Advances in the cellular immunological pathogenesis of type 1 diabetes

Type 1 diabetes is an autoimmune disease caused by the immune‐mediated destruction of insulin‐producing pancreatic β cells. In recent years, the incidence of type 1 diabetes continues to increase. It is supposed that genetic, environmental and immune factors participate in the damage of pancreatic β cells. Both the immune regulation and the immune response are involved in the pathogenesis of type 1 diabetes, in which cellular immunity plays a significant role. For the infiltration of CD4⁺ and CD8⁺ T lymphocyte, B lymphocytes, natural killer cells, dendritic cells and other immune cells take part in the damage of pancreatic β cells, which ultimately lead to type 1 diabetes. This review outlines the cellular immunological mechanism of type 1 diabetes, with a particular emphasis to T lymphocyte and natural killer cells, and provides the effective immune therapy in T1D, which is approached at three stages. However, future studies will be directed at searching for an effective, safe and long‐lasting strategy to enhance the regulation of a diabetogenic immune system with limited toxicity and without global immunosuppression.     

Investigation of the age-at-onset heterogeneity in type 1 diabetes through mathematical modeling

The heterogeneity between young- and adult-onset type 1 diabetes (T1D) is well known, but not well understood. We approach this question through mathematical formulation and analysis of the dynamic interactions between the immune cells and the pancreatic islet beta-cells that lead to the beta-cell destruction. Utilizing the perturbation expansion method we investigate the dynamic stability of our system under fast and slow beta-cell turnover limits. We find that if autoimmunity is initiated when the turnover is slow (adult age), a stable steady state can exist with reduced number of beta-cells, where the beta-cell regeneration balances the ongoing autoimmune destruction. This implies that a slow disease process is possible. In contrast, if autoimmunity occurs when the beta-cell turnover is rapid (young age), such a stable state will never be attained and the destruction will progress unabated, leading to an acute disease onset. The major findings of our model are consistent with clinical observations, and it offers an explanation for the dynamic and phenotypic heterogeneity between young- and adult-onset T1D. More importantly, the model analyses point out that pathways regulating beta-cell turnover can be new targets to interfere with the disease process of T1D.     

Statistical modeling of interlocus interactions in a complex disease: rejection of the multiplicative model of epistasis in type 1 diabetes

In general, common diseases do not follow a Mendelian inheritance pattern. To identify disease mechanisms and etiology, their genetic dissection may be assisted by evaluation of linkage in mouse models of human disease. Statistical modeling of multiple-locus linkage data from the nonobese diabetic (NOD) mouse model of type 1 diabetes has previously provided evidence for epistasis between alleles of several Idd (insulin-dependent diabetes) loci. The construction of NOD congenic strains containing selected segments of the diabetes-resistant strain genome allows analysis of the joint effects of alleles of different loci in isolation, without the complication of other segregating Idd loci. In this article, we analyze data from congenic strains carrying two chromosome intervals (a double congenic strain) for two pairs of loci: Idd3 and Idd10 and Idd3 and Idd5. The joint action of both pairs is consistent with models of additivity on either the log odds of the penetrance, or the liability scale, rather than with the previously proposed multiplicative model of epistasis. For Idd3 and Idd5 we would also not reject a model of additivity on the penetrance scale, which might indicate a disease model mediated by more than one pathway leading to beta-cell destruction and development of diabetes. However, there has been confusion between different definitions of interaction or epistasis as used in the biological, statistical, epidemiological, and quantitative and human genetics fields. The degree to which statistical analyses can elucidate underlying biologic mechanisms may be limited and may require prior knowledge of the underlying etiology.     

GPS-MBA: computational analysis of MHC class II epitopes in type 1 diabetes

As a severe chronic metabolic disease and autoimmune disorder, type 1 diabetes (T1D) affects millions of people world-wide. Recent advances in antigen-based immunotherapy have provided a great opportunity for further treating T1D with a high degree of selectivity. It is reported that MHC class II I-A(g7) in the non-obese diabetic (NOD) mouse and human HLA-DQ8 are strongly linked to susceptibility to T1D. Thus, the identification of new I-A(g7) and HLA-DQ8 epitopes would be of great help to further experimental and biomedical manipulation efforts. In this study, a novel GPS-MBA (MHC Binding Analyzer) software package was developed for the prediction of I-A(g7) and HLA-DQ8 epitopes. Using experimentally identified epitopes as the training data sets, a previously developed GPS (Group-based Prediction System) algorithm was adopted and improved. By extensive evaluation and comparison, the GPS-MBA performance was found to be much better than other tools of this type. With this powerful tool, we predicted a number of potentially new I-A(g7) and HLA-DQ8 epitopes. Furthermore, we designed a T1D epitope database (TEDB) for all of the experimentally identified and predicted T1D-associated epitopes. Taken together, this computational prediction result and analysis provides a starting point for further experimental considerations, and GPS-MBA is demonstrated to be a useful tool for generating starting information for experimentalists. The GPS-MBA is freely accessible for academic researchers at: http://mba.biocuckoo.org.     

