Synergistic Activation of RD29A Via Integration of Salinity Stress and Abscisic Acid in Arabidopsis thaliana.

Plants perceive information from the surroundings and elicit appropriate molecular responses. How plants dynamically respond to combinations of external inputs is yet to be revealed, despite the detailed current knowledge of intracellular signaling pathways. We measured dynamics of Response-to-Dehydration 29A (RD29A) expression induced by single or combined NaCl and ABA treatments in Arabidopsis thaliana. RD29A expression in response to a combination of NaCl and ABA leads to unique dynamic behavior that cannot be explained by the sum of responses to individual NaCl and ABA. To explore the potential mechanisms responsible for the observed synergistic response, we developed a mathematical model of the DREB2 and AREB pathways based on existing knowledge, where NaCl and ABA act as the cognate inputs, respectively, and examined various system structures with cross-input modulation, where non-cognate input affects expression of the genes involved in adjacent signaling pathways. The results from the analysis of system structures, combined with the insights from microarray expression profiles and model-guided experiments, predicted that synergistic activation of RD29A originates from enhancement of DREB2 activity by ABA. Our analysis of RD29A expression profiles demonstrates that a simple mathematical model can be used to extract information from temporal dynamics induced by combinatorial stimuli and produce experimentally testable hypotheses

Systemic and stratum corneum biomarkers of severity in infant atopic dermatitis include markers of innate and T helper cell-related immunity and angiogenesis

BACKGROUND: Biomarkers of atopic dermatitis (AD) are largely lacking, especially in infant AD. Those that have been examined to date have focused mostly on serum cytokines with few on non-invasive biomarkers in the skin. OBJECTIVES: We aimed to explore biomarkers obtainable from non-invasive sampling of infant skin. We compared these to plasma biomarkers and structural and functional measures of the skin barrier. METHODS: We recruited 100 infants at first presentation with AD, who were treatment naïve to topical or systemic anti-inflammatory therapies and 20 healthy children. We sampled clinically unaffected skin by tape stripping the stratum corneum (SC). Multiple cytokines and chemokines and natural moisturizing factors (NMF) were measured in the SC and plasma. We recorded disease severity and skin barrier function. RESULTS: 19 SC and 12 plasma biomarkers showed significant difference between healthy and AD skin. Some biomarkers were common to both the SC and plasma, and others were compartment-specific. Identified biomarkers of AD severity included Th2 skewed markers (IL-13, CCL17, CCL22, IL-5), markers of innate activation (IL-18, Il-1α, IL1β, CXCL8), angiogenesis (Flt-1, VEGF) and others (sICAM-1, vCAM-1, IL-16, IL-17A). CONCLUSIONS: We identified clinically relevant biomarkers of AD, including novel markers, easily sampled and typed in infants. These markers may provide objective assessment of disease severity and suggest new therapeutic targets, or response measurement targets for AD. Future studies will be required to determine if these biomarkers, seen in very early AD, can predict disease outcomes or comorbidities

Personalized prediction of daily eczema severity scores using a mechanistic machine learning model.

BACKGROUND: Atopic dermatitis (AD) is a chronic inflammatory skin disease with periods of flares and remission. Designing personalized treatment strategies for AD is challenging, given the apparent unpredictability and large variation in AD symptoms and treatment responses within and across individuals. Better prediction of AD severity over time for individual patients could help to select optimum timing and type of treatment for improving disease control. OBJECTIVE: We aimed to develop a proof of principle mechanistic machine learning model that predicts the patient-specific evolution of AD severity scores on a daily basis. METHODS: We designed a probabilistic predictive model and trained it using Bayesian inference with the longitudinal data from two published clinical studies. The data consisted of daily recordings of AD severity scores and treatments used by 59 and 334 AD children over 6 months and 16 weeks, respectively. Validation of the predictive model was conducted in a forward-chaining setting. RESULTS: Our model was able to predict future severity scores at the individual level and improved chance-level forecast by 60%. Heterogeneous patterns in severity trajectories were captured with patient-specific parameters such as the short-term persistence of AD severity and responsiveness to topical steroids, calcineurin inhibitors and step-up treatment. CONCLUSIONS: Our proof of principle model successfully predicted the daily evolution of AD severity scores at an individual level and could inform the design of personalized treatment strategies that can be tested in future studies. Our model-based approach can be applied to other diseases with apparent unpredictability and large variation in symptoms and treatment responses such as asthma

