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The differential gene expression of CD43 wildtype and knockout macrophages in response to mycobacterial… Vair, Audra Helena 2010

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DIFFERENTIAL GENE EXPRESSION OF CD43 WILDTYPE AND KNOCKOUT MACROPHAGES IN RESPONSE TO MYCOBACTERIAL STIMULATION  by  Audra Helena Vair  B.Sc., The University of Ottawa, 2007  A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF  MASTER OF SCIENCE  in  The Faculty of Graduate Studies (Pathology)  THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver)  May 2010  © Audra Helena Vair, 2010  Abstract Previous studies in our lab show CD43 deficient macrophages have reduced binding and uptake of M.tb bacilli but allow increased intracellular bacterial growth within the host macrophage due to decreased TNF‐α production. It was also found that there are two heat‐shock proteins found on the cell surface of M.tb that are capable of binding CD43: Cpn 60.2 and dnaK. The purpose of this study was to determine the intracellular signalling pathways activated by CD43 upon M.tb stimulation and to determine if Cpn 60.2 or dnaK could reproduce these responses. An RNA‐microarray was performed using mRNA isolated from wildtype (CD43 +/+) and gene‐deleted (CD43‐/‐) bone‐marrow derived murine macrophages that had been co‐incubated with one of M.tb, Cpn 60.2 or dnaK. There was a large quantity of differentially regulated genes for each treatment, ranging from 330 to 990. Only 113 of these genes were statistically significant and shared between treatment types, 21 genes down regulated in the knockout compared to the wild type and 92 genes were up regulated in CD43‐/‐ compared to CD43+/+. Pathway analysis of differential gene expression was performed using lists of statistically significant, differentially‐regulated genes, which indicated there were multiple pathways and cellular processes in which several genes were found to be affected by the absence of CD43. The pathways included p38/JNK/Erk MAP kinase signaling functioning, TNF‐α post‐transcriptional control, cytoskeletal remodeling, phagocytosis and apoptosis. Related genes from both the microarray data and interacting genes from pathway‐data associated with TNF‐α and apoptosis were selected to both validate the microarray and possibly determine if changes in TNF‐related regulation could be controlled by CD43. The microarray indicated several genes involved in TNF‐α regulation and apoptosis were differentially expressed in CD43 +/+ and CD43 ‐/‐ macrophages. Quantitative Reverse‐Transcriptase PCR was used on a selected number of these genes. Differential gene regulation was observed in the Q RT‐PCR results; however results conflicted with the microarray. In conclusion, CD43 influences the course of M.tb infection within the macrophage, possibly through the regulation of TNF‐α and apoptosis. There are implications for CD43‐related control of eicosanoid production and cytoskeletal regulation but these observations require further investigation.  ii  Table of contents Abstract……………………………………………………………………………………………………………………………………………...ii Table of contents……………………………………………………………………………………………………………………………….iii List of tables……………………………………………………………………………………………………………………………………….v List of figures…………………………………………………………………………………………………………………………………..…vi List of abbreviations………………………………………………………………………………………………………………………….vii Acknowledgements…………………..……………………………………………………………………………………………………….ix Dedication…………………………………………….………………………………………………………………………………………….…x 1 Introduction.......................................................................................................................................1 1.1 Mycobacterium tuberculosis .............................................................................................................. 1 1.1.1 Mycobacterium tuberculosis biology .......................................................................................... 2 1.1.2 Mycobacterium tuberculosis infection.........................................................................................2 1.1.3 Prevention ................................................................................................................................... 5 1.1.4 Treatment ................................................................................................................................... 9 1.2 Immune response to TB ................................................................................................................... 11 1.2.1 Macrophages ............................................................................................................................ 11 1.2.2 Cell‐mediated immunity ........................................................................................................... 13 1.3 Bacterial chaperonins and host cell response .................................................................................. 16 1.4 CD43 .................................................................................................................................................. 18 1.4.1 Structure ................................................................................................................................... 19 1.4.2 Expression .................................................................................................................................. 20 1.4.3 Function .................................................................................................................................... 20 1.4.4 Implications in disease .............................................................................................................. 22 1.5 Introduction of thesis ....................................................................................................................... 22 2 Materials and methods ........................................................................................................................... 25 2.1 Cell preparation and tissue culture ................................................................................................... 25 2.1.1 Mice ........................................................................................................................................... 25 2.1.2 Culture of bone‐marrow derived macrophages ........................................................................ 25 2.2 Bacterial culture ............................................................................................................................... 26 2.3 Recombinant molecular chaperones ................................................................................................ 26 2.4 In vitro assay for binding of particles to macrophages and M.tb infection ...................................... 27 2.5 RNA isolation of BMDMφ .................................................................................................................. 28 2.6 RNA amplification ............................................................................................................................. 29 iii  2.7 RNA microarray application ............................................................................................................. 30 2.7.1 Labelling and purification of nucleic acid ............................................................................. 30 2.7.2 Microarray chip hybridization ............................................................................................... 31 2.8 Microarray analysis – Genespring GX analysis ................................................................................ 32 2.8.1 Ingenuity pathway analysis ................................................................................................... 33 2.9 Quantitative reverse‐transcriptase PCR ........................................................................................... 33 2.9.1 Copy‐DNA (cDNA) synthesis.................................................................................................. 33 2.9.2 Quantitative PCR‐primer design and quality analysis ........................................................... 34 2.9.3 Quantiative RT‐PCR efficiency and protocol ........................................................................ 34 2.9.4 RNA isolation‐ for additional qPCR samples ......................................................................... 35 2.9.5 Quantitative reverse‐transcriptase PCR analysis .................................................................. 36 3 Identification of CD43‐mediated gene expression in response to Mycobacterium tuberculosis and Heat‐Shock proteins 65 and 70 using RNA microarray ............................................................................. 37 3.1 Introduction ...................................................................................................................................... 37 3.2 Rationale ........................................................................................................................................... 39 3.3 Results ............................................................................................................................................... 41 3.3.1 CD43 knockout and CD43 wildtype macrophages exhibit differential regulation to each other in response to mycobacterial stimuli ............................................................................................ 41 3.3.2 Mycobacterium tuberculosis, Cpn 60.2 and dnaK elicit differential responses in CD43‐/‐ and CD43+/+ BMDMΦ .................................................................................................................................. 41 3.3.3 CD43 deficiency affects multiple signaling pathways ........................................................... 44 3.3.4 Confirmation of RNA microarray using quantitative reverse‐transcriptase PCR.................. 54 3.3.5 Investigation of specific intracellular signaling pathways .................................................... 60 3.4 Discussion and summary................................................................................................................... 62 4 Conclusion and future directions ............................................................................................................ 69 4.1 Conclusion ........................................................................................................................................ 69 4.2 Future directions ............................................................................................................................... 72 References .................................................................................................................................................. 78 Supplementary tables ................................................................................................................................ 86  iv  List of tables Table 1 Summary of overall changes in differential expression between CD43KO and CD43WT macrophages .............................................................................................................................................. 42 Table 2 Statistically significant genes shared in expression trend between treatments ........................... 45 Table 3 Common molecular and cellular functions related to M.tb, Cpn60.2 and dnaK ........................... 52 Table 4 Pathway‐associated gene lists with relative expression data ....................................................... 55 Table 5 Confirmation of differential expression of selected genes in RNA microarray using quantitative reverse‐transcriptase PCR .......................................................................................................................... 59 Table 6 Investigation of specific intracellular signaling pathways using quantitative RT‐PCR .................. 61  v  List of figures Figure 1 Venn diagram demonstrating the quantities of genes exhibiting differential response with the exposure to one of M.tb, Cpn60.2 or dnaK ................................................................................................ 43 Figure 1.a Down regulated shared genes ...................................................................................... 43 Figure 1.b Up regulated shared genes ........................................................................................... 43 Figure 2 Sample of gene‐gene interactions related to ‘immunity’ pathway in Ingenuity .......................... 51  vi  List of abbreviations AIDS  acquired immune deficiency syndrome  BCG  Bacille Calmette‐Guerin  BMDMΦ  bone‐marrow derived macrophages  CFU  colony forming units  EMB  ethambutol  HIV  human immunodeficiency virus  IFN‐γ  Interferon gamma  INH  isoniazid  KO  knockout  LAM  lipoarabinomannan  LPS  lipopolysaccharide  MΦ  macrophage  MHC  major histocompatability complex  MOI  multiplicity of infection  MTB  Mycobacterium tuberculosis  NO  nitric oxide  PIM  phosphatidylinositol  PPD  purified protein derivative  PZA  pyrazinamide  RA  rheumatoid arthritis  RIF  rifampin  RNI  reactive nitrogen intermediates  ROI  reactive oxygen intermediates  STR  streptomycin vii  Th1  T‐helper cell type 1  Th2  T‐helper cell type 2  TB  Tuberculosis  TNF‐α  tumour necrosis factor  WT  wild type  WHO  World Health Organization  viii  Acknowledgements I thankfully acknowledge Dr. Rick Stokes, whose breadth of knowledge and understanding in the field of Mycobacteria research was humbling and inspiring and whose patience with me as a student was equally if not more so. I thank Dr. David Walker, for his time, advice and constant support. My parents, Egle and Derek Vair, I am grateful to be blessed with parents as supportive and honest as you both.  ix  For my Parents  x  1 Introduction 1.1 Mycobacterium tuberculosis Mycobacterium tuberculosis (M.tb) is a member of the genus Mycobacterium and the order Actinomycetales. The bacillus is slim and rod‐shaped, 0.2‐0.4 x2 to 10 µm in size [1]. The M.tb genome consists of a single, circular chromosome of approximately 4 million base pairs, leading to almost 4000 annotated genes [2]. Mycobacterium bovis , the ‘TB of cattle’ has 99.9 % similarity to M.tb [2], however another notable mycobacterial pathogen, M.leprae, has approximately 1200 fewer annotated genes but is also an obligate parasite. Unable to grow outside of the host environment, it contains what is considered to be the ‘essential’ mycobacterial genome [3]. First isolated and cultured by Robert Koch in 1882, Mycobacterium tuberculosis was one of the first bacteria used to establish Koch’s postulates of disease and infection. These statements were four criteria Koch laid out to determine the connection between an infection and the infectious organism [4]:  1. The microorganism must be found in abundance in all organisms suffering from the disease, but should not be found in healthy animals. 2. The microorganism must be isolated from a diseased organism and grown in pure culture. 3. The cultured microorganism should cause disease when introduced into a healthy organism. 4. The microorganism must be re‐isolated from the inoculated, diseased experimental host and identified as being identical to the original specific causative agent.  Mycobacterium tuberculosis is a slow growing, non‐motile intracellular pathogen that is not known to form spores, is highly resistant to drying and chemical disinfection and has yet to be isolated from an environmental source [1].  1  1.1.1 Mycobacterium tuberculosis biology Mycobacterium tuberculosis (M.tb) has a complex, lipid‐rich cell wall containing mycolic acid, which makes the organism difficult to visualize with common stains. M.tb bacillus can be visualized by specialized methods of staining, such as Ziehl‐Neelson, carbol fuchsin and Auromin O. Once stained, they become acid‐fast. This means M.tb bacillus are highly resistant to decolourization with up to 3% hydrochloric acid, ethanol or both [5]. It is the thick, waxy cell wall of M.tb that has distinguished it from other virulent organisms. It’s unique and highly complex nature is such an integral part of the M.tb bacterium, it indicates that it is a determinant of virulence. The cell wall core consists of a peptidoglycan layer covalently linked with arabinogalactan, to which mycolic acids are attached. The extracellular surface layer is composed of free lipids and fatty acid chains containing various cell wall proteins. Such glycolipids are phosphatylinositols (PIMs), the phthiocerol‐containing lipids (PDIMS, PAT and DAT), lipomannan and lipoarabinomannan (LAM) [6]. Some of these lipids are thought to be highly correlated with virulence. For example, trehalose 6, 6’‐dimycolate (TDM), also known as cord factor, is a mycolic acid that contains glycolipid and is responsible for the characteristic colony morphology of M.tb growth on solid media [6]. The outermost layer of the cell wall, commonly known as the capsule, is a loose polysaccharide layer containing both proteins and lipids. Some of these capsular moieties have been shown to be involved in host interactions [7]. The inability most drugs to pass through the cell wall are considered to be one of the main challenges in drug discovery for novel anti‐ M.tb treatments.  1.1.2 Mycobacterium tuberculosis infection M.tb is spread by aerosol from patients with active pulmonary disease. The dissemination of TB is facilitated by characteristic tissue‐destructive immunopathology, which results in the development of necrotic, liquid‐filled cavities containing large numbers of bacilli that can erode into bronchi and the airways. Inhalation of just a few bacilli can cause infection, but most infections are either eradicated 2  completely or remain latent [8]. The first sentinel of the immune system to encounter M.tb bacillus is the alveolar macrophage, whereby upon phagocytosis, the bacillus arrests phagosome‐lysosome maturation and persists and replicates in the harsh vacuole environment [9, 10]. This cell serves as a double‐edged sword in terms of a tubercular infection. An activated macrophage has sufficient anti‐ microbial function to destroy M.tb bacillus, but if the bacteria is not contained and successfully replicates, it can serve as a vehicle for further dissemination and replication within the lung, liver and spleen [10]. However as previously mentioned, the macrophage does contain sufficient anti‐microbial properties to contain M.tb bacillus when activated. Appropriate conditions for activation and bactericidal activity of the alveolar macrophages are not precisely known, although pro‐inflammatory cytokines such as TNF‐α and IFN‐γ are very important as well as the active metabolite of vitamin D, 1,25‐ dihydroxyvitamin D [11]. The mechanisms of M.tb killing include reactive oxygen intermediate (ROI) and reactive nitrogen intermediate (RNI) production, digestive enzymes of the lysosomal compartment as well as the acidic environment of the lysosome, both of which can be induced by TNF‐α production. It is known that bacilli that have been ingested by activated macrophages are incapable of resisting phagosome‐lysosome fusion, and succumb to the digestive enzymes and acidic environment of the lysosome [11]. In addition to retarding phagosomal maturation [12], M.tb has developed many other mechanisms for defense against host immunity. It is also capable of producing catalase and superoxide dismutase; both of which can neutralize reactive oxygen species which are produced by macrophages and capable of destroying mycobacteria [13]. Pathogenic strains of M.tb have been known to deregulate apoptotic activation, leading to cellular necrosis and improved bacterial dissemination. Apoptosis is also a putative effecter mechanism of the host macrophage to limit replication of the intracellular pathogen [14, 15]. Apoptosis of infected phagocytes may prevent the spread of infection  3  and reduce viability of intracellular mycobacteria, whereas necrosis, the uncontrolled death and lysis of an infected cell, does not [16, 17]. TNF‐α is required for the induction of apoptosis during M.tb infection [14] and it has been observed that pathogenic strains of M.tb induce less host cell apoptosis than related attenuated strains [14] . This decrease in apoptosis was related to release of soluble TNF‐α receptors, a process that is regulated by IL‐10 production [18]. When infection has been established in the lung, a tubercle (or granuloma) is formed. This consists of infected alveolar macrophages in the central core, surrounded by mononuclear cells brought in by the pro‐inflammatory response initiated by the bacillus including additional ‘regular’ antimicrobial phagocytes, such as macrophages and neutrophils. This collection of professional phagocytes is then contained by a peripheral ‘wall’ of lymphocytes and epitheliod cells. It is the containment of the bacteria via these immune cells that build the granuloma. The infected macrophages can fuse together, generating giant cells or they may develop into lipid‐rich ‘foamy‐like‘ macrophages. This bacterial containment controls dissemination and the infection no longer actively spreads to uninfected bystanders. It is thought that during this stage, the bacteria, though still viable, enter a ‘dormant’ stage during which time their metabolism alters and slows considerably. Due to the difficult nature of accessing and modeling this environment for research, not a lot is known for certain about this stage of the infection. The replicative state of the bacteria is still under investigation, but it is generally thought to be significantly slowed or perhaps non‐existent [19‐21]. The granuloma can remain stable and potentially contain the M.tb infection for the entire life of the patient although the eventual failure of the containment is usually anticipated by the medical community. The reason for this lapse in containment is not entirely known, but generally the risk of reactivation occurs with increased age, immunosuppression or co‐infection with another immune‐ related infection (such as HIV/AIDS) [8]. Recent cases of TB reactivation following anti‐TNF‐α  4  treatment of rheumatoid arthritis indicate a pivotal role for TNF‐α in the maintenance of the granuloma [22]. TNF‐α blocking agents have been approved for a number of inflammatory afflictions such as chronic arthritis [23]. If an arthritis sufferer has a latent tuberculosis infection and takes such an immune‐ suppressant, they will experience re‐activation of the latent infection as well as a hyper‐inflammatory response if taken off the TNF‐α blocker. This demonstrates the highly important, yet undeciphered role that intracellular signaling and cytokines have in the control of each stage of infection [22]. When the breakdown of the tubercle occurs, the necrotic, bacteria‐filled central core becomes caseous, meaning the tissue within dies and appears soft and white. This necrotic tissue eventually liquefies, and breaks through the bronchial wall and allowing viable bacteria to flow into the host airways and systemic circulation. From the lungs of the actively infected, the densely populated bacterial liquid waits to be coughed into the atmosphere, and perhaps the lungs of another victim [24]. As previously mentioned, the majority of the infected population experiences latent infection as a life‐long state, changing only upon the disruption of immune functions [25]. Apart from individuals are those on immunosuppressive drugs, those who suffer from alcoholism, diabetes, nutritional deficiencies, stress and, co‐infected with HIV also have higher risk of re‐activation of latent disease. AIDS patients with HIV have an increasing risk (10% chance per year) of developing active TB, whereas immunocompetent patients have a 10% lifetime chance. Out of the global 9.2 million new cases and 1.7 million deaths worldwide, more than 13% of cases and more than 11% of those deaths were in HIV‐positive individuals [8].  