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This article is from Frontiers in Immunology , volume 5 . Abstract The nervous and immune systems have long been considered as compartments that perform separate and different functions. However, recent clinical, epidemiological, and experimental data have suggested that the pathogenesis of several immune-mediated disorders, such as multiple sclerosis (MS), might involve factors, hormones, and neural mediators that link the immune and nervous system. These molecules are members of the same superfamily, which allow the mutual and bi-directional neural–immune interaction. More recently, the discovery of leptin, one of the most abundant adipocyte-derived hormones that control food intake and metabolism, has suggested that nutritional/metabolic status, acting at central level, can control immune self-tolerance, since it promotes experimental autoimmune encephalomyelitis, an animal model of MS. Here, we summarize the most recent advances and the key players linking the central nervous system, immune tolerance, and the metabolic status. Understanding this coordinated interaction may pave the way for novel therapeutic approaches to increase host defense and suppress immune-mediated disorders.

The natural immune system is considered by many specialists as a second brain of vertebrates. In fact, the immune system possesses all the main features of Artificial Intelligence (AI) systems: (1) memory, (2) learning capability, (3) capability to recognize self and non-self, and (4) decision-making capability, that is, the immune system must decide how to treat all macromolecules it encounters even if such molecules are foreign and have never existed before. Of special interest to computer science is the theory of immune networks which describes interactions between immune system specific proteins (antibodies) and foreign macromolecules (antigens). The existence of such immune networks has been established experimentally by molecular immunology which has detected and described the antibody-antigen interaction. Based on the biological principles of the immune system, the field of Artificial Immune Systems (AISs) has been established. It hopes to offer powerful and robust information processing capabilities for solving complex problems. For example, AISs may provide improved techniques to detect and mitigate modern computer network vulnerabilities to intrusions from computer viruses, unauthorized access or other forms of data corruption. Like other modern computer science techniques such as Artificial Neural Networks (ANNs) or Intelligent Agents, AISs can learn new information, recall previously learned information, and perform pattern recognition in a highly decentralized fashion. However, AISs based on natural immune networks differ remarkably from ANNs, intelligent agents, genetic algorithms, and cellular automata in their ability to recognize self and non-self and their highly specific activity. AISs have already been applied to several specific problems including information security, fault detection, robotic control, and others.

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This article is from Arthritis Research & Therapy , volume 13 . Abstract Introduction: Rheumatoid arthritis (RA) is a systemic autoimmune disease characterized by chronic synovitis that progresses to destruction of cartilage and bone. Bone marrow (BM) cells have been shown to contribute to this pathogenesis. In this study, we compared differentially expressed molecules in BM cells from RA and osteoarthritis (OA) patients and analyzed abnormal regulatory networks to identify the role of BM cells in RA. Methods: Gene expression profiles (GEPs) in BM-derived mononuclear cells from 9 RA and 10 OA patients were obtained by DNA microarray. Up- and down-regulated genes were identified by comparing the GEPs from the two patient groups. Bioinformatics was performed by Expression Analysis Systemic Explorer (EASE) 2.0 based on gene ontology, followed by network pathway analysis with Ingenuity Pathways Analysis (IPA) 7.5. Results: The BM mononuclear cells showed 764 up-regulated and 1,910 down-regulated genes in RA patients relative to the OA group. EASE revealed that the gene category response to external stimulus, which included the gene category immune response, was overrepresented by the up-regulated genes. So too were the gene categories signal transduction and phosphate metabolism. Down-regulated genes were dominantly classified in three gene categories: cell proliferation, which included mitotic cell cycle, DNA replication and chromosome cycle, and DNA metabolism. Most genes in these categories overlapped with each other. IPA analysis showed that the up-regulated genes in immune response were highly relevant to the antigen presentation pathway and to interferon signaling. The major histocompatibility complex (MHC) class I molecules, human leukocyte antigen (HLA)-E, HLA-F, and HLA-G, tapasin (TAP) and TAP binding protein, both of which are involved in peptide antigen binding and presentation via MHC class I molecules, are depicted in the immune response molecule networks. Interferon gamma and interleukin 8 were overexpressed and found to play central roles in these networks. Conclusions: Abnormal regulatory networks in the immune response and cell cycle categories were identified in BM mononuclear cells from RA patients, indicating that the BM is pathologically involved in RA.