Biological pattern generation: the cellular and computational logic of networks in motion

In 1900, Ramón y Cajal advanced the neuron doctrine, defining the neuron as the fundamental signaling unit of the nervous system. Over a century later, neurobiologists address the circuit doctrine: the logic of the core units of neuronal circuitry that control animal behavior. These are circuits that can be called into action for perceptual, conceptual, and motor tasks, and we now need to understand whether there are coherent and overriding principles that govern the design and function of these modules. The discovery of central motor programs has provided crucial insight into the logic of one prototypic set of neural circuits: those that generate motor patterns. In this review, I discuss the mode of operation of these pattern generator networks and consider the neural mechanisms through which they are selected and activated. In addition, I will outline the utility of computational models in analysis of the dynamic actions of these motor networks.     

细胞代谢复杂网络研究进展

复杂网络理论为细胞代谢网络研究提供了新的工具,基于复杂网络理论的细胞代谢网络研究可称细胞代谢复杂网络研究．先简要介绍了细胞代谢复杂网络的研究背景;随后详细总结和论述了细胞代谢复杂网络在建模、分析和控制三个方面的研究现状;再进一步指出了细胞代谢复杂网络在建模、分析和控制这三个方面研究中所存在的一些问题．为细胞代谢复杂网络领域的研究指出了一些有意义的方向,具有一定的参考价值。     

Neural network modeling and control of type 1 diabetes mellitus

This paper presents a developed and validated dynamic simulation model of type 1 diabetes, that simulates the progression of the disease and the two term controller that is responsible for the insulin released to stabilize the glucose level. The modeling and simulation of type 1 diabetes mellitus is based on an artificial neural network approach. The methodology builds upon an existing rich database on the progression of type 1 diabetes for a group of diabetic patients. The model was found to perform well at estimating the next glucose level over time without control. A neural controller that mimics the pancreas secretion of insulin into the body was also developed. This controller is of the two term type: one stage is responsible for short-term and the other for mid-term insulin delivery. It was found that the controller designed predicts an adequate amount of insulin that should be delivered into the body to obtain a normalization of the elevated glucose level. This helps to achieve the main objective of insulin therapy: to obtain an accurate estimate of the amount of insulin to be delivered in order to compensate for the increase in glucose concentration.     

Selfish cellular networks and the evolution of complex organisms

Human gametogenesis takes years and involves many cellular divisions, particularly in males. Consequently, gametogenesis provides the opportunity to acquire multiple de novo mutations. A significant portion of these is likely to impact the cellular networks linking genes, proteins, RNA and metabolites, which constitute the functional units of cells. A wealth of literature shows that these individual cellular networks are complex, robust and evolvable. To some extent, they are able to monitor their own performance, and display sufficient autonomy to be termed "selfish". Their robustness is linked to quality control mechanisms which are embedded in and act upon the individual networks, thereby providing a basis for selection during gametogenesis. These selective processes are equally likely to affect cellular functions that are not gamete-specific, and the evolution of the most complex organisms, including man, is therefore likely to occur via two pathways: essential housekeeping functions would be regulated and evolve during gametogenesis within the parents before being transmitted to their progeny, while classical selection would operate on other traits of the organisms that shape their fitness with respect to the environment.     

e-MutPath: computational modeling reveals the functional landscape of genetic mutations rewiring interactome networks

Understanding the functional impact of cancer somatic mutations represents a critical knowledge gap for implementing precision oncology. It has been increasingly appreciated that the interaction profile mediated by a genomic mutation provides a fundamental link between genotype and phenotype. However, specific effects on biological signaling networks for the majority of mutations are largely unknown by experimental approaches. To resolve this challenge, we developed e-MutPath (edgetic Mutation-mediated Pathway perturbations), a network-based computational method to identify candidate ‘edgetic’ mutations that perturb functional pathways. e-MutPath identifies informative paths that could be used to distinguish disease risk factors from neutral elements and to stratify disease subtypes with clinical relevance. The predicted targets are enriched in cancer vulnerability genes, known drug targets but depleted for proteins associated with side effects, demonstrating the power of network-based strategies to investigate the functional impact and perturbation profiles of genomic mutations. Together, e-MutPath represents a robust computational tool to systematically assign functions to genetic mutations, especially in the context of their specific pathway perturbation effect.     

Saturation transfer difference NMR and computational modeling of a sialoadhesin-sialyl lactose complex

