Cooperative virulence can emerge via horizontal gene transfer but is stabilized by transmission

Intestinal inflammation fuels Salmonella Typhimurium ( S .Tm) transmission despite a fitness cost associated with the expression of virulence. Cheater mutants can emerge that profit from inflammation without enduring this cost. Intestinal virulence in S .Tm is therefore a cooperative trait, and its evolution a conundrum. Horizontal gene transfer (HGT) of cooperative alleles may facilitate the emergence of cooperative virulence, despite its instability. To test this hypothesis, we cloned hilD , coding for a master regulator of virulence, into a conjugative plasmid that is highly transferrable during intestinal colonization. We demonstrate that virulence can emerge by hilD transfer between avirulent strains in vivo . However, this was indeed unstable and hilD mutant cheaters arose within a few days. The timing of cheater emergence depended on the cost. We further show that stabilization of cooperative virulence in S .Tm is dependent on transmission dynamics, strengthened by population bottlenecks, leading cheaters to extinction and allowing cooperators to thrive

COVID-19 infectivity profile correction

The infectivity profile of an individual with COVID-19 is attributed to the paper Temporal dynamics in viral shedding and transmissibility of COVID-19 by He et al., published in Nature Medicine in April 2020. However, the analysis within this paper contains a mistake such that the published infectivity profile is incorrect and the conclusion that infectiousness begins 2.3 days before symptom onset is no longer supported. In this document we discuss the error and compute the correct infectivity profile. We also establish confidence intervals on this profile, quantify the difference between the published and the corrected profiles, and discuss an issue of normalisation when fitting serial interval data. This infectivity profile plays a central role in policy and decision making, thus it is crucial that this issue is corrected with the utmost urgency to prevent the propagation of this error into further studies and policies. We hope that this preprint will reach all researchers and policy makers who are using the incorrect infectivity profile to inform their work.Comment: 5 pages, 2 figure

Estimation and worldwide monitoring of the effective reproductive number of SARS-CoV-2

The effective reproductive number; R; e; is a key indicator of the growth of an epidemic. Since the start of the SARS-CoV-2 pandemic, many methods and online dashboards have sprung up to monitor this number through time. However, these methods are not always thoroughly tested, correctly placed in time, or are overly confident during high incidence periods. Here, we present a method for timely estimation of; R; e; , applied to COVID-19 epidemic data from 170 countries. We thoroughly evaluate the method on simulated data, and present an intuitive web interface for interactive data exploration. We show that, in early 2020, in the majority of countries the estimated; R; e; dropped below 1 only after the introduction of major non-pharmaceutical interventions. For Europe the implementation of non-pharmaceutical interventions was broadly associated with reductions in the estimated; R; e; . Globally though, relaxing non-pharmaceutical interventions had more varied effects on subsequent; R; e; estimates. Our framework is useful to inform governments and the general public on the status of epidemics in their country, and is used as the official source of; R; e; estimates for SARS-CoV-2 in Switzerland. It further allows detailed comparison between countries and in relation to covariates such as implemented public health policies, mobility, behaviour, or weather data

Plasmid- and strain-specific factors drive variation in ESBL-plasmid spread in vitro and in vivo

Horizontal gene transfer, mediated by conjugative plasmids, is a major driver of the global rise of antibiotic resistance. However, the relative contributions of factors that underlie the spread of plasmids and their roles in conjugation in vivo are unclear. To address this, we investigated the spread of clinical Extended Spectrum Beta-Lactamase (ESBL)-producing plasmids in the absence of antibiotics in vitro and in the mouse intestine. We hypothesised that plasmid properties would be the primary determinants of plasmid spread and that bacterial strain identity would also contribute. We found clinical Escherichia coli strains natively associated with ESBL-plasmids conjugated to three distinct E. coli strains and one Salmonella enterica serovar Typhimurium strain. Final transconjugant frequencies varied across plasmid, donor, and recipient combinations, with qualitative consistency when comparing transfer in vitro and in vivo in mice. In both environments, transconjugant frequencies for these natural strains and plasmids covaried with the presence/absence of transfer genes on ESBL-plasmids and were affected by plasmid incompatibility. By moving ESBL-plasmids out of their native hosts, we showed that donor and recipient strains also modulated transconjugant frequencies. This suggests that plasmid spread in the complex gut environment of animals and humans can be predicted based on in vitro testing and genetic data

Estimating plasmid conjugation rates: A new computational tool and a critical comparison of methods

Plasmids are important vectors for the spread of genes among diverse populations of bacteria. However, there is no standard method to determine the rate at which they spread horizontally via conjugation. Here, we compare commonly used methods on simulated and experimental data, and show that the resulting conjugation rate estimates often depend strongly on the time of measurement, the initial population densities, or the initial ratio of donor to recipient populations. Differences in growth rate, e.g. induced by sub-lethal antibiotic concentrations or temperature, can also significantly bias conjugation rate estimates. We derive a new ‘end-point’ measure to estimate conjugation rates, which extends the well-known Simonsen method to include the effects of differences in population growth and conjugation rates from donors and transconjugants. We further derive analytical expressions for the parameter range in which these approximations remain valid. We present an easy to use R package and web interface which implement both new and previously existing methods to estimate conjugation rates. The result is a set of tools and guidelines for accurate and comparable measurement of plasmid conjugation rates

Why are different estimates of the effective reproductive number so different? A case study on COVID-19 in Germany

