Network model of immune responses reveals key effectors to single and co-infection dynamics by a respiratory bacterium and a gastrointestinal helminth

Discrete-state models identify pathway specific B cell states across diseases and infections at single-cell resolution

Oxygen (O2) regulated pathways modulate B cell activation, migration and proliferation during infection, vaccination, and other diseases. Modeling these pathways in health and disease is critical to understand B cell states and ways to mediate them. To characterize B cells by their activation of O2 regulated pathways we develop pathway specific discrete state models using previously published single-cell RNA-sequencing (scRNA-seq) datasets from isolated B cells. Specifically, Single Cell Boolean Omics Network Invariant-Time Analysis (scBONITA) was used to infer logic gates for known pathway topologies. The simplest inferred set of logic gates that maximized the number of "OR" interactions between genes was used to simulate B cell networks involved in oxygen sensing until they reached steady network states (attractors). By focusing on the attractors that best represented sequenced cells, we identified genes critical in determining pathway specific cellular states that corresponded to diseased and healthy B cell phenotypes. Specifically, we investigate the transendothelial migration, regulation of actin cytoskeleton, HIF1A, and Citrate Cycle pathways. Our analysis revealed attractors that resembled the state of B cell exhaustion in HIV+ patients as well as attractors that promoted anerobic metabolism, angiogenesis, and tumorigenesis in breast cancer patients, which were eliminated after neoadjuvant chemotherapy (NACT). Finally, we investigated the attractors to which the Azimuth-annotated B cells mapped and found that attractors resembling B cells from HIV+ patients encompassed a significantly larger number of atypical memory B cells than HIV- attractors. Meanwhile, attractors resembling B cells from breast cancer patients post NACT encompassed a reduced number of atypical memory B cells compared to pre-NACT attractors.

Prior infection with unrelated neurotropic virus exacerbates influenza disease and impairs lung T cell responses

Immunity to infectious diseases is predominantly studied by measuring immune responses towards a single pathogen, although co-infections are common. In-depth mechanisms on how co-infections impact anti-viral immunity are lacking, but are highly relevant to treatment and prevention. We established a mouse model of co-infection with unrelated viruses, influenza A (IAV) and Semliki Forest virus (SFV), causing disease in different organ systems. SFV infection eight days before IAV infection results in prolonged IAV replication, elevated cytokine/chemokine levels and exacerbated lung pathology. This is associated with impaired lung IAV-specific CD8+ T cell responses, stemming from suboptimal CD8+ T cell activation and proliferation in draining lymph nodes, and dendritic cell paralysis. Prior SFV infection leads to increased blood brain barrier permeability and presence of IAV RNA in brain, associated with increased trafficking of IAV-specific CD8+ T cells and establishment of long-term tissue-resident memory. Relative to lung IAV-specific CD8+ T cells, brain memory IAV-specific CD8+ T cells have increased TCR repertoire diversity within immunodominant DbNP366+CD8+ and DbPA224+CD8+ responses, featuring suboptimal TCR clonotypes. Overall, our study demonstrates that infection with an unrelated neurotropic virus perturbs IAV-specific immune responses and exacerbates IAV disease. Our work provides key insights into therapy and vaccine regimens directed against unrelated pathogens.

A meta-analysis of Boolean network models reveals design principles of gene regulatory networks

Gene regulatory networks (GRNs) play a central role in cellular decision-making. Understanding their structure and how it impacts their dynamics constitutes thus a fundamental biological question. GRNs are frequently modeled as Boolean networks, which are intuitive, simple to describe, and can yield qualitative results even when data are sparse. We assembled the largest repository of expert-curated Boolean GRN models. A meta-analysis of this diverse set of models reveals several design principles. GRNs exhibit more canalization, redundancy, and stable dynamics than expected. Moreover, they are enriched for certain recurring network motifs. This raises the important question why evolution favors these design mechanisms.

Modeling the contribution of antibody attack rates to single and dual helminth infections in a natural system

Within-host models of infection can provide important insights into the processes that affect parasite spread and persistence in host populations. However, modeling can be limited by the availability of empirical data, a problem commonly encountered in natural systems. Here, we used six years of immune-infection observations of two gastrointestinal helminths (Trichostrongylus retortaeformis and Graphidium strigosum) from a population of European rabbits (Oryctolagus cuniculus) to develop an age-dependent, mathematical model that explicitly included species-specific and cross-reacting antibody (IgA and IgG) responses to each helminth in hosts with single or dual infections. Different models of single infection were formally compared to test alternative mechanisms of parasite regulation. The two models that best described single infections of each helminth species were then coupled through antibody cross-immunity to examine how the presence of one species could alter the host immune response to, and the within-host dynamics of, the other species. For both single infections, model selection suggested that either IgA or IgG responses could equally explain the observed parasite intensities by host age. However, the antibody attack rate and affinity level changed between the two helminths, it was stronger against T. retortaeformis than against G. strigosum and caused contrasting age-intensity profiles. When the two helminths coinfect the same host, we found variation of the species-specific antibody response to both species together with an asymmetric cross-immune response driven by IgG. Lower attack rate and affinity of antibodies in dual than single infections contributed to the significant increase of both helminth intensities. By combining mathematical modeling with immuno-infection data, our work provides a tractable model framework for disentangling some of the complexities generated by host-parasite and parasite-parasite interactions in natural systems.

