Coinfections by noninteracting pathogens are not independent and require new tests of interaction

Experimentally evolved Staphylococcus aureus shows increased survival in the presence of Pseudomonas aeruginosa by acquiring mutations in the amino acid transporter, GltT

When cultured together under standard laboratory conditions Pseudomonas aeruginosa has been shown to be an effective inhibitor of Staphylococcus aureus. However, P. aeruginosa and S. aureus are commonly observed in coinfections of individuals with cystic fibrosis (CF) and in chronic wounds. Previous work from our group revealed that S. aureus isolates from CF infections are able to persist in the presence of P. aeruginosa strain PAO1 with a range of tolerances with some isolates being eliminated entirely and others maintaining large populations. In this study, we designed a serial transfer, evolution experiment to identify mutations that allow S. aureus to survive in the presence of P. aeruginosa. Using S. aureus USA300 JE2 as our ancestral strain, populations of S. aureus were repeatedly cocultured with fresh P. aeruginosa PAO1. After eight coculture periods, S. aureus populations that survived better in the presence of PAO1 were observed. We found two independent mutations in the highly conserved S. aureus aspartate transporter, gltT, that were unique to evolved P. aeruginosa-tolerant isolates. Subsequent phenotypic testing demonstrated that gltT mutants have reduced uptake of glutamate and outcompeted wild-type S. aureus when glutamate was absent from chemically defined media. These findings together demonstrate that the presence of P. aeruginosa exerts selective pressure on S. aureus to alter its uptake and metabolism of key amino acids when the two are cultured together.

Emerging Themes and Approaches in Plant Virus Epidemiology

Plant diseases caused by viruses share many common features with those caused by other pathogen taxa in terms of the host-pathogen interaction, but there are also distinctive features in epidemiology, most apparent where transmission is by vectors. Consequently, the host-virus-vector-environment interaction presents a continuing challenge in attempts to understand and predict the course of plant virus epidemics. Theoretical concepts, based on the underlying biology, can be expressed in mathematical models and tested through quantitative assessments of epidemics in the field; this remains a goal in understanding why plant virus epidemics occur and how they can be controlled. To this end, this review identifies recent emerging themes and approaches to fill in knowledge gaps in plant virus epidemiology. We review quantitative work on the impact of climatic fluctuations and change on plants, viruses, and vectors under different scenarios where impacts on the individual components of the plant-virus-vector interaction may vary disproportionately; there is a continuing, sometimes discordant, debate on host resistance and tolerance as plant defense mechanisms, including aspects of farmer behavior and attitudes toward disease management that may affect deployment in crops; disentangling host-virus-vector-environment interactions, as these contribute to temporal and spatial disease progress in field populations; computational techniques for estimating epidemiological parameters from field observations; and the use of optimal control analysis to assess disease control options. We end by proposing new challenges and questions in plant virus epidemiology.

Reconstructing multi-strain pathogen interactions from cross-sectional survey data via statistical network inference

Infectious diseases often involve multiple pathogen species or multiple strains of the same pathogen. As such, knowledge of how different pathogens interact is key to understand and predict the outcome of interventions targeting only a subset of species or strains involved in disease. Population-level data may be useful to infer pathogen strain interactions, but most previously used inference methods only consider uniform interactions between all strains or focus on marginal pairwise interactions. As such, these methods are prone to bias induced by indirect interactions through other strains. Here, we evaluated statistical network inference for reconstructing heterogeneous interactions from cross-sectional surveys detecting joint presence/absence patterns of pathogen strains within hosts. We applied various network models to simulated survey data, representing endemic infection states of multiple pathogen strains with potential interactions in acquisition or clearance of infection. Satisfactory performance was demonstrated by the estimators converging to the true interactions. Accurate reconstruction of interaction networks was achieved by regularization or penalization for sample size. Although performance deteriorated in the presence of host heterogeneity, this was overcome by correcting for individual-level risk factors. Our work demonstrates how statistical network inference could prove useful for detecting multi-strain pathogen interactions and may have applications beyond epidemiology.