1.1.3 Prevention Indication of past or present TB infection is typically identified by an initial tuberculin skin test (TST). One’s reactivity to purified protein derivative (PPD), is a precipitate of non‐species specific molecules obtained from filtrates of sterilized, concentrated cultures. The reaction that ensues reflects 5  sensitization of a patient to highly antigenic proteins originating from M.tb [22, 26]. Sensitization in this case refers to the delayed‐type hypersensitivity response of T‐cells that recognize and bind the tuberculin antigens, prompting them to release cytokines and chemokines that attract more lymphocytes and macrophages to the site of PPD injection [26]. It is the TH1 helper subset (CD4+) cells that are responsible for this response. The cell‐mediated response to the injection is measured via the area of induration, palpable raised hardened area around the injection site; in most countries 5‐10 mm counts as either a positive or ‘intermediate’ result, requiring further investigation into patient history and more tests [22]. An unfortunate aspect to this test is the reliance on a fully functioning immune system to demonstrate a positive result. Individuals on immunosuppressive therapies and HIV‐positive patients do not always demonstrate a strong reaction, due to CD4+ T‐cell depletion, and they are the greatest at‐risk groups for active infection [22]. False positives are also common occurrences in certain areas of the globe. Mycobacteria are more common in equatorial regions and many non‐tuberculous mycobacterial peptides cross‐react with PPD. Individuals vaccinated with Bacille Calmette‐Guerin (BCG) also frequently present with a positive result. The ambiguity of the TST is reflected in terms of how to interpret the induration as a reflection of disease state, with national standards for TB control and the experience of the health‐care worker providing the service all impacting the interpretation of the reaction. Improvements to this test are currently under examination, utilizing M.tb‐specific antigens such as ESAT‐ 6 and CFP10 to minimize the impact of cross‐reactivity with BCG [27]. Considering the lengthy history between mankind and Mycobacterium tuberculosis [28], there is very little that has been found to be effective against infection of this highly evolved pathogen. Overall patient health proves to be the most significant factor in the difference between control of the infection and the establishment of active, replicating bacilli. Where populations typically suffer from poor  6  nutrition, unclean drinking water and crowded conditions, TB is more common [29]. There is a live, attenuated vaccine currently available, Bacille Calmette‐Guerin (BCG), which was developed from a virulent strain of Mycobacterium bovis. Over the span of 13 years, it was passaged 230 times over glycerinated bile potato medium [30], resulting in a loss of virulence in animal models and it has been used since the mid‐1920’s to prevent TB infection. However, its discovery led to the dissemination of the original BCG strain to research centres worldwide. The variety of methodologies for bacterial growth and strain maintenance has resulted in great variation in BCG strains with significant differences in immunogenicity and vaccine efficacy [31‐33]. These efficacies range from 0‐94% depending on the recipient human population and the BCG substrain used [30, 34]. Despite the evidence for variation of efficacy, there are still a number of sub‐strains of BCG used for vaccine purposes [30] with the type one is likely to receive simply depending on geographical location. Two independent studies, one from the Tuberculosis Research Centre of India [35], and another conducted in Malawi by the Karonga Prevention Trial Group [36], both gave results in which BCG vaccine showed 0% efficacy. The study in Malawi tested the efficacy of this vaccine using single and repeated BCG doses, with an additional group consisting of combined BCG and heat‐killed Mycobacterium leprae. Individuals lacking a BCG vaccination scar were randomly assigned a prophylaxis and observed for infection over the course of 9 years. Although some protection was afforded by BCG vaccination for Mycobacterium leprae infection, there was no evidence that any of the trial vaccines contributed to protection against pulmonary tuberculosis. Studies in the Chingleput district of south India have also demonstrated 0% efficacy [37]. This study was a double‐blind randomized controlled trial initiated in 1968 and carried on for 15 years, using low , high and placebo doses of BCG. Over the 15‐year period, the incidence of culture‐positive tuberculosis was virtually the same in all three groups; 55, 56 and 54 per 100 000, respectively. There was no trend  7  worth remarking upon nor statistical significance for the case of protection with BCG vaccination in this study whatsoever. The reasons that BCG vaccination tends to fail in equatorial climes are based predominantly on the environmental consequences that occur from inhabiting these regions of the globe. Individuals who live within 30 ˚ of the equator are most likely to lack protection from BCG. Skin tests in these countries indicate that almost everyone has been sero‐converted to at least one environmental mycobacterial antigen [38]. However in northern latitudes, where environmental mycobacteria are less common, a false positive is correspondingly unusual. The cross‐reactivity of the immune response generated by exposure to environmental mycobacterial species, results in very little increased immunological response from BCG vaccination due to previous priming of the TH1 response by environmental mycobacterial infection [39]. This has led to a theory that BCG is destroyed by the host immune response that has already been primed by the non‐pathogenic mycobacterial species before it can establish a greater M.tb – specific response. Typically, a TH1 response is initially desired in a response to M.tb, as this induces the production of IFN‐γ and TNF‐α [40], powerful stimulatory cytokines that activate not only T‐cells but surrounding macrophages, enabling them to release nitrous oxide (NO) and commence the anti‐microbial response. The improved understanding of the immune responses to TB has led to a push for better, more comprehensive vaccine strategies. Complimentary ‘booster’ vaccines to improve BCG efficiency are commonly regarded as the best method of improvement [40]. Primarily consisting of recombinant proteins, these shots would boost CD4+ and cytotoxic CD8+ T‐cell response via exposure to additional mycobacterial antigen. Although these have been considered as ‘likely unhelpful in countries where there is an already adequate TH1‐cell response following exposure to environmental mycobacteria’[40], some fusion proteins (Mtb72F for example) have been recognized in BCG‐vaccinated infants and healthy  8  individuals from TB endemic regions [41]. This would indicate an ability to prime the immune system beyond that of environmental exposure. Improving cell‐specific responses, such as boosting the BCG‐ induced CD8+ response have also been considered. BCG with improved ‘self‐destruct’ mechanisms would enable MHC class I presentation to occur more readily, thereby improving presentation to this cell type. DNA vaccines that encode specific mycobacterial antigens, such as HSP65, are capable of infecting host‐cells and expressing antigen from within, like an intracellular pathogen. These types of vaccines induce both CD4 + and CD8+ T‐cell response as well as down regulating TH2‐cell responses in animal models of TB [42, 43].  1.1.4 Treatment The advent of chemotherapeutic agents against TB came upon the scene mid 20th‐century with the use of streptomycin and soon after the capacity of M.tb to become drug‐resistant became apparent [44]. Current treatment for TB infection includes isoniazid (INH), rifampicin (RMP), pyrazinamide (PZA), ethambutol (EMB), as well as fluoroquinolones (FQs). Short‐course chemotherapy achieves good success when all four first‐line drugs (INH, RMP, PZA, and EMB) are used concurrently throughout 6 months of treatment. Reduction of the number of drugs used together increases the relapse rate of the patient. It is therefore recommended that with smaller drug combinations, longer courses of drug regimen should be followed [45]. Isoniazid is currently the most commonly used anti‐tuberculosis drug, whether on its own or in combinatory treatment [8]. It has been the basis for most effective treatments of TB disease. Typically, INH has a minimum inhibitory concentration of 0.02‐0.2 µg/ml for M.tb, but is only active against growing bacilli and not active against non‐replicating bacteria or in hypoxic conditions, such as may be found with dormant M.tb inside a granuloma. Administered as a prodrug, INH is activated by catalase‐ peroxidase enzyme (KatG) [46], generating a highly reactive species that is effective against multiple 9  drug‐susceptible functions of M.tb. The primary target of INH appears to be the enoyl‐acyl carrier protein reductase (InhA enzyme) which is involved in elongation of fatty acids in mycolic acid synthesis [47]. Resistance to INH occurs frequently, generally occurring due to the loss of the catalase and peroxidase enzyme encoded by KatG [48]. Rifampicin (RMP) is active against both growing and stationary phase bacilli with low metabolic activity; it has high sterilizing activity in vivo and can shorten TB treatments from over a year to 9 months [49]. RMP binds the β subunit of the RNA polymerase thereby interfering with RNA synthesis. Pyrazinamide (PZA) can further shorten treatment from 9 months to 6 because of its ability to kill ‘persister’ bacteria in low pH environments that are not killed by other drugs [49]. It has high bactericidal activity in vivo but little if any activity in normal culture conditions near neutral pH. PZA activity is enhanced under low oxygen or anaerobic conditions and is therefore used to treat latent infection [49‐51]. Ethambutol (EMB) is used to prevent drug resistance. It is a bacteriostatic agent against growing bacilli only and is ineffective against non‐replicating organisms [44]. It’s method of action is to interfere with the biosynthesis of arabinogalactan, which comprises part of the cell wall [52]. Aminoglycosides such as streptomycin, kanamycin, amikacin and capreomycin are antibiotics used to treat a number of bacteria, and are also active against M.tb. Bactericidal against replicating bacteria, these drugs are not active against non‐growing (latent) or intracellular bacteria. These drugs are not typically used as they cannot be administered orally, and their bactericidal activity is comparable to INH [53]. They are only used when the primary agents (INH, RMP, PZA) are ineffective [54]. Ethionamide, prothionamide and thioamides are commonly used anti‐mycobacterial drugs and are active against M.tb, M. avium‐intracellulare and M.leprae [44]. They inhibit the same target as INH (the  10  InhA of the mycolic acid synthesis pathway) [47]. They are used as a second‐line therapy for drug‐ resistant TB, and are administered with multiple agents due to the rapid development of resistance to them when used as a single therapy [54]. The success of a drug‐treatment for TB is highly dependent on patient compliance to a regimen of administration of therapy. Unfortunately, this singularly important area is most commonly compromised due to length of treatment, multi‐drug side effects and inadequate access to health care. The advent of both multi‐drug resistant (MDR) and extremely drug resistant (XDR) –TB has further complicated the situation. MDR‐TB describes a tuberculosis strain that is resistant to two or more front‐line pharmaceuticals. In these cases, second‐line antibiotics (aminoglycocides, thiamides and fluoroquinolones) are added to the drug treatment, increasing the complications and cost involved in patient treatment. XDR‐TB strains are now emerging in places such as Asia and Africa [29], as strains that are both resistant to first and second line therapies [55]. Although not yet as common, these strains are even more difficult to treat and are highly challenging to control in even a well‐equipped health care system [44].  1.2 Immune response to TB Due to the complexity of TB infection and disease progression, the interplay of the immune system and bacteria is a delicate balance. Both protective immunity and pathogenesis related to this disease are mediated by the host immune system [56, 57]. The macrophage is both the primary defender against and replicating vector of M.tb [58] and the cell‐mediated immune response provides support in long term control, but does not provide much in the way of immunological protection against re‐infection [57].  11  1.2.1 Macrophages The alveolar macrophages are the first line of host‐cell defense to TB, being a permanent defense feature of the lung. Animal models have demonstrated, however, that these macrophages are also capable of further dissemination of the bacteria to deeper parts of the lung and other lymphoid tissues [58, 59]. Macrophages have been identified as modulators of the immune response to infections since the early 1960’s [60, 61]. They are activated, not only by host signals ( interferon γ)[62], but by bacterial moieties as well. For example, lipopolysaccharide, also known as LPS, is a highly stimulating factor found on the external coat of Gram‐negative bacteria. The way in which macrophages respond to these types of signals differs depending on the signal nature and source [63]. LPS and IFN‐γ induce a ‘classical’ (currently known as M1) macrophage activation characterized by antimicrobial activities such as production of IL‐12, NO and oxygen‐derived free radicals. Macrophages exposed to IL‐4 and IL‐13 produce an alternative cytokine response, increasing production of IL‐10 and scavenger receptors. This is known as the ‘M2’ macrophage that is predominantly involved in immunomodulatory activities [64 6235, 65 7247]. The M2 state has been sub‐categorized even further into M2a, M2b and M2c based on the origin of immuno‐modulating materials [66]. M2 macrophages have a demonstrated role in parasitic infection [64, 67, 68]. Bacilli may gain entry into the macrophage by a number of methods; there is evidence of receptor‐ mediated phagocytosis with a number of macrophage ligands [69‐71]. Such ligands include complement receptors, mannose receptor, scavenger receptors, CD14, and most recently, CD43 [70, 72]. The consequences of the interactions of each receptor with M.tb are not yet well characterized. It is known that some M.tb receptor interactions result in pro‐inflammatory cytokine production and similar host‐ defense mechanisms [56]. This reaction is beneficial for both host and bacterium. The activation of 12  additional immunity increases the probability of infection control and clearance; however the increased inflammation to the site of infection also brings an influx of new cells for infection and dissemination of bacteria. Macrophages are entirely capable of mediating the inhibition and killing of intracellular pathogens through mechanisms such as the production of reactive nitrogen and reactive oxygen intermediates (RNI, ROI), phagolysosome fusion leading to phagosomal acidification and antigen presentation and macrophage apoptosis, which would lead to bacterial killing and antigen presentation as well [73, 74]. It is the ability of the mycobacterium to evade these host‐effecter functions that can dictate the next steps in pathology. More virulent strains of M.tb demonstrate higher functionality in terms of these traits: arrest of phagosome‐lysosome fusion, down‐regulation of apoptotic signals and induction of necrosis [75‐77]. The preference of virulent M.tb for necrosis versus apoptosis is considered a means of intramacrophage survival [75]. The apoptotic cellular breakdown is considered to contain intracellular replication, and is facilitated during M.tb infection by increasing anti‐apoptotic signaling within the host macrophage [78]. As previously mentioned, once ingested by the macrophage, the M.tb bacillus resides within the phagosome and arrests phagosomal maturation [79]. The bacillus then replicates intracellularly and is either controlled by macrophage and immune activation or continues to replicate and disseminate throughout the lung. The ability of the macrophage to prepare the mycobacterial antigen for presentation to immature T‐cells is crucial for the subsequent control of the infection [79].  1.2.2 Cell-mediated immunity Adaptive immunity develops 2‐6 weeks post infection, following the flurry of cytokine and chemokine signals produced by infected macrophages and other affected antigen‐presenting cells. By this time, mycobacterial antigens have been presented to T cells, either in the circulation or within the draining 13  lymph nodes [74, 80] and the T‐cell subsets have initialized a response against the infection. As previously mentioned, the type of T‐cell response is important not only for the control of an established TB infection, but more so in later stages of the disease. The subtypes most universally recognized for their importance are CD4+ and CD8+ T‐cells [74, 81]. The CD4+ T‐cell has been recognized for its importance in the control of granuloma development at the site of infection. As previously mentioned, these dormant colonies of bacteria are enclosed by a wall of T and B lymphocytes, as well as monocytes, fibroblasts and multi‐nucleated giant cells [82, 83]. The activation and maintenance of this containment is crucial to prevent re‐activation of the disease; just as sufficient control of inflammation is important to prevent unnecessary immunopathology. The observations made of latent TB infections have shown that the cellular responses and cytokines signals that are immunologically important for bacterial containment and elimination in later stages is typically not the same as for the immediate response to the microbe. Studies have shown that interferon‐γ (IFN‐γ), which promotes further differentiation into TH1 cells, has a ‘plateau’ of immune efficiency, and when this is reached, boosters designed to improve this response are ineffectual in terms of protection against M.tb if used when IFN‐γ levels have already peaked. In patients that have TB, it has been observed that there is a careful balance between the TH1 and TH2 responses, demonstrating the complex nature of this infection. In a latent infection, the TH2 response is somewhat preferable, since heavy infiltration of lymphocytes into the lungs is a main cause of gross pathology [38, 40]. High‐ dose exposure of M.tb induces production of IL‐4, a cytokine that inhibits antimicrobial TH1 production, allowing the bacterium to take advantage of this response [40]. Patients with TB in developing countries have greater amounts of IL‐4, compared to controls in northern climates, possibly as a result of the constant exposure to environmental mycobacteria [40].  14  The CD4+ T‐cell is regarded as one of the most important cell populations for the formation of the protective cell wall surrounding the granuloma [84]. Also known as CD4+/αβ/TCR+ T‐lymphocyte cells, these cells secrete many different immunostimulatory cytokines, such as the aforementioned TNF‐α and IFN‐γ, as well as IL‐12 which is critical to promote additional TH1 differentiation [40]. Presentation of antigen to immature T‐cells initiates differentiation into either TH1 or T H2 –type T‐cells, defining the immunological response, thus heavily affecting disease outcome. At the initial point of infection, TH1 is clearly preferable. It is the highly inflammatory, anti‐microbial response that releases activation cytokines such as TNF‐α, IFN‐γ and IL‐12. These cytokines activate macrophages to produce Reactive Oxygen Intermediates and Reactive Nitrogen Intermediates (ROI and RNI) which have been shown to eliminate mycobacteria [85, 86] as well as promote phagosomal maturation of the macrophage; a critical step in bacterial killing and antigen presentation. The initial TH1 response is so important to the control of M.tb infection that genetic defects in the receptors of IL‐12 or IFN‐γ lead to increased susceptibilities and progressive mycobacterial disease [87, 88]. The cytokines of the T H2 phenotype include IL‐6, IL‐10 and TGFβ. As previously mentioned, virulent M. tuberculosis can stimulate macrophages to secrete certain quantities of these molecules and could act to control (and limit) the T‐cell responsiveness. [74, 89‐91]. TH2 cells are more important in parasite‐ response (such as helminthes) and eosinophil production and activation. They activate B cells to produce antibodies for protein‐specific long term immunity and as previously stated, release cytokines that down regulate anti‐microbial macrophage functions. Since M.tb infection resides within the endosomal compartment, it was commonly thought that MHC class I presentation was unlikely to occur, and therefore the role of CD8+ T‐lymphocytes, also known as cytotoxic killer T‐cells, has been historically underappreciated [74]. However, Flynn et al found that deficiencies in MHC I presentation render mice highly susceptible to TB infection, and therefore  15  could demonstrate a higher role of importance than initially anticipated [92]. CD8+ also migrate to the sites of infection and are also IFN‐γ producing cells which are capable of killing infected macrophages [81, 93]; so their role in the initial infection and containment of replicating bacteria might be more important than previously considered. More studies should be performed in this area in order to determine the potentially important roles of MHC class I presentation in M.tb infection. CD1+ ‘nonclassically restricted’ T‐cells are important for receiving antigens presented via the CD1‐receptor (instead of MHC‐class receptors). CD1 has an altered binding site to MHC 1 and is capable of presenting polar hydrophobic antigens, such as lipids and glycolipids [94] These cells would play a significant role in the presentation of important mycobacterial lipids, such as mycolic acids and phosphatidyl‐inosital mannosides (PIM) [95]. These T‐cells are also capable of secreting IFN‐γ for inflammatory activation and are capable of cytotoxic activities towards M.tb infected macrophages [95]. B cells, also known as ‘B lymphocytes’ play a large role in the humeral immune response. They function primarily to make antibodies against foreign bodies known as antigens. They are not only antibody‐ producers, but work as antigen‐presenting cells and also maintain adaptive immunity via ‘memory B cells’. These cells are capable of producing microbe‐specific antibodies long after their production was triggered either by infection or vaccination [96] . B cells, particularly in intracellular infection, impact T‐ cell activation by acting as or upon antigen presenting cells [97]. Many studies involving a variety of intracellular infectious agents have demonstrated susceptibility of B‐cell deficient mice, although studies with M.tb have been variable, resulting in delay of pathologic progression and diminished immunity, but no dramatic effects [97].  