Biology subjects

Zwart, M. P.

Publications and source records attributed to Zwart, M. P..

7 recordsLinked to original sources

bioRxiv2026

RdRpCATCH: A unified resource for RNA virus discovery using viral RNA-dependent RNA polymerase profile Hidden Markov models

Recent advances in large-scale sequence mining have expanded our knowledge of RNA virus diversity. Most genome mining approaches for detecting RNA viruses that encode RNA-dependent RNA polymerase (RdRp) rely on identifying this conserved protein by employing profile Hidden Markov Models (pHMMs) to scan sequencing datasets. Recently, several new pHMM databases for RdRp detection have been released, each following distinct design principles. However, their relative performance is unclear and their accessibility to users without specialized computational expertise is limited. Here, we introduce the RdRp Collaborative Analysis Tool with Collections of pHMMs (RdRpCATCH: https://github.com/dimitris-karapliafis/RdRpCATCH), developed to consolidate publicly available RdRp pHMM resources into a single, accessible platform. RdRpCATCH enables the scanning of (meta)transcriptomic assemblies to discover RNA viruses and provides subsequent taxonomic annotation of detected contigs. A comparative analysis of RdRp pHMM databases reveals that most are highly effective at detecting known diversity of RNA viruses while minimizing false positives, supporting their joint use within RdRpCATCH. RdRpCATCH is distributed as both a conda package and a web server application (https://rdrpcatch.bioinformatics.nl), facilitating access for researchers with diverse expertise. By integrating multiple pHMM resources, this unified framework addresses fragmentation in the field and reduces technical barriers, enabling comprehensive viral discovery.

bioinformatics↗

bioRxiv2023

Biological characterization of an emergent virus infecting vegetables in diversified production systems: physostegia chlorotic mottle virus

With the emergence of high throughput sequencing (HTS) technologies, the discovery of new plant viruses has outpaced their biological characterization. However, it is crucial to understand the biology of these viruses to evaluate the risks they pose for the production of crops and natural ecosystems and to manage them properly. In 2018, Physostegia chlorotic mottle virus (PhCMoV) was detected in Austria in a Physostegia plant (Lamiaceae) using HTS, and subsequent prepublication data sharing associated the presence of the virus with severe fruit symptoms on important crops like tomato, eggplant, and cucumber across nine European countries. This discovery led to a collaborative effort to understand better the viruss genetic diversity, host range, symptomatology, and distribution. Still, specific knowledge gaps remained. In this study, the authors address these gaps by examining the transmission mode, prevalence, and disease severity of PhCMoV. Bioassay and field survey confirmed the causal association between the presence of the virus and symptoms on tomato and eggplant. The investigation also mapped out the historical and geographic footprint of the virus, spanning back 30 years and including a new location, Switzerland. Based on field survey, PhCMoV was found to naturally infect 11 new host plant species across seven families, extending the host range of PhCMoV to 20 plant species across 14 plant families. Greenhouse assays with mechanical inoculation showed that yield losses could reach 100% depending on the phenological stage of the plant at the time of infection. The study also identified a polyphagous leafhopper species (Anaceratagallia sp.) as the natural vector of PhCMoV. PhCMoV was widespread in diversified vegetable farms in Belgium where tomato is grown in soil, occurring in approximately one-third of such farms. However, outbreaks were sporadic and it can be suggested that they were associated with specific cultural practices, such as the cultivation of perennial plants in tomato tunnels that can serve as a host for both the virus and its vector. To further explore this phenomenon and better manage the virus, studying the ecology of the Anaceratagalliae vector would be beneficial.

plant biology↗

bioRxiv2022

The fitness landscape of TEM-1 β-lactamase is stratified and inverted by sublethal concentrations of cefotaxime

Adaptive evolutionary processes are constrained by the availability of mutations which cause a fitness benefit - a concept that may be illustrated by fitness landscapes which map the relationship of genotype space with fitness. Experimentally derived landscapes have demonstrated a predictability to evolution by identifying limited mutational routes that evolution by natural selection may take between low and high-fitness genotypes. However, such studies often utilise indirect measures to determine fitness. We estimated the competitive fitness of each mutant relative to all of its single-mutation neighbours to describe the fitness landscape of three mutations in a {beta}-lactamase enzyme at sub-lethal concentrations of the antibiotic cefotaxime in a structured and unstructured environment. We found that in the unstructured environment the antibiotic selected for higher-resistance types - but with an equivalent fitness for subsets of mutants, despite substantial variation in resistance - resulting in a stratified fitness landscape. In contrast, in a structured environment with low antibiotic concentration, antibiotic-susceptible genotypes had a relative fitness advantage, which was associated with antibiotic-induced filamentation. These results cast doubt that highly resistant genotypes have a unique selective advantage in environments with sub-inhibitory concentrations of antibiotics, and demonstrate that direct fitness measures are required for meaningful predictions of the accessibility of evolutionary routes. ImportanceThe evolution of antibiotic resistant bacterial populations underpins the ongoing antibiotic-resistance crisis. We aim to understand how antibiotic-degrading enzymes can evolve to cause increased resistance, how this process is constrained and whether it can be predictable. To this end we performed competition experiments with a combinatorially-complete set of mutants of a {beta}-lactamase gene subject to sub-inhibitory concentrations of the antibiotic cefotaxime. While some mutants confer their hosts with high resistance to cefotaxime, in competition these mutants do not always confer a selective advantage. Similarly, we identified conditions involving spatial structure where mutations causing high resistance result in a selective disadvantage. Together, this work suggests that the relationship between resistance level and fitness at sub-inhibitory concentrations is complex; predicting the evolution of antibiotic resistance requires knowledge of the conditions that select for resistant genotypes and the selective advantage evolved types have over their predecessors.

