Pavlina Pithova

Healthy first-degree relatives of patients with type 1 diabetes exhibit significant differences in basal gene expression pattern of immunocompetent cells compared to controls: expression pattern as predeterminant of autoimmune diabetes.

Expression features of genetic landscape which predispose an individual to the type 1 diabetes are poorly understood. We addressed this question by comparing gene expression profile of freshly isolated peripheral blood mononuclear cells isolated from either patients with type 1 diabetes (T1D), or their first‐degree relatives or healthy controls. Our aim was to establish whether a distinct type of ‘prodiabetogenic’ gene expression pattern in the group of relatives of patients with T1D could be identified. Whole‐genome expression profile of nine patients with T1D, their ten first‐degree relatives and ten healthy controls was analysed using the human high‐density expression microarray chip. Functional aspects of candidate genes were assessed using the MetaCore software. The highest number of differentially expressed genes (547) was found between the autoantibody‐negative healthy relatives and the healthy controls. Some of them represent genes critically involved in the regulation of innate immune responses such as TLR signalling and CCR3 signalling in eosinophiles, humoral immune reactions such as BCR pathway, costimulation and cytokine responses mediated by CD137, CD40 and CD28 signalling and IL‐1 proinflammatory pathway. Our data demonstrate that expression profile of healthy relatives of patients with T1D is clearly distinct from the pattern found in the healthy controls. That especially concerns differential activation status of genes and signalling pathways involved in proinflammatory processes and those of innate immunity and humoral reactivity. Thus, we posit that the study of the healthy relative’s gene expression pattern is instrumental for the identification of novel markers associated with the development of diabetes.

Protein microarray analysis as a tool for monitoring cellular autoreactivity in type 1 diabetes patients and their relatives

Background:  Autoreactive T cells have a crucial role in type 1 diabetes (T1D) pathogenesis.

Influence of Maternal Hyperglycaemia on Cord Blood Mononuclear Cells in Response to Diabetes‐associated Autoantigens

Perfect maternal diabetes compensation is crucial for the outcome of the baby. However, little is known how hyperglycaemia influences the specific immune response. Furthermore, babies of type 1 diabetes (T1D) mothers have less risk of development T1D than babies with a T1D father. This study aimed to analyze the effect of maternal hyperglycaemia on newborns with focus on the response to diabetes‐associated autoantigens. Populations: (1) Newborns of T1D mothers split into groups according to maternal diabetes compensation during the 3rd trimester: perfect (n = 15) or acceptable (n = 25) compensation. (2) newborns with T1D father (n = 12) (3) newborns with a mother treated for either gestational or type 2 diabetes (n = 10) (4) control newborns (n = 25). Spontaneous as well as diabetes‐associated autoantigen‐stimulated production of 23 cytokines and chemokines were tested using protein microarray. In addition, the influence of glucose on cytokine and chemokine responsiveness was analyzed in vitro. The study groups differed in their spontaneous as well as stimulated cytokine and chemokine spectra. A prominent Th1 response (high IFN‐gamma) from autoantigen stimulation was observed especially in babies of T1D fathers (P = 0.001) and also in mothers with perfect diabetes compensation during the 3rd trimester (P = 0.016) in comparison with control newborns. By contrast, cord blood mononuclear cells cultivated in vitro in high glucose concentration decreased the diabetogenic stimulated Th1 cytokine response. Maternal ‘sweet’ as well as ‘autoimmune environment’ may both lead to lower occurrence of T1D within their offspring. Further studies will reveal the exact immunological mechanism of this observation.

Cord Blood Cytokine Profile Detection in Neonates with T1D Parents – Monitoring of Cellular Auto‐reactivity Using Protein Microarray

Type 1 diabetes (T1D) is a great medical challenge and its incidence rises rapidly. T lymphocytes and their cytokine production are supposed to play a major role in T1D development. So far, there is no potent tool to recognize the early signs of cellular auto‐reactivity which leads to β‐cell damage. The naïve immune system of the newborn (not yet influenced by external factors) can be used as an important model for T1D pathogenesis studies. Cord blood samples of 22 healthy neonates born at term to a diabetic parent (T1DR) and 15 newborns with no family history of any autoimmune disease (controls) were collected. Determination of 23 cytokines was performed before and after the stimulation with diabetogenic autoantigens using protein microarray. We observed lower basal production of all detected cytokines in the T1DR group – granulocyte/macrophage colony‐stimulating factor (GM‐CSF) (P = 0.025), growth regulated protein (GRO) (P = 0.002), GRO‐α (P = 0.027), interleukin (IL)‐1‐α (P = 0.051), IL‐3 (P = 0.008), IL‐7 (P = 0.027), IL‐8 (P = 0.042), monocyte chemoattractant proteins (MCP)‐3 (P = 0.022), monokine‐induced by IFN‐γ (MIG) (P = 0.034) and regulated upon activation normal T‐cell express sequence (RANTES) (P = 0.004). Exclusively lower post‐stimulative levels of G‐CSF (P = 0.030) and GRO‐α (P = 0.04) were observed in controls in comparison with the basal levels. A significant post‐stimulative decrease in G‐CSF (P = 0.030) and MCP‐2 (P = 0.009) levels was observed in controls in comparison with T1DR neonates. We also observed the interesting impact of the risky genotype on the protein microarray results. Protein microarray seems to be a useful tool to characterize a risk pattern of the immune response for T1D also in newborns.