1.3 Bacterial chaperonins and host cell response Heat shock proteins, or Chaperonins, are historically known as proteins that ‘non‐enzymatically aid in the folding or refolding of proteins’ [63], found intracellularly near the Golgi apparatus. However, 16  evidence is mounting that these proteins contain alternative functions and are expressed throughout the cell [63, 98]. Current dogma states that these proteins are solely intracellular, and only upon cellular lysis would they be located in the extracellular space. Chaperonins are typically located in or near the cellular endoplasmic reticulum and assist polypeptide folding as soon as the amino acid chain comes out from the ribosomal ‘exit’ tunnel [63, 99, 100]. However, it has become increasingly apparent in current research, that these proteins are not only secreted, but are capable of having cellular activation capabilities [63]. This idea is not new; a circulating immunosuppressive chaperone known as Early Pregnancy Factor (EPF) was discovered in 1977 [101] and later identified as Chaperonin 10 (also known as Hsp10) by Morton and Cavanagh in 1994 [102]. Since this period, a number of mammalian stress proteins have been characterized as extracellular [63] having been identified in bodily fluids or in cell culture medium. The methods by which HSP secretion occurs are still unknown, but studies have shown that migration via non‐ER‐Golgi exocytosis is possible [103, 104]. One HSP that has received extensive review in light of these observations is Hsp60. It is highly conserved and found in most all organisms [105] and demonstrates a broad variety of responses in macrophages, depending on its source [63]. These variations of response can result in the stimulation of CD14/TLR2/TLR4‐dependant signaling (Human and Chlamydia Hps60) to cytokine production that is TLR2/4/Myd‐88‐independent (H. pylori) to a complete failure to activate macrophages at all [106‐108]. Hsp60 in humans is homologous to the GroEL gene in E.coli and Hsp65 in M.tb [109, 110]. Mycobacterium tuberculosis has 2 Hsp65 variants: Cpn60.1 and Cpn60.2. Cpn60.2 is also known as Hsp65. This protein has been documented to have pro‐inflammatory properties for human monocytes [111] and it has been suggested that Cpn60.2 and 60.1 can both be used to induce pro‐inflammatory protein production in human monocytes in microgram per milliliter quantities. Cpn60.2 has been 17  determined to be essential to M.tb and no knockout mutant has been created yet. Cpn60.1 however, can be deleted without consequences to bacterial survival and growth, but results in a failure of the mutant to induce granulomatous response [112]. The two proteins share 96% sequence similarity but have differential effects in vivo in during the course of infection. Classical macrophage activation and anti‐microbial properties are crucial for the controlling of M.tb infection. Tuberculosis, however, is a disease that has been characterized by the ability of virulent M.tb to inhibit the antibacterial Th1 response, and instead, patients with tuberculosis infection have been characterized with a Th2 phenotype [40] and specific Hsps like Cpn60.1 have been observed to inhibit the formation of IL‐12 p40 subunit; a critical cytokine in the activation of Th1 response [113]. DnaK, from the Hsp 70 family, is important in chaperoning nascent polypeptide chains and is known to assist the folding of newly synthesized polypeptides together [100]. As a protein‐folding chaperonin, dnaK is ATP‐dependant but not physically tethered to ribosomes. It is monomeric, with an amino‐ terminal ATPase and carboxy‐terminal polypeptide binding domain, capable of recognizing short, hydrophobic polypeptides in extended confirmations [63, 100]. In Mycobacterium tuberculosis, Hsp 70 has been documented as stimulatory to human Peripheral Blood Monocytes (PBMs) and produces classical activation; stimulates production of TNF‐α, IL‐12, NO, CCL3, CCL4 and CCL5 [63]. It is also found to bind to many macrophage receptors, including TLR4, TLR2, CD14 and CD40.  1.4 CD43 CD43, also known as leukosialin or sialophorin, is a glycoprotein expressed by most hematopoietic cells; with the exception of platelets, red blood cells and mature B cells [114, 115]. It is a transmembrane protein containing a large, highly glycosylated extracellular domain, and a membrane‐spanning domain and highly conserved cytoplasmic domain [116]. The roles it plays in cellular function are as vast and variable as the types of cells on which it is located [115]. It has been implicated to influence both 18  adhesive and anti‐adhesive cellular properties, cellular differentiation, cytoskeletal changes, apoptosis, intracellular signaling and chemokine signaling[115, 117, 118]. Although there is only a single gene present for CD43, post‐translational modifications and protein cleavage provide ample opportunity for a heterogeneous population of structures. These changes result in differentially sialyated forms of the extracellular protein backbone [119].  1.4.1 Structure Leukosialin (CD43) is made of 400 amino acids in humans and 395 in mice[114, 120]; an extracellular domain of 224 to 234 amino acids (from rat and human, respectively) with an average of one O‐linked carbohydrate chain per three amino acids that extends approximately 45 nm in length from the cell surface [116]. The extracellular domain contains anywhere from 70 to 85 O‐linked carbohydrate chains and over 100 negatively charged sialic acid residues [116]. CD43 exists in two isoforms, 115 kDa and 130 kDa. The 115 kDa isoform contains tetrasaccharide carbohydrate chains and is predominantly found on immature thymocytes and resting monocytes. The 130 kDa form contains hexasaccharide chains and is found on activated T cells and macrophages [117, 121]. The extracellular domain can also be proteolytically cleaved and is commonly found in blood plasma [122]. The differential glycosylation of the amino acid backbone is an essential feature of CD43 for many cell functions, particularly in T cell responses [123, 124]. The cytoplasmic domain is relatively large (123 aa) and demonstrates a higher degree of conservation than the extracellular portion. Mouse and humans demonstrate 65% similarity in the cytoplasmic domain versus 42 % similarity in the extracellular portion [125, 126]. CD43 has longtime been implicated in the involvement of signal transduction, as evidenced by the conservation of the cytoplasmic tail and its ability to become both phosphorylated (and hyperphosphorylated), as well as sulfated [114, 120, 121,  19  123]. Both sets of post‐transcriptional modifications lend themselves to indications of intracellular signaling capabilities.  1.4.2 Expression CD43 is a single gene transcript; the murine CD43 gene is found on chromosome 7, contained within two exons [120]. The post translational modifications of CD43 demonstrate activation‐specific isoforms on certain cell types. Specifically, the differential glycosylation generates glycoproteins of different molecular weights and antibody specificities [119]. T lymphocytes derived from either human or rodent sources express both the 115kDa and 130kDa isoform. Resting T‐lymphocytes express tetrasaccharides, but activated human T‐cells carry more complex hexasaccharide structures due to a shift in activation of glycosylating enzymes [117]. CD43 can be found on T lymphocytes, B lymphocytes and is highly expressed on polymorphonuclear neutrophils, macrophages, mast cells and NK cells[116, 127‐130]. Its expression can vary depending on the activation state of the cell in question; CD43 is expressed on the surface of mature mast cells, but not the monocyte progenitors [127]. Similarly, peritoneal macrophages express CD43 constitutively at low levels, but upon activation, there is a significant positive shift in CD43 expression [128]. Neutrophils experience an opposite effect. When activated, CD43 is shed via proteolysis [130]. It has been demonstrated that activated macrophages also have increased expression of CD43 [131] and increased CD43 expression also appears to correlate with increased TNF alpha production upon immunological challenge [131, 132].  20  1.4.3 Function As previously stated, the putative functions of CD43 are diverse and variable within each cell population that they are observed. Initially, it was widely accepted that CD43 acted only as a cell‐adhesion molecule [115, 121, 133, 134] but further investigation into the subject revealed that CD43 is also capable of transmitting some form of signals that are able to regulate cellular functions [117, 121, 133, 135, 136]. The methods by which CD43 is altered to induce cellular reaction vary between studies which lead to occasionally confusing results. Engagement of CD43 with specific monoclonal antibodies regulates integrin‐mediated T‐cell adhesion and promotes cell aggregation in monocytes and T lymphocytes, suggesting a role in the regulation of integrin function [115, 137, 138]. The intracellular portion has been documented to undergo a number of different modifications, such as ubiquitination and phosphorylation. Phosphorylation of cytoplasmic domain involves src family kinases lyn and hck. The involvement of tyrosine kinase activity associated with CD43 may play a role in the transduction of regulatory signals in neutrophils [133, 139]. CD43 appears to have the capacity to act as an ‘activation molecule ‘to transduce positive activation signals. Its activation stimulates secretion of chemokines RANTES, MIP‐1alpha, MIP‐1beta as well as increased tyrosine kinase activity in anti‐phosphotyrosine immune complexes of cell lysates [129]. Addition of CD43‐specific monoclonal antibody (thus mimicking CD43 stimulation) induces monocyte‐ dependent T‐cell proliferation and homotypic adhesion in T‐cells. It also increases hydrogen peroxide production in monocytes, and elevates natural killer T‐cell activity [117, 128, 129, 134, 140]. The functioning of CD43 affects T‐ cell differentiation and TH2 regulation [141], CD43‐/‐ T‐cells demonstrate defects in Ca2+ transportation, leading to an enhanced TH2 response which can be reversed  21  via the reintroduction of CD43. TH1 differentiation appears unaffected, but an elevation of TH2 cytokines was observed [142]. Subpopulations of high CD43‐expressing macrophages also express higher quantities of TNF‐α than do low CD43‐expressing MΦ, 71% versus 32% respectively, and have also been found to have a somewhat antagonistic function upon NF kappa B activation [131, 143]. Additionally, Randhawa et al demonstrated that CD43‐/‐ murine macrophages demonstrated a defect in TNF‐alpha production upon stimulation with Mycobacterium tuberculosis infection when compared to their wild type counterparts [132]. CD43 has been implicated as a mediator of apoptosis in a number of different cell types; its down‐ regulation has been linked to caspase 3 activation [132, 144]. The intracellular signaling portion of CD43 has also been shown to be marked by ubiquitin, cleaved and translocated to the nucleus during apoptotic activities [145].  1.4.4 Implications in disease Although the phenotype for CD43‐disrupted individuals is not very dramatic [145], there have been documented changes in CD43 in certain diseases. This is not surprising, given the mounting evidence of the importance of CD43 in states of cellular activation and immunological defense. Glycan structuring alone can affect disease progression and outcome: core2 branches in O‐linked oligosaccharides are found in activated T cells (converted from the tetrasaccharide form), and the same structural change is also found on lymphocytes from patients with immunodeficiency conditions (Wiskott –Aldrich and AIDS) [146].  22  1.5 Introduction of thesis The information presented in this chapter outlines the importance of TB as a global‐concern and M.tb as a relevant pathogen. Research into the nature of TB infection and disease progression is not only crucial for world‐health but is important for the insights it gives into the nature of the human immune‐system and the search for novel methods by which we might be able to treat similar infections and ask relevant questions about disease‐control in the human body. TB disease is fascinating because of the possibility of life‐long infection being maintained within the host as well as the possibility of reactivation at any time. As we struggle to understand how this bacterium is capable of evading and controlling the host immune response, we are also forced to investigate ‘both sides of the coin’ of infection and immunity. The failure to provide an adequate vaccine based on current ‘immunological‐memory’ dogma demonstrates that this infection is not quite like any other, and in order to help the millions of people who are infected, it is crucial to understand the interactions between bacillus and host. For M.tb in particular, examination of the innate immune system, including macrophage functions, cytokine production and intracellular signals that lead to them, is a critical first‐step. CD43 has been implicated as both an important mediator of cell signaling and immunological functions, as well as an important intermediary of M.tb binding and uptake [69, 132, 147]. CD43 is a requirement for fully‐efficient binding and uptake of M.tb not only in CD43‐transfected HeLa cells, but CD43 WT vs. KO macrophages as well [69, 132, 147]. Furthermore, Randhawa et al found that intracellular replication of M.tb was enhanced in CD43 KO macrophages, and that this enhancement of growth was controlled only by the addition of recombinant TNF‐α. Conversely, CD43 WT macrophages had similar levels of uninhibited growth when anti‐ TNF‐α antibody was added. Decreased levels of apoptosis and increased levels of necrosis were also observed when CD43 KO macrophages were infected with a virulent strain of Mycobacterium tuberculosis compared to their CD43 WT counterparts [132]. 23  Chaperonins have been observed as possible modulators of immune responses for some time [148]. Their capabilities to stimulate specific immune‐responses have been thoroughly documented for both mammalian and bacterial sources [98] and have been seriously looked at as vaccine‐candidates in mycobacterial prophylactic treatments [40, 42]. Mycobacterial proteins Cpn 60.2 and dnaK have both demonstrated CD43‐binding capabilities [7], as well as immunomodulatory capacities [42, 149]. The aforementioned cytokine deregulation with the lack of CD43 given M.tb stimulus, as well as the potential for immune‐control via extracellular chaperonins led us to hypothesize that the two might be related at the junction of the CD43 mucin. We chose to investigate the possible intracellular macrophage signaling pathways affected by CD43 following stimulus with M.tb, Cpn 60.2 and dnaK via RNA‐microarray and further qualification via quantitative‐reverse‐transcriptase PCR (q‐RT‐PCR). We hoped to determine if there were any similarities between the chaperonin‐host mRNA expression and M.tb‐host cellular mRNA expression. Additionally we planned to observe possible trends in macrophage responses with CD43 and without CD43 in all three treatment groups.  24  2 Methods 2.1 Cell preparation and tissue culture 2.1.1 Mice Wild‐type (WT) control mice (CD43 +/+) and knock‐out (KO), CD43 ‐/‐ mice backcrossed more than 10 times on C57Bl/6 background were housed in a specific pathogen‐free animal facility in micro isolator cages. Experiments were done in accordance with the standards set by the Canadian Council on Animal Care. For all experiments, CD43WT and CD43KO mice were age‐ and sex‐matched homozygous littermates.  2.1.2 Culture of bone-marrow derived macrophages Bone‐Marrow derived Macrophages (BMDMΦ) were isolated from CD43+/+ and CD43 ‐/‐ pairs. Mice were euthanized and dissected to remove femurs and tibiae. The ends of the bones were cut and using a 25 gauge needle, the marrow was flushed out with RPMI (RPMI 1640 medium, Gibco, Burlington ON) supplemented with 10% fetal calf serum, 10mM L‐glutamine and 10 mM sodium pyruvate), all from Gibco (Burlington ON). Bone marrow washes were pooled and erythrocytes were lysed using 0.17 M ammonium chloride (NH4Cl, pH 7.2) After washing, cells were re‐suspended in supplemented RPMI and incubated in tissue culture‐treated flasks (Becton Dickinson Lab ware, Franklin Lakes, NJ) for 3 h at 37°C, 5% CO2 to deplete non‐stem cells by adherence. Non‐adherent cells were then washed, re‐suspended in bone‐marrow media (RPMI + 10% (v/v) L929 cell medium), diluted to 1 million/ml concentration and plated in 125 cm³ flasks and allowed to differentiate for 7 days at 37°C , 5% CO2. Media was replenished on day 5.  25  2.2 Bacterial culture M. tuberculosis strain H37Rv (TMC #102, ATTCC # 27294) was grown to late log phase in Proskauer & Beck medium supplemented with 0.05% Tween 80 under static conditions with intermittent agitation (every 2 days). Cultures were stored at ‐80˚C and tested for viability and approximate concentration of bacteria by CFU counts on 7H10 plates.  2.3 Recombinant molecular chaperones Recombinant Cpn60.2 and DnaK were derived using plasmids pMRLB1 and pMRLB.6, provided through the NIH/NIAID TB Resource Contract (HHSN266200400091c) at Colorado State University. The pMRLB1 (rv0440, Cpn60.2) and pMRLB.6 (rv0350, dnaK) plasmids were designed to include a 6xHiis tag on the gene product and encode for ampicillin resistance. Each of these plasmids was transformed and expressed using BL21Star (DE3) pLysS E.coli competent cells (Invitrogen, Carlsbad, CA). These E.coli strains contain the λ DE3 lysogen (prophage) which enhances T7 promoter function. After successful heat shock transformation of pMRLB1 and pMRLB.6 into respective BL21Star (DE3) pLysS bacteria, 1L volumes of transformed E.coli, grown at 225 rpm in Miller Acros Organics LB Broth (Fisher Scientific, Ottawa ON), at 37°C, were induced with 120 mg mL‐1 isopropyl β‐D‐1‐thiogalactopyranoside (IPTG) to express the recombinant proteins at early log phase, and the cultures were then grown to late log phase, over 3‐4 hours. Growth phases were evaluated throughout, using optical density at 600nm. After logarithmic growth was exhausted, the cultures were centrifuged using 600 mL centrifuge jars (NalgeNunc, Rochester, NY) spun at 10 000 X g. Sedimented bacteria were then resuspended in bacterial lysis buffer (200µg mL‐1 Lysozyme (Sigma, Oakville ON), 250 UL‐1 DNase I (Invitrogen, Burlington ON), one tablet of EDTA‐free protease inhibitor cocktail (Roche) dissolved in 1x Binding Buffer (Calbiochem, San Diego, CA) and ≥18 MΩ H2O). The E.coli lysate was then spun down at 48,400 X g for 1 hour and the supernatant was reserved. A His‐Bind Resin kit (Novagen, Madison , WI) was used for 26  recombinant protein purification, as per manufacturer’s recommendations, and the proteins were washed in the Ni2+ column including a 0.5% (w/v) amidosulfobetaine (ASB) – 14 detergent (Calbiochem, San Diego, CA) treatment to help minimize residual endotoxin presence.  2.4 In vitro assay for binding of particles to macrophages and M.tb infection The macrophage monolayer exposure to recombinant M.tb proteins DnaK, Cpn 60.2 and M.tb was based on previously‐described whole‐bacterial infection protocols. Bone‐marrow derived stem cells were cultured in 125 cm³ flasks at 15 million cells (1 million cells per ml, 15 mls total) for 7 days at 37°C, 5% CO2 to allow for maturation into BMDMΦ (See section 2.1.2). On day 7, monolayers were washed with phagocytosis medium (138 mM NaCl, 8.1mM Na2HPO4, 1.5 mM KH2PO4 , 2.7 mM KCl, 0.6mM CaCl2, 1 mM MgCl2 , and 5.5 mM D‐glucose, pH 7.4) (PBS‐G) twice, and 5ug/ml of either Cpn 60.2, DnaK or PBS‐G. Flasks were rocked in an incubator at 7 days at 37°C, 5% CO2 for 1 hour, and then co‐incubated static for an additional two hours. After 3 hours of total co‐incubation, the flasks were washed three times with PBS‐G and RNA was immediately isolated. For bacterial infection, M.tb bacteria were passed through a 25‐gauge needle 10 times to disperse the bacterial clumps characteristic of mycobacteria, and diluted to the required concentration using PBS‐G. The overlay was removed from BMDMΦ monolayers and replaced with M. tuberculosis in PBS‐G at multiplicities of infection (MOI) of 20:1 or 30:1 (bacteria: MΦ, CD43 +/+ and CD43‐/‐ respectively). Previously in our lab, Randhawa et al found that CD43 deficient mice did not uptake M.tb bacillus as readily as CD43WT mice [72]. It was found that in order to gain the same initial quantities of bacteria within the macrophage itself, 1.5 times more bacteria must be added to the monolayer during the infection. Since this study was an observation of the effects of M.tb on the intracellular signaling of the MΦ, it was imperative that approximately the same numbers of bacteria have entered in the cell for both the CD43WT and CD43KO. Tyler Hickey made a similar observation  27  with the inhibition of bacterial binding and uptake with the blocking of the CD43 extracellular portion using Cpn60.2 [7]. Both of these studies confirm that the addition of 1.5 times more bacteria to CD43KO macrophages is required to obtain similar amounts of intracellular bacilli. Infected MΦs were incubated as for the hsp‐challenged: 1 hour rocking (Nutator, Becton Dickinson, and Mountain View CA) followed by 2 hours stationary at 37˚/5% CO2. Following infection, monolayers were washed three times with PBS‐G and RNA extraction was performed immediately.  2.5 RNA isolation from BMDMΦ RNA was isolated using a trizol‐chloroform extraction followed by a secondary purification using RNeasy RNA purification columns (Qiagen, Mississauga, ON). Wash media was removed by pipetting and 3 ml of Trizol (Invitrogen) was added directly to a flask containing 1.5 x 107 BMDMΦ, which was then incubated at room temperature for 2‐3 minutes until the monolayer had sufficiently lysed in the flask. The sample was then subdivided into 1 ml aliquots, to which 200µl of chloroform was added and the sample was shaken vigorously for 15 seconds and incubated at RT for 2‐5 minutes. Samples were then centrifuged at 13 000 rpm (12 000 g) for 15 minutes at 2‐8 °C. The aqueous phase was then extracted and transferred to separate 1.5 ml RNAse‐free tubes, to which 350 µl β2‐mercaptoethanol‐free RLT buffer (Qiagen, Oakville ON) was added. Samples were homogenized by pipetting. 100% Ethanol was then added to the RNA‐RLT buffer mix and the subdivided RNA solutions were re‐pooled in a treatment‐ specific manner and transferred into RNeasy filter‐columns. The columns were centrifuged at 11 000 rpm for 15 seconds and the flow through was discarded. This process was repeated until the entire sample had passed through the filter. Columns were washed with 500 µl RPE buffer, centrifuged for 15 seconds at 11 000 rpm again, and collection tubes were changed. The RNA sample‐columns were washed a final time with 500 µl of RPE buffer and 35 µl of RNase‐free water was added. The RNA columns were centrifuged at 11 000 rpm for 90 seconds, and this step was repeated with an additional 28  30 µl of RNase‐free water. RNA quality and quantity was assessed using conventional spectrometry and Bioanalyzer methods.  