Target Control of Asynchronous Boolean Networks

We study the target control of asynchronous Boolean networks, to identify interventions that can drive the dynamics of a given Boolean network from any initial state to the desired target attractor. Based on the application time, the control can be realised with three types of perturbations, including instantaneous, temporary and permanent perturbations. We develop efficient methods to compute the target control for a given target attractor with these three types of perturbations. We compare our methods with the stable motif-based control method on a variety of real-life biological networks to evaluate their performance. We show that our methods scale well for large Boolean networks and they are able to identify a rich set of solutions with a small number of perturbations.

Gastrointestinal helminths increase Bordetella bronchiseptica shedding and host variation in supershedding

Co-infected hosts, individuals that carry more than one infectious agent at any one time, have been suggested to facilitate pathogen transmission, including the emergence of supershedding events. However, how the host immune response mediates the interactions between co-infecting pathogens and how these affect the dynamics of shedding remains largely unclear. We used laboratory experiments and a modeling approach to examine temporal changes in the shedding of the respiratory bacterium Bordetella bronchiseptica in rabbits with one or two gastrointestinal helminth species. Experimental data showed that rabbits co-infected with one or both helminths shed significantly more B. bronchiseptica, by direct contact with an agar petri dish, than rabbits with bacteria alone. Co-infected hosts generated supershedding events of higher intensity and more frequently than hosts with no helminths. To explain this variation in shedding an infection-immune model was developed and fitted to rabbits of each group. Simulations suggested that differences in the magnitude and duration of shedding could be explained by the effect of the two helminths on the relative contribution of neutrophils and specific IgA and IgG to B. bronchiseptica neutralization in the respiratory tract. However, the interactions between infection and immune response at the scale of analysis that we used could not capture the rapid variation in the intensity of shedding of every rabbit. We suggest that fast and local changes at the level of respiratory tissue probably played a more important role. This study indicates that co-infected hosts are important source of variation in shedding, and provides a quantitative explanation into the role of helminths to the dynamics of respiratory bacterial infections.

Exploring attractor bifurcations in Boolean networks

Background

Boolean networks (BNs) provide an effective modelling formalism for various complex biochemical phenomena. Their long term behaviour is represented by attractors–subsets of the state space towards which the BN eventually converges. These are then typically linked to different biological phenotypes. Depending on various logical parameters, the structure and quality of attractors can undergo a significant change, known as a bifurcation. We present a methodology for analysing bifurcations in asynchronous parametrised Boolean networks.

Results

In this paper, we propose a computational framework employing advanced symbolic graph algorithms that enable the analysis of large networks with hundreds of Boolean variables. To visualise the results of this analysis, we developed a novel interactive presentation technique based on decision trees, allowing us to quickly uncover parameters crucial to the changes in the attractor landscape. As a whole, the methodology is implemented in our tool AEON. We evaluate the method’s applicability on a complex human cell signalling network describing the activity of type-1 interferons and related molecules interacting with SARS-COV-2 virion. In particular, the analysis focuses on explaining the potential suppressive role of the recently proposed drug molecule GRL0617 on replication of the virus.

Conclusions

The proposed method creates a working analogy to the concept of bifurcation analysis widely used in kinetic modelling to reveal the impact of parameters on the system’s stability. The important feature of our tool is its unique capability to work fast with large-scale networks with a relatively large extent of unknown information. The results obtained in the case study are in agreement with the recent biological findings.

Identification of dynamic driver sets controlling phenotypical landscapes

Controlling phenotypical landscapes is of vital interest to modern biology. This task becomes highly demanding because cellular decisions involve complex networks engaging in crosstalk interactions. Previous work on control theory indicates that small sets of compounds can control single phenotypes. However, a dynamic approach is missing to determine the drivers of the whole network dynamics. By analyzing 35 biologically motivated Boolean networks, we developed a method to identify small sets of compounds sufficient to decide on the entire phenotypical landscape. These compounds do not strictly prefer highly related compounds and show a smaller impact on the stability of the attractor landscape. The dynamic driver sets include many intervention targets and cellular reprogramming drivers in human networks. Finally, by using a new comprehensive model of colorectal cancer, we provide a complete workflow on how to implement our approach to shift from in silico to in vitro guided experiments.

