Doença enxerto-versus hospedeiro: determinação da expressão de genes e alterações de vias associadas ao sistema imunológico usando metodos da biofísica

O transplante de medula óssea (TMO) é um procedimento terapêutico importante em casos relacionados à pacientes com leucemia ou linfoma. Em decorrência desse processo, uma reação conhecida como doença enxerto-versus-hospedeiro (GVHD) pode ocorrer em pacientes susceptíveis como conseqüência da presença de células imunocompetentes do doador. Entretanto, não existe um modelo para descrever completamente as ações relacionadas ao mecanismo imunológico da GVHD desde a fase que inicializa a doença até a fase efetora. O Objetivo geral deste estudo é a investigação da resposta imunológica considerando-se o sistema HLA (antígenos leucocitários humano) em pacientes que desenvolveram a GVHD em decorrência do TMO. O National Cancer Institute (NCI) – Pathway interaction Database e Reactome foram usados como bases de dados com o objetivo de se estudar a expressão de genes e vias relacionados às Classes I e II do sistema HLA (antígenos leucocitários humano). O estudo considerou a mudança de expressão de genes relacionados às 17 vias do sistema imunológico com potencialidade para se expressar em pacientes que desenvolveram a GVHD associada à TMO. Dados referentes aos transcriptomas foram obtidos utilizando-se a plataforma GPL570 Affymetrix Genoma Humano U133 Plus. A atividade relativa foi usada para determinar as alterações das vias em amostras de GVHD em relação ao controle. As análises foram realizadas utilizando-se o software Via Complex e Bioconductor. Observou-se aumento significativo da expressão de genes ralacionados às vias do sistema imune adaptativo, antígenos associados às Classe I e II do HLA, fosforilação de CD3 e CD247, sinalização dos receptores de células T em CD4+ nativas e ativação de NF-kapa β nas células B. Também observou-se alterações significativas na mudança de expressão dos genes associados às vias relacionadas à super família de moléculas B7:CD28\CTLA-4 quando comparadas ao controle. Isso pode indicar a necessidade de geração de um segundo sinal co-estimulador em GVHD, acionado pelas moléculas dessa super família. O aumento da expressão do gene CD69 nas amostras experimentais caracteriza a ativação celular e, portanto, a sinalização de estímulos em GVHD. Os achados obtidos neste estudo contribuem para melhor elucidar o mecanismo imunopatogênico associado à GVHD. P

The Danger Theory and Its Application to Artificial Immune Systems

Over the last decade, a new idea challenging the classical self-non-self viewpoint has become popular amongst immunologists. It is called the Danger Theory. In this conceptual paper, we look at this theory from the perspective of Artificial Immune System practitioners. An overview of the Danger Theory is presented with particular emphasis on analogies in the Artificial Immune Systems world. A number of potential application areas are then used to provide a framing for a critical assessment of the concept, and its relevance for Artificial Immune Systems.

'On the Effects of Idiotypic Interactions for Recommendation Communities in Artificial Immune Systems'

It has previously been shown that a recommender based on immune system idiotypic principles can outperform one based on correlation alone. This paper reports the results of work in progress, where we undertake some investigations into the nature of this beneficial effect. The initial findings are that the immune system recommender tends to produce different neighbourhoods, and that the superior performance of this recommender is due partly to the different neighbourhoods, and partly to the way that the idiotypic effect is used to weight each neighbour's recommendations.

'Investigating Artificial Immune Systems For Job Shop Rescheduling In Changing Environments'

ABSTRACT Artificial immune system can be used to generate schedules in changing environments and it has been proven to be more robust than schedules developed using a genetic algorithm. Good schedules can be produced especially when the number of the antigens is increased. However, an increase in the range of the antigens had somehow affected the fitness of the immune system. In this research, we are trying to improve the result of the system by rescheduling the same problem using the same method while at the same time maintaining the robustness of the schedules.