Associative network models featuring multi-tasking properties have been introduced recently and studied in the low load regime, where the number $P$ of simultaneously retrievable patterns scales with the number $N$ of nodes as $P\sim \log N$. In addition to their relevance in artificial intelligence, these models are increasingly important in immunology, where stored patterns represent strategies to fight pathogens and nodes represent lymphocyte clones. They allow us to understand the crucial ability of the immune system to respond simultaneously to multiple distinct antigen invasions. Here we develop further the statistical mechanical analysis of such systems, by studying the medium load regime, $P \sim N^{\delta}$ with $\delta \in (0,1]$. We derive three main results. First, we reveal the nontrivial architecture of these networks: they exhibit a high degree of modularity and clustering, which is linked to their retrieval abilities. Second, by solving the model we demonstrate for $\delta

Associative network models featuring multi-tasking properties have been introduced recently and studied in the low load regime, where the number $P$ of simultaneously retrievable patterns scales with the number $N$ of nodes as $P\sim \log N$. In addition to their relevance in artificial intelligence, these models are increasingly important in immunology, where stored patterns represent strategies to fight pathogens and nodes represent lymphocyte clones. They allow us to understand the crucial ability of the immune system to respond simultaneously to multiple distinct antigen invasions. Here we develop further the statistical mechanical analysis of such systems, by studying the medium load regime, $P \sim N^{\delta}$ with $\delta \in (0,1]$. We derive three main results. First, we reveal the nontrivial architecture of these networks: they exhibit a high degree of modularity and clustering, which is linked to their retrieval abilities. Second, by solving the model we demonstrate for $\delta

This article is from Immune Network , volume 13 . Abstract Co-signaling molecules are surface glycoproteins that positively or negatively regulate the T cell response to antigen. Co-signaling ligands and receptors crosstalk between the surfaces of antigen-presenting cells (APCs) and T cells, and modulate the ultimate magnitude and quality of T cell receptor (TCR) signaling. In the past 10 years, the field of co-signaling research has been advanced by the understanding of underlying mechanisms of the immune modulation led by newly identified co-signaling molecules and the successful preclinical and clinical trials targeting co-inhibitory molecules called immune checkpoints in the treatment of autoimmune diseases and cancers. In this review, we briefly describe the characteristics of well-known B7 co-signaling family members regarding the expression, functions and therapeutic implications and to introduce newly identified B7 members such as B7-H5, B7-H6, and B7-H7.

This article is from Immune Network , volume 13 . Abstract Co-signaling molecules are surface glycoproteins that positively or negatively regulate the T cell response to antigen. Co-signaling ligands and receptors crosstalk between the surfaces of antigen-presenting cells (APCs) and T cells, and modulate the ultimate magnitude and quality of T cell receptor (TCR) signaling. In the past 10 years, the field of co-signaling research has been advanced by the understanding of underlying mechanisms of the immune modulation led by newly identified co-signaling molecules and the successful preclinical and clinical trials targeting co-inhibitory molecules called immune checkpoints in the treatment of autoimmune diseases and cancers. In this review, we briefly describe the characteristics of well-known B7 co-signaling family members regarding the expression, functions and therapeutic implications and to introduce newly identified B7 members such as B7-H5, B7-H6, and B7-H7.

In this work we adopt a statistical mechanics approach to investigate basic, systemic features exhibited by adaptive immune systems. The lymphocyte network made by B-cells and T-cells is modeled by a bipartite spin-glass, where, following biological prescriptions, links connecting B-cells and T-cells are sparse. Interestingly, the dilution performed on links is shown to make the system able to orchestrate parallel strategies to fight several pathogens at the same time; this multitasking capability constitutes a remarkable, key property of immune systems as multiple antigens are always present within the host. We also define the stochastic process ruling the temporal evolution of lymphocyte activity, and show its relaxation toward an equilibrium measure allowing statistical mechanics investigations. Analytical results are compared with Monte Carlo simulations and signal-to-noise outcomes showing overall excellent agreement. Finally, within our model, a rationale for the experimentally well-evidenced correlation between lymphocytosis and autoimmunity is achieved; this sheds further light on the systemic features exhibited by immune networks.

