstractde Bruijn graphs play an essential role in bioinformatics, yet they lack a universal scalable representation. Here, we introduce simplitigs as a compact, efficient, and scalable representation, and ProphAsm, a fast algorithm for their computation. For the example of assemblies of model organisms and two bacterial pan-genomes, we compare simplitigs to unitigs, the best existing representation, and demonstrate that simplitigs provide a substantial improvement in the cumulative sequence length and their number. When combined with the commonly used Burrows-Wheeler Transform index, simplitigs reduce memory, and index loading and query times, as demonstrated with large-scale examples of GenBank bacterial pan-genomes.

State-level reopenings in late spring 2020 facilitated the resurgence of severe acute respiratory syndrome coronavirus 2 transmission. Here, we analyze age-structured case, hospitalization, and death time series from three states—Rhode Island, Massachusetts, and Pennsylvania—that had successful reopenings in May 2020 without summer waves of infection. Using 11 daily data streams, we show that from spring to summer, the epidemic shifted from an older to a younger age profile and that elderly individuals were less able to reduce contacts during the lockdown period when compared to younger individuals. Clinical case management improved from spring to summer, resulting in fewer critical care admissions and lower infection fatality rate. Attack rate estimates through 31 August 2020 are 6.2% [95% credible interval (CI), 5.7 to 6.8%] of the total population infected for Rhode Island, 6.7% (95% CI, 5.4 to 7.6%) in Massachusetts, and 2.7% (95% CI, 2.5 to 3.1%) in Pennsylvania.

In the United States, state-level re-openings in spring 2020 presented an opportunity for the resurgence of SARS-CoV-2 transmission. One important question during this time was whether human contact and mixing patterns could increase gradually without increasing viral transmission, the rationale being that new mixing patterns would likely be associated with improved distancing, masking, and hygiene practices. A second key question to follow during this time was whether clinical characteristics of the epidemic would improve after the initial surge of cases. Here, we analyze age-structured case, hospitalization, and death time series from three states – Rhode Island, Massachusetts, and Pennsylvania – that had successful re-openings in May 2020 without summer waves of infection. Using a Bayesian inference framework on eleven daily data streams and flexible daily population contact parameters, we show that population-average mixing rates dropped by >50% during the lockdown period in March/April, and that the correlation between overall population mobility and transmission-capable mobility was broken in May as these states partially re-opened. We estimate the reporting rates (fraction of symptomatic cases reporting to health system) at 96.0% (RI), 72.1% (MA), and 75.5% (PA); in Rhode Island, when accounting for cases caught through general-population screening programs, the reporting rate estimate is 94.5%. We show that elderly individuals were less able to reduce contacts during the lockdown period when compared to younger individuals, leading to the outbreak being concentrated in elderly congregate settings despite the lockdown. Attack rate estimates through August 31 2020 are 6.4% (95% CI: 5.8% − 7.3%) of the total population infected for Rhode Island, 5.7% (95% CI: 5.0% − 6.8%) in Massachusetts, and 3.7% (95% CI: 3.1% − 4.5%) in Pennsylvania, with some validation available through published seroprevalence studies. Infection fatality rates (IFR) estimates are higher in our analysis (>2%) than previously reported values, likely resulting from the epidemics in these three states affecting the most vulnerable sub-populations, especially the most vulnerable of the ≥80 age group. We make several suggestions for enhancements to current data collection practices that could improve response efforts in winter.

Background When three SARS-CoV-2 vaccines came to market in Europe and North America in the winter of 2020–2021, distribution networks were in a race against a major epidemiological wave of SARS-CoV-2 that began in autumn 2020. Rapid and optimized vaccine allocation was critical during this time. With 95% efficacy reported for two of the vaccines, near-term public health needs likely require that distribution is prioritized to the elderly, health care workers, teachers, essential workers, and individuals with comorbidities putting them at risk of severe clinical progression. Methods We evaluate various age-based vaccine distributions using a validated mathematical model based on current epidemic trends in Rhode Island and Massachusetts. We allow for varying waning efficacy of vaccine-induced immunity, as this has not yet been measured. We account for the fact that known COVID-positive cases may not have been included in the first round of vaccination. And, we account for age-specific immune patterns in both states at the time of the start of the vaccination program. Our analysis assumes that health systems during winter 2020–2021 had equal staffing and capacity to previous phases of the SARS-CoV-2 epidemic; we do not consider the effects of understaffed hospitals or unvaccinated medical staff. Results We find that allocating a substantial proportion (>75%) of vaccine supply to individuals over the age of 70 is optimal in terms of reducing total cumulative deaths through mid-2021. This result is robust to different profiles of waning vaccine efficacy and several different assumptions on age mixing during and after lockdown periods. As we do not explicitly model other high-mortality groups, our results on vaccine allocation apply to all groups at high risk of mortality if infected. A median of 327 to 340 deaths can be avoided in Rhode Island (3444 to 3647 in Massachusetts) by optimizing vaccine allocation and vaccinating the elderly first. The vaccination campaigns are expected to save a median of 639 to 664 lives in Rhode Island and 6278 to 6618 lives in Massachusetts in the first half of 2021 when compared to a scenario with no vaccine. A policy of vaccinating only seronegative individuals avoids redundancy in vaccine use on individuals that may already be immune, and would result in 0.5% to 1% reductions in cumulative hospitalizations and deaths by mid-2021. Conclusions Assuming high vaccination coverage (>28%) and no major changes in distancing, masking, gathering size, hygiene guidelines, and virus transmissibility between 1 January 2021 and 1 July 2021 a combination of vaccination and population immunity may lead to low or near-zero transmission levels by the second quarter of 2021.