'Introducing Dendritic Cells as a Novel Immune-Inspired Algorithm for Anomaly Detection'

Abstract. Dendritic cells are antigen presenting cells that provide a vital link between the innate and adaptive immune system. Research into this family of cells has revealed that they perform the role of coordinating T-cell based immune responses, both reactive and for generating tolerance. We have derived an algorithm based on the functionality of these cells, and have used the signals and differentiation pathways to build a control mechanism for an artificial immune system. We present our algorithmic details in addition to some preliminary results, where the algorithm was applied for the purpose of anomaly detection. We hope that this algorithm will eventually become the key component within a large, distributed immune system, based on sound immunological concepts.

'Cooperative Automated worm Response and Detection ImmuNe ALgorithm (CARDINAL) inspired by T-cell Immunity and Tolerance'

The role of T-cells within the immune system is to confirm and assess anomalous situations and then either respond to or tolerate the source of the effect. To illustrate how these mechanisms can be harnessed to solve real-world problems, we present the blueprint of a T-cell inspired algorithm for computer security worm detection. We show how the three central T-cell processes, namely T-cell maturation, differentiation and proliferation, naturally map into this domain and further illustrate how such an algorithm fits into a complete immune inspired computer security system and framework.

'Artificial Immune Systems: A new Decision Making Paradigm for Operational Research'

Over the last few years, more and more heuristic decision making techniques have been inspired by nature, e.g. evolutionary algorithms, ant colony optimisation and simulated annealing. More recently, a novel computational intelligence technique inspired by immunology has emerged, called Artificial Immune Systems (AIS). This immune system inspired technique has already been useful in solving some computational problems. In this keynote, we will very briefly describe the immune system metaphors that are relevant to AIS. We will then give some illustrative real-world problems suitable for AIS use and show a step-by-step algorithm walkthrough. A comparison of AIS to other well-known algorithms and areas for future work will round this keynote off. It should be noted that as AIS is still a young and evolving field, there is not yet a fixed algorithm template and hence actual implementations might differ somewhat from the examples given here

'Artificial Immune Systems Tutorial'

The biological immune system is a robust, complex, adaptive system that defends the body from foreign pathogens. It is able to categorize all cells (or molecules) within the body as self-cells or non-self cells. It does this with the help of a distributed task force that has the intelligence to take action from a local and also a global perspective using its network of chemical messengers for communication. There are two major branches of the immune system. The innate immune system is an unchanging mechanism that detects and destroys certain invading organisms, whilst the adaptive immune system responds to previously unknown foreign cells and builds a response to them that can remain in the body over a long period of time. This remarkable information processing biological system has caught the attention of computer science in recent years. A novel computational intelligence technique, inspired by immunology, has emerged, called Artificial Immune Systems. Several concepts from the immune have been extracted and applied for solution to real world science and engineering problems. In this tutorial, we briefly describe the immune system metaphors that are relevant to existing Artificial Immune Systems methods. We will then show illustrative real-world problems suitable for Artificial Immune Systems and give a step-by-step algorithm walkthrough for one such problem. A comparison of the Artificial Immune Systems to other well-known algorithms, areas for future work, tips & tricks and a list of resources will round this tutorial off. It should be noted that as Artificial Immune Systems is still a young and evolving field, there is not yet a fixed algorithm template and hence actual implementations might differ somewhat from time to time and from those examples given here.

Artificial Immune Tissue using Self-Organizing Networks

As introduced by Bentley et al. (2005), artificial immune systems (AIS) are lacking tissue, which is present in one form or another in all living multi-cellular organisms. Some have argued that this concept in the context of AIS brings little novelty to the already saturated field of the immune inspired computational research. This article aims to show that such a component of an AIS has the potential to bring an advantage to a data processing algorithm in terms of data pre-processing, clustering and extraction of features desired by the immune inspired system. The proposed tissue algorithm is based on self-organizing networks, such as self-organizing maps (SOM) developed by Kohonen (1996) and an analogy of the so called Toll-Like Receptors (TLR) affecting the activation function of the clusters developed by the SOM.

