Biology subjects

Regoes, R. R.

Publications and source records attributed to Regoes, R. R..

At least 19 recordsLinked to original sources

bioRxiv2026

The danger hypothesis of virulence evolution

Multicellular organisms regularly encounter microbes, which are, however, only rarely pathogenic. Our understanding of this phenomenon is currently restricted due to lacking theory on evolutionary transitions between non-pathogenic and pathogenic microbial lifestyles. Here we addressed this gap by investigating a mathematical model of host-microbe interactions that is based on the danger theory of immunology, which states that danger signals related to host tissue damage play a key role in activating immune responses. We formally implemented this idea by assuming that immune activation increases with costs that microbes cause to their host, and we compared this to scenarios in which immune activation depends only on the presence or load of infecting microbes. Our model analysis revealed that cost-based - but not presence or load-based - immune activation favours the evolution of avirulence and associated non-pathogenic microbial lifestyles. Based on our results, we propose the danger hypothesis of virulence evolution which states that evolution towards avirulence and intermediate virulence are both possible - depending on whether hosts can accurately assess costs generated by microbes. The idea that basic host immune responses can select for avirulence offers a new explanation for why most microbes are not pathogenic to a given host.

evolutionary biology↗

bioRxiv2026

The role of the innate immune system in shaping the dynamics of antimicrobial treatment

Contemporary antibiotic treatment almost solely considers the Minimum Inhibitory Concentration (MIC) when deciding to employ an antibiotic, often overlooking the critical role of host immunity. Using Galleria mellonella and a virulent strain of Staphylococcus aureus, we investigate the infection dynamics and treatment outcomes of antibiotics of different classes--including antibiotics to which the bacteria are resistant--and a lytic bacteriophage. Surprisingly, we find that the hosts ability to control bacterial density and survive infection does not depend on the specific type of antimicrobial agent nor the bacterias susceptibility to it. Our results demonstrate that the innate immune system is the primary factor in therapeutic success, capable of clearing even highly resistant infections, such as those involving beta-lactamase-producing or ribosomal mutant-resistant strains. These findings challenge the traditional antibiotic-centric view of infection outcomes and emphasize the need to account for host-pathogen-drug interactions beyond simple MIC measurements when designing clinical treatment regimens.

microbiology↗

bioRxiv2026

Dissecting multi-gene virulence phenotypes with base editing requires consideration of target-independent mutagenesis

Bacterial pathogenicity arises from complex genetic interactions that are difficult to characterise through single-gene deletions. CRISPR base editors can generate multiplexed gene knockouts, yet this technology remains unexplored for dissecting bacterial pathogenicity. Here, we developed a base-editing pipeline for multi-gene knockouts while revealing that target-independent editing can contribute to variability in clonal fitness. In the model pathogen Salmonella Typhimurium, we employed curable plasmids containing a cytidine deaminase base editor and a multi-spacer CRISPR array to introduce premature stop codons in up to nine genes encoding SPI-2 T3SS effector proteins. Target bases were efficiently edited, producing a multi-knockout strain that showed reduced virulence in vivo relative to single knockouts. However, whole-genome sequencing revealed off-target cytidine deaminase activity, which affected virulence in vivo in a clone-dependent manner. A statistical power analysis predicted how many edited mutants are needed to confidently measure fitness functions in the face of off-target mutations. Our work shows the potential and current limitations of multiplexed base editing in bacterial pathogens and highlights the need for properly addressing off-target mutations when deploying base editors to interrogate genotype-phenotype relationships.

microbiology↗

bioRxiv2026

Antimicrobial Combination Effects at Subinhibitory Doses do not Reliably Predict Effects at Inhibitory Concentrations