The characterization of toll-like receptor repertoire in Pinna nobilis after mass mortality events suggests adaptive introgression

The fan mussel Pinna nobilis is currently on the brink of extinction due to a multifactorial disease mainly caused to the highly pathogenic parasite Haplosporidium pinnae, meaning that the selection pressure outweighs the adaptive potential of the species. Hopefully, rare individuals have been observed somehow resistant to the parasite, stretching the need to identify the traits underlying this better fitness. Among the candidate to explore at first intention are fast-evolving immune genes, of which toll-like receptor (TLR). In this study, we examined the genetic diversity at 14 TLR loci across P. nobilis, Pinna rudis and P. nobilis × P. rudis hybrid genomes, collected at four physically distant regions, that were found to be either resistant or sensitive to the parasite H. pinnae. We report a high genetic diversity, mainly observed at cell surface TLRs compared with that of endosomal TLRs. However, the endosomal TLR-7 exhibited unexpected level of diversity and haplotype phylogeny. The lack of population structure, associated with a high genetic diversity and elevated dN/dS ratio, was interpreted as balancing selection, though both directional and purifying selection were detected. Interestingly, roughly 40% of the P. nobilis identified as resistant to H. pinnae were introgressed with P. rudis TLR. Specifically, they all carried a TLR-7 of P. rudis origin, whereas sensitive P. nobilis were not introgressed, at least at TLR loci. Small contributions of TLR-6 and TLR-4 single-nucleotide polymorphisms to the clustering of resistant and susceptible individuals could be detected, but their specific role in resistance remains highly speculative. This study provides new information on the diversity of TLR genes within the P. nobilis species after MME and additional insights into adaptation to H. pinnae that should contribute to the conservation of this Mediterranean endemic species.

Experimentally Evolved Staphylococcus aureus Survives in the Presence of Pseudomonas aeruginosa by Acquiring Mutations in the Amino Acid Transporter, GltT

Staphylococcus aureus and Pseudomonas aeruginosa are the most common bacterial pathogens isolated from cystic fibrosis (CF) related lung infections. When both of these opportunistic pathogens are found in a coinfection, CF patients tend to have higher rates of pulmonary exacerbations and experience a more rapid decrease in lung function. When cultured together under standard laboratory conditions, it is often observed that P. aeruginosa effectively inhibits S. aureus growth. Previous work from our group revealed that S. aureus from CF infections have isolate-specific survival capabilities when cocultured with P. aeruginosa. In this study, we designed a serial transfer evolution experiment to identify mutations that allow S. aureus to adapt to the presence of P. aeruginosa. Using S. aureus USA300 JE2 as our ancestral strain, populations of S. aureus were repeatedly cocultured with fresh P. aeruginosa strain, PAO1. After 8 coculture periods, S. aureus populations that survived better in the presence of PAO1 were observed. We found two independent mutations in the highly conserved S. aureus aspartate transporter, gltT, that were unique to evolved P. aeruginosa-tolerant isolates. Subsequent phenotypic testing demonstrated that gltT mutants have reduced uptake of glutamate and outcompete wild-type S. aureus when glutamate is absent from chemically-defined media. These findings together demonstrate that the presence of P. aeruginosa exerts selective pressure on S. aureus to alter its uptake and metabolism of key amino acids when the two bacteria are cultured together.

Dengue Fever, Crimean-Congo Hemorrhagic Fever, and COVID-19 Triple Co-infection: Out of the Frying Pan Into the Fire

In developing countries, infectious diseases are thriving due to poor hygiene, inadequate public health infrastructure, and socio-cultural factors. Generally, infections are due to a single pathogen, but due to the shared risk factors for transmission, co-infections are not uncommon. The severity and outcome of infections are adversely affected by co-infection. Co-infections present as diagnostic and therapeutic enigmas because of the complex interaction between different pathogens involved and distorted host responses. The southeast Asian region, particularly Pakistan, is known for unique combinations of different infections.

We present a distinctive case of triple co-infection of dengue virus, Crimean-Congo hemorrhagic fever virus, and severe acute respiratory syndrome coronavirus-2. The index case was a 60-year-old gentleman who presented with fever, cough, shortness of breath, bruises, and hemoptysis. He had thrombocytopenia, deranged liver and renal function, coagulopathy, and infiltrates in both lung fields. Subsequent investigations revealed a positive polymerase chain reaction for ribonucleic acid of dengue virus, Crimean-Congo Hemorrhagic fever virus, and severe acute respiratory syndrome coronavirus-2. He received supportive treatment including antibiotics, blood products, ribavirin, and supplemental oxygen. He developed multi-organ failure and succumbed to the triple co-infection. This case will act as a wake-up call for clinicians, public health authorities, and infectious disease specialists to plan before the volcano of co-infections erupts.

