The immune system as drug target

The immune system is perhaps the largest yet most diffuse and distributed somatic system in vertebrates. It plays vital roles in fighting infection and in the homeostatic control of chronic disease. As such, the immune system in both pathological and healthy states is a prime target for therapeutic interventions by drugs-both small-molecule and biologic. Comprising both the innate and adaptive immune systems, human immunity is awash with potential unexploited molecular targets. Key examples include the pattern recognition receptors of the innate immune system and the major histocompatibility complex of the adaptive immune system. Moreover, the immune system is also the source of many current and, hopefully, future drugs, of which the prime example is the monoclonal antibody, the most exciting and profitable type of present-day drug moiety. This brief review explores the identity and synergies of the hierarchy of drug targets represented by the human immune system, with particular emphasis on the emerging paradigm of systems pharmacology. © the authors, publisher and licensee Libertas Academica Limited.

Functional study of ephrins and eph receptors in the immune system

Thèse numérisée par la Direction des bibliothèques de l'Université de Montréal.

An investigation of the molecular interactions between statins and bacterial pathogens, and their combined impact on the human immune system

Statins are a class of drug that inhibits cholesterol biosynthesis, and are used to treat patients with high serum cholesterol levels. They exert this function by competitively binding to the enzyme 3-hydroxy-3-methylglutaryl-CoenzymeA reductase (HMGR), which catalyses the formation of mevalonate, a rate-limiting step in cholesterol biosynthesis. In addition, statins have what are called “pleiotropic effects”, which include the reduction of inflammation, immunomodulation, and antimicrobial effects. Statins can also improve survival of patients with sepsis and pneumonia. Cystic fibrosis (CF) is the most common recessive inherited disease in the Caucasian population, which is characterised by factors including, but not limited to, excessive lung inflammation and increased susceptibility to infection. Therefore, the overall objective of this study was to examine the effects of statins on CFassociated bacterial pathogens and the host response. In this work, the prevalence of HMGR was examined in respiratory pathogens, and several CF-associated pathogens were found to possess homologues of this enzyme. HMGR homology was analysed in Staphylococcus aureus, Burkholderia cenocepacia and Streptococcus pneumoniae, and the HMGR of B. cenocepacia was found to have significant conservation to that of Pseudomonas mevalonii, which is the most widely-characterised bacterial HMGR. However, in silico analysis revealed that, unlike S. aureus and S. pneumoniae, B. cenocepacia did not possess homologues of other mevalonate pathway proteins, and that the HMGR of B. cenocepacia appeared to be involved in an alternative metabolic pathway. The effect of simvastatin was subsequently tested on the growth and virulence of S. aureus, B. cenocepacia and S. pneumoniae. Simvastatin inhibited the growth of all 3 species in a dose-dependent manner. In addition, statin treatment also attenuated biofilm formation of all 3 species, and reduced in vitro motility of S. aureus. Interestingly, simvastatin also increased the potency of the aminoglycoside antibiotic gentamicin against B. cenocepacia. The impact of statins was subsequently tested on the predominant CF-associated pathogen Pseudomonas aeruginosa, which does not possess a HMGR homologue. Mevastatin, lovastatin and simvastatin did not influence the growth of this species. However, sub-inhibitory statin concentrations reduced the swarming motility and biofilm formation of P. aeruginosa. The influence of statins was also examined on Type 3 toxin secretion, quorum sensing and chemotaxis, and no statin effect was observed on any of these phenotypes. Statins did not appear to have a characteristic effect on the P. aeruginosa transcriptome. However, a mutant library screen revealed that the effect of statins on P. aeruginosa biofilm was mediated through the PvrR regulator and the Cup fimbrial biosynthesis genes. Furthermore, proteomic analysis demonstrated that 6 proteins were reproducibly induced by simvastatin in the P. aeruginosa swarming cells. The effect of statins on the regulation of the host-P. aeruginosa immune response was also investigated. Statin treatment increased expression of the pro-inflammatory cytokines IL-8 and CCL20 in lung epithelial cells, but did not attenuate P. aeruginosa-mediated inflammatory gene induction. In fact, simvastatin and P. aeruginosa caused a synergistic effect on CCL20 expression. The expression of the transcriptional regulators KLF2 and KLF6 was also increased by statins and P. aeruginosa, with the induction of KLF6 by simvastatin proving to be a novel effect. Interestingly, both statins and P. aeruginosa were capable of inducing alternative splicing of KLF6. P. aeruginosa was found to induce KLF6 alternative splicing by way of the type 3 secreted toxin ExoS. In addition, a mechanistic role was elucidated for KLF6 in the lung, as it was determined that statin-mediated induction of this protein was responsible for the induction of the host response genes CCL20 and iNOS. Moreover, statin treatment caused a slight increase in infection-related cytotoxicity, and increased bacterial adhesion to cells. Taken together, these data demonstrate that statins can reduce the virulence of CFassociated bacterial pathogens and alter host response effectors. Furthermore, novel statin effectors were identified in both bacterial and host cells.

