Omics-Based Mathematical Modeling Unveils Pathogenesis of Periodontitis in an Experimental Murine Model.

Periodontitis is a multifactorial disease that progresses via dynamic interaction between bacterial and host-derived genetic factors. The recent trend of omics analyses has discovered many periodontitis-related risk factors. However, how much the individual factor affects the pathogenesis of periodontitis is still unknown. This article aims to identify multiple key factors related to the pathogenesis of periodontitis and quantitatively predict the influence of each factor on alveolar bone resorption by omics analysis and mathematical modeling. First, we induced periodontitis in mice (n = 3 or 4 at each time point) by tooth ligation. Next, we assessed alveolar bone resorption by micro-computed tomography, alterations in the gene expression by RNA sequencing, and the microbiome of the gingivae by 16S ribosomal RNA sequencing during disease pathogenesis. Omics data analysis identified key players (bacteria and molecules) involved in the pathogenesis of periodontitis. We then constructed a mathematical model of the pathogenesis of periodontitis by employing ordinary differential equations that described the dynamic regulatory interplay between the key players and predicted the alveolar bone integrity as output. Finally, we estimated the model parameters using our dynamic experimental data and validated the model prediction of influence on alveolar bone resorption by in vivo experiments. The model predictions and experimental results revealed that monocyte recruitment induced by bacteria-mediated Toll-like receptor activation was the principal reaction regulating alveolar bone resorption in a periodontitis condition. On the other hand, osteoblast-mediated osteoclast differentiation had less impact on bone integrity in a periodontitis condition.

Can serum biomarkers predict the outcome of systemic immunosuppressive therapy in adult atopic dermatitis patients?

Background: Atopic dermatitis (AD or eczema) is a most common chronic skin disease. Designing personalised treatment strategies for AD based on patient stratification is of high clinical relevance, given a considerable variation in the clinical phenotype and responses to treatments among patients. It has been hypothesised that the measurement of biomarkers could help predict therapeutic responses for individual patients. Objective: We aim to assess whether serum biomarkers can predict the outcome of systemic immunosuppressive therapy in adult AD patients. Methods: We developed a statistical machine learning model using the data of an already published longitudinal study of 42 patients who received azathioprine or methotrexate for over 24 weeks. The data contained 26 serum cytokines and chemokines measured before the therapy. The model described the dynamic evolution of the latent disease severity and measurement errors to predict AD severity scores (Eczema Area and Severity Index, (o)SCORing of AD and Patient Oriented Eczema Measure) two-weeks ahead. We conducted feature selection to identify the most important biomarkers for the prediction of AD severity scores. Results: We validated our model in a forward chaining setting and confirmed that it outperformed standard time-series forecasting models. Adding biomarkers did not improve predictive performance. Conclusions: In this study, biomarkers had a negligible and non-significant effect for predicting the future AD severity scores and the outcome of the systemic therapy.

Stratum corneum markers of innate and T helper cell-related immunity and their relation to the disease severity in Croatian patients with atopic dermatitis.

BACKGROUND: Atopic dermatitis (AD) presents with the wide spectrum of clinical phenotypes within and between various populations. Recent study showed low frequency of filaggrin loss-of-function (FLG LOF) mutations in Croatian AD patients. At present, there are no data on biomarkers of immune response in Croatian AD patients that might be useful in the selection and monitoring of novel immune therapies. OBJECTIVES: To investigate levels of cytokines of various signature in the stratum corneum (SC) collected from lesional and non-lesional skin of AD patients and healthy controls and to evaluate their relationship with the severity of disease and skin barrier function. METHODS: SC samples were collected from 100 adult patients with moderate-to-severe AD and 50 healthy controls. The levels of 21 cytokines were measured by multiplex immunoassay. We conducted machine learning analysis to assess whether a small number of cytokine measurements can discriminate between healthy controls and AD patients and can predict AD severity (SCORAD). RESULTS: The SC levels of thirteen cytokines representing innate immunity, Th-1, Th-2 and Th-17/22 immune response showed significant differences between healthy and AD skin. Our analysis demonstrated that as few as three cytokines measured in lesional skin can discriminate healthy controls and AD with an accuracy of 99% and that the predictive models for SCORAD did not achieve a high accuracy. Cytokine levels were highly correlated with the levels of filaggrin degradation products and skin barrier function. CONCLUSIONS: Stratum corneum analysis revealed aberrant levels of cytokines representing innate immunity, Th-1-, Th-2- and Th-17/22-mediated immune response in Croatian AD patients. Increased Th-2 cytokines and their strong association with natural moisturizing factor (NMF) can explain low NMF levels despite of low frequency of FLG LOF mutations in Croatian population. Predictive models for SCORAD identified cytokines associated with SCORAD but warrants further investigation.

