Corrigendum: Animal-based factors prior to infection predict histological disease outcome in porcine reproductive and respiratory syndrome virus- and Actinobacillus pleuropneumoniae-infected pigs.

[This corrects the article DOI: 10.3389/fvets.2021.742877.].

Animal-Based Factors Prior to Infection Predict Histological Disease Outcome in Porcine Reproductive and Respiratory Syndrome Virus- and Actinobacillus pleuropneumoniae-Infected Pigs.

A large variety of clinical manifestation in individual pigs occurs after infection with pathogens involved in porcine respiratory disease complex (PRDC). Some pigs are less prone to develop respiratory disease symptoms. The variation in clinical impact after infection and the recovery capacity of an individual animal are measures of its resilience. In this paper, we examined which ones of a range of animal-based factors (rectal temperature, body weight, skin lesion scores, behavior, natural antibody serum levels, serum levels of white blood cells, and type of T and granulocyte subsets) when measured prior to infection are related to disease severity. These animal-based factors and the interaction with housing regimen of the piglets (conventional or enriched) were modeled using linear regression to predict disease severity using a dataset acquired from a previous study using a well-established experimental coinfection model of porcine reproductive and respiratory syndrome virus (PRRSV) and Actinobacillus pleuropneumoniae. Both PRRSV and A. pleuropneumoniae are often involved in PRDC. Histological lung lesion score of each animal was used as a measure for PRDC severity after infection. Prior to infection, higher serum levels of lymphocytes (CD3+), naïve T helper (CD3+CD4+CD8-), CD8+ (as well as higher relative levels of CD8+), and memory T helper (CD3+CD4+CD8+) cells and higher relative levels of granulocytes (CD172a) were related to reduced disease severity in both housing systems. Raised serum concentrations of natural IgM antibodies binding to keyhole limpet hemocyanin (KLH) were also related to reduced disease severity after infection. Increased levels of skin lesions at the central body part (after weaning and before infection) were related to increased disease severity in conventional housing systems only. High resisters showed a lower histological lung lesion score, which appeared unrelated to sex. Body temperature, behavior, and growth prior to infections were influenced by housing regimen but could not explain the variation in lung lesion scores after infection. Raised basal lymphocyte counts and lower skin lesion scores are related to reduced disease severity independent of or dependent on housing system, respectively. In conclusion, our study identifies intrinsic animal-based measures using linear regression analysis that predicts resilience to infections in pigs.

Development of a model forecasting Dermanyssus gallinae's population dynamics for advancing Integrated Pest Management in laying hen facilities.

The poultry red mite, Dermanyssus gallinae, is the most significant pest of egg laying hens in many parts of the world. Control of D. gallinae could be greatly improved with advanced Integrated Pest Management (IPM) for D. gallinae in laying hen facilities. The development of a model forecasting the pests' population dynamics in laying hen facilities without and post-treatment will contribute to this advanced IPM and could consequently improve implementation of IPM by farmers. The current work describes the development and demonstration of a model which can follow and forecast the population dynamics of D. gallinae in laying hen facilities given the variation of the population growth of D. gallinae within and between flocks. This high variation could partly be explained by house temperature, flock age, treatment, and hen house. The total population growth variation within and between flocks, however, was in part explained by temporal variation. For a substantial part this variation was unexplained. A dynamic adaptive model (DAP) was consequently developed, as models of this type are able to handle such temporal variations. The developed DAP model can forecast the population dynamics of D. gallinae, requiring only current flock population monitoring data, temperature data and information of the dates of any D. gallinae treatment. Importantly, the DAP model forecasted treatment effects, while compensating for location and time specific interactions, handling the variability of these parameters. The characteristics of this DAP model, and its compatibility with different mite monitoring methods, represent progression from existing approaches for forecasting D. gallinae that could contribute to advancing improved Integrated Pest Management (IPM) for D. gallinae in laying hen facilities.

Effect of Nickel and Cobalt on Methanogenic Enrichment Cultures and Role of Biogenic Sulfide in Metal Toxicity Attenuation.

Metals play an important role in microbial metabolism by acting as cofactors for many enzymes. Supplementation of biological processes with metals may result in improved performance, but high metal concentrations are often toxic to microorganisms. In this work, methanogenic enrichment cultures growing on H2/CO2 or acetate were supplemented with trace concentrations of nickel (Ni) and cobalt (Co), but no significant increase in methane production was observed in most of the tested conditions. However, high concentrations of these metals were detrimental to methanogenic activity of the cultures. Cumulative methane production (after 6 days of incubation) from H2/CO2 was 40% lower in the presence of 8 mM of Ni or 30 mM of Co, compared to controls without metal supplementation. When acetate was used as substrate, cumulative methane production was also reduced: by 18% with 8 mM of Ni and by 53% with 30 mM of Co (after 6 days of incubation). Metal precipitation with sulfide was further tested as a possible method to alleviate metal toxicity. Anaerobic sludge was incubated with Co (30 mM) and Ni (8 mM) in the presence of sulfate or sulfide. The addition of sulfide helped to mitigate the toxic effect of the metals. Methane production from H2/CO2 was negatively affected in the presence of sulfate, possibly due to competition of hydrogenotrophic methanogens by sulfate-reducing bacteria. However, in the enrichment cultures growing on acetate, biogenically produced sulfide had a positive effect and more methane was produced in these incubations than in similar assays without sulfate addition. The outcome of competition between methanogens and sulfate-reducing bacteria is a determinant factor for the success of using biogenic sulfide as detoxification method.