Functional study of ephrins and eph receptors in the immune system

Thèse numérisée par la Direction des bibliothèques de l'Université de Montréal.

'An Artificial Immune System as a Recommender System for Web Sites'

Artificial Immune Systems have been used successfully to build recommender systems for film databases. In this research, an attempt is made to extend this idea to web site recommendation. A collection of more than 1000 individuals' web profiles (alternatively called preferences / favourites / bookmarks file) will be used. URLs will be classified using the DMOZ (Directory Mozilla) database of the Open Directory Project as our ontology. This will then be used as the data for the Artificial Immune Systems rather than the actual addresses. The first attempt will involve using a simple classification code number coupled with the number of pages within that classification code. However, this implementation does not make use of the hierarchical tree-like structure of DMOZ. Consideration will then be given to the construction of a similarity measure for web profiles that makes use of this hierarchical information to build a better-informed Artificial Immune System.

'Immune System Approaches to Intrusion Detection - A Review'

Abstract. The use of artificial immune systems in intrusion detection is an appealing concept for two reasons. Firstly, the human immune system provides the human body with a high level of protection from invading pathogens, in a robust, self-organised and distributed manner. Secondly, current techniques used in computer security are not able to cope with the dynamic and increasingly complex nature of computer systems and their security. It is hoped that biologically inspired approaches in this area, including the use of immune-based systems will be able to meet this challenge. Here we collate the algorithms used, the development of the systems and the outcome of their implementation. It provides an introduction and review of the key developments within this field, in addition to making suggestions for future research.

'Movie Recommendation Systems Using An Artificial Immune System'

We apply the Artificial Immune System (AIS)technology to the collaborative Filtering (CF)technology when we build the movie recommendation system. Two different affinity measure algorithms of AIS, Kendall tau and Weighted Kappa, are used to calculate the correlation coefficients for this movie recommendation system. From the testing we think that Weighted Kappa is more suitable than Kendall tau for movie problems.

'On Affinity Measures for Artificial Immune System Movie Recommenders'

Abstract. We combine Artificial Immune Systems (AIS) technology with Collaborative Filtering (CF) and use it to build a movie recommendation system. We already know that Artificial Immune Systems work well as movie recommenders from previous work by Cayzer and Aickelin ([3], [4], [5]). Here our aim is to investigate the effect of different affinity measure algorithms for the AIS. Two different affinity measures, Kendall's Tau and Weighted Kappa, are used to calculate the correlation coefficients for the movie recommender. We compare the results with those published previously and show that that Weighted Kappa is more suitable than others for movie problems. We also show that AIS are generally robust movie recommenders and that, as long as a suitable affinity measure is chosen, results are good.

'Malicious Code Execution Detection and Response Immune System inspired by the Danger Theory'

The analysis of system calls is one method employed by anomaly detection systems to recognise malicious code execution. Similarities can be drawn between this process and the behaviour of certain cells belonging to the human immune system, and can be applied to construct an artificial immune system. A recently developed hypothesis in immunology, the Danger Theory, states that our immune system responds to the presence of intruders through sensing molecules belonging to those invaders, plus signals generated by the host indicating danger and damage. We propose the incorporation of this concept into a responsive intrusion detection system, where behavioural information of the system and running processes is combined with information regarding individual system calls.