Noninvasive Method for Monitoring Pneumocystis carinii Pneumonia

The progression of Pneumocystis carinii pneumonia was temporally monitored and quantified by real-time polymerase chain reaction of P. carinii–specific DNA in oral swabs and lung homogenates from infected rats. DNA levels correlated with the number of P. carinii organisms in the rats’ lungs, as enumerated by microscopic methods. This report is the first of a noninvasive, antemortem method that can be used to monitor infection in a host over time

Human and computational models of atopic dermatitis:A review and perspectives by an expert panel of the International Eczema Council

Atopic dermatitis (AD) is a prevalent disease worldwide and is associated with systemic comorbidities representing a significant burden on patients, their families, and society. Therapeutic options for AD remain limited, in part because of a lack of well-characterized animal models. There has been increasing interest in developing experimental approaches to study the pathogenesis of human AD in vivo, in vitro, and in silico to better define pathophysiologic mechanisms and identify novel therapeutic targets and biomarkers that predict therapeutic response. This review critically appraises a range of models, including genetic mutations relevant to AD, experimental challenge of human skin in vivo, tissue culture models, integration of “omics” data sets, and development of predictive computational models. Although no one individual model recapitulates the complex AD pathophysiology, our review highlights insights gained into key elements of cutaneous biology, molecular pathways, and therapeutic target identification through each approach. Recent developments in computational analysis, including application of machine learning and a systems approach to data integration and predictive modeling, highlight the applicability of these methods to AD subclassification (endotyping), therapy development, and precision medicine. Such predictive modeling will highlight knowledge gaps, further inform refinement of biological models, and support new experimental and systems approaches to AD

Modelling the Influence of Foot-and-Mouth Disease Vaccine Antigen Stability and Dose on the Bovine Immune Response

Foot and mouth disease virus causes a livestock disease of significant global socio-economic importance. Advances in its control and eradication depend critically on improvements in vaccine efficacy, which can be best achieved by better understanding the complex within-host immunodynamic response to inoculation. We present a detailed and empirically parametrised dynamical mathematical model of the hypothesised immune response in cattle, and explore its behaviour with reference to a variety of experimental observations relating to foot and mouth immunology. The model system is able to qualitatively account for the observed responses during in-vivo experiments, and we use it to gain insight into the incompletely understood effect of single and repeat inoculations of differing dosage using vaccine formulations of different structural stability

Role of the RNA-Binding Protein Nrd1 in Stress Granule Formation and Its Implication in the Stress Response in Fission Yeast

We have previously identified the RNA recognition motif (RRM)-type RNA-binding protein Nrd1 as an important regulator of the posttranscriptional expression of myosin in fission yeast. Pmk1 MAPK-dependent phosphorylation negatively regulates the RNA-binding activity of Nrd1. Here, we report the role of Nrd1 in stress-induced RNA granules. Nrd1 can localize to poly(A)-binding protein (Pabp)-positive RNA granules in response to various stress stimuli, including heat shock, arsenite treatment, and oxidative stress. Interestingly, compared with the unphosphorylatable Nrd1, Nrd1DD (phosphorylation-mimic version of Nrd1) translocates more quickly from the cytoplasm to the stress granules in response to various stimuli; this suggests that the phosphorylation of Nrd1 by MAPK enhances its localization to stress-induced cytoplasmic granules. Nrd1 binds to Cpc2 (fission yeast RACK) in a phosphorylation-dependent manner and deletion of Cpc2 affects the formation of Nrd1-positive granules upon arsenite treatment. Moreover, the depletion of Nrd1 leads to a delay in Pabp-positive RNA granule formation, and overexpression of Nrd1 results in an increased size and number of Pabp-positive granules. Interestingly, Nrd1 deletion induced resistance to sustained stresses and enhanced sensitivity to transient stresses. In conclusion, our results indicate that Nrd1 plays a role in stress-induced granule formation, which affects stress resistance in fission yeast