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Although cancer is generally characterised as group of cells that have acquired the ability to grow uncontrollably, certain alterations in cell physiology dictate whether or not a cell become transformed into malignant growth. These essential alterations identified as self-sufficiency in growth signals, insensitivity to growth-inhibitory signals, evasion of programmed cell death, limitless replicative potential, sustained angiogenesis, and tissue invasion and metastasis are collectively referred to as the hallmarks of cancer and were first described in 2000 by Hanahan and Weinberg [1]. A decade following their initial publication and due to intensive cancer research, the duo revisited the hallmarks of cancer, this time adding two emerging hallmarks (deregulating cellular energetics and avoiding immune destruction) and two enabling characteristics (genome instability and mutation, and tumour-promoting inflammation) to the roster, while concomitantly giving recent research updates to the previously described hallmarks [2]. These hallmarks are summarised and depicted in Figure 1 below together with specific therapeutic targeting strategies. The roles that the immune system plays in preventing formation of incipient neoplasias and in eradicating late-stage tumours and micrometastasis as well as its role in promoting tumour formation were clarified in the second publication of the hallmarks of cancer [2]. We now know that there is a crosstalk between the immune system and incipient cancer cells, the tumour they form and their micrometastases, and that these intimate interactions could inhibit or promote tumour growth, development and metastasis [2, 3]. In order to proliferate, survive and become a clinically detectable tumour, cancer cells must have to devise strategies to evade the immunosurveillance imposed by the immune system. Consequently, considerable efforts have been made in our understanding of how cancer cells evade immune destruction, which has given insights into how to specifically target cancer cells using our body’s natural defenses; a field that is broadly classified as cancer immunotherapy. This essay will attempt to explain why immune evasion by cancer cells, relative to other hallmarks of cancer, is the most important hallmark for cancer biology. In addition, it will provide insights into the mechanisms employed by cancer cells to evade immunosurveillance and finally attempt to discuss the various cancer immunotherapeutic approaches that have been used in the treatment of cancer.
Figure 1: The hallmarks of cancer with various therapeutic targeting strategies. Some of the drugs or agents against each hallmark of cancer are already approved or in different phases of clinical trials. These drugs have been designed based on knowledge of the pathways that are dysregulated in each hallmark that eventually lead to the development and progression of cancer. While some of them have shown clinical benefits, unfortunately, others have not. Figure adapted from [2].
Figure 1: The hallmarks of cancer with various therapeutic targeting strategies. Some of the drugs or agents against each hallmark of cancer are already approved or in different phases of clinical trials. These drugs have been designed based on knowledge of the pathways that are dysregulated in each hallmark that eventually lead to the development and progression of cancer. While some of them have shown clinical benefits, unfortunately, others have not. Figure adapted from [2].

Reasons why immune evasion by cancer cells is an important hallmark of cancer biology relative to other cancer hallmarks

All of the hallmarks of cancer as alluded to above are equally important for the development and progression of cancer. For example, in order to become transformed, a cell or groups of cells must acquire the ability to sustain chronic proliferation by deregulating growth-promoting signals that control their entry into and progression through the cell division cycle [1, 2, 4]. This is largely driven by the ability of cancer cells to overexpress their own growth factors, while simultaneously responding to the same growth factors by expressing their cognate receptors in a paracrine or autocrine fashion [5]. As well as sustaining proliferative signalling, cancer cells must also acquire the ability to evade growth suppressors. This is achieved by inactivating tumour suppressor genes that would otherwise function to limit cell growth and proliferation [2]. For example, by inactivating the retinoblastoma (RB) tumour suppressor pathway, cancer cells are able to proliferate uncontrollably [6]. While the preceding hallmarks together with the ones mentioned in the introduction paragraph above contribute to the cancer phenotypes, evading immune destruction is one challenge that the incipient cancer cells must overcome [2]. This essay, therefore, considers immune evasion as the most important and critical hallmark of cancer biology due to the following factors and/or reasons. Having acquired the ability to proliferate uncontrollably and evade growth suppressors and resist cell death via loss of tumour suppressor proteins, cancer cells must also devise strategies to avoid immune destruction. If cancer cells were unable to avoid immune destruction even after acquiring all of the other hallmark characteristics, the immune system would