Quiescence Generates Moving Average in a Stochastic Epidemiological Model with One Host and Two Parasites

Mathematical modelling of epidemiological and coevolutionary dynamics is widely being used to improve disease management strategies of infectious diseases. Many diseases present some form of intra-host quiescent stage, also known as covert infection, while others exhibit dormant stages in the environment. As quiescent/dormant stages can be resistant to drug, antibiotics, fungicide treatments, it is of practical relevance to study the influence of these two life-history traits on the coevolutionary dynamics. We develop first a deterministic coevolutionary model with two parasite types infecting one host type and study analytically the stability of the dynamical system. We specifically derive a stability condition for a five-by-five system of equations with quiescence. Second, we develop a stochastic version of the model to study the influence of quiescence on stochasticity of the system dynamics. We compute the steady state distribution of the parasite types which follows a multivariate normal distribution. Furthermore, we obtain numerical solutions for the covariance matrix of the system under symmetric and asymmetric quiescence rates between parasite types. When parasite strains are identical, quiescence increases the variance of the number of infected individuals at high transmission rate and vice versa when the transmission rate is low. However, when there is competition between parasite strains with different quiescent rates, quiescence generates a moving average behaviour which dampen off stochasticity and decreases the variance of the number of infected hosts. The strain with the highest rate of entering quiescence determines the strength of the moving average and the magnitude of reduction of stochasticity. Thus, it is worth investigating simple models of multi-strain parasite under quiescence/dormancy to improve disease management strategies.

Distinguishing viruses responsible for influenza-like illness

The many respiratory viruses that cause influenza-like illness (ILI) are reported and tracked as one entity, defined by the CDC as a group of symptoms that include a fever of 100 degrees Fahrenheit, a cough, and/or a sore throat. In the United States alone, ILI impacts 9-49 million people every year. While tracking ILI as a single clinical syndrome is informative in many respects, the underlying viruses differ in parameters and outbreak properties. Most existing models treat either a single respiratory virus or ILI as a whole. However, there is a need for models capable of comparing several individual viruses that cause respiratory illness, including ILI. To address this need, here we present a flexible model and simulations of epidemics for influenza, RSV, rhinovirus, seasonal coronavirus, adenovirus, and SARS/MERS, parameterized by a systematic literature review and accompanied by a global sensitivity analysis. We find that for these biological causes of ILI, their parameter values, timing, prevalence, and proportional contributions differ substantially. These results demonstrate that distinguishing the viruses that cause ILI will be an important aspect of future work on diagnostics, mitigation, modeling, and preparation for future pandemics.

The pitfalls of inferring virus-virus interactions from co-detection prevalence data: application to influenza and SARS-CoV-2

There is growing experimental evidence that many respiratory viruses-including influenza and SARS-CoV-2-can interact, such that their epidemiological dynamics may not be independent. To assess these interactions, standard statistical tests of independence suggest that the prevalence ratio-defined as the ratio of co-infection prevalence to the product of single-infection prevalences-should equal unity for non-interacting pathogens. As a result, earlier epidemiological studies aimed to estimate the prevalence ratio from co-detection prevalence data, under the assumption that deviations from unity implied interaction. To examine the validity of this assumption, we designed a simulation study that built on a broadly applicable epidemiological model of co-circulation of two emerging or seasonal respiratory viruses. By focusing on the pair influenza-SARS-CoV-2, we first demonstrate that the prevalence ratio systematically underestimates the strength of interaction, and can even misclassify antagonistic or synergistic interactions that persist after clearance of infection. In a global sensitivity analysis, we further identify properties of viral infection-such as a high reproduction number or a short infectious period-that blur the interaction inferred from the prevalence ratio. Altogether, our results suggest that ecological or epidemiological studies based on co-detection prevalence data provide a poor guide to assess interactions among respiratory viruses.

Epidemiological and ecological consequences of virus manipulation of host and vector in plant virus transmission