Coinfection and infection duration shape how pathogens affect the African buffalo gut microbiota

Changes in the gut microbiota during pathogen infection are often predicted to influence disease outcomes. However, studies exploring whether pathogens induce microbiota shifts have yielded inconsistent results. This suggests that variation in infection, rather than the presence of infection alone, might shape pathogen-microbiota relationships. For example, most hosts are coinfected with multiple pathogens simultaneously, and hosts vary in how long they are infected, which may amplify or diminish microbial shifts expected in response to a focal pathogen. We used a longitudinal anthelmintic treatment study of free-ranging African buffalo (Syncerus caffer) to examine whether (i) coinfection with bovine tuberculosis (Mycobacterium bovis, TB) and gastrointestinal nematodes, and (ii) the duration of TB infection, modified effects of single pathogens on the gut microbiota. By accounting for the interaction between TB and nematodes, we found that coinfection affected changes in microbial abundance associated with single infections. Furthermore, the duration of TB infection predicted more microbiota variation than the presence of TB. Importantly, coinfection and infection duration had nearly as much influence on microbial patterns as demographic and environmental factors commonly examined in microbiota research. These findings demonstrate that acknowledging infection heterogeneities may be crucial to understanding relationships between pathogens and the gut microbiota.

Within-host mechanisms of immune regulation explain the contrasting dynamics of two helminth species in both single and dual infections

Variation in the intensity and duration of infections is often driven by variation in the network and strength of host immune responses. While many of the immune mechanisms and components are known for parasitic helminths, how these relationships change from single to multiple infections and impact helminth dynamics remains largely unclear. Here, we used laboratory data from a rabbit-helminth system and developed a within-host model of infection to investigate different scenarios of immune regulation in rabbits infected with one or two helminth species. Model selection suggests that the immunological pathways activated against Trichostrongylus retortaeformis and Graphidium strigosum are similar. However, differences in the strength of these immune signals lead to the contrasting dynamics of infections, where the first parasite is rapidly cleared and the latter persists with high intensities. In addition to the reactions identified in single infections, rabbits with both helminths also activate new pathways that asymmetrically affect the dynamics of the two species. These new signals alter the intensities but not the general trend of the infections. The type of interactions described can be expected in many other host-helminth systems. Our immune framework is flexible enough to capture different mechanisms and their complexity, and provides essential insights to the understanding of multi-helminth infections.

Antifragility Predicts the Robustness and Evolvability of Biological Networks through Multi-Class Classification with a Convolutional Neural Network

Robustness and evolvability are essential properties to the evolution of biological networks. To determine if a biological network is robust and/or evolvable, it is required to compare its functions before and after mutations. However, this sometimes takes a high computational cost as the network size grows. Here, we develop a predictive method to estimate the robustness and evolvability of biological networks without an explicit comparison of functions. We measure antifragility in Boolean network models of biological systems and use this as the predictor. Antifragility occurs when a system benefits from external perturbations. By means of the differences of antifragility between the original and mutated biological networks, we train a convolutional neural network (CNN) and test it to classify the properties of robustness and evolvability. We found that our CNN model successfully classified the properties. Thus, we conclude that our antifragility measure can be used as a predictor of the robustness and evolvability of biological networks.

External disturbances impact helminth-host interactions by affecting dynamics of infection, parasite traits, and host immune responses

External perturbations, such as multispecies infections or anthelmintic treatments, can alter host-parasite interactions with consequences on the dynamics of infection. While the overall profile of infection might appear fundamentally conserved at the host population level, perturbations can disproportionately affect components of parasite demography or host responses, and ultimately impact parasite fitness and long-term persistence.We took an immuno-epidemiological approach to this reasoning and examined a rabbit-helminth system where animals were trickle-dosed with either one or two helminth species, treated halfway through the experiment with an anthelmintic and reinfected one month later following the same initial regime. Parasite traits (body length and fecundity) and host immune responses (cytokines, transcription factors, antibodies) were quantified at fixed time points and compared before and after drug treatment, and between single and dual infections.Findings indicated a resistant host phenotype to Trichostrongylus retortaeformis where abundance, body length, and fecundity were regulated by a protective immune response. In contrast, Graphidium strigosum accumulated in the host and, while it stimulated a clear immune reaction, many genes were downregulated both following reinfection and in dual infection, suggestive of a low host resistance.External perturbations affected parasite fecundity, including body length and number of eggs in utero, more significantly than abundance; however, there was no consistency in the parasite-immune relationships.Disentangling the processes affecting parasite life history, and how they relate to host responses, can provide a better understanding of how external disturbances impact disease severity and transmission, and how parasites strategies adjust to secure persistence at the host and the population level.