2.6 RNA amplification After RNA quality was confirmed using an Agilent 2100 bioanalyzer system from Agilent Technologies, aliquots of 1 µg total RNA were subdivided and stored at ‐80 °C. Amplification of the mRNA from the 1 µg total RNA aliquots was performed using a Message Amp II kit from Applied Biosciences. RNA was amplified according to manufacturer’s instructions. Samples were thawed and 1 µl of T7 Oligo (dT) was added to the 1 µg total RNA aliquot. Sufficient RNAse‐free water was added to bring the total volume to 12 µl. Samples were incubated for 10 minutes at 70°C in an Eppendorf Mastercycle, spun for 10 seconds to gather sample at the bottom of the tubes and incubated on ice. Each reaction then had 2 µl 10X first strand buffer, 1 µl of ribonuclease inhibitor, 4 µl dNTP mix and 1 µl of reverse transcriptase added. Reactions were incubated for 2 hours at 42°C in an Eppendorf Mastercycle thermocycler, spun and incubated on ice again. The cDNA that was produced from this reverse‐transcriptase reaction immediately underwent RNA amplification; 63 µl of nuclease‐free water, 10 µl of 10X second strand buffer, 4 µl dNTP mix, 2 µl DNA polymerase and 1 µl of RNAse H were added to all cDNA samples then incubated at 16 °C for 2 hours. cDNA filter cartridges and subsequent solutions were prepped during this time: cDNA filter cartridges were co‐located in 2ml wash (Eppendorf) tubes and 50 µl of cDNA binding buffer was added to each filter cartridge and incubated for a minimum of 5 minutes. When the cDNA amplification was complete, 250 µl of cDNA binding buffer was added to each cDNA sample, mixed via gentle pipetting and transferred into an equilibrated cDNA filter cartridge. Samples were then centrifuged for 1 minute at 10 000Xg, flow through discarded and the centrifugation step repeated. Filters were then transferred to cDNA elution tubes and 10 µl of nuclease‐free water (preheated to 50° C) was added and samples were left for 2 minutes and then centrifuged for 1.5 min at 10 000 Xg.  29  Another 10 µl of pre‐heated nuclease‐free water was added, incubated and eluted a second time. The eluate was then incubated on ice. A dNTP solution of T7 ATP, T7 CTP, T7 GTP, T7 UTP and T7 10x Reaction buffer and T7 Enzyme Mix was added to each sample, as per manufacturer’s instructions. The cDNA samples were then incubated for 14 hours at 37°C, during which the cDNA was transcribed into amplified RNA (aRNA). Reactions were stopped by addition of 60µl of nuclease‐free water and gently vortexed to mix. RNA was then purified by the addition of 350 µl of aRNA binding buffer, thoroughly mixed and passed through an aRNA filter cartridge and centrifuged for 1 minute at 10 000 Xg. 650 µl of aRNA wash buffer was applied to the filter and the sample was spun again for 1 minute at 10 000 Xg. After an additional spin to assure the removal of any trace ethanol, the filter was then transferred into a clean microfuge tube and the centre of the filter was re‐hydrated with 50µl of pre‐heated (60°C) nuclease‐free water. Rehydrated columns were left at room temperature for 2 minutes and centrifuged for 1.5 minutes at 10 000 g. A second elution was performed with another 50µl aliquot of nuclease‐free water. Samples of aRNA totaling 100 µl were quantified using a spectrophotometer, diluted to an approximate concentration of 1 µg/µl, aliquotted into 10 µl volumes and stored at ‐80°C until needed.  2.7 RNA micro-array application Amplified RNA aliquots were transported to the Vancouver Prostate Centre Microarray Facility at 2660 Oak Street, Vancouver, BC V6H 3Z6. All microarray applications (Labeling, Hybridization, Scanning and Analysis) took place at this location, by members of the Stokes lab (Audra Vair, Sharlene Eivemark). CD43KO and CD43WT RNA samples of the same biological replicate and treatment were directly compared using opposing dyes and ‘dye swap’ normalizations were performed.  2.7.1 Labeling and purification of nucleic acid In a 1.5 ml microfuge tube, 2‐3 µg of amplified RNA was added to 2µl of 10x labelling buffer and enough nuclease‐free water to complete the 20 µl reaction volumes. Tubes were incubated in for 5 minutes at 30  95°C and then tubes were spun briefly to gather sample at the bottom of the tube and placed on ice for a minimum of 1 minute. 1 µl of Cy3 or Cy5 fluorescent label was added to the reaction and mixed by pipetting. From here on, samples were shielded from light to avoid quenching of the dyes. Samples were then incubated for 1 hour at 37°C to incorporate dyes. After 1 hour of incubation, labeled aRNA was purified using the CyScribe GFX Purification kit (GE Healthcare, formerly Amersham biosciences) (Baie d’Urfe, Quebec) as per manufacturer’s directions. 500 µl of capture buffer was added to the purification column and labeled aRNA was mixed with the capture buffer by pipetting 4‐5 times. Columns were spun at 13 000 rpm for 30 seconds and the flow‐through was discarded. 600 µl of wash buffer was added to each column and spun for 13 000 rpm for 30 seconds, flow through was discarded. Columns were washed twice more in the same fashion and finally spun for an additional 10 seconds to remove any residual wash buffer. Columns were then transferred to clean 1.5 ml microfuge tubes. 30 µl of pre‐heated (to 65°C) elution buffer was then added to the centre of the column and samples were then incubated at room‐temperature (25°C) and spun at 13 000 rpm for 1 minute. A second addition of 30 µl of elution buffer was applied, incubated and centrifuged. Dye incorporation was then measured using a Nanodrop Spectrophotometer. RNA concentration, A260/280 ratio and dye incorporation were all calculated using these readings. 30pmol of dye per RNA sample was calculated and used to hybridize to the microarray.  2.7.2 Microarray chip hybridization Operon OpArray, Mouse Genome Oligo Set version 4.0 (Eurofins MWG Operon, Hunstville AL) glass slides were given a pre‐hybridization treatment, whereby they were incubated in 1x Saline‐Sodium Citrate (SSC) buffer (150mM sodium chloride, 300mM trisodium citrate, pH 7.0) for 45 minutes at 50˚C. Slides were then washed with distilled water three times and dipped in 100% isopropanol 10 times. Slides were dried by centrifugation in a 50 ml falcon tube for 2 minutes at 2 000 xg.  31  In a clean, nuclease‐free tube, the Cy3 and Cy5 samples from CD43KO and CD43WT macrophages (same co‐incubation medium) were combined and an equal volume of Genesphere Hybridization buffer (2x formamide) was added and tubes were subsequently incubated at 80°C for 10 minutes. Hybridization cassettes were arranged (for the containment of the glass sides and sample), glass slides were placed inside and glass coverslips gently placed on top. After the 10 minute incubation was terminated, the sample was gently applied to the slide, between glass slide and coverslip, making sure to avoid bubbles. Arrays were incubated overnight in a 53°C hybridization oven. Cassettes were then disassembled and washed in the first wash buffer (2x SSC/0.2%SDS) at 65°C for 15 minutes. The second wash (2xSSC) was pre‐warmed to 50°C and slides were removed from wash 1 and incubated in wash 2 for 15 minutes (while shaking). Samples were then transferred to a room‐temperature third wash buffer, 0.2X SSC and shaken for 15 minutes and subsequently dried by centrifugation as before (2 000 xg for 2 minutes) Slides were scanned using the ScanArray Express from Perkin Elmer (Waltham, MA) and images were analyzed using Imagene ™ (BioDiscovery, El Segundo, CA). After scanning, slides were stored shielded from light at ‐20°C.  2.8 Microarray analysis- Genespring GX analysis Microarray analysis was done using Genespring GX ®. Samples were subjected to dye‐swap normalization and average signal was recorded. Gene lists were first subjected to a confidence filter (Students t‐test) between triplicate biological samples; All genes with p‐value greater than 0.05 were rejected as poorly‐replicating results. The model for relative RNA quantization was the ‘Inactive cross‐ gene error’ model and ‘significant differential regulation’ was set to be 1.2 times above or 0.833 times below that of the wild‐type, reference sample.  32  2.8.1 Ingenuity ™ pathways analysis Lists of statistically significant, differentially regulated genes were subjected to a rigorous database analysis via Ingenuity ™ (version 8.0, accessed April 17‐May 15, 2009), an online‐ program that investigates annotated genes of interest and presents the candidate genes that share canonical cell‐ signaling pathways, both at the tissue and sub‐cellular level. Statistical significance of the differential regulation of each candidate pathway is given by a relative comparison of total candidate genes vs. ‘listed’ pathway genes, which is updated online weekly. Pathways and genes were selected from this list based on relativity to the research question and past observations.  2.9 Quantitative RT-PCR Two‐step quantitative RT‐PCR was performed on original, non amplified samples of RNA from storage in the ‐80˚C freezer. Copy‐DNA (cDNA) was performed first and quantitative PCR was applied to the cDNA.  2.9.1 Copy-DNA (cDNA) synthesis Original (non‐amplified) samples of RNA from storage in the ‐80˚C freezer. 1 µg of RNA (to a maximum of 11 µl) was added to a mix of 1 µl of Oligo dT, 1 µl of 10 µM dNTP. Sufficient RNAse‐free DNAse‐free water was added to a final volume of 13 µl. Samples were then incubated for 10 minutes at 60˚C and put on ice for minimum of 1 minute. A second master mix consisting of 4µl of first strand synthesis buffer, 1 µl of 0.1 M DTT, 1µl RNAse‐Out and 1 µl of Superscript III was added per tube of sample. Samples were centrifuged briefly to collect at the bottom of the tube and then incubated for one hour at 42˚C. cDNA was aliquotted and diluted 1:8 in nuclease‐free water prior to qPCR analysis.  33  2.9.2 Quantitative PCR-primer design and quality analysis Gene‐specific primers for Quantitative RT‐PCR amplification were ordered from Invitrogen (Invitrogen, Carlsbad, CA) in 25 nanomolar quantities, desalted. They were resuspended to give a 100µM concentration stock solution in pH 8, 10 µM, and filter‐sterilized Tris buffer. Primers were then subjected to a trial PCR to test specificity, using the same temperature and timing of the Q‐PCR machine to be used. cDNA created from the reference control group (PBS‐G CD43WT) amplified RNA was used to test primer specificity. Each amplification run consisted of 10 minutes at 95 ˚C, 2 minutes at 52˚ C, 60 seconds at 60˚ C and 15 seconds at 95˚C, the later three temperatures repeating in cycle 40 times. The PCR master mix consisted of a 20 µl reaction (as is reflective of the sybr‐green qPCR reaction) containing the following (per tube): 2µl of magnesium‐free PCR buffer (Invitrogen) , 1.6 µl 10 µM dNTP, 1.2 µl 50 µM MgCl2 , 0.5 µl of sense and antisense primers, 0.2 µl of Taq , 12 µl H2O and 1 µl of template cDNA. PCR product was visualized on a 5% agarose gel with 1% SybrSafe additive, and compared to a 100bp DNA ladder for product size and specificity.  2.9.3 Quantitative-RT-PCR efficiency and protocol Template cDNA was generated by combining 1 aliquot from all three biological reps of CD43+/+, PBS‐G incubated cDNA samples. This master‐combination was then diluted 2‐fold from 1:1 dilution to 1:32 dilution in RNASE/DNASE‐free water and used as a template for determining quantitative RT‐PCR efficiency. One µl of each cDNA sample was pipetted into 1 well of a 96‐well PCR plate, to which a master mix consisting of 8.5 µl of Nuclease‐free water, 9.5 µl of Sybr Green, 0.5 µl of sense primer and 0.5 µl of antisense primer was added. Plates were sealed using clear plastic cap‐strips and spun at 4 000 g for 5 minutes at 4˚C to ensure proper mixing and to displace any bubbles. The plates were then analyzed in an Applied Biosystems 7300 Real‐Time PCR system, whereby they were incubated at 50˚ C for 2 minutes, then incubated at 90˚C for 10 minutes, then cycled through a PCR amplification of 15 34  seconds at 95 ˚C and 60.0˚C for 1 minute, 40 times. Dissociation cycles were added to the end of every run in order to ensure appropriate binding and amplification of a single‐gene target.  2.9.4 RNA isolation for additional qPCR samples For tissue culture and infection details, refer to sections 2.1.2 and 2.4. BMDMΦ from both CD43+/+ and CD43‐/‐ mice, seeded at 4 million per flask and adhered to 75cm3 flasks were removed from CO2 incubator and washed three times with 5mls of PBS‐G. One ml of Trizol was added to each flask and adherent cells were then harvested with a cell scraper, transferred to a 1.5 ml RNase‐free microfuge tube and frozen until RNA extraction could take place (an average of 5 days later). Frozen tubes of Trizol were then thawed at room temperature for 5‐10 minutes. 200 µl of chloroform was added to each sample, shaken vigorously for 10‐15 seconds and then incubated for 3‐5 minutes sitting static at room temperature. Tubes were then centrifuged at 13 000 rpm for 15 minutes at 4˚C. The aqueous layer was then transferred into a new tube containing 500 µl of >96% ethanol and incubated at ‐80˚C for a minimum of 4 hours and up to 7 days. Samples in ethanol were then removed from the freezer, thawed and spun at 13 000 rpm for 20 minutes at 4 ˚C. The supernatant was poured off and discarded and the pellet was washed in 500ul of 75% ethanol, spun at 13000 rpm for 1 minute a 4˚C, the supernatant was poured off and the pellet was washed in 500 µl of 96% ethanol, spun for 1 minutes at 13 000 rpm and the wash was discarded. RNA pellets were then allowed to air‐dry for a maximum of 5 minutes, and resuspended in 100 µl of RNase‐free water and then cleaned using the RNeasy kit from Qiagen. Three hundred fifty µl of RLT buffer was added to the re‐suspended RNA sample and mixed by gentle pipetting. Two hundred fifty µl of 96% ethanol were then added, mixed a second time and the entire solution was added to an RNeasy column. Columns were spun according to manufacturer’s instructions, 15 seconds at 8 000 Xg. Eluate was discarded, and columns were washed twice with 500 µl of RPE buffer and spun for 15 seconds at 8 000 Xg each time. After the second wash, the columns were  35  spun for a longer, 2 minute period. Dry columns were then transferred to clean , RNase‐free microfuge tubes and filters were hydrated with 30 µl of water, incubated at room temperature for 1 minute, and spun for 1 minute at 8 000 Xg. Eluate was carefully removed and re‐applied to the column, incubated a second time at room‐temperature and respun. RNA quantity was then measured by Nanodrop and aliquotted into approximate quantities of 1µg per tube and stored at ‐80°C.  2.9.5 Quantitative reverse-transcriptase PCR analysis Q‐RT‐PCR analysis was performed using Relative Expression Software Tool (REST) 2009 from Qiagen (Oakville, ON). Quantitative Cycle (Cq) values were exported from the ABI 7300 Q‐PCR thermocycler and entered into this program in a treatment‐specific manner for each biological replicate. Gene expression was normalized to the expression average of three reference genes, glyceraldehyde 3‐phosphate dehydrogenase (GAPDH), beta actin and 18S ribosomal RNA (18s rRNA). CD43 KO expression of each gene was compared to that of the WT control for each biological replicate. All relative expression values were then averaged and statistics were performed using Student’s t‐test to determine significance.  36  Chapter 3: Differential gene expression between CD43 WT and KO macrophages exposed to M.tb, Cpn 60.2 and dnaK  3.1 Introduction Gene‐expression microarrays are a common method of observing global changes in gene expression. Typically consisting of a glass slide (referred to as a chip) upon which thousands of spots of DNA oligonucleotides are embedded. Each oligonucleotide (probe) is especially designed to bind a portion of a single gene or gene transcript variant. The microarray used in this study was RNA‐specific, and therefore the probes coded for expressed sequence tags (ESTs) of the genome, both annotated (for functional proteins) and non‐annotated. There must be a treatment and control sample for every cell type and/or treatment studied. In the current study, there were separate controls for the two areas of interest, the first being the affect of CD43 on cellular functioning and the second being the effects of M.tb infection on these macrophages versus hsp stimulation. The controls were as follows, 1) CD43 WT (control), for normalizing the differential CD43KO macrophage gene expression and 2) stimulated versus unstimulated macrophages. The isolated RNA must be labelled with two different fluorophores to visualize the binding on the chip and to differentiate signals between control and experimental RNA populations. The two differentially‐labelled RNA sets (KO and WT) are then hybridized onto the same oligonucleotide chip. During this process, the labelled mRNA will bind their specific oligonucleotide sequence and emit a fluorescent signal which is then read by a ‘chip‐reader’, specialized piece of fluorometric machinery. The chip is read in the two optimal wavelengths of the ‘competing’ (control and experimental) dyes, and individual levels of fluorescence are quantified for each oligonucleotide spot. The levels of the fluorescent signal (representing quantities of RNA) are first normalized within the chip, by comparing values of technical replicate spots. The treatment sample is then normalized to the 37  control sample, and final values are assigned to each gene transcript. In order to control for preferential binding or signaling of one dye over another, an additional method of normalization control, known as the ‘dye swap’ method is commonly used. This involves dividing one population of RNA and binding both dyes to it separately and then treating two glass slides with the opposing dye types. For example, of two slides used, one will have ‘Cy5’ control and ‘Cy3’ experimental and other slide will have ‘Cy5’ experimental and ‘Cy3’ control. The two sets of normalized RNA values (from within the chip) are then compared and normalized to each other (chip‐to‐chip), in order to reduce possible error generated by chemistry. This secondary normalization is performed in the microarray data‐analysis program. These programs are designed to deal with large quantities of data as our microarray chips have over 35 000 spots on them, representing approximately 25 000 different genes. In our experiment, dye‐swap normalization was performed. The gene‐expression lists are then filtered based on whatever criteria the researcher chooses, with the predominant two being 1) a filter based on relative gene expression, in which a minimal level of relative difference between the experimental and control samples is set, and 2) a filter based on statistical significance. This current study filtered data on both criteria, however a preferred stringency was set for confidence between biological replicates (p‐value of 0.05 or less) and the relative changes in expression had lower cutoffs than generally used ( 1.2 or 0.833 fold difference). In order to validate the data that was generated using the microarray, quantitative reverse‐transcriptase polymerase chain reaction (Q RT‐PCR) was employed on specific genes selected from the microarray. Q RT‐PCR is a highly specific and sensitive method to measure differential RNA levels, compared to a whole‐genome microarray. This method involves the use of gene‐specific primers and fluorescent markers in the way of either a gene‐specific probe, which fluoresces upon cleavage by the polymerase, or Sybr green dye, a specially formulated dye that intercalates in double‐stranded (and therefore amplified) DNA. Both methods measure the amount of amplification that occurs for a specific gene in a single PCR reaction, the fluorescence of which is measured and analyzed on a specialized plate‐reading 38  thermocycler. Relative values of amplification of genes of interest are compared to genes of reference, which are then compared to the other experimental samples in the study. Because the primers must bind and amplify specific sections of the mRNA, and it is the amplified cDNA that is measured, this method is more specific than a microarray, where nonspecific binding of the single probe to mRNA sequence is more common and background binding must be taken into account. The genes that are examined do not necessarily have to be the focus of the study itself, but their levels of relative expression are compared to those in the microarray. It is ideal to see similar trends of expression with perhaps a higher level of expression in the Q RT‐PCR, eg. 2 fold up‐regulations in microarray can become 200‐fold up regulation in Q RT‐PCR. To find the levels of mRNA for a specific gene being exactly the same for the two methods is relatively uncommon; one usually looks for trend similarities between datasets.  3.2 Rationale Previous research indicated that CD43 is an important element in macrophage interactions with M.tb bacterial invasion, cytokine signaling and apoptosis [72, 131, 132, 143, 145, 147]. Previous studies also indicate that the heat shock protein Cpn 60.2 is primarily responsible for the bacterial interaction with CD43. In addition, dnaK is another heat shock protein found on the M.tb capsule and also demonstrates binding capabilities with CD43 [7]. Two separate studies by Randhawa et al. explored the impact of CD43 deficiency in M.tb infection. It was first demonstrated that there was a deficiency in bacterial binding and uptake of M.tb , which could be countered for at initial infection by increasing the multiplicity of infection 1.5 times, for example from 20:1 to 30:1 for CD43KO macrophages [72]. Once internalized, there was a relative increase of intracellular bacterial growth in the CD43KO compared to CD43WT. This phenomenon was maintained in vivo, a whole‐mouse infection, with a significant increase in bacterial load in the lungs, liver and 39  spleen, but did not affect mouse survival over the 65‐day time‐course. This study also demonstrated that there was a gene‐dose effect of bacterial binding and uptake, CD43 heterozygote macrophages (CD43 +/‐) demonstrated binding and uptake that was intermediate between both CD43WT (CD43+/+) and CD43KO (CD43‐/‐). A second study demonstrated that inactivated CD43KO macrophages were deficient in pro‐inflammatory cytokines TNF‐α, IL‐12 and IL‐6, and that the addition of recombinant TNF‐ α to CD43KO macrophages controlled the intracellular bacterial growth to levels comparable to CD43WT macrophages [132]. The molecular mechanism for deficient binding and uptake of M.tb bacillus to CD43KO macrophages was also explored in our lab, by Tyler Hickey. He found that two heat shock proteins Cpn60.2 and dnaK, were found extracellularly in the M.tb capsular material, and were capable of binding the extracellular portion of CD43 [7]. Further study demonstrated that Cpn60.2 was capable of preventing bacterial binding and uptake in a dose‐dependent fashion to a maximal inhibition of up to 60% reduction of binding and uptake. DnaK did not demonstrate the same strong affinity for CD43. The concentration at which maximal inhibition of binding and uptake was found was 5µg/ml, which was therefore the concentration of recombinant protein used in the current study. The use of heat shock proteins including Cpn60.2 as immunological stimuli as well as potential vaccine candidates has been well‐ documented [42, 150, 151], however it has also been found that the concentrations used in this study are lower than those at which maximal stimulation was achieved elsewhere [151]. The microarray was performed with live M.tb bacillus, recombinant Cpn 60.2 and dnaK in order to investigate the possibility of specific pathways that are differentially regulated between CD43 KO and WT macrophages and ascertain the ability of Cpn 60.2 and dnaK to stimulate CD43 to differentially regulate mRNA expression compared to M.tb.  40  3.3 Results 3.3.1 CD43 knockout and CD43 wild type macrophages exhibit differential regulation to each other in response to mycobacterial stimuli The changes in mRNA expression in CD43 WT and CD43 KO macrophages were tested using whole, live M.tb bacillus, recombinant Cpn 60.2 and recombinant dnaK on murine BMDMφ in the absence of serum, for three hours as outlined in the methods, section 2.4. This was done in three independent experiments using three sets of age‐matched littermate CD43WT and CD43KO mice. It was determined that (i) the mRNA expression of BMDMφ is altered when CD43 is disrupted (Table 1), the (ii) differential gene expression between the two genotypes was much higher upon stimulus with M.tb than with either dnaK or Cpn60.2, and (iii) that there was a high level of basal differential expression between WT and KO macrophages. The basal levels of differential expression were taken into account for genes and pathways that were selected for further investigation and it was taken into account that trends in expression were enhanced with stimulation compared to the phagocytosis‐medium control.  