Assessing whether drug combinations synergise or antagonise is difficult for several reasons: (i) measuring bacterial death rates at clinically relevant inhibitory drug concentrations is methodologically challenging, (ii) there is no unifying definition of what constitutes synergy or antagonism, and (iii) both synergy and antagonism may be concentration- and mixing-ratio-dependent. To assess how well sub-inhibitory measurements predict inhibitory behaviour, we quantified drug interactions for 15 pairwise drug combinations on a concentration checkerboard covering a wide range of inhibitory and sub-inhibitory concentrations. To this end, we tracked the population dynamics of 8640 bioluminescent E. coli cultures by recording their light-intensity trajectories. To handle time-varying treatment effects and allow fair comparisons between drugs with distinct killing dynamics, we used a time-weighted net growth rate {psi} to summarise each trajectory and assigned interaction labels (synergistic/independent/antagonistic) based on Bliss independence and Loewe additivity. We found that the interaction label depends on both the concentration and the mixing ratio, frequently changing between the sub-inhibitory and inhibitory regimes. Characterising drug combinations at a single sub-inhibitory concentration is therefore not sufficient. Instead, their combined effects should be assessed at the conditions of their intended use.

microbiology↗

bioRxiv2025

Parallel evolution of full-length genomes in a long-term evolution experiment with phage ΦX174

The study of evolution in organisms with high mutation rates requires detailed data on the genomic composition of the population. Here we report on an innovative use of high-throughput sequencing technology combined with rapid experimental evolution that allowed us to follow the genetic diversification of four independent bacteriophage populations that we evolved without actively imposing any selection pressures (in addition to those inherent in the bacterial cell environment) for 412 generations in unprecedented detail. Tracing over 80000 bacteriophage genomes, representing 884 distinct genotypes, we find that the patterns of viral diversification result in largely non-congruent genotype distributions, but also document multiple instances of parallel evolution. Unlike previous observations of parallelism in viral evolution, we do not just see the parallel evolution of mutations at single sites, but on the level of full-length genomes. Computer simulations that recapitulate our experiments in great detail show that this degree of parallelism is inconsistent with neutral evolution. We further show that the observed extent of parallel evolution biases phylodynamic analysis of migration rates, and wrongly estimates significant migration between the independent evolution lines. Our approach is applicable to many other viruses and paves the way to advanced population genetic investigations that aim to shed light on phenomena that relate to genetic linkage across the genome.

evolutionary biology↗

bioRxiv2025

Viral Simulation Reveals Overestimation Bias in Within-Host Phylodynamic Migration Rate Estimates Under Selection

Phylodynamic methods are widely used to infer the population dynamics of viruses between and within hosts. For HIV-1, these methods have been used to estimate migration rates between different anatomical compartments within a host. These methods typically assume that the genomic regions used for reconstruction are evolving without selective pressure, even though other parts of the viral genome are known to experience strong selection. In this study, we investigate how selection affects phylodynamic migration rate estimates. To this end, we developed a novel agent-based simulation tool, virolution, to simulate the evolution of virus within two anatomical compartments of a host. Using this tool, we generated viral sequences and genealogies assuming both, neutral evolution and purifying selection that is concordant in both compartments. We found that, under the selection regime, migration rates are significantly over-estimated with a stochastic mixture model and a structured coalescent model in the Bayesian inference framework BEAST2. Our results reveal that commonly used phylogeographic methods, which assume neutral evolution, can significantly bias migration rate estimates in selective regimes. This study underscores the need for assessing the robustness of phylodynamic analysis with respect to more realistic selection regimes.

evolutionary biology↗

bioRxiv2024

Evolution of ivermectin resistance in the nematode model Caenorhabditis elegans: critical influence of population size and unexpected cross-resistance to emodepside

The emergence and spread of anthelmintic resistance represent a major challenge for treating parasitic nematodes, threatening mass-drug control programs in humans and zoonotic species. Currently, experimental evidence to understand the influence of management (e.g., treatment intensity and frequency) and parasite-associated factors (e.g., genetic variation, population size and mutation rates) is lacking. To rectify this knowledge gap, we performed controlled evolution experiments with the model nematode Caenorhabditis elegans and further evaluated the evolution dynamics with a computational model. Large population size was critical for rapid ivermectin resistance evolution in vitro and in silico. Male nematodes were favored during resistance evolution, indicating a selective advantage of sexual recombination under drug pressure in vitro. Ivermectin resistance evolution led to the expected emergence of cross-resistance to the structurally related anthelmintic moxidectin but unexpectedly also to the structurally unrelated anthelmintic emodepside that has an entirely different mode of action. In contrast, albendazole, levamisole, and monepantel efficacy were not influenced by the evolution of Ivermectin resistance. We conclude that combining computational modeling with in vitro evolution experiments to test specific aspects of evolution directly represents a promising approach to guide the development of novel treatment strategies to anticipate and mitigate resistance evolution in parasitic nematodes.