The Effect of Diabetes-Associated Autoantigens on Cell Processes in Human PBMCs and Their Relevance to Autoimmune Diabetes Development

Type 1 Diabetes (T1D) is considered to be a T-helper- (Th-) 1 autoimmune disease; however, T1D pathogenesis likely involves many factors, and sufficient tools for autoreactive T cell detection for the study of this disease are currently lacking. In this study, using gene expression microarrays, we analysed the effect of diabetes-associated autoantigens on peripheral blood mononuclear cells (PBMCs) with the purpose of identifying (pre)diabetes-associated cell processes. Twelve patients with recent onset T1D, 18 first-degree relatives of the TD1 patients (DRL; 9/18 autoantibody positive), and 13 healthy controls (DV) were tested. PBMCs from these individuals were stimulated with a cocktail of diabetes-associated autoantigens (proinsulin, IA-2, and GAD65-derived peptides). After 72 hours, gene expression was evaluated by high-density gene microarray. The greatest number of functional differences was observed between relatives and controls (69 pathways), from which 15% of the pathways belonged to “immune response-related” processes. In the T1D versus controls comparison, more pathways (24%) were classified as “immune response-related.” Important pathways that were identified using data from the T1D versus controls comparison were pathways involving antigen presentation by MHCII, the activation of Th17 and Th22 responses, and cytoskeleton rearrangement-related processes. Genes involved in Th17 and TGF-beta cascades may represent novel, promising (pre)diabetes biomarkers.

Particle Swarm Optimization Based Adaptable Predictor of Glycemia Values

A multiple-steps ahead prediction of glucose level from real time continuous glucose monitoring system (RT-CGMS) device is presented. Both linear and nonlinear autoregressive models with exogenous inputs are used for the system identification. Insulin and nutritional income are used as the exogenous inputs. To better represent the dynamical character of those external factors, simple compartment models are used providing a signal of the influence of the insulin and nutrition. Those signals are used as inputs to regressive models. The main problem of adaptation of those compartment models to particular patient is solved using continuous particle swarm optimization algorithm. The proposed approach is demonstrated on data from type I diabetic patients with RT-CGMS and insulin pump. The results provide the first step of creation of future mobile application for decision support of type 1 diabetics.

A Review of Model Prediction in Diabetes and of Designing Glucose Regulators Based on Model Predictive Control for the Artificial Pancreas

The present work presents a comparative assessment of glucose prediction models for diabetic patients using data from sensors monitoring blood glucose concentration as well as data from in silico simulations. The models are based on neural networks and linear and nonlinear mathematical models evaluated for prediction horizons ranging from 5 to 120 min. Furthermore, the implementation of compartment models for simulation of absorption and elimination of insulin, caloric intake and information about physical activity is examined in combination with neural networks and mathematical models, respectively. This assessment also addresses the recent progress and challenges in designing glucose regulators based on model predictive control used as part of artificial pancreas devices for type 1 diabetic patients. The assessments include 24 papers in total, from 2006 to 2016, in order to investigate progress in blood glucose concentration prediction and in Artificial Pancreas devices for type 1 diabetic patients.

Not Only Glycaemic But Also Other Metabolic Factors Affect T Regulatory Cell Counts and Proinflammatory Cytokine Levels in Women with Type 1 Diabetes

Type 1 diabetic (T1D) patients suffer from insulinopenia and hyperglycaemia. Studies have shown that if a patients hyperglycaemic environment is not compensated, it leads to complex immune dysfunctions. Similarly, T1D mothers with poor glycaemic control exert a negative impact on the immune responses of their newborns. However, questions concerning the impact of other metabolic disturbances on the immune system of T1D mothers (and their newborns) have been raised. To address these questions, we examined 28 T1D women in reproductive age for the relationship between various metabolic, clinical, and immune parameters. Our study revealed several unexpected correlations which are indicative of a much more complex relationship between glucose and lipid factors (namely, glycosylated haemoglobin Hb1Ac, the presence of one but not multiple chronic diabetic complications, and atherogenic indexes) and proinflammatory cytokines (IL-1alpha and TNF-alpha). Regulatory T cell counts correlated with HbA1c, diabetic neuropathy, lipid spectra parameters, and IL-6 levels. Total T-helper cell count was interconnected with BMI and glycaemia variability correlated with lipid spectra parameters, insulin dose, and vitamin D levels. These and other correlations revealed in this study provide broader insight into the association of various metabolic abnormalities with immune parameters that may impact T1D mothers or their developing child.