3.3.2 Mycobacterium tuberculosis, Cpn 60.2 and dnaK elicit differential responses in CD43-/- and CD43+/+ BMDMφ In CD43 KO macrophages, there were more genes differentially regulated in response to M.tb stimulation than any other co‐incubation factor (figure 1 A and B). There were 728 uniquely down‐ regulated and 940 uniquely up‐regulated genes in CD43KO macrophages compared to CD43WT macrophages, in response to M.tb, versus 531 and 748 for dnaK and 321 and 843 for Cpn 60.2 (Supplemental Tables 1, 2, 3, 4). There were relatively few genes that were shared in both statistical significance and minimum fold change between the different stimuli, and only one gene shared in both 41  Table 1 Summary of overall changes in differential expression between CD43KO and CD43WT macrophages Gene Expression  M. tuberculosis  Cpn 60.2  dnaK  Phagocytosis medium  Total number of up regulated RNA sequences (KO)  1003  907  805  759  Total number of down regulated RNA sequences (KO)  748  330  543  402  Total number of differentially expressed RNA sequences  1751/38 000  1237/38 000  1348/38 000  1161/38 000  Relative expression is given as CD43KO expression compared to CD43WT control. M. tuberculosis infection was performed with an MOI (Multiplicity of Infection) of 20:1 for CD43WT macrophages and 30:1 for CD43KO macrophages, in order to normalize for differential bacterial binding and uptake. Recombinant proteins Cpn60.2 and dnaK were co‐incubated with CD43WT or CD43KO macrophages in the phagocytosis medium at a concentration of 5 µg/ml. Genes were filtered based on confidence between biological replicates (p<0.05), then normalized expression of either greater than 1.2 (up regulated) or less than 0.8333 (down‐regulated) in the CD43KO macrophages compared to CD43WT. Approximately 25 000 genes were represented in the microarray, with 38 000 gene transcripts (genes including transcript variants)  42  Figure 1 Venn diagram demonstrating the quantities of genes exhibiting differential response with exposure to one of M.tb Cpn 60.2 or dnaK a M.tb  728 9  11 0  321  1  531  60.2  dnaK  b M.tb  940 35 845 60.2  1 29  27 748 dnaK  Genes that are down regulated (a) or up regulated (b) and shared between treatments. Statistically significant based on Confidence between biological replicates (p‐value >0.05). Differential expression is of the CD43KO MΦ compared to CD43WT MΦ control. Normalized cutoff values for differential expression are 0.8333 and 1.2 respectively. 43  statistical significance and minimum fold change in all three stimuli (figure 1B). This gene was a non‐ annotated region of the mus muscles chromosome 14 genomic contig, flanked by a hypothetical protein and a voltage‐dependent calcium channel. DnaK and Cpn 60.2 demonstrated little expressional similarity between each other, when compared to the similarity between either of the recombinant proteins and whole M.tb infection. For genes that are down regulated in the knockout, only one gene meets both stringency requirements for both 60.2 and dnaK, however the hsps have 9 and 11 genes in common with whole M.tb, respectively (Supplemental Table 5). Up regulated genes were similarly matched in numbers between Cpn 60.2 and dnaK, with 29 genes shared between the two recombinant proteins and 35 and 27 genes shared between M.tb and 60.2 or dnak respectively. If reproducibility between biological replicates (statistical significance) specific to each treatment type is disregarded, there are 9 genes up regulated in the knockout compared to the wild type shared between all three treatments (Supplemental Table 5). The lack of similarity in down regulated genes between all three treatment types however indicates that dnaK and 60.2 do not appear to be stimulating the macrophages with the same type of CD43 dependence. There were no apparent relationships between shared genes although many functional categories are shared. There were very few genes that belonged to a unique functional sub‐set which was only activated by a single stimulus (Table 2).This initially caused difficulty in determining the true effect that a lack of CD43 had on the intracellular functions of BMDMφ and how they might be affected by M.tb, Cpn60.2 or dnaK.  3.3.3 CD43 deficiency affects multiple signaling pathways To determine the significance of CD43 in terms of intracellular signaling pathways, a network analysis was performed using an internet‐based knowledge network. Using the Ingenuity Systems Pathway Analysis (IPA, to model potentially significant signaling networks affected by the loss of CD43 when comparing stimulation by Cpn 60.2, dnaK or M.tb, it was determined that a  44  Table 2 Trends in the expression of statistically significant genes shared between different treatments. Table 2.a Genes up‐regulated in CD43KO macrophages compared to CD43WT macrophages shared by dnaK and M.tb‐treated phagocytes. Gene Category  Description  Receptors  Normalized Expression Dnak  M.tb  Olfr770  Olfactory receptor 770  1.913  1.654  Olfr 894  Olfactory receptor 894  1.663  1.391  Signalling proteins  Dnak  M.tb  Nek10  Never in Mitosis gene A‐related kinase 10, NIMA related kinase 10  2.276  1.356  EG209380  Similar to Very Large Inducible GTPase 1  1.827  1.624  Rgs17  Regulator of G‐protein signaling  1.746  1.339  Plxna2  Plexin A2, Multicellular organismal development, receptor activity Map/Microtubule affinity‐regulated kinase 2, Par‐1, Mark2‐cell differentiation amino acid phosphorylation  1.468  1.528  1.368  1.545  Dnak  M.tb  1.342  1.345  Dnak  M.tb  1.936  2.451  Transcription/Translation  Dnak  M.tb  Similar to short coil‐coil protein HCF‐binding transcription factor, CREB/ATF bZIP transcription factor, Zf, LAZip, tyrosine kinase‐associate leucine zipper protein LAZipII, Mus Musclus, CREB/ATF bZIP LAzip transcription factor  2.119  1.433  1.459  1.343  coiled‐coil domain containing 25  1.421  1.411  Structural  Dnak  M.tb  1.94  1.346  Mark2 Chemokines  Interferon regulatory factor 2 binding protein 2, negative regulation of Interferon transcription from RNA polymerase II promoter  Irf2bp2 Transporters  Chloride channel, calcium activated 5‐ chloride transport/ligand‐gated ion channel activity  Clca5  Col9a2  procallagen, typ IX, alpha 2, cell adhesion, phosphate transport, skeletal development  The genes in the above table were determined to be statistically significant in both dnaK and M.tb – challenged macrophages and observed to share the same trend in expression profile. Expression values provided are of CD43KO macrophages compared to CD43WT macrophage control. Macrophages were infected with an MOI of 20:1 for CD43WT and 30:1 CD43KO. Macrophages were co‐incubated with dnaK at a concentration of 5 µg/ml.  45  Table 2.b Genes up‐regulated in CD43KO macrophages compared to CD43WT macrophages shared by Cpn 60.2 and M.tb treated phagocytes Gene Category  Description  Receptors  Normalized Expression 60.2  M.tb  V1rb1  Vomonasal receptor B1, Vn2, V1RA5  1.701  1.477  Olf609  MOR9‐1  1.722  1.454  Olfr818  Olfactory receptor 818  1.631  1.462  5‐Ht2b  5‐hydroxytryptamine (serotonin) receptor 2b  1.400  1.460  60.2  M.tb  1.496  1.888  60.2  M.tb  1.678  1.842  1.266  1.709  Transcription/Translation  60.2  M.tb  AT rich interactive domain 5A (Mrf1 like, modulator recognition factor 1) Arid5a,  1.603  1.836  Ayp1  1.370  1.346  1.266  1.539  Cell differentiation/growth  60.2  M.tb  Fgfr‐1  1.834  1.502  60.2  M.tb  Keratin associate protein 6‐1  1.297  2.288  Dynein axonemal, heavy chain 10  1.292  1.387  Plasma Membrane  60.2  M.tb  Tmem 139  1.756  1.763  1.462  1.535  Metabolism  60.2  M.tb  Gdpd3  2.235  6.843  1.432 60.2 2.068  1.227 M.tb 1.811  Chemokines Caspase recruitment domain family member 11, BIMP3, CARMA1, endocytosis , positive regulation of I‐kappa B kinase, NF‐kappa B cascade, postitive regulation of interleukin‐2 biosynthetic process`  Card11 Transporters  Gamma‐aminobutyric acid receptor, subunit rho 1' chloride transport‐gamma aminobutyric acid signaling pathway, ion transport Guanylate kinase 1, atp binding, guanylate kinase activity, transferase activity, pruine metabolism  Gabrr1 Guk1  ribonuclease H2, subunit C, Rnase H2c Putative S1 RNA binding domain protein, predicted riken cdna, zinc finger cchc domain containing 17  ZccHc17  Basic fibroblast growth factor receptor 1 precursor  Structural krtap6‐1  Mir 16  Transmembrane protain 139, integral to membrane Membrane interacting protein of RGS16, g‐coupled receptor protein signaling pathway, glycerol metabolic process  glycerophosphodiester phosphodiesterase domain containing 3, glycerol metabolic process Enyol coenzyme A hydratase 1, proxisomal/mitochondrial dienyol‐coa isomerase , fatty acid Ech1 metabolic process, lipid metabolism Ubiquitin/proteasome Usp37 Ubiquitin specific peptidase 37, cystein ‐ type peptidase activity  The genes in the above table were determined to be statistically significant in both Cpn60.2 and M.tb –challenged macrophages and observed to share the same trend in expression profile. Expression values provided are of CD43KO macrophages compared to CD43WT macrophage control. Macrophages were infected with an MOI of 20:1 for CD43WT and 30:1 CD43KO. Macrophages were co‐incubated with Cpn60.2 at a concentration of 5 µg/ml 46  Table 2.c Genes up‐regulated in CD43KO macrophages compared to CD43WT macrophages shared by dnaK and Cpn 60.2 treated phagocytes  Gene Category  Description  Normalized Expression  Receptors  60.2  Dnak  Asgr2  1.785  2.424  1.459  1.860  1.270  1.462  1.3  1.21  60.2  Dnak  asialoglycoprotein receptor 2 (Asgr2) Proteinase activated receptor 3 precursor (PAR‐3) (Thrombin receptor‐ like 2) (Coagulation factor F2rl2 II receptor‐like 2). [Source:Uniprot/SWISSPROT;Acc:O08675] Antxr1 anthrax toxin receptor 1 Signal sequence receptor Delta, Trap, endoplasmic reticulum receptor activity, integral to the Ssr4 membrane Signalling proteins  1.423  2.548  Insl6  Wnt‐16 protein precursor, wingles‐related MMTV integration site 16, Wnt receptor signaling pathway, calcium modulating pathway Insulin‐like 6, homone activity  1.452  1.822  Rragd  Ras‐related GTP bindin D, GTP binding, nucleotide binding, protein binding  1.408  1.750  60.2  Dnak  1.297  2.763  60.2  Dnak  2.240  1.987  1.428  1.673  Transcription/Translation  60.2  Dnak  Brunol4  Bruno‐like 4, RNA binding protein  2.796  2.551  Ccdc57  Coilded‐coil domain containing 57  1.347  2.330  SCR2_MOUSE  Scratch homolog 2 zinc finger protein, Similar to Transcriptional repressor scratch 2  3.024  2.329  4931407K02Rik  CAP‐binding protein complex interacting protein 1  2.120  2.256  Smug1  Single strand selective monofunctional uracil DNA glycosylase, dna‐repair, base‐exision repair Proline, glutamic acid and leucine rich protein 1, Mus musclus praline, glutamic acid and leucin e rich protein 1  1.715  1.829  1.897  1.693  60.2  Dnak  1.502  1.931  Wnt16  Adhesion Caspr4  Contactin associated protein‐like 4 precursor (cell‐recognition protein)  Transporters Glutamate receptor 2, precursor GluR‐2, GluR‐B, GluR‐K2, glutamate receptor , ionotropic, AMPA2, Gria2, potassium ion transport Coatomer protein complex, subunit gamma, isoform 2 is encoded by transcript variant, transporter activity, structural molecule activity  GluR‐2 Copg  Pelp1 Structural Myo5c  myosin VC  Villin‐like protein, Villp, actin bidning, barbed‐end actin filament capping, cytoskeleton organization and biogenesis Plasma Membrane  1.489  1.331  60.2  Dnak  Clec2e  1.617  2.923  Vill  C lectin‐related protein A, sugar binding  The genes in the above table were determined to be statistically significant in both dnaK and Cpn 60.2 –incubated samples and observed to share the same trend in expression profile. Expression values provided are of CD43KO macrophages compared to CD43WT macrophage control. Macropahges were co‐incubated with recombinant protein concentrations of 5µg/ml. 47  Table 2.d Genes down‐regulated in CD43KO macrophages compared to CD43WT macrophages shared by M.tb and dnaK treated phagocytes  Gene Category  Description  Downregulated Receptors  Normalized Expression Dnak  M.tb  advanced glycosylation end product‐specific receptor precursor (receptor for advanced glycosylation end product)  0.714  0.583  Peroxisomal 3, 2‐trans‐enyol‐CoA isomerase  0.764  0.802  Phospho2 Phosphatase, orphan2, novel protein containing a putative phosphatase domain Signalling proteins Pleckstrin and Sec7 domain containing 4, regulation of ARF protein signal Psd4 transduction, guanyl‐nucleotide exchange factor activity Adhesion  0.640  0.807  0.706  0.782  Comp Cartilage oligomeric matrix protein , Calcium ion binding, protein binding Transcription/Translation  0.540  0.557  EST AI449063  0.558  0.659  Ager Enzymes Peci  putative nucleic acid binding protein RY‐1  The genes in the above table were determined to be statistically significant in both dnaK and M.tb – incubated samples and observed to share the same trend in expression profile. Expression values provided are of CD43KO macrophages compared to CD43WT macrophage control. Macrophages were infected with M.tb at a MOI of 20:1 for CD43WT and 30:1 for CD43KO. Macrophages were co‐ incubated with dnaK at a concentration of 5µg/ml.  48  Table 2.e Genes down‐regulated in CD43KO macrophages compared to CD43WT macrophages shared by M.tb and 60.2 treated phagocytes Gene Category  Description  Downregulated Receptors Notch gene homolog 1, Mis6, Tan1, Major Type A protein, tranmembrane receptor Notch 1 Notch1, activation regulateds T‐cell linieage commitment and early tcell development Epo‐R erythropeoitin receptor precursor Integrin alpha ‐ M precursor, Cell surface glycoprotein MAC‐1 alpha subunit, CR‐3 alpha Itgam chain CD11b, leukocyte adhesion receptor MO1 Enzymes Sps2 Selenophosphate Synthetase 2, Ysg3, Sephs2 AMP deaminase 3 AMP deaminase isoform E Signalling proteins Leucine rich repeat containing 10, heart‐restricted leucine‐rich repeat protein, Lrrc10 important in heart development Tle3 Transducin‐like enhancer of split 3, homolog of DrosophilaE (spl) MAPKAPK‐2 MAP kinase‐activated protein kinase 2 Plasma Membrane Fibronectin leucine Rich transmembrane protein 3  Normalized Expression 60.2  M.tb  0.782 0.623  0.572 0.540  0.613  0.611  0.705  0.831  0.588  0.687  0.749 0.638  0.599 0.485  0.634  0.657  0.518  0.619  The genes in the above table were determined to be statistically significant in both Cpn 60.2 and M.tb –incubated samples and observed to share the same trend in expression profile. Expression values provided are of CD43KO macrophages compared to CD43WT macrophage control. Macrophages were infected with M.tb at a MOI of 20:1 for CD43WT and 30:1 for CD43KO. Macrophages were co‐ incubated with Cpn60.2 at a concentration of 5µg/ml.  49  number of intracellular signaling pathways are potentially affected by CD43 presence or absence. The changes in gene expression (n‐fold) for each treatment were analyzed using this program and the most significant molecular and cellular function pathways were examined to determine if there was a functional significance of CD43 in M.tb‐related stimuli. Maps of inter‐connected molecules were generated (example shown in figure 2). However, the details of gene‐gene interactions were not clear or particularly detailed using this aspect of the program. For that reason, a more detailed, analytical approach was used. The program is also capable of individually associating specific genes to various cell functions and pathways. Genes that are broadly‐used cell signaling molecules would be associated with a number of pathways for example, however statistical significance for the genes of interest when compared to the total number of genes listed in a specific pathway demonstrates the likelihood of true association. A summary of the top five pathways identified from my data as differentially regulated by CD43 is shown in Table 3. Upon investigation with common signaling pathways involving all three stimuli, it appeared that among the top molecular and cellular functions that were differentially expressed were 1) cellular development, 2) growth and proliferation, 3) death, 4) movement and 5) cellular function and maintenance. The master lists of the variety of pathways is quite extensive, and there is a range of statistical significance in terms of the probability that a pathway is associated by chance (or not) based on the total number of genes associated with a specific function compared to the total number of genes in the list of that function. All of the aforementioned molecular and cellular functions had significant p‐values of less than 0.05 (range of values provided in table 3). The pathways analysis system tends to sub‐categorize genes not only based on cellular functions but disease states and physical disorders. I chose to investigate the pathways based around cellular and molecular functions as opposed to investigating genes that are disease‐state specific in order to simplify the search based solely on cellular functionality. Additionally, certain genes transcend various ‘categories’ and are applicable to a number of cellular functions.  50  Figure 2 Example of gene‐gene interactions related to ‘immunity’ pathway in Ingenuity ™  The genes presented are associated with the ‘Immunity’ gene lists and the relative expression values are connected to M. tuberculosis infection. Genes are annotated, protein‐coding and all linear connections denote direct protein‐protein interactions. Data is represented as fold‐change in CD43KO macrophages compared to CD43WT macrophages. Green color represents genes that are down regulated in CD43KO macrophages and red color represents genes that are up regulated in CD43WT macrophages. 51  Table 3 Common Molecular and Cellular Functions related to M.tb, hsp65 and dnaK stimulus  Associated Pathway/Function  Number of molecules present from pathway  P‐value (Significance of pathway‐association)  Cellular Development  381  <0.01  Differentiation of cells  240  <0.001  Development Processes  269  <0.001  Development  207  <0.001  Cell Death  48  <0.001  Growth  39  <0.001  Morphogenesis  73  <0.001  Maturation  39  <0.001  Apoptosis  41  <0.001  Expansion  24  <0.001  Branching Morphogenesis  6  <0.01  Cellular Growth and Proliferation  438  <0.01  Proliferation  340  <0.01  Growth  238  <0.001  Colony formation  68  <0.001  Expansion  24  <0.001  Cell Death  433  <0.01  Cell Death  376  <0.01  Apoptosis  378  <0.001  Survival  143  <0.01  Cytotoxicity  16  <0.001  52  Killing  7  <0.001  Necrosis  33  <0.001  Inhibition  36  <0.001  Cell viability  45  <0.01  Cellular Movement  258  <0.01  Migration  196  <0.001  Cell Movement  141  <0.001  Infiltration  51  <0.001  Homing  77  <0.01  Mobilization  10  <0.001  Movement  88  <0.001  Chemotaxis  75  <0.01  Invasion  77  <0.01  Emigration  6  <0.001  Chemoattraction  13  <0.001  Recruitment  13  <0.001  Transmigration  17  <0.001  Cellular Function and Maintenance  121  <0.001  Homeostasis  101  <0.001  Function  34  <0.001  Development  93  <0.001  Respiratory Burst  15  <0.001  Cell death  48  <0.001  Apoptosis  41  <0.001  Differentiation  47  <0.001  Pathways determined to be differentially regulated by CD43 when stimulated by three ligands: M.tb, Cpn60.2 and dnaK. Pathways listed result from comparing the 3 treatments and selecting those present in two or more of the aforementioned treatment lists. P‐ values represent statistical significance of the genes provided representing the aforementioned pathway. 53  Further inspection of these categorized gene lists revealed that eicosanoid signaling, p38 MapK signaling, apoptosis, inflammatory signaling and cytoskeletal remodeling were all differentially regulated pathways between CD43 WT and KO macrophages (Table 4). Because of the previously observed results concerning the role of CD43 in the induction of inflammatory cytokines and apoptosis [132], further investigation into apoptosis and inflammatory signaling was performed. Networks of directly‐relating genes from these pathways were generated, and it was apparent that CD43 KO macrophages induced higher levels of intrinsic apoptosis compared to their WT counterparts, as well as expressed lower quantities of mRNA involved in extrinsic apoptosis and TNF‐α production (Table 4.4). Genes from these three areas were chosen as candidates for Q RT‐PCR validation.  3.3.4 Confirmation of RNA microarray using Quantitative ReverseTranscriptase PCR There were several candidate genes selected for expression analysis (Supplemental, Table 6). The final set of genes (Table 5) were selected on the basis of either being present in the microarray and sharing expression trends in at least two of the three treatments (Cpn60.2, M.tb or dnaK). We were also interested in looking at other possible differentially regulated genes in the pathways identified (Table 4) that were not deemed differentially regulated via the microarray analysis (Table 6). As previously mentioned due to earlier observations concerning the differential apoptosis and TNF‐α production in CD43KO compared to WT macrophages [132], these areas were of primary interest for further investigation. RNA samples from the dnaK exposed macrophages were omitted from this portion of the study due to the previous lack of evidence for any specific role in the interaction of M.tb and macrophages that was mediated by dnaK and CD43 [7]. The same study  54  Table 4 Pathway‐associated gene lists with relative expression data Table 4.a Cytoskeletal Remodeling‐Associated genes Relative Expression level per treatment  Gene Phagocytosis medium  M.tb  60.2  dnaK  Gene Function  ASAP1  1.130  1.03  1.124  0.738  Phospholipid‐dependent Arf GTPasse‐Activating protein (associated with Src)  CAPN9  0.797  0.726  1.316  0.893  Calpain 9, calcium‐dependent cystein endopeptidase. Calcium‐ion binding  CAPN11  0.983  1.186  1.555  1.749  Calpain 11, Calcium‐dependent thiol protease  GRB7  0.260  1.523  0.658  1.317  Growth factor receptor bound protein 7  ITGAL  1.043  0.766  0.749  1.345  Alpha L integrin  ITGAM  0.937  0.610  0.612  0.916  Alpha M Integrin  ITGAX  1.528  3.859  1.625  1.194  Alpha X Integrin  ITGB2 ITGB8 MAP2K1  1.219 1.199 0.887  1.293 0.780 1.074  0.710 0.59342 0.788  0.859 1.291 0.705  Integrin, beta 2 Integrin, beta 8 Mitogen‐activated protein kinase kinase 1  MRAS  0.555  0.726  0.743  0.793  Muscle and microspikes RAS  PIK3CA  0.615  1.339  0.676  0.906  phosphatidylinositol 3‐kinase, catalytic alpha polypeptide  PPP1CB  1.049  0.771  0.668  0.862  protein phosphatase 1, catalytic subunit, beta isoform  RAP2A  1.164  1.295  0.878  1.324  member of RAS oncogene family  TLN2  0.605  1.375  0.689  0.565  talin 2, actin assembly  TSPAN3  1.250  0.984  1.079  0.651  tetraspanin 3  TSPAN5  1.041  1.411  0.958  1.003  tetraspanin 5  TTN  0.649  1.622  1.017  1.104  Titin  WAS  0.977  1.198  1.149  1.204  WAS protein family, member 1  ACTR1A  1.198  0.837  0.561  0.947  ARP1 actin‐related protein, homolog A  Gsn  1.166  1.312  1.247  1.258  gelsolin (amyloidosis, Finnish type)  Genes filtered on confidence between biological replicates (p<0.05) and with expression values of either greater than 1.2 or less than 0.8333 in either M.tb, Cpn 60.2 or dnaK‐treated CD43KO MΦ compared to CD43WT and associated with cytoskeletal remodelling as determined by Ingenuity.  