Continued discovery of transcriptional units expressed in cells of the mouse mononuclear phagocyte lineage

The current RIKEN transcript set represents a significant proportion of the mouse transcriptome but transcripts expressed in the innate and acquired immune systems are poorly represented. In the present study we have assessed the complexity of the transcriptome expressed in mouse macrophages before and after treatment with lipopolysaccharide, a global regulator of macrophage gene expression, using existing RIKEN 19K arrays. By comparison to array profiles of other cells and tissues, we identify a large set of macrophage-enriched genes, many of which have obvious functions in endocytosis and phagocytosis. In addition, a significant number of LPS-inducible genes were identified. The data suggest that macrophages are a complex source of mRNA for transcriptome studies. To assess complexity and identify additional macrophage expressed genes, cDNA libraries were created from purified populations of macrophage and dendritic cells, a functionally related cell type. Sequence analysis revealed a high incidence of novel mRNAs within these cDNA libraries. These studies provide insights into the depths of transcriptional complexity still untapped amongst products of inducible genes, and identify macrophage and dendritic cell populations as a starting point for sampling the inducible mammalian transcriptome.

Mediators of gram-positive septic shock

Septic shock can occur as a result of Gram-negative or Gram-positive infection and involves a complex interaction between bacterial factors and the host immune system producing a systemic inflammatory state that may progress to multiple organ failure and death. Gram-positive bacteria are increasingly becoming more prevalent especially Staphylococcus epidermidis in association with indwelling devices. Lipopolysaccaride (LPS) is the key Gram-negative component involved in this process, but it is not clear which components of Gram-positive bacteria are responsible for progression of this often fatal disease. The aim of this thesis was to investigate the effect of bacterial components on the immune systems. Lipid S, a short chain form of lipoteichoic acid (LTA) found to be excreted from bacteria during growth in culture medium was examined along with other Gram-positive cell wall components: LTA, peptidoglycan (PG) and wall teichoic acids (WTA) and LPS from Gram-negative bacteria. Lipid S, LTA, PG and LPS but not WTA all stimulated murine macrophages and cell lines to produce significant amounts of NO, TNF-a, IL-6 and IL-1 and would induce fever and tissue damage seen in inflammatory diseases. Lipid S proved to be the most potent out of the Gram-positive samples tested. IgG antibodies in patients serum were found to bind to and cross react with lipid S and LTA. Anti-inflammatory antibiotics, platelet activating factor (PAF), PAF receptor antagonists and monoclonal antibodies (mAbs) directed to LTA, CD14 and toll-like receptors were utilised to modulate cytokine and NO production. In cell culture the anti-LTA and the anti-CD14 mAbs failed to markedly attenuate the production of NO, TNF-a, IL-6 or IL-1, the anti-TLR4 antibody did greatly inhibit the ability of LPS to stimulate cytokine production but not lipid S. The tetracyclines proved to be the most effective compounds, many were active at low concentrations and showed efficacy to inhibit both lipid S and LPS stimulated macrophages to produce NO.

Nootropia : a user profiling model based on a self-organising term network

Artificial Immune Systems are well suited to the problem of using a profile representation of an individual’s or a group’s interests to evaluate documents. Nootropia is a user profiling model that exhibits similarities to models of the immune system that have been developed in the context of autopoietic theory. It uses a self-organising term network that can represent a user’s multiple interests and can adapt to both short-term variations and substantial changes in them. This allows Nootropia to drift, constantly following changes in the user’s multiple interests, and, thus, to become structurally coupled to the user.

Immunoinformatics:predicting immunogenicity in silico

This volume both engages the reader and provides a sound foundation for the use of immunoinformatics techniques in immunology and vaccinology. It addresses databases, HLA supertypes, MCH binding, and other properties of immune systems. The book contains chapters written by leaders in the field and provides a firm background for anyone working in immunoinformatics in one easy-to-use, insightful volume.

Optimizing amino acid groupings for GPCR classification

MOTIVATION: There is much interest in reducing the complexity inherent in the representation of the 20 standard amino acids within bioinformatics algorithms by developing a so-called reduced alphabet. Although there is no universally applicable residue grouping, there are numerous physiochemical criteria upon which one can base groupings. Local descriptors are a form of alignment-free analysis, the efficiency of which is dependent upon the correct selection of amino acid groupings. RESULTS: Within the context of G-protein coupled receptor (GPCR) classification, an optimization algorithm was developed, which was able to identify the most efficient grouping when used to generate local descriptors. The algorithm was inspired by the relatively new computational intelligence paradigm of artificial immune systems. A number of amino acid groupings produced by this algorithm were evaluated with respect to their ability to generate local descriptors capable of providing an accurate classification algorithm for GPCRs.