evolutionary biology↗

bioRxiv2024

Investigating the consequences of the mating system for drug resistance evolution in C. elegans

The rise of anthelmintic-resistant strains in livestock threatens both animal and human health. Understanding the factors influencing anthelmintic resistance is crucial to mitigate the threat posed by these parasites. Due to difficulties in studying parasitic worms in the laboratory, the non-parasitic nematode Caenorhabditis elegans is used as a model organism to investigate anthelmintic resistance evolution. However, the suitability of this free-living nematode as a model for parasitic worms is debatable due to its rare androdioecious reproductive system, raising questions about the generalizability of findings from evolutionary experiments in C. elegans to other species. In this study, we developed a polygenic, population genetic model combined with pharmacodynamic approaches to investigate the effects of reproductive strategy and other aspects, such as dominance, mutational effects, the number of loci, and population size, on determining the dynamics and outcome of evolutionary processes. We found that androdioecious populations showed both rapid initial adaptation typical for hermaphrodites and tolerance to high drug concentrations observed in dioecious populations. They also exhibited the highest diversity and shortest time for the fixation of the beneficial allele. These results suggest that androdioecious populations can harness the advantages of both selfing and outcrossing, optimizing their reproductive strategy in response to drug selection.

evolutionary biology↗

bioRxiv2024

The dynamics of Staphylococcal infection and their treatment with antibiotics and bacteriophage in the Galleria mellonella model system

Critical to our understanding of infections and their treatment is the role the innate immune system plays in controlling bacterial pathogens. Nevertheless, many in vivo systems are made or modified such that they do not have an innate immune response. Use of these systems denies the opportunity to examine the synergy between the immune system and antimicrobial agents. In this study we demonstrate that the larva of Galleria mellonella is an effective in vivo model for the study of the population and evolutionary biology of bacterial infections and their treatment. To do this we test three hypotheses concerning the role of the innate immune system during infection. We show: i) sufficiently high densities of bacteria are capable of saturating the innate immune system, ii) bacteriostatic drugs and bacteriophages are as effective as bactericidal antibiotics in preventing mortality and controlling bacterial densities, and iii) minority populations of bacteria resistant to a treating antibiotic will not ascend. Using a highly virulent strain of Staphylococcus aureus and a mathematical computer-simulation model, we further explore how the dynamics of the infection within the short term determine the ultimate infection outcome. We find that immune activation in response to high densities of bacteria leads to a strong but short-lived immune response which ultimately results in a high degree of mortality. Overall, our findings illustrate the utility of the G. mellonella model system in conjunction with established in vivo models in studying infectious disease progression and treatment. Significance statementCentral to our understanding of the course of bacterial infections and their treatment is the contribution of the innate immune system. We use the larvae of Galleria mellonella to test hypothesis about the role of the innate immune system on Staphylococcus aureus infections. We demonstrate that the innate immune system of these larvae can control the infection and be saturated by high bacterial densities. As a consequence of this innate immune system, bacteriostatic drugs and phages are as effective as bactericidal drugs, and minority populations of bacteria resistant to antibiotics do not ascend. Our findings illustrate the utility of G. mellonella as a model for studying infections dynamics and therapeutic strategies.

microbiology↗

bioRxiv2024

The evolution of antimicrobial peptide resistance in Pseudomonas aeruginosa is severely constrained by random peptide mixtures