55  Table 4.b Mapk/p38signalling‐associated genes Gene  Relative Expression level per treatment M.tb  60.2  dnaK  Gene Function  DUSP1  Phagocytosis medium 1.490  0.487  1.174  1.392  dual specificity phosphatase 1  IL1A IL1B IL1F9 IL1R1 IL1RN JMJD7‐PLA2G4B MAP3K7IP2  0.677 1.149 1.488 1.667 0.791 1.065 1.209  0.294 0.472 0.791 1.652 0.444 0.779 1.000  0.998 0.614 1.766 1.440 0.771 1.106 0.872  1.273 0.962 0.445 2.085 1.161 1.715 0.643  interleukin 1, alpha interleukin 1, beta interleukin 1 family, member 9 interleukin 1 receptor, type I interleukin 1 receptor antagonist JMJD7‐PLA2G4B readthrough mitogen‐activated protein kinase kinase kinase 7 interacting protein 2  MAPK14  0.903  0.615  0.791  1.357  mitogen‐activated protein kinase 14  MAPKAPK2  0.973  0.657  0.634  1.042  mitogen‐activated protein kinase‐activated protein kinase 2  MAPKAPK3  1.159  1.020  0.986  1.117  mitogen‐activated protein kinase‐activated protein kinase 3  MYC  0.091  1.731  1.721  1.903  v‐myc myelocytomatosis viral oncogene homolog (avian)  PLA2G4A  1.354  0.663  1.323  1.097  phospholipase A2, group IVA (cytosolic, calcium‐dependent)  TGFBR2 TNFRSF1A  0.967 1.147  1.503 0.967  0.803 0.652  1.140 1.083  transforming growth factor, beta receptor II (70/80kDa) tumor necrosis factor receptor superfamily, member 1A  TNFRSF1B  0.999  0.810  0.551  0.798  tumor necrosis factor receptor superfamily, member 1B  DEDD HRK TNFRSF1A  0.940 0.499 1.147  0.500 0.735 0.967  0.583 1.405 0.652  1.07 0.696 1.083  harakiri, BCL2 interacting protein (contains only BH3 domain) tumor necrosis factor receptor superfamily, member 1A tumor necrosis factor receptor superfamily, member 1B  Traf1  1.158  0.810  0.981  1.630  TNF receptor‐associated factor 1  Traf5  0.867  0.784  1.278  0.565  TNF receptor‐associated factor 5  Tnfaip3  1.314  0.432  0.785  0.938  Tumor necrosis factor, alpha‐induced protein 3 (Putative DNA binding protein A20)  timp3 zfp36  1.086 0.965  0.673 0.456  0.498 0.614  0.806 0.891  Metalloproteinase inhibitor 3 precursor zinc finger protein 36, tristetraprolin  Genes filtered on confidence between biological replicates (p<0.05) and with expression values of either greater than 1.2 or less than 0.8333 in either M.tb, Cpn 60.2 or dnaK‐treated cells and associated with MAP‐kinase and p38 activity as determined by Ingenuity.  56  Table 4.c Eicosanoid Signaling Relative Expression level per treatment  Gene Phagocytosis medium  M.tb  60.2  dnaK  Gene Function  PTGFR  1.213  0.708  0.967  0.816  prostaglandin F recptor  PLA2G2D  0.852  1.883  1.612  1.156  phospholipase A2, group IID  PTGS2  0.902  0.340  1.258  1.267  prostaglandin‐endoperoxide synthase 2 (prostaglandin G/H synthase and cyclooxygenase)  Pla2g4a  1.354  0.663  1.323  1.097  phospholipase A2, group IVA (cytosolic, calcium‐ dependent)  Pla2g4b  1.065  0.779  1.106  1.715  phospholipase A2, group IVB (cytosolic)  PTGDS  1.198  1.0162  1.265  1.299  prostaglandin D2 synthase 21kDa (brain)  PTGER4  0.542  0.832  0.653  1.464  prostaglandin E receptor 4 (subtype EP4)  PTGES  0.392  0.685  0.878  0.712  prostaglandin E synthase  PTGS2  0.902  0.339  1.258  1.267  prostaglandin‐endoperoxide synthase 2 (prostaglandin G/H synthase and cyclooxygenase)  Il12b  1.067  0.520  0.528  0.278  interleukin 12B (natural kill cell stimulatory factor 2)  IL18  0.855  0.683  1.0139  1.338  interleukin 18 (interferon‐gamma‐inducing factor)  Genes filtered on confidence between biological replicates (p<0.05) and with expression values of either greater than 1.2 or less than 0.8333 in either M.tb, Cpn 60.2 or dnaK‐treated cells and associated with Eicosanoid production and metabolism as determined by Ingenuity.  57  Table 4.d Apoptosis‐related signaling Relative Expression level per treatment  Gene  M.tb  60.2  dnaK  Gene Function  Fadd  Phagocytosis medium 1.582  0.888  0.895  0.898  Fas (TNFRSF6) ‐associated via death domain  Adam17  0.999  0.810  0.551  0.798  ADAM metallopeptidase domain 17  CASP7  0.948  1.239  0.461  0.799  caspase 7, apoptosis‐related cysteine peptidase  CASP9  0.664  0.238  0.917  1.481  caspase 9, apoptosis‐related cysteine peptidase  DAPK1  1.530  0.917  1.142  1.087  death‐associated protein kinase 1  DAPK2  1.379  1.611  1.108  1.837  death‐associated protein kinase 2  DEDD  0.940  0.500  0.583  1.070  death effector domain containing  HRK  0.499  0.735  1.405  0.696  TNFRSF1A  1.147  0.967  0.652  1.083  TNFRSF1B  0.999  0.810  0.551  0.798  Traf1  1.158  0.784  0.981  1.630  TNF receptor‐associated factor 1  Tnfaip3  1.314  0.432  0.785  0.938  Tumor necrosis factor, alpha‐induced protein 3 (Putative DNA binding protein A20)  timp3  1.086  0.673  0.497  0.806  Metalloproteinase inhibitor 3 precursor  zfp36  0.965  0.456  0.615  0.890  zinc finger protein 36, tristetraprolin  Traf5  0.867  1.327  1.278  0.565  TNF receptor‐associated factor 5  harakiri, BCL2 interacting protein (contains only BH3 domain) tumor necrosis factor receptor superfamily, member 1A tumor necrosis factor receptor superfamily, member 1B  Genes filtered on confidence between biological replicates (p<0.05) and with expression values of either greater than 1.2 or less than 0.8333 in either M.tb, Cpn 60.2 or dnaK‐treated cells and associated with Apoptosis as determined by Ingenuity.  58  Table 5 Confirmation of differential expression of selected genes in RNA microarray using Quantitative Reverse‐Transcriptase PCR  Phagocytosis medium Gene Name  Microarray  M.tb  q‐RT‐PCR  Microarray  Common  Normalized  SE  Normalized  SE  Normalized  SE  Zfp36 Tnfrsf1b Casp7 Casp9  0.9653 0.9997 0.9479 0.6642  0.32 0.491 0.194 0.516  0.688 0.733 0.888 0.989  0.3496 0.1188 0.0777 0.2772  0.456 0.81 1.239 0.238  0.107 0.088 0.26 0.576  Cpn 60.2 q‐RT‐PCR  Normalized  SE  0.7427 0.2717 1.9613 1.627 1.5233 0.6386 1.0773 0.5407  Microarray  q‐RT‐PCR  Normalized  SE  Normalized  SE  0.615 0.551 0.461 0.917  0.174 0.123 0.1 0.452  1.042 7.949 1.338 1.603  0.274 3.942 0.084 0.265  RNA microarray data using amplified mRNA compared to Quantitative RT‐PCR data using original RNA sample. Gene expression was normalized with the average expression of three reference genes (GAPDH, 16S rRNA and β‐actin) and KO expression is presented in reference to WT control. Data presented is the mean of three biological replicates.  59  confirmed Cpn60.2 binds with sufficient specificity to CD43 that M.tb bacillus binding and uptake is blocked by up to 60% in its presence [7]. Baseline differences in expression between CD43KO and CD43WT macrophages (incubated in phagocytosis medium alone) were not replicated with Q RT‐PCR for ¾ genes. Trends in differential expression were replicated with Q RT‐PCR in 2 out of 4 of the selected genes when CD43KO macrophages were infected with M.tb although none of the trends were maintained in macrophages exposed to Cpn60.2. As measured by Q RT‐PCR, gene expression in CD43KO macrophages treated with Cpn 60.2 appeared to be over expressed when compared to the microarray data.  3.3.5 Investigation of specific intracellular signaling pathways using Quantitative RT-PCR In addition to the genes that were determined to be differentially regulated in the RNA microarray, genes that were not found in the analysis but were related to the pathways of interest were examined for differential expression using Q RT‐PCR. The data taken from the microarray analysis appeared to indicate a possible change in TNF‐α post‐transcriptional control as well as a tendency towards intrinsic apoptosis over extrinsic apoptosis in CD43 KO macrophages when compared to WT. Results from Q RT‐ PCR (Table 6) indicate that these trends require further investigation. When CD43KO macrophages were stimulated with M.tb, there appeared to be an increase in genes related to intrinsic apoptosis, although the quantity of differential expression was not determined to be statistically significant. Cpn60.2 also demonstrated similar trends for 2 of the 3 non‐microarray genes. Additional genes associated with extrinsic apoptosis also appeared to be up‐regulated in CD43KO macrophages in the majority of treatments. TNF‐α production appeared to be deficient in KO macrophages, but the level of differential expression was not statistically significant.  60  Table 6 Investigation of specific intracellular signaling pathways using Quantitative RT‐PCR Phagocytosis medium Gene Name / Related Pathway Intrinsic Apoptosis Bax Casp9 Apaf1 Extrinsic Apoptosis TRADD Casp3 TNF‐α production TNF‐α  Normalized  SE  M.tb Normalized  SE  Cpn 60.2 Normalized  SE  1.0572 0.246 0.9893 0.277 0.8227 0.184  1.927 0.811 1.077 0.5407 1.259 0.5596  0.888 0.1313 1.6027 0.265 1.7437 0.3319  0.8693 0.315 0.8157 0.192  0.571 0.1125 1.507 0.1362  1.3655 0.3765 1.5583 0.5024  1.119 0.184  0.61 0.2312  0.8153 0.3831  Additional genes coding for proteins that had direct protein‐protein interactions with the genes and pathways of interest were also investigated for differential regulation. Gene expression was normalized with the average expression of three reference genes (GAPDH, 16S rRNA and β‐actin) and KO expression is presented in reference to WT control. Data is the mean of three biological replicates  61  3.4 Discussion and Summary Understanding how the host cells interact with M.tb during their initial encounter is an important part of gaining understanding of what it takes to establish an infection. Important cytokines, proteins, receptors and their subsequent stimulatory functions such as inducing anti‐microbial responses are all vital in this process. Deficiencies in pro‐inflammatory cytokines such as TNF‐α, IFN‐γ and IL‐12 have all been shown to result in increased host susceptibility to M.tb [132]. Phagocytes play crucial roles as initiators and directors of the immune response, both by cytokine production and antigen presentation. It is known that M.tb can evade many of these processes by entering the macrophage and preventing activation [152]. This can be done via binding and uptake through the complement receptors CR1, CR3 or CR4 [153]. Mannose receptor and type A scavenger receptors are also known as ‘back door’ entries for the bacterium because the binding of these receptors is not directly linked to an inflammatory response [154]. As previously mentioned, research in our lab has demonstrated strong binding interactions between Cpn60.2, an hsp found on the mycobacterial capsular surface, and CD43, a transmembrane glycoprotein found on macrophages [7]. Using Cpn60.2 and dnaK, another capsular hsp with weaker affinity for CD43 [7], we examined the possible changes in intracellular signaling due to CD43 stimulation with the hsps in comparison to whole bacteria to investigate how they relate to each other. Our lab has also previously determined a link between CD43, TNF‐α production, control of bacterial infection and increased apoptosis upon infection with M.tb [132]. Given the already‐known pro‐inflammatory capabilities of Cpn60.2 [151], we hypothesized that CD43 stimulation by this hsp could, at least in part, be responsible for the effect of the bacterial interactions with macrophage CD43. An RNA‐microarray was performed by which RNA from bone‐marrow derived murine macrophages from CD43 KO and WT mice that had been co‐incubated with one of Cpn60.2, dnaK, M.tb or phagocytosis  62  medium (control) was applied to a mouse whole‐genome expressed sequence tag array. The initial analysis of this gene‐expression array demonstrated that the pattern of differential expression of the genome attributable to CD43 was quite different between all treatment types. This then led us to a more functionally‐oriented investigation. The exploration into differential pathway regulation demonstrated a number of interesting findings. There were many different areas of cellular function possibly regulated by CD43, several of them linked to in vivo and in vitro results found in our lab[69, 132]. Cell death, morphogenesis, cellular movement, respiratory burst and chemo‐attraction were all categories that had a statistically significant number of genes differentially regulated. These areas all had physiological significance in the form of previously published observations, both from our group and others [132, 147]. Other pathways of interest that had high numbers of differentially expressed genes included eicosanoid signaling, p53 and MAPK signaling. All of these processes have been related to cellular function both in general and in reference to microbial infection and activation. Because these signaling mediators are so diversely involved in cellular functions, it is difficult to say if their differential expression is directly related to CD43 or a result of a subsequently differentially regulated process. That being said, these data should not be regarded as insignificant. The scope of this project is such that their investigation could not be conducted, but could be highly relevant to these and other physiological observations made about cells deficient in CD43. The areas that were investigated further with quantitative RT‐PCR were apoptosis and TNF‐α regulation, wherein the microarray was partially validated in expression trends with the Q RT‐PCR. The trends for the control phagocytosis‐medium treatment were similar. Both microarray and Q RT‐PCR were around one, and not significantly differentiated. However not all of the genes were consistently validated for M.tb and Cpn60.2. Zfp36, a gene responsible for TNF‐α mRNA control was down regulated in the CD43 KO macrophages compared to the CD43 WT macrophages with both M.tb and  63  Cpn60.2 in the microarray data, but appeared to be unchanged when examined with Q RT‐PCR. Tnfrsf1b had a very interesting expression profile, the microarray data inferred that it would be down regulated in Cpn60.2‐incubated CD43KO macrophages but up regulated in M.tb‐infected CD43KO macrophages. However the Q RT‐PCR results demonstrated opposing effects; it appeared to be significantly up regulated in the CD43KO macrophages when stimulated with Cpn60.2 and down regulated in M.tb infected macrophages. Caspase 7 gave similar results for Cpn60.2 treatment; it was down regulated in the microarray and up regulated in the Q RT‐PCR. In contrast, M.tb infected macrophages gave similar results for caspase 7 expressions in the microarray and Q RT‐PCR. Caspase 9 was significantly differentially expressed in the microarray analysis of dnaK treated and M.tb infected samples and was included in the Q RT‐PCR reactions due to its biological significance. Both M.tb and Cpn60.2 appear to have opposing expression levels with Q RT‐PCR data when the microarray showed down regulation in M.tb and unchanged relative expression in Cpn 60.2, but with Q RT‐PCR data, unchanged relative expression with M.tb and relative up regulation in Cpn60.2. It would appear that the Q‐PCR validation of the microarray was inconclusive and that more differentially expressed genes should have been chosen. The pathway‐related genes however did give some interesting results in terms of biological trends. Although no differential expression was statistically significant, both Bax and Apaf1, which are key players in intrinsic apoptosis were up regulated in CD43KO mice when infected with M.tb. Apaf1 and Caspase 9 were up regulated in the CD43KO compared to CD43WT when stimulated with Cpn60.2. The genes that had been selected for their involvement in extrinsic apoptosis, TRADD and Tnfrsf1b, both appear to be up regulated in the CD43KO macrophages when exposed to Cpn 60.2 as well, making this story more complex than initially anticipated. TNF‐α expression appears to remain higher in the CD43 WT than the CD43KO although this was not statistically significant, it would appear that this pathway could still be affected at the transcriptional level. 64  It would appear that the genes that had been selected involved in TNF‐α production and extrinsic apoptosis appear to be predominantly in agreement with previous studies performed in our lab [132]. M.tb infection and CD43 deficiency appears to have a negative effect on the production of TNF‐α‐ associated genes as well one out of the two extrinsic‐apoptotic signaling genes. The previous observations made regarding the affects of CD43 lend credit to this trend, but as previously mentioned, the effects of mRNA quantity on protein production are not always directly‐linked, and the significance found here may or may not be completely reflective of the biological significance of TNF‐α underproduction. The disconnect between the RNA microarray and Q RT‐PCR could be due to a number of factors. The many steps of sample preparation invite a number of sources of error that cannot always be accounted for. Due to the typically low quantities of mRNA that working with primary cell‐culture provides, a mRNA amplification was done to a portion of the original RNA sample for the microarray work. The rest of the RNA was stored at ‐80˚C until such a time that it was required for Quantitative‐RT‐PCR, which was at least one and a half years later. In the case of M.tb‐infected macrophages, the RNA was more than two years old at the time of cDNA synthesis. . RNA storage at ‐80˚ is typically secure up to a year as long as the sample remains undisturbed however contamination of these samples would have gone unchecked after the initial bioanalyzer examination pre‐RNA amplification By conventional standards, RNA that is over a year old is too old to use. The possible degradation taken place during this time makes any true verification of RNA‐expression somewhat suspect, particularly in the case of the M.tb‐ infected samples. The Q RT‐PCR was also performed on cDNA that was made from original RNA sample, and not amplified RNA that was used in the microarray itself. The amplification process could have had an effect on mRNA  65  that was present in either very low or very high quantities, with higher concentrations of mRNA being more likely to amplify and lower concentrations being less likely to amplify. Examination of the technical repeats of the data suggests that at the time of Q‐RT‐PCR, there was little to indicate errors in reproducibility within a single, aliquotted cDNA sample. That being said however, the manner in which the experiment was set‐up could have been altered to lessen the probability of variation between genotypes and biological replicates. The reactions were performed in a genotype‐ specific manner, then normalized within the plate and compared to their other genotype‐plate. Although in‐plate normalizations were performed, plate to plate variations could affect the comparisons of relative expression levels. There appears to be inconsistencies between plate values for the reference genes as well as the genes of interest. The differences in gene expression for a CD43WT to CD43KO comparison would be better compared on the same plate, in order to eliminate any error due to run‐to‐run variation. This would also make it more apparent if a reference gene was not only affected by treatment, but genotype as well; which could be the case in this example. Three reference genes were chosen to lessen the likelihood of this effect, so that target genes could be normalized to an average expression of reference. However the second comparison, of that CD43WT to CD43KO data, could be influenced by the possible changes in expression of the reference genes per genotype. Previously observed data from Randhawa et al demonstrated both lower amounts of apoptosis and TNF‐ α production in CD43KO macrophages, when infected with M.tb [132]. The molecular data shown here shows the disconnect that is physiologically possible between transcription of a gene, its translation into protein and the biological protein release. TNF‐α is a major apoptotic signaling factor in macrophage biology and is crucial in the containment of M.tb infection. When TNF‐α levels are hindered, either by genomic disruption or protein‐inhibition, intracellular replication of M.tb increases significantly. This effect is returned to normal upon the addition of recombinant TNF‐α. Although current Q‐RT‐PCR data  66  suggest that the TNF‐α mRNA levels are not statistically significantly different between the KO and WT macrophages, the trend is in agreement with previously published data. It has already been observed in vitro that these results are physiologically significant to the survival of the host cell and the subsequent life cycle of the bacteria [132]. This could indicate that the significant alterations that occur are at a post‐translational level, and involve other signaling enzymes such as kinases and acetylases. The post‐ translational modification sites for CD43 are well‐documented [115, 119, 120, 145] The alternate pathways that were differentially regulated included several kinase‐related pathways. An interesting result that was found but left unexplored was the differentially‐related eicoasnoid signaling. Several enzymes in the prostaglandin synthesis pathway were differentially regulated between CD43 KO and WT macrophages. Although phospholipase A2 (Pla2g2d) appeared to be up regulated in KO macrophages, subsequent enzymes such as Prostaglandin‐endoperozide synthase 2 which helps break down the arachadonic acid produced by phospholipase A2, is only up regulated in Cpn60.2‐treated macrophages compared to M.tb‐infected macrophages. Additionally, prostaglandin E synthase , ptges, is down regulated in both Cpn60.2 treated and M.tb‐infected CD43 KO macrophages. There have been studies that demonstrate ptges knockout mice are more susceptible to M.tb [155] , however this observation is based on the theory that the bacterium inhibits prostaglandin‐E2 mediated plasma membrane repair in the lysosome. If this mechanism disruption was a factor in CD43 KO mice, it could help to explain the observed increased necrosis in CD43 KO macrophages, as observed previously in our lab [132]. Cytoskeletal remodeling was also differentially affected between the two genotypes. Previous studies have already determined a link between CD43 deficiency and impaired binding and uptake of M.tb bacillus [69, 147]. Blocking of CD43 on the host surface with Cpn60.2 also prevents bacterial entry into the macrophage [7], indicating that CD43 is not only a player in cytokine production and intracellular  67  regulation, but in the very beginnings of bacteria‐host interactions. It has not yet been determined what the link is between CD43, cytoskeletal modifications and phagocytosis but our microarray determined that there are differentially regulated genes related to these processes. Recently, Kumar et al [156] performed a similar type of study to ours, using a genome‐wide bank of siRNAs for macrophages, infecting them with M.tb and observing the resulting growth patterns. Several genes from their study that were up regulated by M.tb infection were also significantly differentially regulated in this study as well. EMP2, SLC26A11, ST8SIA4 and TLR8 were all up regulated in the CD43WT macrophages upon infection with M.tb compared to the CD43KO counterparts in the microarray performed. The investigation in the study of Kumar et al also focused on pathway‐analysis, and found that autophagy is a key process in the control of intracellular replication. The aforementioned, shared genes all produce protein products that interact within the autophagy pathway, but having both inhibitory and stimulatory roles for autophagy in the cell. It was observed that M.tb can possibly partially control the advent of autophagy from within the cell via manipulation of intracellular signaling. Some of the aforementioned proteins, EMP2 and ST8SIA4 are down regulated in infected macrophages that are stimulated by IFN‐γ, further indicating a possible role in their importance to control of intracellular infection. Autophagy and apoptosis are tightly regulated intracellular functions. The former is a signal for the cell to begin ‘digesting itself’, usually instigated in times of severe cell stress and nutrient‐ deprivation, whereas the latter, apoptosis, is also a path which the cell controls from within, initiating the dismantling of intracellular structures within a highly organized procedure. It is clear to see that both of these processes would be detrimental to the successful replication of M.tb bacillus within its host macrophage niches, and therefore must be managed in some way. The inability to carry out either of these functions could very possibly be detrimental to the chances of survival for the host cell and certainly result in a less than optimal control of infection.  