Characterization of L-Serine Deaminase and its Potential Role in the Pathogenicity of Pseudomonas aeruginosa

All pathogens require high energetic influxes to counterattack the host immune system and without this energy bacterial infections are easily cleared. This study is an investigation into one highly bioenergetic pathway in Pseudomonas aeruginosa involving the amino acid L-serine and the enzyme L-serine deaminase (L-SD). P. aeruginosa is an opportunistic pathogen causing infections in patients with compromised immune systems as well as patients with cystic fibrosis. Recent evidence has linked L-SD directly to the pathogenicity of several organisms including but not limited to Campylobacter jejuni, Mycobacterium bovis, Streptococcus pyogenes, and Yersinia pestis. We hypothesized that P. aeruginosa L-SD is likely to be critical for its virulence. Genome sequence analysis revealed the presence of two L-SD homo logs encoded by sdaA and sdaB. We analyzed the ability of P. aeruginosa to utilize serine and the role of SdaA and SdaB in serine deamination by comparing mutant strains of sdaA (PAOsdaA) and sdaB (PAOsdaB) with their isogenic parent P. aeruginosa P AO 1. We demonstrated that P. aeruginosa is unable to use serine as a sole carbon source. However, serine utilization is enhanced in the presence of glycine and this glycine-dependent induction of L-SD activity requires the inducer serine. The amino acid leucine was shown to inhibit L-SD activity from both SdaA and SdaB and the net contribution to L-serine deamination by SdaA and SdaB was ascertained at 34% and 66 %, respectively. These results suggest that P. aeruginosa LSD is quite different from the characterized E. coli L-SD that is glycine-independent but leucine-dependent for activation. Growth mutants able to use serine as a sole carbon source were also isolated and in addition, suicide vectors were constructed which allow for selective mutation of the sdaA and sdaB genes on any P. aeruginosa strain of interest. Future studies with a double mutant will reveal the importance of these genes for pathogenicity.

An overview on Human Leukocyte Antigen (HLA) region gene expression during Pseudomonas aeruginosa infection

The human leukocyte antigen (HLA) complex is an extensively studied cluster of genes with immunoregulatory function. Pseudomonas aeruginosa is capable of infecting individuals with weakened immune systems, and is associated with a high mortality rate. Previous genetic studies of the HLA region have found correlations between bacterial infection and its effect on regulating HLA gene expressions to establish their infection. This project analyzes the expression of classical HLA loci (A, B, C, DR, DQ, DP) in human B cells and macrophage cells during the infection of virulent strains of P. aeruginosa. Cells were cultured and infected with different virulent live, and heat-killed strains of P. aeruginosa for different time periods. The mRNA was extracted and converted into cDNA followed by real-time quantitative PCR and data analysis. The Western Blot technique was used to identify the targeted protein’s cell surface expression. Infection with P. aeruginosa was found to inhibit the expression of HLA proteins. The PA14 strain inhibited expression of all targeted genes in all experiments. Infections with PA01 and PA103 showed different patterns depending on the incubation time and the targeted gene. These differences suggest that the three strains use various mechanisms to inhibit HLA protein expression.

FdDCA : A Novel Fuzzy Deterministic Dendritic Cell Algorithm

Postprint

Molecular Insights of CD4+ T Cell Differentiation, Effector Formation and Helper Function

CD4+ T cells play a crucial in the adaptive immune system. They function as the central hub to orchestrate the rest of immunity: CD4+ T cells are essential governing machinery in antibacterial and antiviral responses by facilitating B cell affinity maturation and coordinating the innate and adaptive immune systems to boost the overall immune outcome; on the contrary, hyperactivation of the inflammatory lineages of CD4+ T cells, as well as the impairments of suppressive CD4+ regulatory T cells, are the etiology of various autoimmunity and inflammatory diseases. The broad role of CD4+ T cells in both physiological and pathological contexts prompted me to explore the modulation of CD4+ T cells on the molecular level.

microRNAs (miRNAs) are small RNA molecules capable of regulating gene expression post-transcriptionally. miRNAs have been shown to exert substantial regulatory effects on CD4+ T cell activation, differentiation and helper function. Specifically, my lab has previously established the function of the miR-17-92 cluster in Th1 differentiation and anti-tumor responses. Here, I further analyzed the role of this miRNA cluster in Th17 differentiation, specifically, in the context of autoimmune diseases. Using both gain- and loss-of-function approaches, I demonstrated that miRNAs in miR-17-92, specifically, miR-17 and miR-19b in this cluster, is a crucial promoter of Th17 differentiation. Consequently, loss of miR-17-92 expression in T cells mitigated the progression of experimental autoimmune encephalomyelitis and T cell-induced colitis. In combination with my previous data, the molecular dissection of this cluster establishes that miR-19b and miR-17 play a comprehensive role in promoting multiple aspects of inflammatory T cell responses, which underscore them as potential targets for oligonucleotide-based therapy in treating autoimmune diseases.