The prevalence of antibiotic-resistant pathogens has become a major threat to public health, requiring swift initiatives for discovering new strategies to control bacterial infections. Hence, antibiotic stewardship and rapid diagnostics, but also the development, and prudent use, of novel effective antimicrobial agents are paramount. Ideally, these agents should be less likely to select for resistance in pathogens than currently available conventional antimicrobials. The usage of antimicrobial Peptides (AMPs), key components of the innate immune response, and combination therapies, have been proposed as strategies to diminish the emergence of resistance. Herein, we investigated whether newly developed random antimicrobial peptide mixtures (RPMs) can significantly reduce the risk of resistance evolution in vitro to that of single sequence AMPs, using the ESKAPE pathogen Pseudomonas aeruginosa (P. aeruginosa) as a model Gram-negative bacterium. Infections of this pathogen are difficult to treat due the inherent resistance to many drug classes, enhanced by the capacity to form biofilms. P. aeruginosa was experimentally evolved in the presence of AMPs or RPMs, subsequentially assessing the extent of resistance evolution and cross-resistance/collateral sensitivity between treatments. Furthermore, the fitness costs of resistance on bacterial growth were studied, and whole-genome sequencing used to investigate which mutations could be candidates for causing resistant phenotypes. Lastly, changes in the pharmacodynamics of the evolved bacterial strains were examined. Our findings suggest that using RPMs bears a much lower risk of resistance evolution compared to AMPs and mostly prevents cross-resistance development to other treatments, while maintaining (or even improving) drug sensitivity. This strengthens the case for using random cocktails of AMPs in favour of single AMPs, against which resistance evolved in vitro, further providing an alternative to classic antibiotics worth pursuing.

microbiology↗

bioRxiv2023

Dose-dependent interaction of parasites with tiers of host defence generates "wormholes" that prolong infection at intermediate inoculum sizes

Immune responses are induced by parasite exposure and can in turn reduce parasite burden. Despite such apparently simple rules of engagement, key drivers of within-host dynamics, including dose-dependence of defence and infection duration, have proven difficult to predict. Here, we model how varied inoculating doses interact with host defences. Defence is multi-tiered, with 3 tiers invoked for all animals: barrier, innate, and adaptive. We model how these tiers interact with replicating and non-replicating parasites across six orders of magnitude of dose. We find that, in general, intermediate parasite doses generate infections of longest duration because they are sufficient in number to breach barrier defences, but insufficient to strongly induce subsequent tiers of defence. Deviation from the hypothesis of independent action, which postulates that each parasite has an independent probability of establishing infection, may therefore be widespread. Most interestingly, our model predicts local maxima of duration at two doses - one for each tier transition. While empirical evidence is consistent with nonlinear dose-dependencies, the profiles with multiple turning points that we predict will require finer-scale dose experiments than are usually undertaken. Our results help explain varied infection duration among differentially-exposed hosts and elucidate evolutionary pressures that shape both virulence and defence.

ecology↗

bioRxiv2022

Assessing the importance of resistance, persistence and hyper-mutation for antibiotic treatment success with stochastic modelling

Antimicrobial resistance poses a rising threat to global health, making it crucial to understand the routes of bacterial survival during antimicrobial treatments. Treatment failure can result from genetic or phenotypic mechanisms, which diminish the effect of antibiotics. By assembling empirical data, we find that, for example, Pseudomonas aeruginosa infections in cystic fibrosis patients frequently contain persisters, transiently non-growing and antibiotic-refractory subpopulations, and hyper-mutators, mutants with elevated mutation rates and thus higher probability of genetic resistance emergence. Resistance, persistence and hyper-mutation dynamics are difficult to disentangle experimentally. Hence, we use stochastic population modelling and deterministic fitness calculations of bacterial evolution under antibiotic treatment to investigate how genetic resistance and phenotypic mechanisms affect treatment success. We find that treatment failure is caused by resistant mutants at lower antibiotic concentrations (with high final bacterial numbers), but by persistence phenotypes at higher antibiotic concentrations (with low final bacterial numbers). Facilitation of resistance occurs through hyper-mutators during treatment, but through persistence only after treatment is discontinued, which allows for persisters to resume growth and evolve resistance in the absence of antibiotics. Our findings highlight the time- and concentration-dependence of different bacterial mechanisms to escape antibiotic killing, which should be considered when designing resistance-proof antimicrobial treatments.