The aim of this work is to try to bridge over theoretical immunology and disordered statistical mechanics. Our long term hope is to contribute to the development of a quantitative theoretical immunology from which practical applications may stem. In order to make theoretical immunology appealing to the statistical physicist audience we are going to work out a research article which, from one side, may hopefully act as a benchmark for future improvements and developments, from the other side, it is written in a very pedagogical way both from a theoretical physics viewpoint as well as from the theoretical immunology one. Furthermore, we have chosen to test our model describing a wide range of features of the adaptive immune response in only a paper: this has been necessary in order to emphasize the benefit available when using disordered statistical mechanics as a tool for the investigation. However, as a consequence, each section is not at all exhaustive and would deserve deep investigation: for the sake of completeness, we restricted details in the analysis of each feature with the aim of introducing a self-consistent model.

This paper deals with a new model for clonal network dynamics. We describe in detail this model and derive special equations governing immune system dynamics based on the general gradient type principles that can be inherent to a wide class of real living objects. A special clonal network is modeled by two symmetric projector matrix variables simultaneously taking into account both asymmetry of the interaction to each other and adaptation states that can be realized owing to possible idiotypic clonal suppresions. We perform computer simulations of the model dynamics for some simple cases of relatively low dimension, paying special attention to the dynamics of amounts of activated receptor strings within clonal network.

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This article is from PLoS ONE , volume 9 . Abstract Obese individuals are at greater risk for death from influenza virus infection. Paralleling human evidence, obese mice are also more susceptible to influenza infection mortality. However, the underlying mechanisms driving greater influenza severity in the obese remain unclear. Metabolic profiling has been utilized in infectious disease models to enhance prognostic or diagnostic methods, and to gain insight into disease pathogenesis by providing a more global picture of dynamic infection responses. Herein, metabolic profiling was used to develop a deeper understanding of the complex processes contributing to impaired influenza protection in obese mice and to facilitate generation of new explanatory hypotheses. Diet-induced obese and lean mice were infected with influenza A/Puerto Rico/8/34. 1H nuclear magnetic resonance-based metabolic profiling of urine, feces, lung, liver, mesenteric white adipose tissue, bronchoalveolar lavage fluid and serum revealed distinct metabolic signatures in infected obese mice, including perturbations in nucleotide, vitamin, ketone body, amino acid, carbohydrate, choline and lipid metabolic pathways. Further, metabolic data was integrated with immune analyses to obtain a more comprehensive understanding of potential immune-metabolic interactions. Of interest, uncovered metabolic signatures in urine and feces allowed for discrimination of infection status in both lean and obese mice at an early influenza time point, which holds prognostic and diagnostic implications for this methodology. These results confirm that obesity causes distinct metabolic perturbations during influenza infection and provide a basis for generation of new hypotheses and use of this methodology in detection of putative biomarkers and metabolic patterns to predict influenza infection outcome.

We are interested in modeling theoretical immunology within a statistical mechanics flavor: focusing on the antigen-independent maturation process of B-cells, in this paper we try to revise the problem of self vs non-self discrimination by mature B lymphocytes. We consider only B lymphocytes: despite this is of course an oversimplification, however such a toy model may help to highlight features of their interactions otherwise shadowed by main driven mechanisms due to i.e. helper T-cell signalling. By analyzing possible influences of the ontogenesis of the immune system on the final behavior of B lymphocytes, we try to merge over the purely negative selection mechanism at their birth with the adult self-regulation process. The final goal is a "thermodynamical picture" by which both the scenarios can exist and, actually, be synergically complementary: Trough numerical simulations we impose on a recent scheme for B-cell interactions, that part of self-reactive lymphocytes are killed during the ontogenesis by which two observations stem: At first the so built system is able to show anergy with respect to the previously encountered self even in its mature life, then this naturally leads to an increasing variance (and average) in the connectivity distribution of the resulting idiotypic network. As a consequence, following Varela perspective, this shift may contribute to push to anergy those self-directed cells which are free to explore the body: identifying the latter as the highly connected ones, anergy is imposed even via the B-network regulation, and its strength is influenced by the negative selection.

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