The siglecs are a family of I-type lectins binding to sialic acids on the cell surface. Sialoadhesin (siglec-1) is expressed at much higher levels in inflammatory macrophages and specifically binds to alpha-2,3-sialylated N-acetyl lactosamine residues of glycan chains. The terminal disaccharide alpha-D-Neu5Ac-(2-->3)-beta-D-Gal is thought to be the main epitope recognized by sialoadhesin. To understand the basis of this biological recognition reaction we combined NMR experiments with a molecular modeling study. We employed saturation transfer difference (STD) NMR experiments to characterize the binding epitope of alpha-2,3-sialylated lactose, alpha-D-Neu5Ac-(2-->3)-beta-D-Gal-(1-->4)-D-Glc 1 to sialoadhesin at atomic resolution. The experimental results were compared to a computational docking model and to X-ray data of a complex of sialyl lactose and sialoadhesin. The data reveal that sialoadhesin mainly recognizes the N-acetyl neuraminic acid and a small part of the galactose moiety of 1. The crystal structure of a complex of sialoadhesin with sialyl lactose 1 was used as a basis for a modeling study using the FlexiDock algorithm. The model generated was very similar to the original crystal structure. Therefore, the X-ray data were used to predict theoretical STD values utilizing the CORCEMA-STD protocol. The good agreement between experimental and theoretical STD values indicates that a combined modeling/STD NMR approach yields a reliable structural model for the complex of sialoadhesin with alpha-D-Neu5Ac-(2-->3)-beta-D-Gal-(1-->4)-D-Glc 1 in aqueous solution.     

Hybrid cellular automaton modeling of nutrient modulated cell growth in tissue engineering constructs

Mathematic models help interpret experimental results and accelerate tissue engineering developments. We develop in this paper a hybrid cellular automata model that combines the differential nutrient transport equation to investigate the nutrient limited cell construct development for cartilage tissue engineering. Individual cell behaviors of migration, contact inhibition and cell collision, coupled with the cell proliferation regulated by oxygen concentration were carefully studied. Simplified two-dimensional simulations were performed. Using this model, we investigated the influence of cell migration speed on the overall cell growth within in vitro cell scaffolds. It was found that intense cell motility can enhance initial cell growth rates. However, since cell growth is also significantly modulated by the nutrient contents, intense cell motility with conventional uniform cell seeding method may lead to declined cell growth in the final time because concentrated cell population has been growing around the scaffold periphery to block the nutrient transport from outside culture media. Therefore, homogeneous cell seeding may not be a good way of gaining large and uniform cell densities for the final results. We then compared cell growth in scaffolds with various seeding modes, and proposed a seeding mode with cells initially residing in the middle area of the scaffold that may efficiently reduce the nutrient blockage and result in a better cell amount and uniform cell distribution for tissue engineering construct developments.     

A tetrameric allyl complex of sodium, and computational modeling of the Na-allyl chemical shift

Transmetallation of Li[A′] (A′ = [1,3-(SiMe₃)₂C₃H₃]⁻) with sodium tert-butoxide produces the corresponding sodium salt, which crystallizes from THF/toluene in the form of a cyclic tetramer, {Na[A′](thf)}₄. The Na atoms are in a square planar arrangement, bridged with π-bound allyl ligands; the Na-C distances range from 2.591(3)-2.896(3) Å, with an average of 2.70 Å. The geometries of several model organosodium complexes containing cyclopentadienyl and allyl ligands were optimized with density functional theory methods. The optimized structures were used with the gauge-including atomic orbital (GIAO) method to calculate their ²³Na NMR magnetic shielding values. Unlike the case with NaCp, the chemical shift of unsubstituted Na(C₃H₅) is very sensitive to the presence of coordinated THF (causing a 20 ppm upfield shift); silyl substitution has an even larger effect (30 ppm upfield shift). The observed ²³Na shift of δ −3.3 ppm for Na[A′] in THF-d₈, however, cannot be reliably distinguished from that calculated for the [Na(thf)₄]⁺ cation alone.     

The role of low avidity T cells in the protection against type 1 diabetes: a modeling investigation

Cytotoxic T lymphocytes (CTLs) play a dominant role in the pathogenesis of autoimmune diabetes, commonly denoted Type 1 Diabetes (T1D). These CTLs (notably CD8⁺ T cells) recognize and kill insulin-secreting pancreatic β cells, reducing their number by ∼90%. The resulting reduction of insulin secretion causes the defective regulation of glucose metabolism, leading to the characteristic symptoms of diabetes. Recognition of β cells as targets by CTLs depends on the interactions between MHC-peptide complexes on the surface of β cells and receptors (TCRs) on T cells. Those CTLs with high affinity TCRs (also called high avidity T cells) cause most of the harm, while those with low affinity TCRs (also called low avidity T cells) play a more mysterious role. Recent experimental evidence suggests that low avidity T cells accumulate as memory T cells during the disease and may be protective in NOD mice (a strain prone to developing T1D), delaying disease progression. It has been hypothesized that such low avidity T cells afford disease protection either by crowding the islets of Langerhans, where β cells reside, or by killing antigen presenting cells (APCs).In this paper, we explore the hypothesized mechanisms for this protective effect in the context of a series of models for (1) the interactions of low and high avidity T cells, (2) the effect of APCs and (3) the feedback from β cell killing to autoantigen-induced T cell proliferation. We analyze properties of these models, noting consistency of predictions with observed behaviour. We then use the models to examine the influence of various treatment strategies on the progression of the disease. The model reveals that progressive accumulation of memory low avidity autoreactive T cells during disease progression makes treatments aimed at expanding these protective T cell types more effective close to, or at the onset of clinical disease. It also provides evidence for the hypothesis that low avidity T cells kill APCs (rather than the alternate hypothesis that they crowd the islets).