Changes in parasite traits, rather than intensity, affect the dynamics of infection under external perturbation

Understanding the mechanisms that generate complex host-parasite interactions, and how they contribute to variation between and within hosts, is important for predicting risk of infection and transmission, and for developing more effective interventions based on parasite properties. We used the T. retortaeformis (TR)-rabbit system and developed a state-space mathematical framework to capture the variation in intensity of infection and egg shedding in hosts infected weekly, then treated with an anthelminthic and subsequently re-challenged following the same infection regime. Experimental infections indicate that parasite intensity accumulates more slowly in the post-anthelminthic phase but reaches similar maximum numbers. By contrast, parasite EPG (eggs per gram of feces) shed from rabbits in the post-treatment phase is lower and less variable through time. Inference based on EPG alone suggests a decline in parasite intensity over time. Using a state-space model and incorporating all sources of cross-sectional and longitudinal data, we show that while parasite intensity remains relatively constant in both experimental phases, shedding of eggs into the environment is increasingly limited through changes in parasite growth. We suggest that host immunity directly modulates both the accumulation and the growth of the parasite, and indirectly affects transmission by limiting parasite length and thus fecundity. This study provides a better understanding of how within-host trophic interactions influence different components of a helminth population. It also suggests that heterogeneity in parasite traits should be addressed more carefully when examining and managing helminth infections in the absence of some critical data on parasite dynamics.

Developing Network Models of Multiscale Host Responses Involved in Infections and Diseases

Complex interactions involved in host response to infections and diseases require advanced analytical tools to infer drivers of the response in order to develop strategies for intervention. This chapter discusses approaches to assemble interactions ranging from molecular to cellular levels and their analysis to investigate the cross talk between immune pathways. Particularly, construction of immune networks by either data-driven or literature-driven methods is explained. Next, graph theoretic approaches for probing static network properties as well as visualization of networks are discussed. Finally, development of Boolean models for simulation of network dynamics to investigate cross talk and emergent properties are considered along with Boolean-like models that may compensate for some of the limitations encountered in Boolean simulations. In conclusion, the chapter will allow readers to construct and analyze multiscale networks involved in immune responses.

Discrete Dynamical Modeling of Influenza Virus Infection Suggests Age-Dependent Differences in Immunity

Immunosenescence, an age-related decline in immune function, is a major contributor to morbidity and mortality in the elderly. Older hosts exhibit a delayed onset of immunity and prolonged inflammation after an infection, leading to excess damage and a greater likelihood of death. Our study applies a rule-based model to infer which components of the immune response are most changed in an aged host. Two groups of BALB/c mice (aged 12 to 16 weeks and 72 to 76 weeks) were infected with 2 inocula: a survivable dose of 50 PFU and a lethal dose of 500 PFU. Data were measured at 10 points over 19 days in the sublethal case and at 6 points over 7 days in the lethal case, after which all mice had died. Data varied primarily in the onset of immunity, particularly the inflammatory response, which led to a 2-day delay in the clearance of the virus from older hosts in the sublethal cohort. We developed a Boolean model to describe the interactions between the virus and 21 immune components, including cells, chemokines, and cytokines, of innate and adaptive immunity. The model identifies distinct sets of rules for each age group by using Boolean operators to describe the complex series of interactions that activate and deactivate immune components. Our model accurately simulates the immune responses of mice of both ages and with both inocula included in the data (95% accurate for younger mice and 94% accurate for older mice) and shows distinct rule choices for the innate immunity arm of the model between younger and aging mice in response to influenza A virus infection.IMPORTANCE Influenza virus infection causes high morbidity and mortality rates every year, especially in the elderly. The elderly tend to have a delayed onset of many immune responses as well as prolonged inflammatory responses, leading to an overall weakened response to infection. Many of the details of immune mechanisms that change with age are currently not well understood. We present a rule-based model of the intrahost immune response to influenza virus infection. The model is fit to experimental data for young and old mice infected with influenza virus. We generated distinct sets of rules for each age group to capture the temporal differences seen in the immune responses of these mice. These rules describe a network of interactions leading to either clearance of the virus or death of the host, depending on the initial dosage of the virus. Our models clearly demonstrate differences in these two age groups, particularly in the innate immune responses.