Many plant viruses are transmitted by insect vectors. Transmission can be described as persistent or non-persistent depending on rates of acquisition, retention, and inoculation of virus. Much experimental evidence has accumulated indicating vectors can prefer to settle and/or feed on infected versus noninfected host plants. For persistent transmission, vector preference can also be conditional, depending on the vector’s own infection status. Since viruses can alter host plant quality as a resource for feeding, infection potentially also affects vector population dynamics. Here we use mathematical modelling to develop a theoretical framework addressing the effects of vector preferences for landing, settling and feeding–as well as potential effects of infection on vector population density–on plant virus epidemics. We explore the consequences of preferences that depend on the host (infected or healthy) and vector (viruliferous or nonviruliferous) phenotypes, and how this is affected by the form of transmission, persistent or non-persistent. We show how different components of vector preference have characteristic effects on both the basic reproduction number and the final incidence of disease. We also show how vector preference can induce bistability, in which the virus is able to persist even when it cannot invade from very low densities. Feedbacks between plant infection status, vector population dynamics and virus transmission potentially lead to very complex dynamics, including sustained oscillations. Our work is supported by an interactive interface https://plantdiseasevectorpreference.herokuapp.com/. Our model reiterates the importance of coupling virus infection to vector behaviour, life history and population dynamics to fully understand plant virus epidemics.

Plant virus diseases–which cause devastating epidemics in plant populations worldwide–are most often transmitted by insect vectors. Recent experimental evidence indicates how vectors do not choose between plants at random, but instead can be affected by whether plants are infected (or not). Virus infection can cause plants to “smell” different, because they produce different combinations of volatile chemicals, or “taste” different, due to chemical changes in infected tissues. Vector reproduction rates can also be affected when colonising infected versus uninfected plants. Potential effects on epidemic spread through a population of plants are not yet entirely understood. There are also interactions with the mode of virus transmission. Some viruses can be transmitted after only a brief probe by a vector, whereas others are only picked up after an extended feed on an infected plant. Furthermore there are differences in how long vectors remain able to transmit the virus. This ranges from a matter of minutes, right up to the entire lifetime of the insect, depending on the plant-virus-vector combination under consideration. Here we use mathematical modelling to synthesise all this complexity into a coherent theoretical framework. We illustrate our model via an online interface https://plantdiseasevectorpreference.herokuapp.com/.

Reconstructing contact network structure and cross-immunity patterns from multiple infection histories

Interactions within a population shape the spread of infectious diseases but contact patterns between individuals are difficult to access. We hypothesised that key properties of these patterns can be inferred from multiple infection data in longitudinal follow-ups. We developed a simulator for epidemics with multiple infections on networks and analysed the resulting individual infection time series by introducing similarity metrics between hosts based on their multiple infection histories. We find that, depending on infection multiplicity and network sampling, multiple infection summary statistics can recover network properties such as degree distribution. Furthermore, we show that by mining simulation outputs for multiple infection patterns, one can detect immunological interference between pathogens (i.e. the fact that past infections in a host condition future probability of infection). The combination of individual-based simulations and analysis of multiple infection histories opens promising perspectives to infer and validate transmission networks and immunological interference for infectious diseases from longitudinal cohort data.

Viewing Emerging Human Infectious Epidemics through the Lens of Invasion Biology

Invasion biology examines species originated elsewhere and moved with the help of humans, and those species’ impacts on biodiversity, ecosystem services, and human well-being. In a globalized world, the emergence and spread of many human infectious pathogens are quintessential biological invasion events. Some macroscopic invasive species themselves contribute to the emergence and transmission of human infectious agents. We review conceptual parallels and differences between human epidemics and biological invasions by animals and plants. Fundamental concepts in invasion biology regarding the interplay of propagule pressure, species traits, biotic interactions, eco-evolutionary experience, and ecosystem disturbances can help to explain transitions between stages of epidemic spread. As a result, many forecasting and management tools used to address epidemics could be applied to biological invasions and vice versa. Therefore, we advocate for increasing cross-fertilization between the two disciplines to improve prediction, prevention, treatment, and mitigation of invasive species and infectious disease outbreaks, including pandemics.

The Epidemiology of Plant Virus Disease: Towards a New Synthesis

Epidemiology is the science of how disease develops in populations, with applications in human, animal and plant diseases. For plant diseases, epidemiology has developed as a quantitative science with the aims of describing, understanding and predicting epidemics, and intervening to mitigate their consequences in plant populations. Although the central focus of epidemiology is at the population level, it is often necessary to recognise the system hierarchies present by scaling down to the individual plant/cellular level and scaling up to the community/landscape level. This is particularly important for diseases caused by plant viruses, which in most cases are transmitted by arthropod vectors. This leads to range of virus-plant, virus-vector and vector-plant interactions giving a distinctive character to plant virus epidemiology (whilst recognising that some fungal, oomycete and bacterial pathogens are also vector-borne). These interactions have epidemiological, ecological and evolutionary consequences with implications for agronomic practices, pest and disease management, host resistance deployment, and the health of wild plant communities. Over the last two decades, there have been attempts to bring together these differing standpoints into a new synthesis, although this is more apparent for evolutionary and ecological approaches, perhaps reflecting the greater emphasis on shorter often annual time scales in epidemiological studies. It is argued here that incorporating an epidemiological perspective, specifically quantitative, into this developing synthesis will lead to new directions in plant virus research and disease management. This synthesis can serve to further consolidate and transform epidemiology as a key element in plant virus research.