evolutionary biology↗

bioRxiv2022

Per-Pathogen Virulence of HIV-1 subtypes A, C and D

HIV-1 subtypes differ, among other things, in their clinical manifestations and the speed in which they spread. In particular, the frequency of subtype C is increasing relative to subtype A and D. We aim to investigate whether HIV-1 subtype A, C and D differ in their per-pathogen virulence and to what extend this can explain the difference in spread between these subtypes. We use data from the Hormonal Contraception and HIV-1 Genital Shedding and Disease Progression among Women with Primary HIV Infection (GS) Study. For each study participant, we determine the set-point viral load value, CD4+ T cell level after primary infection and CD4+ T cell decline. Based on both the CD4+ T cell count after primary infection and CD4+ T cell decline, we estimate the time until AIDS for each individual. We then obtain our newly introduced measure of virulence as the inverse of the estimated time until AIDS. This new measure of virulence has an improved correlation with the set-point viral load compared to the decline of CD4+ T cells alone. After fitting a model to the measured virulence and set-point viral load values, we tested if this relation varies per subtype. We found that subtype C has a significantly higher per-pathogen virulence than subtype A. Based on an evolutionary model, we then hypothesize that differences in the primary length of infection period cause the observed variation in the speed of spread of the subtypes. Author summaryHIV-1 subtype C is currently spreading relatively fast in various parts of the world. Data from a study that followed many women infected with different HIV-1 subtypes (A, C and D) before they started treatment shows that neither their viral load nor the disease duration are increased for subtype C compared to subtype A and D. Thus, it seems that subtype C does not have a transmission advantage, neither per contact nor due to longer infection, making the observed relative rise in subtype C a puzzle. We used the same data to test if subtype C has optimized its potential to spread by decreasing the disease duration per unit of viral load (per-pathogen virulence) compared to subtype A and D. However, we find that subtype C has a significantly higher per-pathogen virulence than subtype A and D. This result makes the rise of subtype C even more counter-intuitive. In a last step, we develop an evolutionary model, in which we synthesize all our empirical results. With this model we can show that the most likely explanation for the global spread of subtype C is a difference in the duration of primary infection between the subtypes.

evolutionary biology↗

bioRxiv2021

Pathogen invasion-dependent tissue reservoirs and plasmid-encoded antibiotic degradation promote within-host plasmid transfer

Many plasmids encode antibiotic resistance genes. Through conjugation, plasmids can be rapidly disseminated. Previous work identified gut-luminal donor/recipient blooms and tissue-lodged plasmid-bearing persister cells of the enteric pathogen Salmonella enterica serovar Typhimurium (S.Tm) that survive antibiotic-therapy in host tissues, as factors promoting plasmid dissemination among Enterobacteriaceae. However, the buildup of tissue reservoirs and their contribution to plasmid spread await experimental demonstration. Here, we asked if re-seeding-plasmid acquisition-invasion cycles by S.Tm could serve to diversify tissue-lodged plasmid reservoirs, and thereby promote plasmid spread. Starting with intraperitoneal mouse infections, we demonstrate that S.Tm cells re-seeding the gut lumen initiate clonal expansion. ESBL plasmid-encoded gut luminal antibiotic degradation by donors can foster recipient survival under beta-lactam antibiotic treatment, enhancing transconjugant formation upon re-seeding. S.Tm transconjugants can subsequently re-enter host tissues introducing the new plasmid into the tissue-lodged reservoir. Population dynamics analyses pinpoint recipient migration into the gut lumen as rate-limiting for plasmid transfer dynamics in our model. Priority effects may be a limiting factor for reservoir formation in host tissues. Overall, our proof-of-principle data indicates that luminal antibiotic degradation and shuttling between the gut lumen and tissue-resident reservoirs can promote the accumulation and spread of plasmids within a host over time.