Multiplicity of Mathematical Modeling Strategies to Search for Molecular and Cellular Insights into Bacteria Lung Infection

Even today two bacterial lung infections, namely pneumonia and tuberculosis, are among the 10 most frequent causes of death worldwide. These infections still lack effective treatments in many developing countries and in immunocompromised populations like infants, elderly people and transplanted patients. The interaction between bacteria and the host is a complex system of interlinked intercellular and the intracellular processes, enriched in regulatory structures like positive and negative feedback loops. Severe pathological condition can emerge when the immune system of the host fails to neutralize the infection. This failure can result in systemic spreading of pathogens or overwhelming immune response followed by a systemic inflammatory response. Mathematical modeling is a promising tool to dissect the complexity underlying pathogenesis of bacterial lung infection at the molecular, cellular and tissue levels, and also at the interfaces among levels. In this article, we introduce mathematical and computational modeling frameworks that can be used for investigating molecular and cellular mechanisms underlying bacterial lung infection. Then, we compile and discuss published results on the modeling of regulatory pathways and cell populations relevant for lung infection and inflammation. Finally, we discuss how to make use of this multiplicity of modeling approaches to open new avenues in the search of the molecular and cellular mechanisms underlying bacterial infection in the lung.

Network analysis of gut microbiota literature: an overview of the research landscape in non-human animal studies

A wealth of human studies have demonstrated the importance of gut microbiota to health. Research on non-human animal gut microbiota is now increasing, but what insight does it provide? We reviewed 650 publications from this burgeoning field (2009-2016) and determined that animals driving this research were predominantly 'domestic' (48.2%), followed by 'model' (37.5%), with least studies on 'wild' (14.3%) animals. Domestic studies largely experimentally perturbed microbiota (81.8%) and studied mammals (47.9%), often to improve animal productivity. Perturbation was also frequently applied to model animals (87.7%), mainly mammals (88.1%), for forward translation of outcomes to human health. In contrast, wild animals largely characterised natural, unperturbed microbiota (79.6%), particularly in pest or pathogen vectoring insects (42.5%). We used network analyses to compare the research foci of each animal group: 'diet' was the main focus in all three, but to different ends: to enhance animal production (domestic), to study non-infectious diseases (model), or to understand microbiota composition (wild). Network metrics quantified model animal studies as the most interdisciplinary, while wild animals incorporated the fewest disciplines. Overall, animal studies, especially model and domestic, cover a broad array of research. Wild animals, however, are the least investigated, but offer under-exploited opportunities to study 'real-life' microbiota.

Impact of Helminth Infections and Nutritional Constraints on the Small Intestine Microbiota

Helminth infections and nutrition can independently alter the composition and abundance of the gastrointestinal microbiota, however, their combined effect is poorly understood. Here, we used the T. retortaeformis-rabbit system to examine how the helminth infection and host restriction from coprophagy/ready-to-absorb nutrients affected the duodenal microbiota, and how these changes related to the acquired immune response at the site of infection. A factorial experiment was performed where the bacterial community, its functionality and the immune response were examined in four treatments (Infect, Infect+Collar, Control+Collar and Control). Helminths reduced the diversity and abundance of the microbiota while the combination of parasites and coprophagic restriction led to a more diversified and abundant microbiota than infected cases, without significantly affecting the intensity of infection. Animals restricted from coprophagy and free from parasites exhibited the richest and most abundant bacterial community. By forcing the individuals to absorb nutrients from less digested food, the coprophagic restriction appears to have facilitated the diversity and proliferation of bacteria in the duodenum. Changes in the microbiota were more clearly associated with changes in the immune response for the infected than the nutrient restricted animals. The functional and metabolic characteristics of the duodenal microbiota were not significantly different between treatments. Overall, infection and diet affect the gut microbiota but their interactions and outcome can be complex. These findings can have important implications for the development of control measures to helminth infections where poor nutrition/malnutrition can also be a concern.

Host immunity shapes the impact of climate changes on the dynamics of parasite infections

Global climate change is predicted to alter the distribution and dynamics of soil-transmitted helminth infections, and yet host immunity can also influence the impact of warming on host-parasite interactions and mitigate the long-term effects. We used time-series data from two helminth species of a natural herbivore and investigated the contribution of climate change and immunity on the long-term and seasonal dynamics of infection. We provide evidence that climate warming increases the availability of infective stages of both helminth species and the proportional increase in the intensity of infection for the helminth not regulated by immunity. In contrast, there is no significant long-term positive trend in the intensity for the immune-controlled helminth, as immunity reduces the net outcome of climate on parasite dynamics. Even so, hosts experienced higher infections of this helminth at an earlier age during critical months in the warmer years. Immunity can alleviate the expected long-term effect of climate on parasite infections but can also shift the seasonal peak of infection toward the younger individuals.

