A key role for PTP1B in dendritic cell maturation, migration and T cell activation

Acknowledgements We thank the Iain Fraser Flow Cytometry Centre and the Medical Research Facility of the University of Aberdeen. We are grateful to Drs West, Zaru, and Davidson (University of Dundee) for the scientific discussion and technical assistance. Wethank Derek Mitchell (University of Dundee) for aiding with the quantification of focal contacts. Funding This work was supported by Saving Sight in Grampian and the Development Trust of the UoA (both to J.V.F.). Work on this project was partly funded by project grants from British Heart Foundation and European Foundation for the Study of Diabetes/Lilly diabetes programme grant (to M.D.).

A key role for PTP1B in dendritic cell maturation, migration and T cell activation

Acknowledgements We thank the Iain Fraser Flow Cytometry Centre and the Medical Research Facility of the University of Aberdeen. We are grateful to Drs West, Zaru, and Davidson (University of Dundee) for the scientific discussion and technical assistance. Wethank Derek Mitchell (University of Dundee) for aiding with the quantification of focal contacts. Funding This work was supported by Saving Sight in Grampian and the Development Trust of the UoA (both to J.V.F.). Work on this project was partly funded by project grants from British Heart Foundation and European Foundation for the Study of Diabetes/Lilly diabetes programme grant (to M.D.).

Classification and clinical behavior of blastic plasmacytoid dendritic cell neoplasms according to their maturation-associated immunophenotypic profile

Blastic plasmacytoid dendritic cell neoplasm (BPDCN) is a rare subtype of leukemia/lymphoma, whose diagnosis can be difficult to achieve due to its clinical and biological heterogeneity, as well as its overlapping features with other hematologic malignancies. In this study we investigated whether the association between the maturational stage of tumor cells and the clinico-biological and prognostic features of the disease, based on the analysis of 46 BPDCN cases classified into three maturation-associated subgroups on immunophenotypic grounds. Our results show that blasts from cases with an immature plasmacytoid dendritic cell (pDC) phenotype exhibit an uncommon CD56- phenotype, coexisting with CD34+ non-pDC tumor cells, typically in the absence of extramedullary (e.g. skin) disease at presentation. Conversely, patients with a more mature blast cell phenotype more frequently displayed skin/extramedullary involvement and spread into secondary lymphoid tissues. Despite the dismal outcome, acute lymphoblastic leukemia-type therapy (with central nervous system prophylaxis) and/or allogeneic stem cell transplantation appeared to be the only effective therapies. Overall, our findings indicate that the maturational profile of pDC blasts in BPDCN is highly heterogeneous and translates into a wide clinical spectrum -from acute leukemia to mature lymphoma-like behavior-, which may also lead to variable diagnosis and treatment.

Dendritic Cells for SYN Scan Detection

Artificial immune systems have previously been applied to the problem of intrusion detection. The aim of this research is to develop an intrusion detection system based on the function of Dendritic Cells (DCs). DCs are antigen presenting cells and key to the activation of the human immune system, behaviour which has been abstracted to form the Dendritic Cell Algorithm (DCA). In algorithmic terms, individual DCs perform multi-sensor data fusion, asynchronously correlating the fused data signals with a secondary data stream. Aggregate output of a population of cells is analysed and forms the basis of an anomaly detection system. In this paper the DCA is applied to the detection of outgoing port scans using TCP SYN packets. Results show that detection can be achieved with the DCA, yet some false positives can be encountered when simultaneously scanning and using other network services. Suggestions are made for using adaptive signals to alleviate this uncovered problem.

Transcriptional analysis of PRRSV-infected porcine dendritic cell response to Streptococcus suis infection reveals up-regulation of inflammatory-related genes expression

The porcine reproductive and respiratory syndrome virus (PRRSV) is one of the most important swine pathogens and often serves as an entry door for other viral or bacterial pathogens, of which Streptococcus suis is one of the most common. Pre-infection with PRRSV leads to exacerbated disease caused by S. suis infection. Very few studies have assessed the immunological mechanisms underlying this higher susceptibility. Since antigen presenting cells play a major role in the initiation of the immune response, the in vitro transcriptional response of bone marrow-derived dendritic cells (BMDCs) and monocytes in the context of PRRSV and S. suis co-infection was investigated. BMDCs were found to be more permissive than monocytes to PRRSV infection; S. suis phagocytosis by PRRSV-infected BMDCs was found to be impaired, whereas no effect was found on bacterial intracellular survival. Transcription profile analysis, with a major focus on inflammatory genes, following S. suis infection, with and without pre-infection with PRRSV, was then performed. While PRRSV pre-infection had little effect on monocytes response to S. suis infection, a significant expression of several pro-inflammatory molecules was observed in BMDCs pre-infected with PRRSV after a subsequent infection with S. suis. While an additive effect could be observed for CCL4, CCL14, CCL20, and IL-15, a distinct synergistic up-regulatory effect was observed for IL-6, CCL5 and TNF-α after co-infection. This increased pro-inflammatory response by DCs could participate in the exacerbation of the disease observed during PRRSV and S. suis co-infection.