have been able to selectively target and destroy them even before they are clinically detectable. This is because the increased genetic instability (leading to increased mutational burden) within cancer cells leads to generation of tumour-specific antigens (neoantigens) that eventually become sensed by the innate immune system leading to tumour detection and rejection by the immune system [7-9]. Moreover, the immune system has long been known to play important role in tumour growth and control. This is backed up by the fact that immunocompromised individuals are at higher risk of having cancer, and that spontaneous regression of some tumours is possible, although in very rare cancer cases [10-12]. This idea of the involvement of the body’s natural defense system to fight off cancer also stems from the effective use of the bacterium Bacillus Calmette-Guérin (BCG) in treating bladder cancer back in 1976 [13]. Moreover, current immunotherapies, especially immune checkpoint inhibitors, have demonstrated improved patient outcome for multiple solid and haematologic malignancies [14], highlighting an important role of the immune system in suppressing cancer growth. Lastly, compared to other cancer therapies, the immunological memory of the adaptive arm of the immune system together with their ability to detect and eradicate tumour variants as they emerge makes the immune system a very useful biological cancer therapeutic tool to exploit [15]. Hence, in an ideal situation, our immune system would have been able to eradicate cancer cells. However, cancer cells adopt strategies to evade immune response by promoting immunosuppressive signals, which lead to their own growth advantage [16, 17]. In summary, immune evasion by cancer cells represents an important hallmark for cancer biology since without attack by the immune system, cancer cells would continue to grow, divide and metastasize. The next section of this essay will attempt to explain the complex interactions between the immune system and the developing cancer cells

At the crosstalk between cancer cells and the immune system

During the process of cancer development, progression and metastasis, the immune system interacts intimately with both the cancer cells and the normal stromal cells, a complex crosstalk that can both suppress and enhance the growth of the tumour [18, 19]. For a tumour to be clinically detectable, it must devise strategies to evade the immunosurveillance techniques employed by the immune system. The adaptive arm of the immune system uses its CD8+ cytotoxic T-cells and CD4+ helper T-cells to target a developing neoplasia for eradication by the production of cytokines such as interferon gamma (IFN-g) [3, 20]. Moreover, the cytotoxic effects of CD8+ T-cells also contribute majorly to the killing of tumour cells [21]. Cross-priming and activation of the adaptive arm of the immune system above is dependent on antigen presentation by professional antigen presenting cells (APCs), such as dendritic cells (DCs), which are a part of the innate immune response [22-25]. However, there are reports to show that APCs may be dysfunctional in tumour-bearing animals due to production of immunosuppressive factors by tumours that prevent CD34+ stem cell maturation into DCs [26-28]. This and other factors contribute to immune evasion strategies employed by cancer cells, which the next section describes.

Mechanisms of immune evasion

Despite the barriers imposed by both the innate and adaptive arms of the immune system as described above, cancer cells are still able to subvert their host’s anti-tumour immune responses to become clinically detectable. Certain characteristics of the tumours as well as the stromal and chronic inflammatory cells contribute to the eventual escape of the developing tumour [29]. As well as suppressing tumour growth, the immune system can also select for tumours with decreased immunogenicity, thereby enhancing tumour growth [18]. This is explained by the concept of cancer immunoediting, a major escape strategy employed by developing cancer cells to avoid immune-mediated elimination [30-32]. Three separate phases of immunoediting is widely accepted: elimination, equilibrium and escape.  Elimination is the state where both the innate and adaptive immune systems detect and kill the most immunologically vulnerable developing cancer cells that characteristically express tumour antigens. However due to heterogeneity between individual cancer cells, orchestrated by genetic instability, a subset of cancer cells with reduced immunogenicity can remain dormant for years as suggested by experimental models [33]. During this time, the cancer cells continue to divide, and acquire additional mutations in response to immune-induced inflammation or by chance. This stage, where there is a balance between new tumour cell variants and elimination by the immune system, is termed ‘’equilibrium’’. Ultimately, tumour cells are able to subvert immunosurveillance through mechanisms such as loss of tumour antigens, increased resistance to attack by immune cells [34], or by recruitment of immunosuppressive cells to the tumour microenvironment (TME) [35, 36]. This stage represents how tumour cells escape the immune system and become a clinically overt tumour. A schematic view of immunoediting is depicted in Figure 2 below. These myriad of escape strategies are explained further below.