Boolean Modeling of Cellular and Molecular Pathways Involved in Influenza Infection

Systems virology integrates host-directed approaches with molecular profiling to understand viral pathogenesis. Self-contained statistical approaches that combine expression profiles of genes with the available databases defining the genes involved in the pathways (gene-sets) have allowed characterization of predictive gene-signatures associated with outcome of the influenza virus (IV) infection. However, such enrichment techniques do not take into account interactions among pathways that are responsible for the IV infection pathogenesis. We investigate dendritic cell response to seasonal H1N1 influenza A/New Caledonia/20/1999 (NC) infection and infer the Boolean logic rules underlying the interaction network of ligand induced signaling pathways and transcription factors. The model reveals several novel regulatory modes and provides insights into mechanism of cross talk between NFκB and IRF mediated signaling. Additionally, the logic rule underlying the regulation of IL2 pathway that was predicted by the Boolean model was experimentally validated. Thus, the model developed in this paper integrates pathway analysis tools with the dynamic modeling approaches to reveal the regulation between signaling pathways and transcription factors using genome-wide transcriptional profiles measured upon influenza infection.

The Oral Tolerance as a Complex Network Phenomenon

The phenomenon of oral tolerance refers to a local and systemic state of tolerance induced in the gut after its exposure to innocuous antigens. Recent findings have shown the interrelationship between cellular and molecular components of oral tolerance, but its representation through a network of interactions has not been investigated. Our work aims at identifying the causal relationship of each element in an oral tolerance network, and also to propose a phenomenological model that's capable of predicting the stochastic behavior of this network when under manipulation. We compared the changes of a "healthy" network caused by "knock-outs" (KOs) in two approaches: an analytical approach by the Perron Frobenius theory; and a computational approach, which we describe within this work in order to find numerical results for the model. Both approaches have shown the most relevant immunological components for this phenomena, that happens to corroborate the empirical results from animal models. Besides explain in a intelligible fashion how the components interacts in a complex manner, we also managed to describe and quantify the importance of KOs that hasn't been empirically tested.

Boolean modeling: a logic-based dynamic approach for understanding signaling and regulatory networks and for making useful predictions

The biomolecules inside or near cells form a complex interacting system. Cellular phenotypes and behaviors arise from the totality of interactions among the components of this system. A fruitful way of modeling interacting biomolecular systems is by network-based dynamic models that characterize each component by a state variable, and describe the change in the state variables due to the interactions in the system. Dynamic models can capture the stable state patterns of this interacting system and can connect them to different cell fates or behaviors. A Boolean or logic model characterizes each biomolecule by a binary state variable that relates the abundance of that molecule to a threshold abundance necessary for downstream processes. The regulation of this state variable is described in a parameter free manner, making Boolean modeling a practical choice for systems whose kinetic parameters have not been determined. Boolean models integrate the body of knowledge regarding the components and interactions of biomolecular systems, and capture the system's dynamic repertoire, for example the existence of multiple cell fates. These models were used for a variety of systems and led to important insights and predictions. Boolean models serve as an efficient exploratory model, a guide for follow-up experiments, and as a foundation for more quantitative models.

Multi-level modeling of light-induced stomatal opening offers new insights into its regulation by drought

Plant guard cells gate CO2 uptake and transpirational water loss through stomatal pores. As a result of decades of experimental investigation, there is an abundance of information on the involvement of specific proteins and secondary messengers in the regulation of stomatal movements and on the pairwise relationships between guard cell components. We constructed a multi-level dynamic model of guard cell signal transduction during light-induced stomatal opening and of the effect of the plant hormone abscisic acid (ABA) on this process. The model integrates into a coherent network the direct and indirect biological evidence regarding the regulation of seventy components implicated in stomatal opening. Analysis of this signal transduction network identified robust cross-talk between blue light and ABA, in which [Ca2+]c plays a key role, and indicated an absence of cross-talk between red light and ABA. The dynamic model captured more than 10(31) distinct states for the system and yielded outcomes that were in qualitative agreement with a wide variety of previous experimental results. We obtained novel model predictions by simulating single component knockout phenotypes. We found that under white light or blue light, over 60%, and under red light, over 90% of all simulated knockouts had similar opening responses as wild type, showing that the system is robust against single node loss. The model revealed an open question concerning the effect of ABA on red light-induced stomatal opening. We experimentally showed that ABA is able to inhibit red light-induced stomatal opening, and our model offers possible hypotheses for the underlying mechanism, which point to potential future experiments. Our modelling methodology combines simplicity and flexibility with dynamic richness, making it well suited for a wide class of biological regulatory systems.