evolutionary biology↗

bioRxiv2021

Decomposing virulence to understand bacterial clearance in persistent infections

Hosts are not always successful at controlling and eliminating a pathogen and the factors causing variation in pathogen clearance are not well understood. To address this problem, we used Drosophila melanogaster to investigate how infections with different bacterial pathogens affects virulence, clearance and persistence. In this context we developed novel hypotheses that focus on how variation in clearance should be related to variation in different components of virulence, where virulence is the infection-related reduction in host fitness. To achieve this, virulence was decomposed into exploitation, i.e., how well bacteria can replicate inside the host, and per parasite pathogenicity (PPP), i.e., the amount of damage per parasite inflicted on the host. We used four bacterial species: Enterobacter cloacae, Providencia burhodogranariea, Lactococcus lactis and Pseudomonas entomophila. The injection doses spanned four orders of magnitude, and survival was followed to estimate virulence. Bacterial load was quantified in live flies during the acute (1-4 days) and chronic (7-35 days) phases of infection, and we tested infection status of flies that had died up to ten weeks post infection. We show that sustained persistent infection and clearance are both possible outcomes for bacterial species across a range of virulence. Bacteria of all species could persist inside the host for at least 75 days, and injection dose partly predicted within-species variation in clearance. Our decomposition of virulence showed that species differences in bacterial virulence could be explained by a combination of variation in both exploitation and PPP. In addition, we found that that higher exploitation leads to lower bacterial clearance, whereas we could not detect any effect of PPP on clearance. The differing effects of exploitation and PPP imply that there can be different means by which variation in virulence is related to clearance, which could critically affect pathogen transmission and the evolution of pathogen virulence. Author summaryFollowing an infection, hosts are not always able to quickly clear the pathogen, and they instead either die or survive with a persistent infection. Such variation is ecologically and evolutionarily important, because it can affect infection prevalence and transmission, and also virulence evolution. But what causes variation in infection outcomes? Here we contribute towards answering this question by investigating infection dynamics in flies infected with one of four bacterial species. We first establish that the bacterial species differ in virulence, i.e., the host death rate after infection. We find that variation in virulence arises because the bacteria differ in the two components of virulence: bacterial growth inside the host (exploitation), and the amount of damage caused per bacterium (per parasite pathogenicity). Furthermore, as early-phase exploitation increases, bacterial clearance later in the infection decreases. This finding can be explained by increasing exploitation leading to increasing clearance costs for the host. Taken together we demonstrate that variation in infection outcomes can be partly explained by how virulence, and its components, relate to the rate of pathogen clearance. We propose that the decomposition of virulence is valuable for understanding variation in infection outcomes - potentially also beyond the interrelation between virulence and clearance.

evolutionary biology↗

bioRxiv2021

Epidemiological and evolutionary consequences of CRISPR-Cas reactivity

Adaptive immune systems face a control challenge: they should react with enough strength to clear an infection while avoiding to harm their organism. CRISPR-Cas systems are adaptive immune systems of prokaryotes that defend against fast evolving viruses. Here, we explore the CRISPR-Cas control challenge and look how its reactivity, i.e. its probability to acquire a new resistance, impacts the epidemiological outcome of a phage outbreak and the prokaryotes fitness. We show that in the absence of phage evolution, phage extinction is driven by the probability to acquire at least one resistance. However, when phage evolution is fast, phage extinction is characterised by an epidemiological critical threshold: any reactivity below this critical threshold leads to phage survival whereas any reactivity above it leads to phage extinction. We also show that in the absence of autoimmunity, high levels of reactivity evolve. However, when CRISPR-Cas systems are prone to autoimmune reactions, intermediate levels of reactivity are evolutionarily optimal. These results help explaining why natural CRISPR-Cas systems do not show high levels of reactivity. Author summaryCRISPR-Cas systems are adaptive immune systems that use a complex 3-step molecular mechanism to defend prokaryotes against phages. Viral infections of populations defending with CRISPR-Cas can result in rapid phage extinction or in medium-term phage maintenance. What controls phage fate? Using mathematical modeling, we show that two parameters control this outcome: the phage escape rate and CRISPR-Cas reactivity (i.e. its probability of resistance acquisition upon infection). Furthermore, CRISPR-Cas reactivity impacts host fitness. From this, we derive that 1) CRISPR-Cas reactivity is a key predictor of the efficiency and of the cost of a CRISPR-Cas system, 2) there is an optimal reactivity balancing the cost of autoimmunity and immune efficiency and 3) high phage escape rate selects for higher CRISPR-Cas reactivities.