The effective reproductive number R$_t$ has taken a central role in the scientific, political, and public discussion during the COVID-19 pandemic, with numerous real-time estimates of this quantity routinely published. Disagreement between estimates can be substantial and may lead to confusion among decision-makers and the general public. In this work, we compare different estimates of the national-level effective reproductive number of COVID-19 in Germany in 2020 and 2021. We consider the agreement between estimates from the same method but published at different time points (within-method agreement) as well as retrospective agreement across eight different approaches (between-method agreement). Concerning the former, estimates from some methods are very stable over time and hardly subject to revisions, while others display considerable fluctuations. To evaluate between-method agreement, we reproduce the estimates generated by different groups using a variety of statistical approaches, standardizing analytical choices to assess how they contribute to the observed disagreement. These analytical choices include the data source, data pre-processing, assumed generation time distribution, statistical tuning parameters, and various delay distributions. We find that in practice, these auxiliary choices in the estimation of R$_t$ may affect results at least as strongly as the selection of the statistical approach. They should thus be communicated transparently along with the estimates

Sex and gender in infection and immunity: addressing the bottlenecks from basic science to public health and clinical applications

Although sex and gender are recognized as major determinants of health and immunity, their role israrely considered in clinical practice and public health. We identified six bottlenecks preventing theinclusion of sex and gender considerations from basic science to clinical practice, precision medicineand public health policies. (i) A terminology-related bottleneck, linked to the definitions of sex andgender themselves, and the lack of consensus on how to evaluate gender. (ii) A data-relatedbottleneck, due to gaps in sex-disaggregated data, data on trans/non-binary people and genderidentity. (iii) A translational bottleneck, limited by animal models and the underrepresentation ofgender minorities in biomedical studies. (iv) A statistical bottleneck, with inappropriate statisticalanalyses and results interpretation. (v) An ethical bottleneck posed by the underrepresentation ofpregnant people and gender minorities in clinical studies. (vi) A structural bottleneck, as systemicbias and discriminations affect not only academic research but also decision makers. We specifyguidelines for researchers, scientific journals, funding agencies and academic institutions to addressthese bottlenecks. Following such guidelines will support the development of more efficient andequitable care strategies for all

Sex and gender in infection and immunity: addressing the bottlenecks from basic science to public health and clinical applications.

Although sex and gender are recognized as major determinants of health and immunity, their role is rarely considered in clinical practice and public health. We identified six bottlenecks preventing the inclusion of sex and gender considerations from basic science to clinical practice, precision medicine and public health policies. (i) A terminology-related bottleneck, linked to the definitions of sex and gender themselves, and the lack of consensus on how to evaluate gender. (ii) A data-related bottleneck, due to gaps in sex-disaggregated data, data on trans/non-binary people and gender identity. (iii) A translational bottleneck, limited by animal models and the underrepresentation of gender minorities in biomedical studies. (iv) A statistical bottleneck, with inappropriate statistical analyses and results interpretation. (v) An ethical bottleneck posed by the underrepresentation of pregnant people and gender minorities in clinical studies. (vi) A structural bottleneck, as systemic bias and discriminations affect not only academic research but also decision makers. We specify guidelines for researchers, scientific journals, funding agencies and academic institutions to address these bottlenecks. Following such guidelines will support the development of more efficient and equitable care strategies for all

Sex and gender in infection and immunity: addressing the bottlenecks from basic science to public health and clinical applications

Although sex and gender are recognized as major determinants of health and immunity, their role is rarely considered in clinical practice and public health. We identified six bottlenecks preventing the inclusion of sex and gender considerations from basic science to clinical practice, precision medicine and public health policies. (i) A terminology-related bottleneck, linked to the definitions of sex and gender themselves, and the lack of consensus on how to evaluate gender. (ii) A data-related bottleneck, due to gaps in sex-disaggregated data, data on trans/non-binary people and gender identity. (iii) A translational bottleneck, limited by animal models and the underrepresentation of gender minorities in biomedical studies. (iv) A statistical bottleneck, with inappropriate statistical analyses and results interpretation. (v) An ethical bottleneck posed by the underrepresentation of pregnant people and gender minorities in clinical studies. (vi) A structural bottleneck, as systemic bias and discriminations affect not only academic research but also decision makers. We specify guidelines for researchers, scientific journals, funding agencies and academic institutions to address these bottlenecks. Following such guidelines will support the development of more efficient and equitable care strategies for all

Practical considerations for measuring the effective reproductive number, Rt.

Estimation of the effective reproductive number Rt is important for detecting changes in disease transmission over time. During the Coronavirus Disease 2019 (COVID-19) pandemic, policy makers and public health officials are using Rt to assess the effectiveness of interventions and to inform policy. However, estimation of Rt from available data presents several challenges, with critical implications for the interpretation of the course of the pandemic. The purpose of this document is to summarize these challenges, illustrate them with examples from synthetic data, and, where possible, make recommendations. For near real-time estimation of Rt, we recommend the approach of Cori and colleagues, which uses data from before time t and empirical estimates of the distribution of time between infections. Methods that require data from after time t, such as Wallinga and Teunis, are conceptually and methodologically less suited for near real-time estimation, but may be appropriate for retrospective analyses of how individuals infected at different time points contributed to the spread. We advise caution when using methods derived from the approach of Bettencourt and Ribeiro, as the resulting Rt estimates may be biased if the underlying structural assumptions are not met. Two key challenges common to all approaches are accurate specification of the generation interval and reconstruction of the time series of new infections from observations occurring long after the moment of transmission. Naive approaches for dealing with observation delays, such as subtracting delays sampled from a distribution, can introduce bias. We provide suggestions for how to mitigate this and other technical challenges and highlight open problems in Rt estimation