Severity of bovine tuberculosis is associated with co-infection with common pathogens in wild boar

Co-infections with parasites or viruses drive tuberculosis dynamics in humans, but little is known about their effects in other non-human hosts. This work aims to investigate the relationship between Mycobacterium bovis infection and other pathogens in wild boar (Sus scrofa), a recognized reservoir of bovine tuberculosis (bTB) in Mediterranean ecosystems. For this purpose, it has been assessed whether contacts with common concomitant pathogens are associated with the development of severe bTB lesions in 165 wild boar from mid-western Spain. The presence of bTB lesions affecting only one anatomic location (cervical lymph nodes), or more severe patterns affecting more than one location (mainly cervical lymph nodes and lungs), was assessed in infected animals. In addition, the existence of contacts with other pathogens such as porcine circovirus type 2 (PCV2), Aujeszky's disease virus (ADV), swine influenza virus, porcine reproductive and respiratory syndrome virus, Mycoplasma hyopneumoniae, Actinobacillus pleuropneumoniae, Haemophilus parasuis and Metastrongylus spp, was evaluated by means of serological, microbiological and parasitological techniques. The existence of contacts with a structured community of pathogens in wild boar infected by M. bovis was statistically investigated by null models. Association between this community of pathogens and bTB severity was examined using a Partial Least Squares regression approach. Results showed that adult wild boar infected by M. bovis had contacted with some specific, non-random pathogen combinations. Contact with PCV2, ADV and infection by Metastrongylus spp, was positively correlated to tuberculosis severity. Therefore, measures against these concomitant pathogens such as vaccination or deworming, might be useful in tuberculosis control programmes in the wild boar. However, given the unexpected consequences of altering any community of organisms, further research should evaluate the impact of such measures under controlled conditions. Furthermore, more research including other important pathogens, such as gastro-intestinal nematodes, will be necessary to complete this picture.

Infections do not predict shedding in co-infections with two helminths from a natural system

Given the health and economic burden associated with the widespread occurrence of co-infections in humans and agricultural animals, understanding how coinfections contribute to host heterogeneity to infection and transmission is critical if we are to assess risk of infection based on host characteristics. Here, we examine whether host heterogeneity to infection leads to similar heterogeneity in transmission in a population of rabbits single and co-infected with two helminths and monitored monthly for eight years. Compared to single infections, co-infected rabbits carried higher Trichostrongylus retortaeformis intensities, shorter worms with fewer eggs in utero, and shed similar numbers of parasite eggs. In contrast, the same co-infected rabbits harbored fewer Graphidium strigosum with longer bodies and more eggs in utero, and shed more eggs of this helminth. A positive density-dependent relationship between fecundity and intensity was found for T. retortaeformis but not G. strigosum in co-infected rabbits. Juvenile rabbits contributed to most of the infection and shedding of T. retortaeformis, while adult hosts were more important for G. strigosum dynamics of infection and transmission, and this pattern was consistent in single and co-infected individuals. This host-parasite system suggests that we cannot predict the pattern of parasite shedding during co-infections based on intensity of infection alone. We suggest that a mismatching between susceptibility and infectiousness should be expected in helminth coinfections and should not be overlooked.

Co-infection by Escherichia coli O157 and gastrointestinal strongyles in sheep

This research investigated the prevalence of gastrointestinal (GI) strongyles and Escherichia coli O157 in naturally infected sheep, as well as the possible correlation between the pathogens examined. A total of 314 sheep, randomly selected from 21 farms located in southern Italy, were examined. GI strongyles and E. coli O157 were detected by using the FLOTAC double technique and culture media, respectively. GI strongyles were detected on 19/21 farms (90.5%) and E. coli O157 on 12/21 (57.4%). At the animal level, GI strongyles were detected from 193/314 (61.5%) sheep analysed, whereas E. coli O157 was isolated from 20/314 (6.4%) sheep. Statistical analysis performed at animal-level showed a negative correlation between E. coli O157 and GI strongyle EPGs (Spearman's ρ=-0.128; P=0.03). Caution should be exercised in interpreting the research findings because a number of different confounding factors possibly influenced the trend of negative correlation between the two pathogenic agents investigated. Further studies, including molecular diagnostics, production data and multivariable analytical approaches, are needed to assess the actual impact of multiple pathogen infections in grazing sheep and other livestock species.

Variability in the intensity of nematode larvae from gastrointestinal tissues of a natural herbivore

The migration of infective nematode larvae into the tissues of their hosts has been proposed as a mechanism of reducing larval mortality and increase parasite lifetime reproductive success. Given that individual hosts differ in the level of exposure, strength of immune response and physiological conditions we may expect the number of larvae in tissue to vary both between and within hosts. We used 2 gastrointestinal nematode species common in the European rabbit (Oryctolagus cuniculus) and examined how the number of larvae in the tissue changed with the immune response, parasite intensity-dependent constraints in the lumen and seasonal weather factors, in rabbits of different age, sex and breeding status. For both nematode species, larvae from the gastrointestinal tissue exhibited strong seasonal and host age-related patterns with fewer larvae recovered in summer compared to winter and more in adults than in juveniles. The number of larvae of the 2 nematodes was positively associated with intensity of parasite infection in the lumen and antibody responses while it was negatively related with air temperature and rainfall. Host sex, reproductive status and co-infection with the second parasite species contributed to increase variability between hosts. We concluded that heterogeneities in host conditions are a significant cause of variability of larval abundance in the gastrointestinal tissues. These findings can have important consequences for the dynamics of nematode infections and how parasite's life-history strategies adjust to host changes.