Evolutionary ecology of Lyme Borrelia

The bacterial genus, Borrelia, is comprised of vector-borne spirochete species that infect and are transmitted from multiple host species. Some Borrelia species cause highly-prevalent diseases in humans and domestic animals. Evolutionary, ecological, and molecular research on many Borrelia species have resulted in tremendous progress toward understanding the biology and natural history of these species. Yet, many outstanding questions, such as how Borrelia populations will be impacted by climate and land-use change, will require an interdisciplinary approach. The evolutionary ecology research framework incorporates theory and data from evolutionary, ecological, and molecular studies while overcoming common assumptions within each field that can hinder integration across these disciplines. Evolutionary ecology offers a framework to evaluate the ecological consequences of evolved traits and to predict how present-day ecological processes may result in further evolutionary change. Studies of microbes with complex transmission cycles, like Borrelia, which interact with multiple vertebrate hosts and arthropod vectors, are poised to leverage the power of the evolutionary ecology framework to identify the molecular interactions involved in ecological processes that result in evolutionary change. Using existing data, we outline how evolutionary ecology theory can delineate how interactions with other species and the physical environment create selective forces or impact migration of Borrelia populations and result in micro-evolutionary changes. We further discuss the ecological and molecular consequences of those micro-evolutionary changes. While many of the currently outstanding questions will necessitate new experimental designs and additional empirical data, many others can be addressed immediately by integrating existing molecular and ecological data within an evolutionary ecology framework.

Predicting N-Strain Coexistence from Co-colonization Interactions: Epidemiology Meets Ecology and the Replicator Equation

Multi-type infection processes are ubiquitous in ecology, epidemiology and social systems, but remain hard to analyze and to understand on a fundamental level. Here, we study a multi-strain susceptible-infected-susceptible model with coinfection. A host already colonized by one strain can become more or less vulnerable to co-colonization by a second strain, as a result of facilitating or competitive interactions between the two. Fitness differences between N strains are mediated through \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$N^2$$\end{document}N2 altered susceptibilities to secondary infection that depend on colonizer-cocolonizer identities (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$K_{ij}$$\end{document}Kij). By assuming strain similarity in such pairwise traits, we derive a model reduction for the endemic system using separation of timescales. This ‘quasi-neutrality’ in trait space sets a fast timescale where all strains interact neutrally, and a slow timescale where selective dynamics unfold. We find that these slow dynamics are governed by the replicator equation for N strains. Our framework allows to build the community dynamics bottom-up from only pairwise invasion fitnesses between members. We highlight that mean fitness of the multi-strain network, changes with their individual dynamics, acts equally upon each type, and is a key indicator of system resistance to invasion. By uncovering the link between N-strain epidemiological coexistence and the replicator equation, we show that the ecology of co-colonization relates to Fisher’s fundamental theorem and to Lotka-Volterra systems. Besides efficient computation and complexity reduction for any system size, these results open new perspectives into high-dimensional community ecology, detection of species interactions, and evolution of biodiversity.

Implications of mixed viral infections on plant disease ecology and evolution

Mixed viral infections occur more commonly than would be expected by chance in nature. Virus-virus interactions may affect viral traits and leave a genetic signature in the population, and thus influence the prevalence and emergence of viral diseases. Understanding about how the interactions between viruses within a host shape the evolutionary dynamics of the viral populations is needed for viral disease prevention and management. Here, we first synthesize concepts implied in the occurrence of virus-virus interactions. Second, we consider the role of the within-host interactions of virus-virus and virus-other pathogenic microbes, on the composition and structure of viral populations. Third, we contemplate whether mixed viral infections can create opportunities for the generation and maintenance of viral genetic diversity. Fourth, we attempt to summarize the evolutionary response of viral populations to mixed infections to understand how they shape the spatio-temporal dynamics of viral populations at the individual plant and field scales. Finally, we anticipate the future research under the reconciliation of molecular epidemiology and evolutionary ecology, drawing attention to the need of adding more complexity to future research in order to gain a better understanding about the mechanisms operating in nature.