MicroRNA analysis of childhood atopic dermatitis reveals a role for miR-451a.

BACKGROUND: MicroRNAs (miRNAs), important regulators of gene expression, have been implicated in a variety of disorders. The expression pattern of miRNAs in paediatric atopic dermatitis (AD) has not been well studied. OBJECTIVES: We sought to investigate miRNA expression profiles in different blood compartments of infants with AD. METHODS: Small RNA and analysis with the HTG EdgeSeq system were performed to identify differentially expressed miRNAs in peripheral blood mononuclear cells (PBMCs) and plasma of infants with AD vs. age-matched healthy controls, with reverse transcription quantitative real-time polymerase chain reaction (RT-qPCR) used for validation and measurement of miRNA targets. Logistic regression models with area under the receiving operating characteristic estimation was used to evaluate the diagnostic potential of chosen miRNAs for AD. RESULTS: RNA sequencing was performed to access miRNA expression profiles in paediatric AD. We identified 10 differentially expressed miRNAs in PBMCs and eight dysregulated miRNAs in plasma of infants with AD compared with controls. Upregulated miRNAs in PBMCs included miRNAs known to be involved in inflammation: miR-223-3p, miR-126-5p and miR-143-3p. Differential expression of only one miRNA, miR-451a, was observed in both PBMCs and plasma of children with AD. Dysregulation of three miRNAs (miR-451a, miR-143-3p and miR-223-3p) was validated in larger numbers of samples and miR-451a was identified as a predictive biomarker for the early diagnosis of the disease. Experimentally verified targets of miR-451a, interleukin 6 receptor (IL6R) and proteasome subunit beta type-8 (PSMB8), were increased in patients with AD, negatively correlated with miR-451a levels and upregulated following inhibition of miR-451a in PBMCs. CONCLUSIONS: In infants with AD, a distinct peripheral blood miRNA signature is seen, highlighting the systemic effects of the disease. miR-451a is uniquely expressed in different blood compartments of patients with AD and may serve as a promising novel biomarker for the early diagnosis of AD.

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 noninvasive biomarkers in the skin. OBJECTIVES: We aimed to explore biomarkers obtainable from noninvasive sampling of infant skin. We compared these with plasma biomarkers and structural and functional measures of the skin barrier. METHODS: We recruited 100 infants at first presentation with AD, who were treatment naive 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 factor were measured in the SC and plasma. We recorded disease severity and skin barrier function. RESULTS: Nineteen SC and 12 plasma biomarkers showed significant differences 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 T helper 2-skewed markers [interleukin (IL)-13, CCL17, CCL22, IL-5]; markers of innate activation (IL-18, IL-1α, IL1ß, CXCL8) and angiogenesis (Flt-1, vascular endothelial growth factor); and others (soluble intercellular adhesion molecule-1, soluble vascular cell adhesion molecule-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 whether these biomarkers, seen in very early AD, can predict disease outcomes or comorbidities.

Stochastic approach to molecular interactions and computational theory of metabolic and genetic regulations.

The underlying molecular mechanisms of metabolic and genetic regulations are computationally identical and can be described by a finite state Markov process. We establish a common computational model for both regulations based on the stationary distribution of the Markov process with the aim of establishing a unified, quantitative model of general biological regulations. Various existing results regarding intracellular regulations are derived including the classical Michaelis-Menten equation and its generalization to more complex allosteric enzymes in a systematic way. The notion of probability flow is introduced to distinguish the equilibrium stationary distribution from the non-equilibrium one; it plays a crucial role in the analysis of stationary state equations. A graphical criterion to guarantee the existence of an equilibrium stationary distribution is derived, which turns out to be identical to the classical Wegscheider condition. Simple graphical methods to compute the equilibrium and non-equilibrium stationary distributions are derived based crucially on the probability flow, which dramatically simplifies the classical methods still used in enzymology.