68  4 Conclusions and future directions 4.1 Conclusions Mycobacterium tuberculosis is the etiological agent of TB, an infection that has plagued mankind for centuries. It is capable of evading host defenses without any initial detrimental effects to host or pathogen, enabling successful intracellular replication and efficient dissemination. Although the advent of antimicrobial treatments gave hope to the millions of sufferers worldwide, it is evident that none of these treatments is without weakness, and drug‐resistance is a common phenomenon [55]. The best prophylaxis for infection that we have is the vaccine BCG. The efficacy of this vaccine has been documented to be inconsistent: it varies from 0% to 80%[157] [31‐33]. Most disturbingly, it shows the least protection in equatorial climes, inhabited by people who need protection from M.tb the most [40]. The main reasons for this effect are predominantly related to cross‐antigen reactivity with environmental mycobacteria, which are more prevalent in southern climes. The already primed immune system easily kills the attenuated Mycobacterium bovis, BCG, (thus compromising its efficacy) but is still ineffectual against the more pathogenic Mycobacterium tuberculosis. Subsequent infection with M.tb ensues and the immune system is only activated enough to simply contain the infection at the latent stage, offering no protection from the re‐activation that commonly occurs with age and infirmity. Additional vaccines have been developed as ‘booster’ shots, to further prime the immune system and initiate stronger activation responses. Subunit and DNA‐based vaccines are in the forefront of this field, using highly antigenic M.tb‐specific proteins. These vaccines are intended to both further stimulate the host immune response as well as generate a more species‐specific defense. Mycobacterial HSPs such as Cpn60.2, ESAT‐6 and 85kDa are common players in these studies. Fusion proteins of one or more of these HSPs are regularly developed and tested for improved efficacy. T‐cell responses vary depending  69  on the stimulus used, and it has been surmised that gaining the appropriate balance of CD4+ and CD8+ T‐cell response with a vaccine could be the key to attaining effective prophylaxis [40]. The intricate interactions between M.tb and the host immune system over the course of a possible life‐ long infection present a unique challenge in terms of improving the host immune response. The ability of M.tb to survive and manipulate the adaptive and cell‐mediated sides of immunity demonstrates a need to better understand these interactions, so that we may eventually be able to use them to the host advantage. Understanding the initial macrophage‐host interaction is key to finding the mediating factors that can control the balance of power between the host and pathogen. The research presented here is an investigation into the possible role of macrophage CD43 in the regulation of the host response to M.tb infection and to bacterial HSPs located on the cell surface. Previously published results show both the ability of Cpn 60.2 and dnaK to demonstrate immunomodulatory functions [151, 158] as well as evidence suggesting a prominent role of CD43 in M.tb‐macrophage association and mediation of bacterial growth [7, 72, 132, 147]. Additional studies demonstrated binding of Cpn 60.2 and dnaK to the CD43 extracellular domain and the ability of exogenous Cpn 60.2 to significantly block the binding and uptake of bacteria by CD43 WT macrophages [7]. This was of interest because it provided possible evidence for a direct ligand‐receptor signaling mechanism of CD43 within M.tb infection. The purpose of this current study was to determine if Cpn 60.2 or dnaK were either entirely or partially responsible for mediating the cellular responses previously observed in vitro between M.tb and CD43 WT macrophages versus CD43 KO macrophages and to determine other possible functions of CD43 when stimulated by mycobacterial ligands. These responses included differential cytokine responses and bacterial binding and uptake, which could involve a number of possible areas of cellular control. To gain a broader understanding of the intracellular differences between CD43 KO and WT 70  macrophages, we performed an RNA microarray on mRNA isolated from CD43 KO and WT BMDMφ that had been either infected with M.tb or co‐incubated with one of Cpn 60.2 or dnaK. The differential expression was then examined between WT and KO for each treatment, and expression profiles of KO macrophages were compared between treatments. Results from this study showed that there were a great number of transcriptional differences between WT and KO macrophages as a whole, despite CD43 KO mice being typically described as phenotypically unremarkable in a passive, unstimulated state [145]. There were more differences than similarities between M.tb and either HSP in the expression profiles they induced. However a number of shared functional categories and signaling pathways were elucidated. CD43 deficiency combined with bacterial ligand stimulation led to differential regulation of a variety of areas of cellular regulation including intracellular signaling molecules such as p53/MapK, cytoskeletal maintenance, eicosanoid production, TNF‐α production and apoptosis. Previously published in vitro results [132, 147] implicated CD43 has a strong affect on apoptosis and TNF‐α production and so the latter two categories were chosen for further investigation using quantitative RT‐PCR. In terms of differential change of gene expression between CD43 KO and CD43WT macrophages, the data obtained lacks statistical significance. It would appear, however, that some of the trends that were observed in the microarray are supported by the Q RT‐PCR, although not for all treatments. This is somewhat anticipated, because both Cpn 60.2 and dnaK have different cytokine expression profiles and binding affinity for CD43 and M.tb is clearly more likely to induce greater transcriptional changes than a single purified protein. The Q RT‐PCR results were not entirely consistent with the microarray results. Gene expression (as measured by Q RT‐PCR) was not consistently differentially regulated between Cpn60.2 and M.tb, and expression values did not entirely reflect those of the microarray. However, additional genes from implicated pathways were investigated and these results demonstrated trends that indicated some of  71  the observed pathways are directly affected by the loss of CD43. Intrinsic apoptotic signaling and TNF‐α production both demonstrate trends and, as previously mentioned, the statistical insignificance in differential mRNA levels between CD43KO and CD43WT macrophages could indicate that the significant alterations that occur are at a post‐translational level, and involve other signaling enzymes. As mentioned additionally in the discussion, there were a number of unexplored pathways that showed promise as CD43‐related factors. These findings however, would have to be verified using additional tests at either RNA or protein levels, but using fresher samples, with an altered study set‐up. The results presented indicate that CD43 is capable of inducing changes in intracellular signaling which control a variety of cellular functions. Stimulation of its extracellular domain with Mycobacterium tuberculosis and mycobacterial proteins induce a variety of responses, some of which have been closely connected to in vitro observed results. Not surprisingly, there is no known single pathway to infection protection from M.tb. It has a unique and complex relationship with its host, but through this study and similar investigations, these pathways might be elucidated and their connections employed for the benefit of the host.  4.2 Future directions The body of this work examines the initial steps of intracellular change upon infection. There is some in vitro, in vivo and protein‐level work previously done involving CD43 and Mycobacterium tuberculosis, that shows CD43 deficiency results in reduced bacterial binding and uptake and reduced pro‐ inflammatory cytokine production leading to increased intracellular replication of the bacterium in vitro and increased bacterial load of the organs in vivo [72, 132]. Cpn60.2 and dnaK from Mycobacterium tuberculosis both have been shown to bind the extracellular portion of CD43, and the co‐incubation of CD43WT macrophages with recombinant Cpn 60.2 can partially block M.tb tuberculosis in a dose‐  72  dependent manner [7]. This study has demonstrated the capability of Cpn60.2 to initiate changes in the transcriptional response of CD43 WT and KO macrophages, but in order to determine physiological significance, these interactions should also be validated on a protein‐level. It is still not entirely clear if Cpn60.2 is also capable of affecting TNF‐α production via CD43 stimulation. Even though the RNA microarray and Q RT‐PCR indicated post‐translational trends that support this contention, these changes were not statistically significant and their impact on protein production and release (in terms of CD43‐ related TNF‐α) is not yet known. Because of its broad‐ranging immunostimulatory nature, TNF‐α has a tightly‐regulated production pathway. More genes involved in this production pathway, both those found in the microarray study and those not, could be examined using Q RT‐PCR. Adam17, TIMP3 and tnfaip3 were all differentially expressed in the microarray and are closely involved with TNF‐α production. It would be interesting to see if the production of any of these proteins was highly deregulated due to CD43 disruption, and contribute to the understanding of how CD43 and TNF‐α are linked during macrophage activation. Since it had been previously demonstrated that CD43 deficiency resulted in reduced TNF‐α production as well as reduced apoptosis and increased levels of necrosis in vitro [132] only apoptosis and TNF‐α were further examined in this study. Other affected signaling pathways, such as prostaglandin synthesis, and MAPK activation have previously been shown to also have important roles in M.tb infection, affecting cytokine production, plasma membrane stability and endosome function [155]. Our group observed that the lack of CD43‐mediated TNF‐α production resulted in increased mycobacterial growth, decreased apoptosis and increased necrosis [132]. Divangahi et al showed that lack of prostaglandin E2 (PGE2 ) prevents membrane repair and induces necrosis. Their study investigated the ability of virulent M.tb to down regulate PGE2 leading to the result of increased necrosis and decreased apoptosis. It is possible that there is a similar link in CD43KO mice, with decreased PGE2 production, leading to the increased necrosis that was previously observed by Randhawa et al [132]. Our Q RT‐PCR 73  data showed an elevated level of caspase 3 mRNA in CD43KO macrophages when infected with M.tb or co‐incubated with Cpn60.2. Although Randhawa et al found relatively decreased caspase 3 activity, these findings should be validated further, through additional Q RT‐PCR experiments using more recent samples and perhaps involving increased Cpn60.2 concentrations. Another caspase 3 activity assay could also be performed, using both M.tb infection and Cpn60.2 co‐incubation as stimuli for apoptotic activities. It would be interesting to investigate the link between differential caspase 3 mRNA production, protein production and activity in CD43KO macrophages versus CD43WT. The possible link between prostaglandin and necrosis could be further investigated by looking at the potential differential levels of prostaglandin production within these two genotypes in relation to the differential levels of apoptosis during M.tb infection and Cpn60.2 stimulation using ELISA. Cytoskeletal remodeling was also differentially affected between the two macrophage genotypes. Previous studies have already determined a link between CD43 deficiency and impaired binding and uptake of M.tb bacillus. Blocking of CD43 on the host surface with Cpn60.2 also prevents bacterial entry into the macrophage, indicating that CD43 is not only a player in cytokine production and intracellular regulation, but in the very beginnings of bacteria‐host interactions. It has not yet been determined what the link is between CD43, bacterial binding, cytoskeletal modifications and phagocytosis. A simple assay that could be done to investigate the possible cytoskeletal defects in CD43KO mice would be a mobility assay involving CD43KO and CD43WT macrophages: investigating their ability to move towards a chemo attractant while adhered on a solid surface. Latex beads are also commonly used in studies involving phagocytes; to investigate the ability of CD43KO macrophages to phagocytose latex beads, coating the beads in different proteins, such as Cpn60.2 or dnaK to improve or impair binding could also be performed.  74  Similarly, a number of genes found to be statistically differentially regulated in our study have been linked to the results of other groups interested in host‐macrophage interactions with M.tb. As previously mentioned, Kumar et al [156] have recently made a case for the importance of autophagy in the control of M.tb infection and replication. This pathway involves cytoskeletal remodeling as well as a significant involvement of intracellular signaling molecules (due to tight internal regulation). It is feasible to further investigate the differential expression of the genes that are commonly shared between the two studies, to fully explore the possibility of a link between these observations. Q RT‐PCR analysis on EMP2, SLC26A11, ST8SIA4 and TLR8 to verify possible differential expression between CD43KO and CD43WT would be a good starting point to investigate the possible links between these two studies. Lastly, it has yet to be determined if the observed differential changes in mRNA expression mediated by CD43 are a result of its functionality as a single signaling molecule, or its possible work in conjunction with an adapter protein. The concentrations of recombinant Cpn60.2 and dnaK used to elucidate transcriptional response (5 µg/ml) were sufficient to block bacterial binding and uptake [7], but not to produce optimal cytokine response previously observed with Cpn60.2 (10µg/ml) [151]. That being said, differential responses between CD43KO and CD43WT macrophages were observed and several genes relating to apoptosis were shared in expression levels in the Q RT‐PCR data. The intracellular tail of CD43 has the capacity to maintain a phosphorylation or ubiquitin signal, but has no known kinase activity of its own. It is possible that it works in conjunction with additional adaptor proteins. More detailed work involving CD43‐binding moieties (such as Cpn60.2) and macrophages at the protein‐level could further deduce the roles of CD43 as a possible receptor on its own or as a partner in a larger intracellular signaling network. The data provided indicates many possible CD43 interactions previously unconsidered. Although CD43KO mice appear to be normal, their ability to regain and maintain homeostasis appears altered. An investigation with differential dosage of Cpn60.2 and M.tb infection could be performed, using the two known stimulatory concentrations (5 and 10µg/ml) of Cpn60.2, M.tb 75  infections with normalized quantities of bacteria (MOI of 20:1 for CD43WT and 30:1 for CD43KO) looking at specific cellular functions of interest such as apoptotic activity, as previously described. Transfection of CD43KO macrophages with CD43 that has had an altered intracellular tail could determine if the regions of phosphorylation (Ser76 for example, [159] ) are necessary for specific cellular functions or not. Additional experiments that would give strength to these findings might include additional quantitative RT‐PCR experiments using RNA that has not been in storage for so long, and altering the plate setup to accommodate both sets of comparison data on a single plate, to minimize variation. Since the microarray that was performed was a direct comparison of CD43WT and CD43KO gene expression, it would be prudent to include both genotypes on a single plate, as well as space enough to include a small dilution‐set to measure PCR efficiency with each run. Using the REST program, gene concentration is calculated using PCR efficiency first then the relative expression is calculated by the differences in concentrations. Calculations using this method would be more accurate (per plate) if a separate PCR efficiency was performed in each instance. Although this would reduce the number of genes examined per experiment, the data gained from these changes would be more reliable. Many studies have demonstrated an important role of the macrophage in M.tb infection. It is the first cell to encounter M. tuberculosis, the first line of direct defense, and the first cell to initiate the cytokine cascade. The ability to produce an appropriate cytokine response is crucial for the activation as well as infiltration of incoming immune cells to the site of infection. Furthermore, the ability to appropriately regulate tissue homeostasis with functions such as autophagy and apoptosis is vital for gaining the upper hand and controlling intracellular replication. It has been shown that CD43 has multiple possible roles in this process, and is potentially important for macrophage function in the state of infection. Understanding the factors that impact host‐response in M.tb infection is imperative in developing novel  76  prophylactic treatments. Species‐specific stimulation of immunity can be improved by knowing more about the factors of stimulation and cell activation from within the immune system as well as the bacterial interactions.  77  References  1. 2. 3. 4. 5.  6. 7.  8. 9.  10.  11. 12. 13.  14. 15. 16.  17.  18.  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[Source:RefSeq;Acc:NM_028078] Catechol O‐methyltransferase, membrane‐bound form (EC (MB‐ COMT) [Contains: Catechol O‐ methyltransferase, soluble form (S‐COMT)]. [Source:Uniprot/SWISSPROT;Acc:O88587] Lipoamide acyltransferase component of branched‐chain alpha‐keto acid dehydrogenase complex, mitochondrial precursor (EC (Dihydrolipoyllysine‐residue (2‐ methylpropanoyl)transferase) (E2) (Dihydrolipoamide branched chain transacylase) (BCKAD E2 subunit). [Source:Uniprot/SWISSPROT;Acc:P53395]  Jam4  0.775  0.807  1.275  0.841  0.591  Comt  0.802  2.348  1.15  1.068  0.766  Dbt  0.586  1.316  0.922  1.194  0.834  Mkrn2  0.679  0.954  1.528  1.014  1.076  Grasp  1.744  1.485  1.627  1.062  1.03  C1d  0.673  0.825  1.08  0.888  1.33  Cd52  0.547  1.633  1.387  0.989  1.906  Luzp1  0.764  1.38  1.169  1.106  0.658  Slc13a2  2.193  0.621  0.658  0.961  0.995  Agpat3  0.738  1.812  1.096  1.165  0.957  Lsr  1.505  1.113  0.626  1.289  1.393  leucine zipper protein 1. [Source:RefSeq;Acc:NM_024452] Solute carrier family 13, member 2 (Renal sodium/dicarboxylate cotransporter) (Na()/dicarboxylate cotransporter 1) (NaDC‐1). [Source:Uniprot/SWISSPROT;Acc:Q9ES88] 1‐acylglycerol‐3‐phosphate O‐acyltransferase 3. [Source:RefSeq;Acc:NM_053014] liver‐specific bHLH‐Zip transcription factor; Lisch7 protein. [Source:RefSeq;Acc:NM_017405]  1.31  0.608  0.639  1.261  0.853  GRAM domain containing 1A  Cyp51  2.268  0.954  1.1  0.998  0.825  cytochrome P450, 51.  Itfg2  1.776  1.117  1.233  1.012  0.995  Coasy  1.464  1.213  1.121  1.259  1.135  integrin alpha FG‐GAP repeat containing 2 Bifunctional coenzyme A synthase (CoA synthase) [Includes: Phosphopantetheine adenylyltransferase (EC (Pantetheine‐ phosphate adenylyltransferase) (PPAT) (Dephospho‐CoA pyrophosphorylase); Dephospho‐ CoA kinase (EC (DPCK)  Gramd1a  Makorin 2. [Source:Uniprot/SWISSPROT;Acc:Q9ERV1] GRP1 (general receptor for phosphoinositides 1)‐ associated scaffold protein; GRP1‐associated scaffold protein. [Source:RefSeq;Acc:NM_019518] nuclear DNA‐binding protein; C1D protein; small unique nuclear co‐repressor. [Source:RefSeq;Acc:NM_020558] CAMPATH‐1 antigen precursor (CD52 antigen) (Lymphocyte differentiation antigen B7). [Source:Uniprot/SWISSPROT;Acc:Q64389]  86  Common  Incubation Medium (protein‐ incubation)  Incubation Medium (infection )  M.tb  60.2  dnaK  Description von Willebrand factor. [Source:RefSeq;Acc:NM_011708] suppressor of Ty 6 homolog. [Source:RefSeq;Acc:NM_009297] nuclear prelamin A recognition factor‐like. [Source:RefSeq;Acc:NM_026238] transmembrane protein 143; synonyms: AI481604, 2310076O21Rik; Mus musculus transmembrane protein 143 (Tmem143), mRNA.  Vwf  1.645  1.346  1.134  1.326  1.173  Supt6h  2.772  0.035  0.576  0.736  1.315  Narfl  0.731  0.851  0.951  0.984  0.969  Tmem143  0.537  1.087  1.238  0.62  1.191  Btbd6  1.449  1.251  1.052  0.992  1.139  Prkar2a  0.545  0.831  1.03  0.949  0.8  Cyp17a1 expressed sequence AI413414 (AI413414)  1.382  0.747  0.907  0.805  0.885  BTB/POZ domain containing protein 6. [Source:Uniprot/SWISSPROT;Acc:Q8K2J9] cAMP‐dependent protein kinase type II‐beta regulatory chain (Fragment). [Source:Uniprot/SWISSPROT;Acc:P31324] Cytochrome P450 17A1 (EC (CYPXVII) (P450‐ C17) (P450c17) (Steroid 17‐alpha‐hydroxylase/17,20 lyase). [Source:Uniprot/SWISSPROT;Acc:P27786]  1.73  2.38  1.563  1.205  0.617  expressed sequence AI413414 (AI413414)  Cp  1.478  0.486  0.666  0.934  1.341  Brd8  1.438  0.749  1.275  1.068  1.052  Fancl  0.724  1.295  1.07  1.098  0.955  Rpgrip1  0.726  1.563  0.902  0.819  0.749  Dlst  1.624  0.975  1.182  0.676  0.913  Pgf  0.687  1.103  1.056  1.315  0.918  Grap  0.684  1.776  0.908  1.008  1.029  Prdx2  0.733  1.191  1.163  0.962  1.171  Placenta growth factor precursor (PlGF). [Source:Uniprot/SWISSPROT;Acc:P49764] GRB2‐related adaptor protein. [Source:RefSeq;Acc:NM_027817] Peroxiredoxin 2 (EC 1.11.1.‐) (Thioredoxin peroxidase 1) (Thioredoxin‐ dependent peroxide reductase 1) (Thiol‐ specific antioxidant protein) (TSA). [Source:Uniprot/SWISSPROT;Acc:Q61171]  Polr2a  0.616  2.04  1.772  1.269  0.906  DNA‐directed RNA polymerase II largest subunit  Ceruloplasmin precursor (EC (Ferroxidase). [Source:Uniprot/SWISSPROT;Acc:Q61147] bromodomain containing 8. [Source:RefSeq;Acc:NM_030147] Ubiquitin ligase protein PHF9 (EC 6.3.2.‐) (Proliferation of germ cells protein). [Source:Uniprot/SWISSPROT;Acc:Q9CR14] X‐linked retinitis pigmentosa GTPase regulator‐interacting protein 1 (RPGR‐interacting protein 1). dihydrolipoamide S‐succinyltransferase (E2 component of 2‐oxo‐glutarate complex). [Source:RefSeq;Acc:NM_030225]  87  Common  Incubation Medium (protein‐ incubation)  Incubation Medium (infection )  M.tb  60.2  dnaK  Description Rho‐interacting protein 3 (p116RIP) (RIP3). [Source:Uniprot/SWISSPROT;Acc:P97434] Low affinity immunoglobulin epsilon Fc receptor (Lymphocyte IGE receptor) (Fc‐epsilon‐RII) (CD23). [Source:Uniprot/SWISSPROT;Acc:P20693] Parvalbumin alpha. [Source:Uniprot/SWISSPROT;Acc:P32848] Ran‐specific GTPase‐activating protein (Ran binding protein 1) (RANBP1). [Source:Uniprot/SWISSPROT;Acc:P34022] Neutrophil collagenase precursor (EC (Matrix metalloproteinase‐8) (MMP‐8) (Collagenase 2). [Source:Uniprot/SWISSPROT;Acc:O70138] Orphan nuclear receptor TR4 (Orphan nuclear receptor TAK1). [Source:Uniprot/SWISSPROT;Acc:P49117]  RIP3_MOUSE  2.401  1.598  0.447  1.653  0.529  Fcer2a  0.349  1.187  1.228  1.382  1.119  Pvalb  2.426  1.276  1.379  0.456  1.16  Ranbp1  0.803  1.034  1.139  1.124  1.048  Mmp8  1.497  1.12  1.113  0.893  1.121  Nr2c2  1.296  1.061  0.496  1.114  0.858  Otx1  0.465  0.851  2.286  1.353  1.755  Dnase1  2.511  1.167  0.798  0.457  0.795  0.6  1.31  0.653  0.682  1.47  Pak6  2.225  1.467  1.202  1.097  0.811  p21 (CDKN1A)‐activated kinase 6  Herpud2  1.203  1.215  1.271  1.05  0.976  Mgst1  0.66  1.334  1.308  0.81  1.103  Hipk1  1.265  1.006  0.947  0.971  0.524  Apobec3  1.59  1.21  0.803  0.75  1.415  Ccdc109a  2.247  1.278  0.939  0.966  0.71  HERPUD family member 2 Microsomal glutathione S‐transferase 1 (EC (Microsomal GST‐ 1) (Microsomal GST‐I). [Source:Uniprot/SWISSPROT;Acc:Q91VS7] Homeodomain‐interacting protein kinase 1 (EC 2.7.1.‐). [Source:Uniprot/SWISSPROT;Acc:O88904] DNA dC‐>dU editing enzyme APOBEC‐3 (EC 3.5.4.