Tracing the molecular basis of transcriptional dynamics in noisy data by using an experiment-based mathematical model.

Changes in transcription factor levels, epigenetic status, splicing kinetics and mRNA degradation can each contribute to changes in the mRNA dynamics of a gene. We present a novel method to identify which of these processes is changed in cells in response to external signals or as a result of a diseased state. The method employs a mathematical model, for which the kinetics of gene regulation, splicing, elongation and mRNA degradation were estimated from experimental data of transcriptional dynamics. The time-dependent dynamics of several species of adipose differentiation-related protein (ADRP) mRNA were measured in response to ligand activation of the transcription factor peroxisome proliferator-activated receptor δ (PPARδ). We validated the method by monitoring the mRNA dynamics upon gene activation in the presence of a splicing inhibitor. Our mathematical model correctly identifies splicing as the inhibitor target, despite the noise in the data.

Prediction uncertainty assessment of a systems biology model requires a sample of the full probability distribution of its parameters.

Multi-parameter models in systems biology are typically 'sloppy': some parameters or combinations of parameters may be hard to estimate from data, whereas others are not. One might expect that parameter uncertainty automatically leads to uncertain predictions, but this is not the case. We illustrate this by showing that the prediction uncertainty of each of six sloppy models varies enormously among different predictions. Statistical approximations of parameter uncertainty may lead to dramatic errors in prediction uncertainty estimation. We argue that prediction uncertainty assessment must therefore be performed on a per-prediction basis using a full computational uncertainty analysis. In practice this is feasible by providing a model with a sample or ensemble representing the distribution of its parameters. Within a Bayesian framework, such a sample may be generated by a Markov Chain Monte Carlo (MCMC) algorithm that infers the parameter distribution based on experimental data. Matlab code for generating the sample (with the Differential Evolution Markov Chain sampler) and the subsequent uncertainty analysis using such a sample, is supplied as Supplemental Information.

Identifying optimal models to represent biochemical systems.

Biochemical systems involving a high number of components with intricate interactions often lead to complex models containing a large number of parameters. Although a large model could describe in detail the mechanisms that underlie the system, its very large size may hinder us in understanding the key elements of the system. Also in terms of parameter identification, large models are often problematic. Therefore, a reduced model may be preferred to represent the system. Yet, in order to efficaciously replace the large model, the reduced model should have the same ability as the large model to produce reliable predictions for a broad set of testable experimental conditions. We present a novel method to extract an "optimal" reduced model from a large model to represent biochemical systems by combining a reduction method and a model discrimination method. The former assures that the reduced model contains only those components that are important to produce the dynamics observed in given experiments, whereas the latter ensures that the reduced model gives a good prediction for any feasible experimental conditions that are relevant to answer questions at hand. These two techniques are applied iteratively. The method reveals the biological core of a model mathematically, indicating the processes that are likely to be responsible for certain behavior. We demonstrate the algorithm on two realistic model examples. We show that in both cases the core is substantially smaller than the full model.

A mathematical model for the coreceptors SOMATIC EMBRYOGENESIS RECEPTOR-LIKE KINASE1 and SOMATIC EMBRYOGENESIS RECEPTOR-LIKE KINASE3 in BRASSINOSTEROID INSENSITIVE1-mediated signaling.

Brassinosteroids (BRs) are key regulators in plant growth and development. The main BR-perceiving receptor in Arabidopsis (Arabidopsis thaliana) is BRASSINOSTEROID INSENSITIVE1 (BRI1). Seedling root growth and hypocotyl elongation can be accurately predicted using a model for BRI1 receptor activity. Genetic evidence shows that non-ligand-binding coreceptors of the SOMATIC EMBRYOGENESIS RECEPTOR-LIKE KINASE (SERK) family are essential for BRI1 signal transduction. A relatively simple biochemical model based on the properties of SERK loss-of-function alleles explains complex physiological responses of the BRI1-mediated BR pathway. The model uses BRI1-BR occupancy as the central estimated parameter and includes BRI1-SERK interaction based on mass action kinetics and accurately describes wild-type root growth and hypocotyl elongation. Simulation studies suggest that the SERK coreceptors primarily act to increase the magnitude of the BRI1 signal. The model predicts that only a small number of active BRI1-SERK complexes are required to carry out BR signaling at physiological ligand concentration. Finally, when calibrated with single mutants, the model predicts that roots of the serk1serk3 double mutant are almost completely brassinolide (BL) insensitive, while the double mutant hypocotyls remain sensitive. This points to residual BRI1 signaling or to a different coreceptor requirement in shoots.