evolutionary biology↗

bioRxiv2022

Incomplete bunyavirus particles contribute to within-host spread and between-host transmission

Bunyaviruses lack a specific mechanism to ensure the incorporation of a complete set of genome segments into each virion, explaining the generation of incomplete virus particles lacking one or more genome segments. Such incomplete virus particles, which may represent the majority of particles produced, are generally considered to interfere with virus infection and spread. Using the three-segmented Rift Valley fever virus as a model bunyavirus, we here show that two distinct incomplete virus particle populations that are unable to spread autonomously, are able to efficiently complement each other in both mammalian and insect cells following co-infection. We further show that incomplete virus particles are capable of co-infecting mosquitoes, resulting in the rescue of infectious virus that is able to disseminate to the mosquito salivary glands. Our findings reveal a significant role of incomplete particles in within-host spread and between-host transmission, reminiscent of the life cycle of multipartite viruses.

microbiology↗

bioRxiv2021

Empirical estimates of the mutation rate for an alphabaculovirus

Mutation rates are of key importance for understanding evolutionary processes and predicting their outcomes. Empirical estimates of mutation rate are available for a number of RNA viruses, but few are available for DNA viruses, which tend to have larger genomes. Whilst some viruses have very high mutation rates, lower mutation rates are expected for viruses with large genomes to ensure genome integrity. Alphabaculoviruses are insect viruses with large genomes and often have high levels of polymorphism, suggesting high mutation rates despite evidence of proofreading activity by the replication machinery. Here, we report an empirical estimate of the mutation rate per base per strand copying (s/n/r) of Autographa californica multiple nucleopolyhedrovirus (AcMNPV). To avoid biases due to selection, we analyzed mutations that occurred in a stable, non-functional genomic insert after five serial passages in Spodoptera exigua larvae. Population bottlenecks, viral mode of replication and thresholds for mutation detection likely affect mutation rate estimates, and we therefore used population genetic models that account for these processes to infer the mutation rate. We estimated a mutation rate of 1x10-7 s/n/r. This estimate was not sensitive to different model assumptions or including whole genome data. The rates at which different classes of mutations accumulate provide good evidence for neutrality of mutations occurring within the inserted region. We therefore present a robust approach for mutation rate estimation for viruses with stable genomes, and strong evidence of a much lower alphabaculovirus mutation rate than supposed based on the high levels of polymorphism observed. Author SummaryVirus populations can evolve rapidly, driven by the large number of mutations that occur during virus replication. It is challenging to measure mutation rates because selection will affect which mutations are observed: beneficial mutations are overrepresented in virus populations, while deleterious mutations are selected against and therefore underrepresented. Few mutation rates have been estimated for viruses with large DNA genomes, and there are no estimates for any insect virus. Here, we estimate the mutation rate for an alphabaculovirus, a virus that infects caterpillars and has a large, 134 kilobase pair DNA genome. To ensure that selection did not bias our estimate of mutation rate, we studied which mutations occurred in a large artificial region inserted into the virus genome, where mutations did not affect viral fitness. We deep sequenced evolved virus populations, and compared the distribution of observed mutants to predictions from a simulation model to estimate mutation rate. We found evidence for a relatively low mutation rate, of one mutation in every 10 million bases replicated. This estimate is in line with expectations for a virus with self-correcting replication machinery and a large genome.

genetics↗

bioRxiv2021

Population size mediates the contribution of high-rate and large-benefit mutations to parallel evolution

Both mutations with large benefits and mutations occurring at high rates may cause parallel evolution, but their contribution is expected to depend on population size. We show that small and large bacterial populations adapt to a novel antibiotic using similar numbers, but different types of mutations. Small populations repeatedly substitute similar high-rate structural variants, including the deletion of a nonfunctional {beta}-lactamase, and evolve modest resistance levels. Hundred-fold larger populations more frequently use the same low-rate, large-benefit point mutations, including those activating the {beta}-lactamase, and reach 50-fold higher resistance levels. Our results demonstrate a key role of clonal interference in mediating the contribution of high-rate and large-benefit mutations in populations of different size, facilitated by a tradeoff between rates and fitness effects of different mutation classes.

evolutionary biology↗

bioRxiv2017

Mutation supply and the repeatability of selection for antibiotic resistance

Whether evolution can be predicted is a key question in evolutionary biology. Here we set out to better understand the repeatability of evolution, which is a necessary condition for predictability. We explored experimentally the effect of mutation supply and the strength of selective pressure on the repeatability of selection from standing genetic variation. Different sizes of mutant libraries of an antibiotic resistance gene, TEM-1 {beta}-lactamase in Escherichia coli, were subjected to different antibiotic concentrations. We determined whether populations went extinct or survived, and sequenced the TEM gene of the surviving populations. The distribution of mutations per allele in our mutant libraries--generated by error-prone PCR--followed a Poisson distribution. Extinction patterns could be explained by a simple stochastic model that assumed the sampling of beneficial mutations was key for survival. In most surviving populations, alleles containing at least one known large-effect beneficial mutation were present. These genotype data also support a model which only invokes sampling effects to describe the occurrence of alleles containing large-effect driver mutations. Hence, evolution is largely predictable given cursory knowledge of mutational fitness effects, the mutation rate and population size. There were no clear trends in the repeatability of selected mutants when we considered all mutations present. However, when only known large-effect mutations were considered, the outcome of selection is less repeatable for large libraries, in contrast to expectations. Furthermore, we show experimentally that alleles carrying multiple mutations selected from large libraries confer higher resistance levels relative to alleles with only a known large-effect mutation, suggesting that the scarcity of high-resistance alleles carrying multiple mutations may contribute to the decrease in repeatability at large library sizes.

evolutionary biology↗