To systematically study miRNA regulation in effector CD4+ T cells, I devised a large-scale miRNAome profiling to track in vivo miRNA changes in antigen-specific CD4+ T cells activated by Listeria challenge. From this screening, I identified that miR-23a expression tightly correlates with CD4+ effector expansion. Ectopic expression and genetic deletion strategies validated that miR-23a was required for antigen-stimulated effector CD4+ T cell survival in vitro and in vivo. I further determined that miR-23a targets Ppif, a gatekeeper of mitochondrial reactive oxygen species (ROS) release that protects CD4+ T cells from necrosis. Necrosis is a type of cell death that provokes inflammation, and it is prominently triggered by ROS release and its consequent oxidative stress. My finding that miR-23a curbs ROS-mediated necrosis highlights the essential role of this miRNA in maintaining immune homeostasis.

A key feature of miRNAs is their ability to modulate different biological aspects in different cell populations. Previously, my lab found that miR-23a potently suppresses CD8+ T cell cytotoxicity by restricting BLIMP1 expression. Since BLIMP1 has been found to inhibit T follicular helper (Tfh) differentiation by antagonizing the master transcription factor BCL6, I investigated whether miR-23a is also involved in Tfh differentiation. However, I found that miR-23a does not target BLIMP1 in CD4+ T cells and loss of miR-23a even fostered Tfh differentiation. This data indicate that miR-23a may target other pathways in CD4+ T cells regarding the Tfh differentiation pathway.

Although the lineage identity and regulatory networks for Tfh cells have been defined, the differentiation path of Tfh cells remains elusive. Two models have been proposed to explain the differentiation process of Tfh cells: in the parallel differentiation model, the Tfh lineage is segregated from other effector lineages at the early stage of antigen activation; alternatively, the sequential differentiation model suggests that naïve CD4+ T cells first differentiate into various effector lineages, then further program into Tfh cells. To address this question, I developed a novel in vitro co-culture system that employed antigen-specific CD4+ T cells, naïve B cells presenting cognate T cell antigen and BAFF-producing feeder cells to mimic germinal center. Using this system, I were able to robustly generate GC-like B cells. Notably, well-differentiated Th1 or Th2 effector cells also quickly acquired Tfh phenotype and function during in vitro co-culture, which suggested a sequential differentiation path for Tfh cells. To examine this path in vivo, under conditions of classical Th1- or Th2-type immunizations, I employed a TCRβ repertoire sequencing technique to track the clonotype origin of Tfh cells. Under both Th1- and Th2- immunization conditions, I observed profound repertoire overlaps between the Teff and Tfh populations, which strongly supports the proposed sequential differentiation model. Therefore, my studies establish a new platform to conveniently study Tfh-GC B cell interactions and provide insights into Tfh differentiation processes.

Analyse de la valeur pronostique du contexte immunologique du cancer superficiel de la vessie

Le cancer de la vessie est le 5e plus répandu au Canada. Les tumeurs vésicales non-infiltrant le muscle (TVNIM) représentent 70-75% des tumeurs au premier diagnostic. Après une résection transurétrale de tumeur de vessie (RTUTV), 60-70 % des patients souffriront de récidive et 10-20 % de progression vers l’infiltration du muscle (TVIM). Présentement, l’évaluation du risque de récidive ou de progression pour sélectionner le traitement approprié est basée sur les caractéristiques cliniques et pathologiques. La gestion des TVNIM à haut risque est l’un des aspects les plus difficiles à gérer pour un uro-oncologue et il est bien connu que l’issue clinique peut varier significativement entre des patients ayant une tumeur de même stade. Il serait donc important de détecter les tumeurs les plus susceptibles de récidiver et de progresser pour ajuster le traitement en conséquence. L’objectif de mon projet était d’analyser la valeur pronostique du contexte immunologique des TVNIM pour prédire leurs probabilités de récidive ou de progression vers l’infiltration du muscle. Mon premier volet consistait à évaluer la valeur pronostique de l’infiltration des cellules immunes, telles que les cellules dendritiques infiltrant les tumeurs (TIDC), les cellules T infiltrant les tumeurs (TIL) et les macrophages associés aux tumeurs (TAM) dans une cohorte de 106 TVNIM initiales. Les données d’infiltration des TIDC et des TIL dans les TVNIM démontrent leur importance dans l’évolution des patients atteints du cancer de la vessie et pourraient aider à identifier les TVNIM à haut risque. Mon deuxième volet consistait à caractériser un profil d’expression génique associé aux immunités innée et adaptative dans une série de 22 TVNIM. Cependant, le faible nombre de tumeurs disponibles a empêché d’obtenir une conclusion. Notre étude a permis de confirmer que la composition et le phénotype des cellules immunes infiltrant les TVNIM ont un impact sur l’évolution de ces tumeurs.