Figure 2: An illustration of the concept of cancer immuno-editing. The immune-editing process by cancer is divided into three stages: elimination, equilibrium and escape. As normal cells become transformed, they express certain molecules, including tumour-specific antigens on their surface. These antigens become recognised by both the innate and adaptive arms of the immune system which leads to elimination of the incipient cancer cells. However, due to high intratumoural heterogeneity, not all cancer cells are eliminated. In the equilibrium stage, the subpopulation of cancer cells with reduced immunogenicity can remain dormant for years. Subsequently, these cancer cells might outcompete the immune system by downregulating their surface antigens or inducing an immunosuppressive microenvironment. This represents the escape stage. Cancer cells that escaped the immune system are able to grow, divide and manifest as a clinically overt tumour. CTLA-4, cytotoxic T-lymphocyte–associated antigen 4; IDO, indoleamine 2,3-dioxygenase; DC, dendritic cell; MHC, major histocompatibility complex; NK, natural killer; NKT, natural killer T-cell; PD-L1, programmed cell death ligand 1; TGF, transforming growth factor–beta; IFN, interferon; IL, interleukin; PD-1, programmed cell death 1; T-reg, T-regulatory cells. Figure adapted from [86].
Figure 2: An illustration of the concept of cancer immuno-editing. The immune-editing process by cancer is divided into three stages: elimination, equilibrium and escape. As normal cells become transformed, they express certain molecules, including tumour-specific antigens on their surface. These antigens become recognised by both the innate and adaptive arms of the immune system which leads to elimination of the incipient cancer cells. However, due to high intratumoural heterogeneity, not all cancer cells are eliminated. In the equilibrium stage, the subpopulation of cancer cells with reduced immunogenicity can remain dormant for years. Subsequently, these cancer cells might outcompete the immune system by downregulating their surface antigens or inducing an immunosuppressive microenvironment. This represents the escape stage. Cancer cells that escaped the immune system are able to grow, divide and manifest as a clinically overt tumour. CTLA-4, cytotoxic T-lymphocyte–associated antigen 4; IDO, indoleamine 2,3-dioxygenase; DC, dendritic cell; MHC, major histocompatibility complex; NK, natural killer; NKT, natural killer T-cell; PD-L1, programmed cell death ligand 1; TGF, transforming growth factor–beta; IFN, interferon; IL, interleukin; PD-1, programmed cell death 1; T-reg, T-regulatory cells. Figure adapted from [86].
To ensure they continue to survive and escape immune surveillance, tumours recruit immunosuppressive thymus-derived CD4+CD25+FoxP3+ regulatory T-cells (Treg) via production and secretion of certain chemokines [37, 38]. Treg-mediated immunosuppression is considered a major strategy utilised by tumours to escape destruction and it presents a major obstacle to the success of immunotherapy [39-41]. CTLA4-mediated induction of indoleamine 2,3-dioxygenase-expressing APCs (which degrades tryptophan required for T-cell activation)[42] and perforin and granzyme A pathway activation [43] are some of the suppressive mechanisms adopted by Treg cells to mediate killing of CD8+ T-cells and APCs. These and other Treg-mediated mechanisms of immune suppression are summarised in Figure 3 below. In addition, myeloid cells, such as myeloid-derived suppressor cells (MDSCs), modulated DCs and M2 macrophages can form an inflammatory microenvironment to promote tumour initiation, angiogenesis and metastasis [44, 45]. Secretion of immunosuppressive cytokines (including IL-4, IL-6 and IL-10), generation of nitric oxide (NO) and reactive oxygen species together with increased activity of L-arginase have been suggested as some of the mechanisms of immunosuppression by myeloid cells [44, 46]. For example, production of IL-10 correlates with the induction of T-cell anergy and is regarded as a major immunosuppressive factor released by tumour cells, together with TGF-b [47].  The above findings indicate that although some tumours may retain their antigenicity and immunogenicity, which are targets of effector T-cells, they can still evade immune elimination by promoting an immunosuppressive microenvironment.
Figure 3: Proposed immunosuppressive mechanisms of regulatory T-cells. a| Regulatory T-cells can promote the induction of B7-H4 by APCs, which in turn lead to the inhibition of cytotoxic T-cells. b| Via Perforin or granzyme B-mediated pathways, an activated Treg cell can promote apoptosis of both APCs and cytotoxic T-cells. c| CTLA-4-expressing Treg cells can induce the expression of indoleamine 2,3-dioxygenase (IDO) by APCs, which in turn leads to T-cell anergy by reducing the amount of tryptophan required for T-cell activation. d| Treg cells can also secrete cytokines, such as IL10 and TGF, which lead to T-cell anergy and APC dysfunction via decreased expression of MHC molecules, IL12, CD80 and CD86. Figure adapted from [87].