Transcriptional analysis of PRRSV-infected porcine dendritic cell response to Streptococcus suis infection reveals up-regulation of inflammatory-related genes expression

The porcine reproductive and respiratory syndrome virus (PRRSV) is one of the most important swine pathogens and often serves as an entry door for other viral or bacterial pathogens, of which Streptococcus suis is one of the most common. Pre-infection with PRRSV leads to exacerbated disease caused by S. suis infection. Very few studies have assessed the immunological mechanisms underlying this higher susceptibility. Since antigen presenting cells play a major role in the initiation of the immune response, the in vitro transcriptional response of bone marrow-derived dendritic cells (BMDCs) and monocytes in the context of PRRSV and S. suis co-infection was investigated. BMDCs were found to be more permissive than monocytes to PRRSV infection; S. suis phagocytosis by PRRSV-infected BMDCs was found to be impaired, whereas no effect was found on bacterial intracellular survival. Transcription profile analysis, with a major focus on inflammatory genes, following S. suis infection, with and without pre-infection with PRRSV, was then performed. While PRRSV pre-infection had little effect on monocytes response to S. suis infection, a significant expression of several pro-inflammatory molecules was observed in BMDCs pre-infected with PRRSV after a subsequent infection with S. suis. While an additive effect could be observed for CCL4, CCL14, CCL20, and IL-15, a distinct synergistic up-regulatory effect was observed for IL-6, CCL5 and TNF-α after co-infection. This increased pro-inflammatory response by DCs could participate in the exacerbation of the disease observed during PRRSV and S. suis co-infection.

Follicular dendritic cell disruption as a novel mechanism of virus-induced immunosuppression

Arboviruses (Arthropod-borne viruses) cause acute diseases that are increasingly affecting both human and animal health. Currently, there is a critical lack of understanding about the nature of arbovirus-host interactions in the lymph nodes (LNs), the place where the adaptive immune response is initiated and shaped. In this study, we used bluetongue virus (BTV) and its natural sheep host, to characterise the early events of an arbovirus infection with particular focus on the LNs. Our findings reveal a previously uncharacterized mechanism used by an arbovirus to manipulate host immunity. This study shows that BTV, similarly to other antigens delivered through the skin, is transported rapidly via the lymph to the peripheral lymph nodes. Here, BTV infects and disrupts the stromal network of marginal reticular cells and follicular dendritic cells composing the scaffolding of the follicular area. These cells contribute to antigen presentation and affinity maturation of B-cells for the production of antibodies. Consequently, we observed a loss of germinal centre structure, which hinders B-cell proliferation. This process results in a delayed production of high affinity and virus neutralizing antibodies that is directly related to the virulence of the BTV strain used and the severity of disease. Moreover the humoral immune response to a different antigen is also hampered in BTV-infected animals. Our data show that an arbovirus can evade the host antiviral responses by inducing an acute immunosuppression. Although transient, this immunosuppression occurs at the critical early stages of infection when a delayed host humoral immune response likely affects virus systemic dissemination and the clinical outcome of disease.

An Agent-based Classification Model

The major function of this model is to access the UCI Wisconsin Breast Cancer data-set[1] and classify the data items into two categories, which are normal and anomalous. This kind of classification can be referred as anomaly detection, which discriminates anomalous behaviour from normal behaviour in computer systems. One popular solution for anomaly detection is Artificial Immune Systems (AIS). AIS are adaptive systems inspired by theoretical immunology and observed immune functions, principles and models which are applied to problem solving. The Dendritic Cell Algorithm (DCA)[2] is an AIS algorithm that is developed specifically for anomaly detection. It has been successfully applied to intrusion detection in computer security. It is believed that agent-based modelling is an ideal approach for implementing AIS, as intelligent agents could be the perfect representations of immune entities in AIS. This model evaluates the feasibility of re-implementing the DCA in an agent-based simulation environment called AnyLogic, where the immune entities in the DCA are represented by intelligent agents. If this model can be successfully implemented, it makes it possible to implement more complicated and adaptive AIS models in the agent-based simulation environment.

DCA for bot detection

Ensuring the security of computers is a non-trivial task, with many techniques used by malicious users to compromise these systems. In recent years a new threat has emerged in the form of networks of hijacked zombie machines used to perform complex distributed attacks such as denial of service and to obtain sensitive data such as password information. These zombie machines are said to be infected with a dasiahotpsila - a malicious piece of software which is installed on a host machine and is controlled by a remote attacker, termed the dasiabotmaster of a botnetpsila. In this work, we use the biologically inspired dendritic cell algorithm (DCA) to detect the existence of a single hot on a compromised host machine. The DCA is an immune-inspired algorithm based on an abstract model of the behaviour of the dendritic cells of the human body. The basis of anomaly detection performed by the DCA is facilitated using the correlation of behavioural attributes such as keylogging and packet flooding behaviour. The results of the application of the DCA to the detection of a single hot show that the algorithm is a successful technique for the detection of such malicious software without responding to normally running programs.