Safe Visiting, March 20, 2103

For most of us a cold, or even the flu, poses nothing more than a minor inconvenience, some time at home in bed, and maybe a trip to see your physician. For residents and tenants in long-term care settings, however, coming down with the flu can be life threatening. Compromised immune systems and respiratory or cardiac issues can become more than just a challenge. Because of this, it is important to think about how a safe environment can be maintained while at the same time allowing for visitors and social activities.

'Danger Theory: The Link between AIS and IDS?'

Abstract We present ideas about creating a next generation Intrusion Detection System (IDS) based on the latest immunological theories. The central challenge with computer security is determining the difference between normal and potentially harmful activity. For half a century, developers have protected their systems by coding rules that identify and block specific events. However, the nature of current and future threats in conjunction with ever larger IT systems urgently requires the development of automated and adaptive defensive tools. A promising solution is emerging in the form of Artificial Immune Systems (AIS): The Human Immune System (HIS) can detect and defend against harmful and previously unseen invaders, so can we not build a similar Intrusion Detection System (IDS) for our computers? Presumably, those systems would then have the same beneficial properties as HIS like error tolerance, adaptation and self-monitoring. Current AIS have been successful on test systems, but the algorithms rely on self-nonself discrimination, as stipulated in classical immunology. However, immunologist are increasingly finding fault with traditional self-nonself thinking and a new 'Danger Theory' (DT) is emerging. This new theory suggests that the immune system reacts to threats based on the correlation of various (danger) signals and it provides a method of 'grounding' the immune response, i.e. linking it directly to the attacker. Little is currently understood of the precise nature and correlation of these signals and the theory is a topic of hot debate. It is the aim of this research to investigate this correlation and to translate the DT into the realms of computer security, thereby creating AIS that are no longer limited by self-nonself discrimination. It should be noted that we do not intend to defend this controversial theory per se, although as a deliverable this project will add to the body of knowledge in this area. Rather we are interested in its merits for scaling up AIS applications by overcoming self-nonself discrimination problems.

'Towards a Conceptual Framework for Innate Immunity'

Innate immunity now occupies a central role in immunology. However, artificial immune system models have largely been inspired by adaptive not innate immunity. This paper reviews the biological principles and properties of innate immunity and, adopting a conceptual framework, asks how these can be incorporated into artificial models. The aim is to outline a meta-framework for models of innate immunity.

Sensing Danger: Innate Immunology for Intrusion Detection

The immune system provides an ideal metaphor for anomaly detection in general and computer security in particular. Based on this idea, artificial immune systems have been used for a number of years for intrusion detection, unfortunately so far with little success. However, these previous systems were largely based on immunological theory from the 1970s and 1980s and over the last decade our understanding of immunological processes has vastly improved. In this paper we present two new immune inspired algorithms based on the latest immunological discoveries, such as the behaviour of Dendritic Cells. The resultant algorithms are applied to real world intrusion problems and show encouraging results. Overall, we believe there is a bright future for these next generation artificial immune algorithms

Investigating a Hybrid Metaheuristic For Job Shop Rescheduling

Previous research has shown that artificial immune systems can be used to produce robust schedules in a manufacturing environment. The main goal is to develop building blocks (antibodies) of partial schedules that can be used to construct backup solutions (antigens) when disturbances occur during production. The building blocks are created based upon underpinning ideas from artificial immune systems and evolved using a genetic algorithm (Phase I). Each partial schedule (antibody) is assigned a fitness value and the best partial schedules are selected to be converted into complete schedules (antigens). We further investigate whether simulated annealing and the great deluge algorithm can improve the results when hybridised with our artificial immune system (Phase II). We use ten fixed solutions as our target and measure how well we cover these specific scenarios.