microbiology↗

bioRxiv2020

Multi-step vs. single-step resistance evolution under different drugs, pharmacokinetics and treatment regimens

The success of antimicrobial treatment is threatened by the evolution of drug resistance. Population genetic models are an important tool in mitigating that threat. However, most such models consider resistance emergence via a single mutational step. Here, we assembled experimental evidence that drug resistance evolution follows two patterns: i) a single mutation, which provides a large MIC increase, or ii) multiple mutations, each conferring a small increase, which combine to yield high-level resistance. Using stochastic modeling we then investigated the consequences of these two patterns for treatment failure and population diversity under various treatments. We find that resistance evolution is substantially limited if more than two mutations are required and that the most efficacious drug type depends on the pharmacokinetic profile. Further, we demonstrate that, for resistance evolution in multiple steps, adaptive treatment, which only suppresses the bacterial population, is favored over aggressive treatment, which aims at eradication.

evolutionary biology↗

bioRxiv2020

Estimating seroprevalence with imperfect serological tests: a cutoff-free approach

Large-scale serological testing in the population is essential to determine the true extent of the current SARS-CoV-2 pandemic. Serological tests measure antibody responses against pathogens and use predefined cutoff levels that dichotomize the quantitative test measures into sero-positives and negatives and use this as a proxy for past infection. With the imperfect assays that are currently available to test for past SARS-CoV-2 infection, the fraction of seropositive individuals in serosurveys is a biased estimator of the cumulative incidence and is usually corrected to account for the sensitivity and specificity. Here we use an inference method -- referred to as mixture-model approach -- for the estimation of the cumulative incidence that does not require to define cutoffs by integrating the quantitative test measures directly into the statistical inference procedure. We confirm that the mixture model outperforms the methods based on cutoffs, leading to less bias and error in estimates of the cumulative incidence. We illustrate how the mixture model can be used to optimize the design of serosurveys with imperfect serological tests. We also provide guidance on the number of control and case sera that are required to quantify the tests ambiguity sufficiently to enable the reliable estimation of the cumulative incidence. Lastly, we show how this approach can be used to estimate the cumulative incidence of classes of infections with an unknown distribution of quantitative test measures. This is a very promising application of the mixture-model approach that could identify the elusive fraction of asymptomatic SARS-CoV-2 infections. An R-package implementing the inference methods used in this paper is provided. Our study advocates using serological tests without cutoffs, especially if they are used to determine parameters characterizing populations rather than individuals. This approach circumvents some of the shortcomings of cutoff-based methods at exactly the low cumulative incidence levels and test accuracies that we are currently facing in SARS-CoV-2 serosurveys. Author SummaryAs other pathogens, SARS-CoV-2 elicits antibody responses in infected people that can be detected in their blood serum as early as a week after the infection until long after recovery. The presence of SARS-CoV-2 specific antibodies can therefore be used as a marker of past infection, and the prevalence of seropositive people, i.e. people with specific antibodies, is a key measure to determine the extent of the SARS-CoV-2 pandemic. The serological tests, however, are usually not perfect, yielding false positive and false negative results. Here we exploit an approach that refrains from classifying people as seropositive or negative, but rather compares the antibody level of an individual to that of confirmed cases and controls. This approach leads to more reliable estimates of cumulative incidence, especially for the low prevalence and low test accuracies that we face during the current SARS-CoV-2 pandemic. We also show how this approach can be extended to infer the presence of specific types of cases that have not been used for validating the test, such as people that underwent a mild or asymptomatic infection.

immunology↗