The DCA:SOMe comparison: a comparative study between two biologically-inspired algorithms

The dendritic cell algorithm (DCA) is an immune-inspired algorithm, developed for the purpose of anomaly detection. The algorithm performs multi-sensor data fusion and correlation which results in a ‘context aware’ detection system. Previous applications of the DCA have included the detection of potentially malicious port scanning activity, where it has produced high rates of true positives and low rates of false positives. In this work we aim to compare the performance of the DCA and of a self-organizing map (SOM) when applied to the detection of SYN port scans, through experimental analysis. A SOM is an ideal candidate for comparison as it shares similarities with the DCA in terms of the data fusion method employed. It is shown that the results of the two systems are comparable, and both produce false positives for the same processes. This shows that the DCA can produce anomaly detection results to the same standard as an established technique.

Artificial immune systems

The human immune system has numerous properties that make it ripe for exploitation in the computational domain, such as robustness and fault tolerance, and many different algorithms, collectively termed Artificial Immune Systems (AIS), have been inspired by it. Two generations of AIS are currently in use, with the first generation relying on simplified immune models and the second generation utilising interdisciplinary collaboration to develop a deeper understanding of the immune system and hence produce more complex models. Both generations of algorithms have been successfully applied to a variety of problems, including anomaly detection, pattern recognition, optimisation and robotics. In this chapter an overview of AIS is presented, its evolution is discussed, and it is shown that the diversification of the field is linked to the diversity of the immune system itself, leading to a number of algorithms as opposed to one archetypal system. Two case studies are also presented to help provide insight into the mechanisms of AIS; these are the idiotypic network approach and the Dendritic Cell Algorithm.

Performance evaluation of DCA and SRC on a single bot detection

Malicious users try to compromise systems using new techniques. One of the recent techniques used by the attacker is to perform complex distributed attacks such as denial of service and to obtain sensitive data such as password information. These compromised machines are said to be infected with malicious software termed a “bot”. In this paper, we investigate the correlation of behavioural attributes such as keylogging and packet flooding behaviour to detect the existence of a single bot on a compromised machine by applying (1) Spearman’s rank correlation (SRC) algorithm and (2) the Dendritic Cell Algorithm (DCA). We also compare the output results generated from these two methods to the detection of a single bot. The results show that the DCA has a better performance in detecting malicious activities.

Interstitial and Langerhans' dendritic cells in chronic periodontitis and gingivitis

The aim of the present study was to compare quantitatively the distribution of dendritic cell subpopulations in chronic periodontitis and gingivitis. Fourteen biopsies from patients with chronic periodontitis and fifteen from patients with gingivitis were studied. An immunoperoxidase technique was used to quantify the number of Langerhans' cells (CD1a) and interstitial dendritic cells (factor XIIIa) in the oral and sulcular and junctional/pocket epithelia and in the lamina propria. A greater number of factor XIIIa+ dendritic cells in the lamina propria and CD1a+ dendritic cells in the oral epithelium were observed in gingivitis compared to the periodontitis group (p = 0.05). In the sulcular and junctional/pocket epithelia and in the lamina propria, the number of CD1a+ dendritic cells was similar in the gingivitis and periodontitis groups. In conclusion, the number of Langerhans' cells in the oral epithelium and interstitial dendritic cells in the lamina propria is increased in gingivitis compared to periodontitis, which may contribute to the different pattern of host response in these diseases.

Increased generation of dendritic cells from myeloid progenitors in autoimmune-prone nonobese diabetic mice

Aberrant dendritic cell (DC) development and function may contribute to autoimmune disease susceptibility. To address this hypothesis at the level of myeloid lineage-derived DC we compared the development of DC from bone marrow progenitors in vitro and DC populations in vivo in autoimmune diabetes-prone nonbese diabetic (NOD) mice, recombinant congenic nonbese diabetes-resistant (NOR) mice, and unrelated BALB/c and C57BL/6 (BL/6) mice. In GM-CSF/IL-4-supplemented bone marrow cultures, DC developed in significantly greater numbers from NOD than from NOR, BALB/c, and BL/6 mice. Likewise, DC developed in greater numbers from sorted (lineage(-)IL-7Ralpha(-)SCA-1(-)c-kit(+)) NOD myeloid progenitors in either GM-CSF/IL-4 or GM-CSF/stem cell factor (SCF)/TNF-alpha. [H-3]TdR incorporation indicated that the increased generation of NOD DC was due to higher levels of myeloid progenitor proliferation. Generation of DC with the early-acting hematopoietic growth factor, flt3 ligand, revealed that while the increased DC-generative capacity of myeloid-committed progenitors was restricted to NOD cells, early lineage-uncommitted progenitors from both NOD and NOR had increased DC-gencrative capacity relative to BALB/c and BL/6. Consistent with these findings, NOD and NOR mice had increased numbers of DC in blood and thymus and NOD had an increased proportion of the putative myeloid DC (CD11c(+)CD11b(+)) subset within spleen. These findings demonstrate that diabetes-prone NOD mice exhibit a myeloid lineage-specific increase in DC generative capacity relative to diabetes-resistant recombinant congenic NOR mice. We propose that an imbalance favoring development of DC from myeloid-committed progenitors predisposes to autoimmune disease in NOD mice.