Influence of substrates composition on immunomodulation by MSCs

Mesenchymal stem cells (MSCs) are non-hematopoietic multipotent stem cells capable to self-renew and differentiate along different cell lineages. MSCs can be found in adult tissues and extra embryonic tissues like the umbilical cord matrix/Wharton’s Jelly (WJ). The latter constitute a good source of MSCs, being more naïve and having a higher proliferative potential than MSCs from adult tissues like the bone marrow, turning them more appealing for clinical use. It is clear that MSCs modulate both innate and adaptive immune responses and its immunodulatory effects are wide, extending to T cells and dendritic cells, being therapeutically useful for treatment of immune system disorders. Mechanotransduction is by definition the mechanism by which cells transform mechanical signals translating that information into biochemical and morphological changes. Here, we hypothesize that by culturing WJ-MSCs on distinct substrates with different stiffness and biochemical composition, may influence the immunomodulatory capacity of the cells. Here, we showed that WJ-MSCs cultured on distinct PDMS substrates presented different secretory profiles from cells cultured on regular tissue culture polystyrene plates (TCP), showing higher secretion of several cytokines analysed. Moreover, it was also shown that WJ-MSCs cultured on PDMS substrates seems to possess higher immunomodulatory capabilities and to differentially regulate the functional compartments of T cells when compared to MSCs maintained on TCP. Taken together, our results suggest that elements of mechanotransduction seem to be influencing the immunomodulatory ability of MSCs, as well as their secretory profile. Thus, future strategies will be further explored to better understand these observation and to envisage new in vitro culture conditions for MSCs aiming at distinct therapeutic approaches, namely for immune-mediated disorders.

Comparison of the modes of action of Apremilast and Roflumilast

The phosphodiesterase 4 (PDE4) family are cAMP specific phosphodiesterases that play an important role in the inflammatory response and is the major PDE type found in inflammatory cells. A significant number of PDE4 specific inhibitors have been developed and are currently being investigated for use as therapeutic agents. Apremilast, a small molecule inhibitor of PDE 4 is in development for chronic inflammatory disorders and has shown promise for the treatment of psoriasis, psoriatic arthritis as well as other inflammatory diseases. It has been found to be safe and well tolerated in humans and in March 2014 it was approved by the US food and drug administration for the treatment of adult patients with active psoriatic arthritis. The only other PDE4 inhibitor on the market is Roflumilast and it is used for treatment of respiratory disease. Roflumilast is approved in the EU for the treatment of COPD and was recently approved in the US for treatment to reduce the risk of COPD exacerbations. Roflumilast is also a selective PDE4 inhibitor, administered as an oral tablet once daily, and is thought to act by increasing cAMP within lung cells. As both (Apremilast and Roflumilast) compounds selectively inhibit PDE4 but are targeted at different diseases, there is a need for a clear understanding of their mechanism of action (MOA). Differences and similarity of MOA should be defined for the purposes of labelling, for communication to the scientific community, physicians, and patients, and for an extension of utility to other diseases and therapeutic areas. In order to obtain a complete comparative picture of the MOA of both inhibitors, additional molecular and cellular biology studies are required to more fully elucidate the signalling mediators downstream of PDE4 inhibition which result in alterations in pro- and anti-inflammatory gene expression. My studies were conducted to directly compare Apremilast with Roflumilast, in order to substantiate the differences observed in the molecular and cellular effects of these compounds, and to search for other possible differentiating effects. Therefore the main aim of this thesis was to utilise cutting-edge biochemical techniques to discover whether Apremilast and Roflumilast work with different modes of action. In the first part of my thesis I used novel genetically encoded FRET based cAMP sensors targeted to different intracellular compartments, in order to monitor cAMP levels within specific microdomains of cells as a consequence of challenge with Apremilast and Roflumilast, which revealed that Apremilast and Roflumilast do regulate different pools of cAMP in cells. In the second part of my thesis I focussed on assessing whether Apremilast and Roflumilast cause differential effects on the PKA phosphorylation state of proteins in cells. I used various biochemical techniques (Western blotting, Substrate kinase arrays and Reverse Phase Protein array and found that Apremilast and Roflumilast do lead to differential PKA substrate phosphorylation. For example I found that Apremilast increases the phosphorylation of Ribosomal Protein S6 at Ser240/244 and Fyn Y530 in the S6 Ribosomal pathway of Rheumatoid Arthritis Synovial fibroblast and HEK293 cells, whereas Roflumilast does not. This data suggests that Apremilast has distinct biological effects from that of Roflumilast and could represent a new therapeutic role for Apremilast in other diseases. In the final part of my thesis, Phage display technology was employed in order to identify any novel binding motifs that associate with PDE4 and to identify sequences that were differentially regulated by the inhibitors in an attempt to find binding motifs that may exist in previously characterised signalling proteins. Petide array technology was then used to confirm binding of specific peptide sequences or motifs. Results showed that Apremilast and Roflumilast can either enhance or decrease the binding of PDE4A4 to specific peptide sequences or motifs that are found in a variety of proteins in the human proteome, most interestingly Ubiquitin-related proteins. The data from this chapter is preliminary but may be used in the discovery of novel binding partners for PDE4 or to provide a new role for PDE inhibition in disease. Therefore the work in this thesis provides a unique snapshot of the complexity of the cAMP signalling system and is the first to directly compare action of the two approved PDE4 inhibitors in a detailed way.