A mathematical model for BRASSINOSTEROID INSENSITIVE1-mediated signaling in root growth and hypocotyl elongation.

Brassinosteroid (BR) signaling is essential for plant growth and development. In Arabidopsis (Arabidopsis thaliana), BRs are perceived by the BRASSINOSTEROID INSENSITIVE1 (BRI1) receptor. Root growth and hypocotyl elongation are convenient downstream physiological outputs of BR signaling. A computational approach was employed to predict root growth solely on the basis of BRI1 receptor activity. The developed mathematical model predicts that during normal root growth, few receptors are occupied with ligand. The model faithfully predicts root growth, as observed in bri1 loss-of-function mutants. For roots, it incorporates one stimulatory and two inhibitory modules, while for hypocotyls, a single inhibitory module is sufficient. Root growth as observed when BRI1 is overexpressed can only be predicted assuming that a decrease occurred in the BRI1 half-maximum response values. Root growth appears highly sensitive to variation in BR concentration and much less to reduction in BRI1 receptor level, suggesting that regulation occurs primarily by ligand availability and biochemical activity.

Integrating two patterning processes in the flower.

Spatial organ arrangement plays an important role in flower development. The position and the identity of floral organs is influenced by various processes, in particular the expression of MADS-box transcription factors for identity and dynamics of the plant hormone auxin for positioning. We are currently integrating patterning processes of MADS and auxin into our computational models, based on interactions that are known from experiments, in order to get insight in how these define the floral body plan. The resulting computational model will help to explore hypothetical interactions between the MADS and auxin regulation networks in floral organ patterning.

Mutational robustness of gene regulatory networks.

Mutational robustness of gene regulatory networks refers to their ability to generate constant biological output upon mutations that change network structure. Such networks contain regulatory interactions (transcription factor-target gene interactions) but often also protein-protein interactions between transcription factors. Using computational modeling, we study factors that influence robustness and we infer several network properties governing it. These include the type of mutation, i.e. whether a regulatory interaction or a protein-protein interaction is mutated, and in the case of mutation of a regulatory interaction, the sign of the interaction (activating vs. repressive). In addition, we analyze the effect of combinations of mutations and we compare networks containing monomeric with those containing dimeric transcription factors. Our results are consistent with available data on biological networks, for example based on evolutionary conservation of network features. As a novel and remarkable property, we predict that networks are more robust against mutations in monomer than in dimer transcription factors, a prediction for which analysis of conservation of DNA binding residues in monomeric vs. dimeric transcription factors provides indirect evidence.

Simulation of organ patterning on the floral meristem using a polar auxin transport model.

An intriguing phenomenon in plant development is the timing and positioning of lateral organ initiation, which is a fundamental aspect of plant architecture. Although important progress has been made in elucidating the role of auxin transport in the vegetative shoot to explain the phyllotaxis of leaf formation in a spiral fashion, a model study of the role of auxin transport in whorled organ patterning in the expanding floral meristem is not available yet. We present an initial simulation approach to study the mechanisms that are expected to play an important role. Starting point is a confocal imaging study of Arabidopsis floral meristems at consecutive time points during flower development. These images reveal auxin accumulation patterns at the positions of the organs, which strongly suggests that the role of auxin in the floral meristem is similar to the role it plays in the shoot apical meristem. This is the basis for a simulation study of auxin transport through a growing floral meristem, which may answer the question whether auxin transport can in itself be responsible for the typical whorled floral pattern. We combined a cellular growth model for the meristem with a polar auxin transport model. The model predicts that sepals are initiated by auxin maxima arising early during meristem outgrowth. These form a pre-pattern relative to which a series of smaller auxin maxima are positioned, which partially overlap with the anlagen of petals, stamens, and carpels. We adjusted the model parameters corresponding to properties of floral mutants and found that the model predictions agree with the observed mutant patterns. The predicted timing of the primordia outgrowth and the timing and positioning of the sepal primordia show remarkable similarities with a developing flower in nature.

Continuous-time modeling of cell fate determination in Arabidopsis flowers.

BACKGROUND: The genetic control of floral organ specification is currently being investigated by various approaches, both experimentally and through modeling. Models and simulations have mostly involved boolean or related methods, and so far a quantitative, continuous-time approach has not been explored. RESULTS: We propose an ordinary differential equation (ODE) model that describes the gene expression dynamics of a gene regulatory network that controls floral organ formation in the model plant Arabidopsis thaliana. In this model, the dimerization of MADS-box transcription factors is incorporated explicitly. The unknown parameters are estimated from (known) experimental expression data. The model is validated by simulation studies of known mutant plants. CONCLUSIONS: The proposed model gives realistic predictions with respect to independent mutation data. A simulation study is carried out to predict the effects of a new type of mutation that has so far not been made in Arabidopsis, but that could be used as a severe test of the validity of the model. According to our predictions, the role of dimers is surprisingly important. Moreover, the functional loss of any dimer leads to one or more phenotypic alterations.