Applying Mathematical Models to Study the Role of the Immune System in Chronic Myelogenous Leukemia

Although tyrosine kinase inhibitors (TKIs) such as imatinib have transformed chronic myelogenous leukemia (CML) into a chronic condition, these therapies are not curative in the majority of cases. Most patients must continue TKI therapy indefinitely, a requirement that is both expensive and that compromises a patient's quality of life. While TKIs are known to reduce leukemic cells' proliferative capacity and to induce apoptosis, their effects on leukemic stem cells, the immune system, and the microenvironment are not fully understood. A more complete understanding of their global therapeutic effects would help us to identify any limitations of TKI monotherapy and to address these issues through novel combination therapies. Mathematical models are a complementary tool to experimental and clinical data that can provide valuable insights into the underlying mechanisms of TKI therapy. Previous modeling efforts have focused on CML patients who show biphasic and triphasic exponential declines in BCR-ABL ratio during therapy. However, our patient data indicates that many patients treated with TKIs show fluctuations in BCR-ABL ratio yet are able to achieve durable remissions. To investigate these fluctuations, we construct a mathematical model that integrates CML with a patient's autologous immune response to the disease. In our model, we define an immune window, which is an intermediate range of leukemic concentrations that lead to an effective immune response against CML. While small leukemic concentrations provide insufficient stimulus, large leukemic concentrations actively suppress a patient's immune system, thus limiting it's ability to respond. Our patient data and modeling results suggest that at diagnosis, a patient's high leukemic concentration is able to suppress their immune system. TKI therapy drives the leukemic population into the immune window, allowing the patient's immune cells to expand and eventually mount an efficient response against the residual CML. This response drives the leukemic population below the immune window, causing the immune population to contract and allowing the leukemia to partially recover. The leukemia eventually reenters the immune window, thus stimulating a sequence of weaker immune responses as the two populations approach equilibrium. We hypothesize that a patient's autologous immune response to CML may explain the fluctuations in BCR-ABL ratio that are regularly seen during TKI therapy. These fluctuations may serve as a signature of a patient's individual immune response to CML. By applying our modeling framework to patient data, we are able to construct an immune profile that can then be used to propose patient-specific combination therapies aimed at further reducing a patient's leukemic burden. Our characterization of a patient's anti-leukemia immune response may be especially valuable in the study of drug resistance, treatment cessation, and combination therapy.

Immune System Approaches to Intrusion Detection - A Review

The use of artificial immune systems in intrusion detection is an appealing concept for two reasons. Firstly, the human immune system provides the human body with a high level of protection from invading pathogens, in a robust, self-organised and distributed manner. Secondly, current techniques used in computer security are not able to cope with the dynamic and increasingly complex nature of computer systems and their security. It is hoped that biologically inspired approaches in this area, including the use of immune-based systems will be able to meet this challenge. Here we review the algorithms used, the development of the systems and the outcome of their implementation. We provide an introduction and analysis of the key developments within this field, in addition to making suggestions for future research.

'Immune System Approaches to Intrusion Detection - A Review'

Abstract. The use of artificial immune systems in intrusion detection is an appealing concept for two reasons. Firstly, the human immune system provides the human body with a high level of protection from invading pathogens, in a robust, self-organised and distributed manner. Secondly, current techniques used in computer security are not able to cope with the dynamic and increasingly complex nature of computer systems and their security. It is hoped that biologically inspired approaches in this area, including the use of immune-based systems will be able to meet this challenge. Here we collate the algorithms used, the development of the systems and the outcome of their implementation. It provides an introduction and review of the key developments within this field, in addition to making suggestions for future research.

'Malicious Code Execution Detection and Response Immune System inspired by the Danger Theory'

The analysis of system calls is one method employed by anomaly detection systems to recognise malicious code execution. Similarities can be drawn between this process and the behaviour of certain cells belonging to the human immune system, and can be applied to construct an artificial immune system. A recently developed hypothesis in immunology, the Danger Theory, states that our immune system responds to the presence of intruders through sensing molecules belonging to those invaders, plus signals generated by the host indicating danger and damage. We propose the incorporation of this concept into a responsive intrusion detection system, where behavioural information of the system and running processes is combined with information regarding individual system calls.

'On Affinity Measures for Artificial Immune System Movie Recommenders'

Abstract. We combine Artificial Immune Systems (AIS) technology with Collaborative Filtering (CF) and use it to build a movie recommendation system. We already know that Artificial Immune Systems work well as movie recommenders from previous work by Cayzer and Aickelin ([3], [4], [5]). Here our aim is to investigate the effect of different affinity measure algorithms for the AIS. Two different affinity measures, Kendall's Tau and Weighted Kappa, are used to calculate the correlation coefficients for the movie recommender. We compare the results with those published previously and show that that Weighted Kappa is more suitable than others for movie problems. We also show that AIS are generally robust movie recommenders and that, as long as a suitable affinity measure is chosen, results are good.