Idiotypic Immune Networks in Mobile Robot Control

Jerne's idiotypic network theory postulates that the immune response involves inter-antibody stimulation and suppression as well as matching to antigens. The theory has proved the most popular Artificial Immune System (AIS) model for incorporation into behavior-based robotics but guidelines for implementing idiotypic selection are scarce. Furthermore, the direct effects of employing the technique have not been demonstrated in the form of a comparison with non-idiotypic systems. This paper aims to address these issues. A method for integrating an idiotypic AIS network with a Reinforcement Learning based control system (RL) is described and the mechanisms underlying antibody stimulation and suppression are explained in detail. Some hypotheses that account for the network advantage are put forward and tested using three systems with increasing idiotypic complexity. The basic RL, a simplified hybrid AIS-RL that implements idiotypic selection independently of derived concentration levels and a full hybrid AIS-RL scheme are examined. The test bed takes the form of a simulated Pioneer robot that is required to navigate through maze worlds detecting and tracking door markers.

Biological Inspiration for Artificial Immune Systems

Artificial immune systems (AISs) to date have generally been inspired by naive biological metaphors. This has limited the effectiveness of these systems. In this position paper two ways in which AISs could be made more biologically realistic are discussed. We propose that AISs should draw their inspiration from organisms which possess only innate immune systems, and that AISs should employ systemic models of the immune system to structure their overall design. An outline of plant and invertebrate immune systems is presented, and a number of contemporary systemic models are reviewed. The implications for interdisciplinary research that more biologically-realistic AISs could have is also discussed.

Introducting Dendritic Cells as a Novel Immune-Inspired Algorithm for Anomaly Detection

Dendritic cells are antigen presenting cells that provide a vital link between the innate and adaptive immune system. Research into this family of cells has revealed that they perform the role of coordinating T-cell based immune responses, both reactive and for generating tolerance. We have derived an algorithm based on the functionality of these cells, and have used the signals and differentiation pathways to build a control mechanism for an artificial immune system. We present our algorithmic details in addition to some preliminary results, where the algorithm was applied for the purpose of anomaly detection. We hope that this algorithm will eventually become the key component within a large, distributed immune system, based on sound imnological concepts.

An Immune Inspired Approach to Anomaly Detection

The immune system provides a rich metaphor for computer security: anomaly detection that works in nature should work for machines. However, early artificial immune system approaches for computer security had only limited success. Arguably, this was due to these artificial systems being based on too simplistic a view of the immune system. We present here a second generation artificial immune system for process anomaly detection. It improves on earlier systems by having different artificial cell types that process information. Following detailed information about how to build such second generation systems, we find that communication between cells types is key to performance. Through realistic testing and validation we show that second generation artificial immune systems are capable of anomaly detection beyond generic system policies. The paper concludes with a discussion and outline of the next steps in this exciting area of computer security.