'Coinfection-helminthes and tuberculosis'

PURPOSE OF REVIEW

Tuberculosis (TB) continues to be a significant problem and a major cause of morbidity and mortality in the developing world despite decades of intensive efforts to combat the disease. The poverty in these endemic areas is associated with an increased incidence of tropical helminthic infections. The purpose of this review is to bring to the fore, the urgent need to unravel the potential consequences of helminth coinfection to tuberculosis disease pathogenesis and transmission.

RECENT FINDINGS

There is now strong experimental evidence that helminth-induced T helper (Th)2 and T regulatory (Treg) responses impinge on host resistance against Mycobacterium tuberculosis (Mtb) infection. Several studies show that Th1 response is reduced in helminth coinfected hosts. Emerging studies also indicate that helminth-induced alternatively activated macrophages contribute to enhanced susceptibility to TB. Despite studies showing an association between helminthes and diminished Th1 immunity, maternal antihelminthic treatment had no effect on an infant's response to bacille Calmette-Guerin vaccination.

SUMMARY

Whether helminthes affect tuberculosis (TB) disease is still an open question and clinical trials are warranted to determine at the population level whether helminthes enhance TB incidence and transmission and diminish the protective immune response to vaccines. Consequently, mass deworming of infected individuals could contribute toward overall improvement of global public health.

Immuno-epidemiology of chronic bacterial and helminth co-infections: observations from the field and evidence from the laboratory

Co-infections can alter the host immune responses and modify the intensity and dynamics of concurrent parasitic species. The extent of this effect depends on the properties of the system and the mechanisms of host-parasite and parasite-parasite interactions. We examined the immuno-epidemiology of a chronic co-infection to reveal the immune mediated relationships between two parasites colonising independent organs, and the within-host molecular processes influencing the dynamics of infection at the host population level. The respiratory bacterium, Bordetella bronchiseptica, and the gastrointestinal helminth, Graphidium strigosum, were studied in the European rabbit (Oryctolagus cuniculus), using long-term field data and a laboratory experiment. We found that 65% of the rabbit population was co-infected with the two parasites; prevalence and intensity of co-infection increased with rabbit age and exhibited a strong seasonal pattern with the lowest values recorded during host breeding (from April to July) and the highest in the winter months. Laboratory infections showed no significant immune-mediated effects of the helminth on bacterial intensity in the lower respiratory tract but a higher abundance was observed in the nasal cavity during the chronic phase of the infection, compared with single bacterial infections. In contrast, B. bronchiseptica enhanced helminth intensity and this was consistent throughout the 4-month trial. These patterns were associated with changes in the immune profiles between singly and co-infected individuals for both parasites. This study confirmed the general observation that co-infections alter the host immune responses but also highlighted the often ignored role of bacterial infection in helminth dynamics. Additionally, we showed that G. strigosum had contrasting effects on B. bronchiseptica colonising different parts of the respiratory tract. At the host population level our findings suggest that B. bronchiseptica facilitates G. strigosum infection, and re-infection with G. strigosum assists in maintaining bacterial infection in the upper respiratory tract and thus long-term persistence.

Snapshot of spatio-temporal cytokine responses to single and co-infections with helminths and bacteria

Cytokines play a key role in maintaining communication between organs and in so doing modulate the interaction between concurrent infections. The extent of these effects depends on the properties of the organ infected and the intensity and type of infections. To determine systemic bystander effects among organs, IFN-γ, IL-4 and IL-10 gene expression was quantified at 7 days post-challenge in directly infected and uninfected organs during single and co-infections with the respiratory bacterium Bordetella bronchiseptica and the gastrointestinal helminths Graphidium strigosum and Trichostrongylus retortaeformis. Results showed that cytokine expression in a specific organ was influenced by the type of infection occurring in another organ, and this bystander effect was more apparent in some organs than others. Within the same organ the relative cytokine expression was consistent across infections, although some cytokines were more affected by bystander effects than others. For the infected gastrointestinal tract, a stronger cytokine response was observed in the tissue that harbored the majority of helminths (i.e. duodenum and fundus). Overall, co-infections altered the intensity but to a lesser extent the relative cytokine profile against the focal infection, indicating clear bystander effects and low organ compartmentalization. However, organs appear to actively modulate cytokine expression to avoid potential immuno-pathological consequences.