‐) (Apolipoprotein B editing complex 3) (Arp3) (CEM15) (CEM‐15). [Source:Uniprot/SWISSPROT;Acc:Q99J72] PREDICTED: similar to chromosome 10 open reading frame 42 (LOC215999)  BY749370  1.894  0.715  0.684  0.578  2.45  0.66  1.072  1.138  0.771  0.96  BC048079 ENSMUSG000 00011263  0.619  1.135  1.36  1.17  0.795  hypothetical protein  2.715  0.511  1.231  0.46  0.516  hypothetical protein  BC040201  0.511  0.914  1.16  1.338  1.195  hypothetical protein  Eps8l1  Wnt2  Homeobox protein OTX1. [Source:Uniprot/SWISSPROT;Acc:P80205] Deoxyribonuclease I precursor (EC (DNase I). [Source:Uniprot/SWISSPROT;Acc:P49183] epidermal growth factor receptor pathway substrate 8‐ like protein 1. [Source:RefSeq;Acc:NM_026146]  hypothetical protein Wnt‐2 protein precursor (IRP protein) (INT‐1 related protein). [Source:Uniprot/SWISSPROT;Acc:P21552]  88  Common  Incubation Medium (protein‐ incubation)  Incubation Medium (infection )  M.tb  60.2  dnaK  Description  Q9D2E9  1.779  0.609  0.864  0.904  2.028  Slc41a1  1.77  0.931  0.64  0.893  0.713  Pten  1.646  0.992  0.983  0.98  0.966  hypothetical protein solute carrier family 41, member 1. [Source:RefSeq;Acc:NM_173865] Phosphatidylinositol‐3,4,5‐trisphosphate 3‐phosphatase PTEN (EC (Mutated in multiple advanced cancers 1). [Source:Uniprot/SWISSPROT;Acc:O08586]  Q9DA53  0.547  0.745  0.759  0.858  1.137  hypothetical protein  Pax5  1.883  0.988  1.569  2.691  0.928  Mrps25  0.689  1.198  1.141  1.229  1.785  Hoxa2  1.608  0.903  1.451  1.483  0.746  Pdcd2  1.451  0.982  1.226  1.105  1.054  Paired box protein Pax‐5 (B‐cell specific transcription factor) (BSAP). [Source:Uniprot/SWISSPROT;Acc:Q02650] Mitochondrial 28S ribosomal protein S25 (S25mt) (MRP‐ S25). [Source:Uniprot/SWISSPROT;Acc:Q9D125] Homeobox protein Hox‐A2 (Hox‐1.11) (Hox1.11). [Source:Uniprot/SWISSPROT;Acc:P31245] Programmed cell death protein 2 (Zinc finger protein Rp‐ 8). [Source:Uniprot/SWISSPROT;Acc:P46718]  AK131125 ENSMUSG000 00014786 ENSMUSG000 00015002  1.565  1.71  0.8  1.274  0.638  hypothetical protein  0.36  0.238  0.822  1.552  0.868  hypothetical protein  1.499  0.161  0.968  0.28  0.858  Hmgb3  1.289  0.793  0.912  0.93  0.968  Zdhhc12 ENSMUSG000 00015542  0.762  1.029  0.957  0.854  1.083  hypothetical protein High mobility group protein 4 (HMG‐4) (High mobility group protein 2a) (HMG‐2a). [Source:Uniprot/SWISSPROT;Acc:O54879] Zinc finger DHHC domain containing protein 12. [Source:Uniprot/SWISSPROT;Acc:Q8VC90]  0.705  0.963  1.337  1.296  1.384  Nkx2‐5  0.547  0.8  1.043  0.728  0.866  hypothetical protein Homeobox protein Nkx‐2.5 (Homeobox protein NK‐2 homolog E) (Cardiac‐ specific homeobox) (Homeobox protein CSX). [Source:Uniprot/SWISSPROT;Acc:P42582]  Dgat2l3  0.495  0.718  0.621  0.275  1.206  diacylglycerol O‐acyltransferase 2‐like 3  ENSMUSG000 00016082  1.601  1.191  1.115  1.267  1.126  hypothetical protein  Lonrf3  0.608  0.995  1.342  1.36  1.664  Matn4  1.227  0.597  0.73  0.788  0.586  Eef1a2  1.371  0.891  0.411  1.277  1.633  ring finger protein 127 Matrilin‐4 precursor (MAT‐4). [Source:Uniprot/SWISSPROT;Acc:O89029] Elongation factor 1‐alpha 2 (EF‐1‐alpha‐2) (Elongation factor 1 A‐2) (eEF1A‐2) (Statin S1). [Source:Uniprot/SWISSPROT;Acc:P27706]  Q9CZJ0  2.17  0.257  0.642  1.158  1.148  0.692  1.531  0.927  1.343  1.041  XM_149454  hypothetical protein PREDICTED: RIKEN cDNA 1700042G07 gene (1700042G07Rik)  89  Common  Incubation Medium (protein‐ incubation)  Incubation Medium (infection )  M.tb  60.2  dnaK  Exosc10  1.222  0.926  0.854  1.159  0.58  Nek8  1.606  0.621  1.279  1.173  0.818  Akap1  0.447  0.813  1.01  0.911  0.982  Description Polymyositis/scleroderma autoantigen 100 kDa (Autoantigen PM/Scl 2) (P100 polymyositis‐scleroderma overlap syndrome associated autoantigen homolog). [Source:Uniprot/SWISSPROT;Acc:P56960] Serine/threonine‐protein kinase Nek8 (EC (NimA‐related protein kinase 8). [Source:Uniprot/SWISSPROT;Acc:Q91ZR4] A kinase anchor protein 1, mitochondrial precursor (Protein kinase A anchoring protein 1) (PRKA1) (Dual specificity A‐Kinase anchoring protein 1) (D‐AKAP‐1) (Spermatid A‐kinase anchor protein) (S‐AKAP). [Source:Uniprot/SWISSPROT;Acc:O08715]  6330403K07R ik  1.616  2.049  1.363  2.009  0.765  UGS148 protein. [Source:RefSeq;Acc:NM_134022]  Ncor1  1.539  1.129  0.929  1.004  0.92  Slc2a4  0.713  1.065  1.521  0.588  1.098  Zfpn1a1  0.659  1.126  0.768  1.159  1.12  Smyd4  2.17  1.039  0.695  1.145  0.566  Tm4sf5  0.701  0.569  1.022  0.89  1.167  Slc35b4  1.264  0.951  0.928  1.057  1.171  Trip10  0.463  0.995  0.618  1.035  0.665  Slc6a8 ENSMUSG000 00019770  1.367  0.876  1.242  1.031  1.01  1.737  1.578  1.12  1.046  1.34  Trdn 1700021F05Ri k  2.631  0.972  0.715  0.273  1.153  0.557  1.173  0.817  1.041  1.268  Ppil6  1.777  5.461  1.487  0.289  1.023  Dcbld1  1.424  0.951  0.598  1.159  1.199  Nuclear receptor corepressor 1 (N‐CoR1) (N‐CoR) (Retinoid X receptor interacting protein 13) (RIP13). [Source:Uniprot/SWISSPROT;Acc:Q60974] Solute carrier family 2, facilitated glucose transporter, member 4 (Glucose transporter type 4, insulin‐ responsive) (GT2). [Source:Uniprot/SWISSPROT;Acc:P14142] DNA‐binding protein Ikaros (Lymphoid transcription factor LyF‐1). [Source:Uniprot/SWISSPROT;Acc:Q03267] SET and MYND domain containing 4. [Source:RefSeq;Acc:NM_177009] transmembrane 4 superfamily member 5. [Source:RefSeq;Acc:NM_029360] solute carrier family 35, member B4 (Slc35b4) thyroid hormone receptor interactor 10; Cdc42 interacting protein‐4. [Source:RefSeq;Acc:NM_134125] solute carrier family 6 (neurotransmitter transporter, creatine), member 8; creatine transporter. [Source:RefSeq;Acc:NM_133987] PREDICTED: similar to nesprin 1 isoform longer; synaptic nuclei expressed gene 1; nesprin 1; enaptin (LOC432424) Cardiac triadin isoform 3. [Source:Uniprot/SPTREMBL;Acc:Q8VIN7] hypothetical protein LOC67851 2900084F20Rik protein (EC (Peptidyl‐prolyl cis‐ trans isomerase) (PPIase) (Rotamase). discoidin, CUB and LCCL domain containing 1. [Source:RefSeq;Acc:NM_025705]  90  Common  Incubation Medium (protein‐ incubation)  Incubation Medium (infection )  M.tb  60.2  dnaK  Description  Ros1  2.321  0.992  0.913  1.105  0.448  Ube2d1  0.721  0.892  0.971  0.905  0.858  Tmpo  0.716  1.627  0.675  0.841  1.578  Ros1 proto‐oncogene. [Source:RefSeq;Acc:NM_011282] Ubiquitin‐conjugating enzyme E2‐17 kDa 1 (EC (Ubiquitin‐ protein ligase) (Ubiquitin carrier protein) (E2(17)KB 1). [Source:Uniprot/SWISSPROT;Acc:P61080] Lamina‐associated polypeptide 2 isoforms beta/delta/epsilon/gamma (Thymopoietin isoforms beta/delta/epsilon/gamma) (TP beta/delta/epsilon/gamma). [Source:Uniprot/SWISSPROT;Acc:Q61029]  Epb4.1l2  0.739  2.119  1.615  0.908  0.923  Band 4.1‐like protein 2 (Generally expressed protein 4.1) (4.1G). [Source:Uniprot/SWISSPROT;Acc:O70318]  ENSMUSG000 00020069  0.647  0.887  1.032  1.077  1.159  Ddit4 ENSMUSG000 00020177  0.584  0.856  0.523  0.707  0.968  2.441  1.347  0.532  1.3  0.664  Mpg 4930524B15R ik  0.554  1.017  0.829  0.962  1.176  hypothetical protein DNA‐3‐methyladenine glycosylase (EC (3‐ methyladenine DNA glycosidase) (ADPG) (3‐alkyladenine DNA glycosylase) (N‐methylpurine‐ DNA glycosirase). [Source:Uniprot/SWISSPROT;Acc:Q04841]  1.907  2.058  0.975  0.796  0.308  hypothetical protein LOC67592  Cpeb4  1.564  0.973  0.982  0.94  1.023  Acsl6  0.696  0.903  0.851  1.107  1.074  Kremen  0.553  1.672  1.037  1.903  0.82  Nefh  0.472  0.814  1.206  0.937  0.753  cytoplasmic polyadenylation element binding protein 4 Long‐chain‐fatty‐acid‐‐CoA ligase 6 (EC (Long‐ chain acyl‐CoA synthetase 6) (LACS 6). [Source:Uniprot/SWISSPROT;Acc:Q91WC3] Kremen protein 1 precursor (Kringle‐containing protein marking the eye and the nose) (Dickkopf receptor). [Source:Uniprot/SWISSPROT;Acc:Q99N43] Neurofilament triplet H protein (200 kDa neurofilament protein) (Neurofilament heavy polypeptide) (NF‐H). [Source:Uniprot/SWISSPROT;Acc:P19246]  1.56  1.168  0.911  0.488  0.478  hypothetical protein LOC67430  4921536K21R ik  PREDICTED: similar to heterogeneous nuclear ribonucleoprotein H3 isoform a (LOC432467) HIF‐1 responsive RTP801; dexamethasone‐induced gene 2. [Source:RefSeq;Acc:NM_029083]  91  Common  Incubation Medium (protein‐ incubation)  Incubation Medium (infection )  M.tb  60.2  dnaK  Patz1 2810021J22Ri k  1.671  1.208  0.871  0.686  1.339  Description zinc finger protein 278; transcription factor (MAZ‐related) MAZR; POZ‐AT hook‐zinc finger protein. [Source:RefSeq;Acc:NM_019574]  1.462  0.66  1.561  1.095  0.781  hypothetical protein LOC69944  Arsg  1.717  0.318  0.646  0.353  1.029  Rnaseh1  1.433  1.832  0.868  1.118  0.892  Dus4l  0.532  1.024  1.086  0.599  0.793  Klf11  0.722  1.664  1.316  0.983  1.037  Ccl11  0.381  1.358  0.499  1.604  1.041  Arylsulfatase G Ribonuclease H1 (EC (RNase H1). [Source:Uniprot/SWISSPROT;Acc:O70338] protein similar to E.coli yhdg and R. capsulatus nifR3. [Source:RefSeq;Acc:NM_028002] Transforming growth factor‐beta‐inducible early growth response protein 3 (TGFB‐inducible early growth response protein 3) (TIEG‐3) (TGFB‐inducible early growth response protein 2b). [Source:Uniprot/SWISSPROT;Acc:Q8K1S5] Eotaxin precursor (Small inducible cytokine A11) (CCL11) (Eosinophil chemotactic protein). [Source:Uniprot/SWISSPROT;Acc:P48298]  Nf1  1.576  0.875  0.893  0.888  1.27  Neurofibromin (Neurofibromatosis‐related protein NF‐1). [Source:Uniprot/SWISSPROT;Acc:Q04690]  1700012C15R ik  1.572  0.936  0.971  1.131  0.83  Aanat  0.698  2.309  0.753  0.904  2.636  hypothetical protein LOC76390 Serotonin N‐acetyltransferase (EC (Aralkylamine N‐ acetyltransferase) (AA‐NAT) (Serotonin acetylase). [Source:Uniprot/SWISSPROT;Acc:O88816]  Mfsd11  0.723  1.068  1.005  0.935  1.159  major facilitator superfamily domain containing 11 Homeobox protein Hox‐B9 (Hox‐2.5). [Source:Uniprot/SWISSPROT;Acc:P20615]  Hoxb9 1500010J02Ri k  1.88  0.865  0.958  1.159  0.699  1.536  1.599  0.994  1.262  1.321  Stat5b  1.488  1.313  1.043  1.029  0.92  1.4  1.132  0.92  0.852  0.832  hypothetical protein LOC68964 isoform a Signal transducer and activator of transcription 5B. [Source:Uniprot/SWISSPROT;Acc:P42232] Maleylacetoacetate isomerase (EC (MAAI) (Glutathione S‐ transferase zeta 1) (EC (GSTZ1‐ 1). [Source:Uniprot/SWISSPROT;Acc:Q9WVL0]  D12Ertd551e  1.823  0.859  1.004  0.969  1.563  hypothetical protein  Mlh3  1.429  0.98  0.891  1.005  1.178  Gli3  2.432  2.313  1.06  1.196  1.882  Gstz1  Similar to mutL (E. coli) homolog 3. [Source:Uniprot/SPTREMBL;Acc:Q99L38] Zinc finger protein GLI3. [Source:Uniprot/SWISSPROT;Acc:Q61602]  92  Common  Incubation Medium (protein‐ incubation)  Incubation Medium (infection )  M.tb  60.2  dnaK  Prl  1.773  0.836  1.362  2.186  0.914  Tcfap2a  1.467  0.856  0.998  0.456  0.545  Tbc1d7  0.685  0.794  1.609  0.994  1.053  Fbp2  1.661  1.128  1.271  1.187  0.858  Auh  0.715  0.806  0.796  0.827  1.044  Description Prolactin precursor (PRL). [Source:Uniprot/SWISSPROT;Acc:P06879] Transcription factor AP‐2 alpha (AP2‐alpha) (Activating enhancer‐ binding protein 2 alpha) (Activator protein‐2) (AP‐2). [Source:Uniprot/SWISSPROT;Acc:P34056] TBC1 domain family, member 7. [Source:RefSeq;Acc:NM_025935] Fructose‐1,6‐bisphosphatase isozyme 2 (EC (D‐ fructose‐1,6‐ bisphosphate 1‐phosphohydrolase) (FBPase) (RAE‐30). [Source:Uniprot/SWISSPROT;Acc:P70695] Methylglutaconyl‐CoA hydratase, mitochondrial precursor (EC (AU‐specific RNA‐binding enoyl‐CoA hydratase) (AU‐binding enoyl‐CoA hydratase) (muAUH). [Source:Uniprot/SWISSPROT;Acc:Q9JLZ3]  Zfp369  1.482  1.247  1.031  0.985  0.559  zinc finger protein 369; neurotrophin receptor interacting factor 2. [Source:RefSeq;Acc:NM_178364]  Dapk1  1.53  0.975  0.917  1.142  1.087  Death‐associated protein kinase 1 (EC (DAP kinase 1). [Source:Uniprot/SWISSPROT;Acc:Q80YE7] Death‐associated protein kinase 1 (EC (DAP kinase 1). [Source:Uniprot/SWISSPROT;Acc:Q80YE7]  Dapk1 2900041A09R ik  1.708  0.991  1.335  0.954  0.986  0.584  1.743  0.973  1.339  1.066  Otp  1.594  0.798  0.936  0.381  1.357  Q80VX9  0.748  1.427  1.218  1.246  1.075  Ppwd1 ENSMUSG000 00021775  1.944  0.903  0.758  0.801  1.18  0.488  0.987  0.935  0.682  1.36  1.83  1.318  0.758  0.856  0.921  Camk2g  1.386  1.466  1.242  1.142  1.324  hypothetical protein ubiquitin specific protease 54. [Source:RefSeq;Acc:NM_030180] Calcium/calmodulin‐dependent protein kinase type II gamma chain (EC (CaM‐kinase II gamma chain) (CaM kinase II gamma subunit) (CaMK‐II gamma subunit). [Source:Uniprot/SWISSPROT;Acc:Q923T9]  Glt8d1  1.862  1.741  0.918  0.812  0.898  glycosyltransferase 8 domain containing 1  3425401B19R ik  2.162  0.563  0.775  0.863  1.23  Rnaseh2b  1.448  1.387  0.935  0.6  1.15  Usp54  hypothetical protein Homeobox protein orthopedia. [Source:Uniprot/SWISSPROT;Acc:O09113] Retinal pigment (Fragment). [Source:Uniprot/SPTREMBL;Acc:Q80VX9] peptidylprolyl isomerase domain and WD repeat containing 1  hypothetical protein 2610207P08Rik protein (Similar to hypothetical 35.1 kDa protein). [Source:Uniprot/SPTREMBL;Acc:Q9D014]  93  Common  Incubation Medium (protein‐ incubation)  Incubation Medium (infection )  2.46  2.024  1.226  2.172  1.024  Description Bone morphogenetic protein 3b precursor (BMP‐3b) (Growth/differentiation factor 10) (GDF‐10). [Source:Uniprot/SWISSPROT;Acc:P97737]  BC051457  1.452  1.124  0.55  0.758  1.099  hypothetical protein  Dnajc15  1.264  1.248  1.246  1.127  1.109  Q8BWQ9  1.438  1.277  0.554  1.433  0.542  Gzme 1500005A01R ik  1.427  0.694  1.187  0.923  0.998  0.665  0.93  1.334  0.942  0.821  DnaJ (Hsp40) homolog, subfamily D, member 1. [Source:RefSeq;Acc:NM_025384] Ubiquitin carboxyl‐terminal esterase L3. [Source:Uniprot/SPTREMBL;Acc:Q8BWQ9] Granzyme E precursor (EC 3.4.21.‐) (Cytotoxic cell protease 3) (CCP3) (CTL serine protease 2) (D12) (Cytotoxic serine protease 2) (MCSP2). [Source:Uniprot/SWISSPROT;Acc:P08884] UPF0172 protein C14orf122 homolog. [Source:Uniprot/SWISSPROT;Acc:Q9DB76]  Q8BWX0  0.385  0.503  1.183  0.873  0.824  Similar to HCDI protein. [Source:Uniprot/SPTREMBL;Acc:Q8BWX0]  Ugt3a2  1.292  0.763  1.06  2.227  0.775  Khdrbs3  1.272  1.062  0.811  0.964  0.843  Zfat1  1.511  1.432  0.837  1.175  1.229  Ttc35  1.297  0.987  1.012  0.999  0.928  Fbxo32  1.306  1.242  1.225  0.878  1.082  Zhx1  0.773  1.302  1.214  0.824  0.819  Slc25a17  1.299  0.954  0.978  1.036  1.122  tetratricopeptide repeat domain 35 F‐box only protein 32 (Muscle atrophy F‐box protein) (MAFbx) (Atrogin‐ 1). [Source:Uniprot/SWISSPROT;Acc:Q9CPU7] Zinc fingers and homeoboxes protein 1. [Source:Uniprot/SWISSPROT;Acc:P70121] Peroxisomal membrane protein PMP34 (34 kDa peroxisomal membrane protein) (Solute carrier family 25, member 17). [Source:Uniprot/SWISSPROT;Acc:O70579]  Cacna1i  2.09  0.693  0.739  0.792  0.549  calcium channel, voltage‐dependent, alpha 1I subunit  ENSMUSG000 00022416  1.611  0.666  1.191  1.035  0.952  Adamts20  5.149  0.808  0.855  0.513  0.824  hypothetical protein ADAMTS‐20 precursor (EC 3.4.24.‐) (A disintegrin and metalloproteinase with thrombospondin motifs 20) (ADAM‐TS 20) (ADAM‐TS20). [Source:Uniprot/SWISSPROT;Acc:P59511]  D15Wsu75e  1.514  1.352  0.941  0.878  1.273  D15Wsu75e protein. [Source:RefSeq;Acc:NM_134095]  Gdf10  M.tb  60.2  dnaK  UDP glycosyltransferases 3 family, polypeptide A2 KH domain containing, RNA binding, signal transduction associated 3; etoile. [Source:RefSeq;Acc:NM_010158] zinc finger protein 406; zinc finger protein 403. [Source:RefSeq;Acc:NM_198644]  94  Common Txndc11  Incubation Medium (protein‐ incubation)  Incubation Medium (infection )  M.tb  60.2  dnaK  Description  0.55  0.882  1.161  0.648  0.977  Zfp263 ENSMUSG000 00022591  1.425  0.899  0.832  0.868  0.746  thioredoxin domain containing 11 isoform 1 zinc finger protein 263; kruppel‐associated box‐zinc finger protein NT2. [Source:RefSeq;Acc:NM_148924]  0.301  0.197  1.436  1.068  0.411  hypothetical protein  Muc19 ENSMUSG000 00022647  1.532  0.533  0.908  0.296  1.717  mucin 19  0.281  1.792  1.157  0.715  0.271  Retnlb  2.311  1.412  1.104  0.884  0.978  hypothetical protein Resistin‐like beta precursor (RELMbeta) (Cysteine‐rich secreted protein FIZZ2) (Cysteine‐rich secreted protein A12‐beta). [Source:Uniprot/SWISSPROT;Acc:Q99P86]  Stxbp5l  1.957  0.829  0.734  1.063  1.213  Cxadr  0.6  2.246  0.878  0.818  0.441  Cxadr  1.525  0.941  0.351  0.75  0.649  Enah  2.157  0.942  0.769  0.857  1.093  0.65  1.074  0.981  1.314  0.874  0.539  0.6  1.153  1.057  0.399  syntaxin binding protein 5‐like Coxsackievirus and adenovirus receptor homolog precursor (mCAR). [Source:Uniprot/SWISSPROT;Acc:P97792] Coxsackievirus and adenovirus receptor homolog precursor (mCAR). [Source:Uniprot/SWISSPROT;Acc:P97792] Enabled protein homolog (NPC derived proline‐rich protein 1) (NDPP‐1). [Source:Uniprot/SWISSPROT;Acc:Q03173] Insulin‐like growth factor binding protein 6 precursor (IGFBP‐6) (IBP‐ 6) (IGF‐binding protein 6). [Source:Uniprot/SWISSPROT;Acc:P47880] tryptophan rich basic protein. [Source:RefSeq;Acc:NM_207301]  0.055  4.673  1.091  0.194  1.121  Cea17 protein (Fragment). [Source:Uniprot/SPTREMBL;Acc:O08932]  1.838  0.353  0.829  0.784  2.12  0.411  0.826  1.714  0.93  1.332  Iqcf3  1.58  0.24  0.469  0.985  1.501  Slc5a7  1.36  0.446  0.591  0.809  0.847  Clic5  1.541  1.178  0.817  0.304  1.235  Tmprss3  2.198  1.082  0.918  0.849  1.313  Igfbp6 ENSMUSG000 00023147 ENSMUSG000 00023159 ENSMUSG000 00023387 ENSMUSG000 00023528  hypothetical protein Homeobox protein Pem (Placenta and embryonic expression protein). [Source:Uniprot/SWISSPROT;Acc:P52651] IQ motif containing F3 solute carrier family 5 (choline transporter), member 7. [Source:RefSeq;Acc:NM_022025] Chloride intracellular channel protein 5. [Source:Uniprot/SWISSPROT;Acc:Q8BXK9] Transmembrane protease, serine 3 (EC 3.4.21.‐). [Source:Uniprot/SWISSPROT;Acc:Q8K1T0]  95  Common  Incubation Medium (protein‐ incubation)  Incubation Medium (infection )  M.tb  60.2  dnaK  DP30_MOUSE  0.721  1.404  1.461  1.098  0.734  Qpct  0.608  1.354  0.935  0.885  1.132  Luc7l ENSMUSG000 00024194  1.375  1.675  0.968  1.682  0.998  0.742  0.632  1.021  0.899  1.148  Map3k8  0.606  0.579  0.477  0.854  0.764  Cox7a2l  0.64  0.976  0.889  0.662  0.813  Wdr46  0.687  0.895  1.119  1.125  0.838  Atp6v1g2  2.385  0.608  0.852  0.86  0.956  Zfp521  0.676  0.82  1.593  0.991  0.474  ENSMUSG000 00024459  0.436  1.937  0.802  0.522  0.544  Description Dpy‐30‐like protein. [Source:Uniprot/SWISSPROT;Acc:Q99LT0] Similar to glutaminyl‐peptide cyclotransferase precursor. [Source:Uniprot/SPTREMBL;Acc:Q8BH20] Putative RNA‐binding protein Luc7‐like 1. [Source:Uniprot/SWISSPROT;Acc:Q9CYI4] divalent cation tolerant protein CUTA. [Source:RefSeq;Acc:NM_026307] Mitogen‐activated protein kinase kinase kinase 8 (EC (COT proto‐oncogene serine/threonine‐protein kinase) (C‐COT) (Cancer Osaka thyroid oncogene). [Source:Uniprot/SWISSPROT;Acc:Q07174] Cytochrome c oxidase subunit VIIa‐related protein, mitochondrial precursor (Silica‐induced protein 81) (SIG‐ 81) (SIG81). [Source:Uniprot/SWISSPROT;Acc:Q61387] WD‐repeat protein BING4. [Source:Uniprot/SWISSPROT;Acc:Q9Z0H1] Vacuolar ATP synthase subunit G 2 (EC (V‐ ATPase G subunit 2) (Vacuolar proton pump G subunit 2) (V‐ATPase 13 kDa subunit 2). [Source:Uniprot/SWISSPROT;Acc:Q9WTT4] zinc finger protein 521; ecotropic viral integration site 3. [Source:RefSeq;Acc:NM_145492]  Rab27b 1110032A13R ik ENSMUSG000 00024593  1.92  0.948  0.743  0.75  0.797  hypothetical protein Ras‐related protein Rab‐27B. [Source:Uniprot/SWISSPROT;Acc:Q99P58]  0.807  1.411  0.961  0.968  0.904  hypothetical protein LOC68731  2.226  0.834  1.022  0.899  0.923  Tmc1  1.772  0.999  1.352  1.464  0.537  Lipf  1.571  0.791  0.825  0.392  1.081  hypothetical protein Transmembrane cochlear‐expressed protein 1 (Beethoven protein) (Deafness protein). [Source:Uniprot/SWISSPROT;Acc:Q8R4P5] Triacylglycerol lipase, gastric precursor (EC (Gastric lipase) (GL). [Source:Uniprot/SWISSPROT;Acc:Q9CPP7]  Ppp2r5b  0.499  0.622  1.485  0.707  1.012  protein phosphatase 2, regulatory subunit B (B56), beta isoform. [Source:RefSeq;Acc:NM_198168]  Il33  0.677  2.516  0.765  0.91  1.131  interleukin 33  Q8BXH7  1.376  0.836  0.951  0.871  1.134  TANKYRASE‐related protein (Fragment). [Source:Uniprot/SPTREMBL;Acc:Q8BXH7]  96  Common  Incubation Medium (protein‐ incubation)  Incubation Medium (infection )  M.tb  60.2  dnaK  Description Fos‐related antigen‐1 (FRA‐1). [Source:Uniprot/SWISSPROT;Acc:P48755] Transcription factor 7‐like 2 (HMG box transcription factor 4) (T‐ cell‐specific transcription factor 4) (TCF‐4) (mTCF‐4). [Source:Uniprot/SWISSPROT;Acc:Q924A0] Transcription factor 7‐like 2 (HMG box transcription factor 4) (T‐ cell‐specific transcription factor 4) (TCF‐4) (mTCF‐4). [Source:Uniprot/SWISSPROT;Acc:Q924A0]  Fosl1  0.639  1.92  1.095  0.628  1.319  Tcf7l2  1.242  1.282  0.576  1.058  1.006  Tcf7l2  1.483  1.101  1.078  1.594  1.065  Trub1  1.432  1.24  0.999  0.723  0.873  Slc18a2  0.339  0.952  0.912  0.959  0.996  Bnc1  1.572  0.746  1.855  1.055  2.126  Mms19l  1.457  1.415  1.06  0.856  0.946  Entpd7  0.63  0.772  1.544  1.045  0.839  Tlx1  3.757  1.143  1.123  0.415  0.645  Zfhx4  0.691  0.861  0.822  0.547  0.729  Hsd17b10  0.743  1.034  1.045  0.999  1.137  Huwe1  1.749  1.508  1.109  1.176  0.831  Jarid1c  1.849  0.461  1.262  1.256  1.52  Gpr143  1.726  1.346  1.037  1.166  0.87  Cdk2  0.546  0.837  0.779  0.715  0.999  Cdk2  1.537  1.143  0.728  1.089  0.827  MMS19 (MET18 S. cerevisiae)‐like. [Source:RefSeq;Acc:NM_028152] ectonucleoside triphosphate diphosphohydrolase; lysosomal apyrase‐like 2. [Source:RefSeq;Acc:NM_053103] T‐cell leukemia homeobox protein 1 (Homeobox protein Hox‐11) (Homeobox TLX‐1). [Source:Uniprot/SWISSPROT;Acc:P43345] zinc finger homeodomain 4. [Source:RefSeq;Acc:NM_030708] 3‐hydroxyacyl‐CoA dehydrogenase type II (EC (Type II HADH) (Endoplasmic reticulum‐associated amyloid beta‐peptide binding protein). [Source:Uniprot/SWISSPROT;Acc:O08756] Upstream regulatory element binding protein 1 (Fragment). [Source:Uniprot/SPTREMBL;Acc:Q8BNJ7] Jumonji/ARID domain‐containing protein 1C (SmcX protein) (Xe169 protein). [Source:Uniprot/SWISSPROT;Acc:P41230] G protein‐coupled receptor 143 (Ocular albinism type 1 protein homolog) (MOA1). [Source:Uniprot/SWISSPROT;Acc:P70259] Cell division protein kinase 2 (EC [Source:Uniprot/SWISSPROT;Acc:P97377] Cell division protein kinase 2 (EC [Source:Uniprot/SWISSPROT;Acc:P97377]  Q9D7M0  1.203  1.229  0.974  1.033  1.555  hypothetical protein  TruB pseudouridine (psi) synthase homolog 1 isoform 1 Synaptic vesicular amine transporter (Monoamine transporter) (Vesicular amine transporter 2) (VAT2) (Solute carrier family 18 member 2). [Source:Uniprot/SWISSPROT;Acc:Q8BRU6] Zinc finger protein basonuclin 1. [Source:Uniprot/SWISSPROT;Acc:O35914]  97  Common  Incubation Medium (protein‐ incubation)  Incubation Medium (infection )  M.tb  60.2  dnaK  Description Transforming protein P21/H‐Ras‐1 (c‐H‐ras). [Source:Uniprot/SWISSPROT;Acc:Q61411]  Hras1  1.775  0.191  0.933  0.724  0.583  Mir  1.389  0.628  0.782  0.699  0.928  Akap13  1.487  1.171  0.746  1.25  1.26  Plk4  0.501  0.985  1.186  1.062  1.109  Sec61a2 ENSMUSG000 00025861  1.937  0.866  0.641  1.024  0.594  1.834  1.29  1.144  1.087  0.327  Gria4  1.566  1.108  1.619  1.115  1.515  Eya1  0.47  0.841  1.015  0.677  1.437  hypothetical protein Glutamate receptor 4 precursor (GluR‐4) (GluR4) (GluR‐D) (Glutamate receptor ionotropic, AMPA 4). [Source:Uniprot/SWISSPROT;Acc:Q9Z2W8] Eyes absent homolog 1 (EC [Source:Uniprot/SWISSPROT;Acc:P97767]  c‐mir, cellular modulator of immune recognition (Mir) Type II cAMP‐dependent protein kinase anchoring protein Ht31 (Fragment). [Source:Uniprot/SPTREMBL;Acc:Q924X9] polo‐like kinase 4 isoform sak‐a; serine/threonine kinase 18. [Source:RefSeq;Acc:NM_011495] Protein transport protein Sec61 alpha subunit isoform 2 (Sec61 alpha‐ 2). [Source:Uniprot/SWISSPROT;Acc:Q9JLR1]  Slco5a1  1.511  0.92  1.494  0.485  1.108  solute carrier organic anion transporter family, member 5A1. [Source:RefSeq;Acc:NM_172841]  Crygf  0.624  0.815  0.714  1.265  0.831  crystallin, gamma F. [Source:RefSeq;Acc:NM_027010]  Ica1l ENSMUSG000 00026023  0.524  1.048  1.14  0.93  1.386  Ica69‐related protein. [Source:RefSeq;Acc:NM_027407]  0.735  0.683  1.023  1.04  1.034  Pdcl3 1700024G10R ik 4930544G21R ik  1.783  0.616  1.047  1.46  1.012  hypothetical protein IAP‐associated factor VIAF1. [Source:RefSeq;Acc:NM_026850]  0.623  1.342  1.577  1.266  1.408  0.549  0.406  1.341  0.176  1.295  Slc23a3  1.512  2.109  0.546  0.834  1.372  Rnpepl1  1.274  1.189  0.978  1.124  0