Figure 3: Proposed immunosuppressive mechanisms of regulatory T-cells. a| Regulatory T-cells can promote the induction of B7-H4 by APCs, which in turn lead to the inhibition of cytotoxic T-cells. b| Via Perforin or granzyme B-mediated pathways, an activated Treg cell can promote apoptosis of both APCs and cytotoxic T-cells. c| CTLA-4-expressing Treg cells can induce the expression of indoleamine 2,3-dioxygenase (IDO) by APCs, which in turn leads to T-cell anergy by reducing the amount of tryptophan required for T-cell activation. d| Treg cells can also secrete cytokines, such as IL10 and TGF, which lead to T-cell anergy and APC dysfunction via decreased expression of MHC molecules, IL12, CD80 and CD86. Figure adapted from [87].
  Defective antigen presentation represents another mechanism that tumours utilise to escape immune surveillance. A variety of mutated and nonmutated antigens with the potential to induce an immune response against the tumour are [over]expressed on the surface of cancer cells [48]. In an attempt to evade immune destruction, cancer cells downregulate antigen processing and presentation machinery (APM) that control the major histocompatibility complex  (MHC)-I pathway and other related proteins [49-52]. This leads to reduced tumour cell surface antigen presentation which prevents cytotoxic T-cells from recognising target antigens on tumour cells, thus promoting tumour growth and metastasis. In addition, tumours are able to induce T-cell tolerance by their failure to express co-stimulatory molecules which leads to T-cell anergy [53]. In the same vein, tumours can also undergo immune deviation by shifting the balance from the anti-tumour Th1 response to  the tumour-promoting Th2 response, a process that is TGF-b and IL-10-dependent [54]. Moreover, tumours that possess sufficient antigenicity are still able to subvert the immune response by upregulating the inhibitory molecule programmed death (PD)-L1 on their cell surface [55], whose receptors are known to be significantly upregulated on T-cells that infiltrate the tumour [56, 57].  This suggests tumours may use these PD-1/PD-L-1 signalling pathway to negatively regulate an immune response against it. Consequently, tumours overexpressing PD-L1, including oesophageal, kidney, ovarian and pancreatic cancers, are associated with poor prognosis [58-61]. Thus, cancer cells can induce T-cell tolerance and immune deviation by dysregulating certain pathway and/or upregulating immune-inhibitory molecules on their cell surface. These upregulated molecules also present a better avenue to develop agents or antibodies that specifically target and inhibit them, as will be discussed below. Solid tumours also evade immune destruction by excluding T-cells. A higher immunoscore, which is dependent on increased T-cell infiltration, correlates with good prognosis for patients. This T-cell infiltration is dependent on certain chemokines (e.g., CXCL9 and CXCL10) and cytokines expressed on the surfaces of tumour cells or within the TME [62, 63], which serve as ligands for chemokine receptors on the surface of T-cells [64]. Tumours can, however, exclude anti-tumour T-cells from the TME by downregulating the ligands for chemokine receptors expressed on T-cells via epigenetic silencing as recently observed in ovarian cancer [65]. In addition, tumours can also exclude T-cells by expressing FasL, which induces apoptosis in T-cells expressing its cognate Fas receptor [66]. Lastly, certain oncogenic mutations within tumours indirectly modulate the TME by interfering with immunity. For example, Kras mutations induce the expression of granulocyte macrophage colony-stimulating factor (GM-CSF), which in turn recruit myeloid cells leading to reduced CD8+ T-cell infiltration in pancreatic ductal adenocarcinoma [67, 68]. The above tumour and stromal cell-mediated processes illustrate the complex mechanisms that act in concert to orchestrate immune evasion by cancer cells. Figure 4 below is an overview of some of the immune evasion strategies highlighted above as well as other evasion strategies not described above due to space limitation. The next section will discuss strategies that have been exploited to boost host immune response against tumours.
Figure 4: Mechanisms of immune evasion by cancer cells. Tumours employ several mechanisms in order to escape immune destruction. From recruiting immunosuppressive cells to downregulating pathways that are involved in antigen presentation, tumours are able to subvert the anti-tumour effects of cytotoxic T lymphocytes, allowing them to proliferate and metastasize to new sites. Figure adapted from [88].
Figure 4: Mechanisms of immune evasion by cancer cells. Tumours employ several mechanisms in order to escape immune destruction. From recruiting immunosuppressive cells to downregulating pathways that are involved in antigen presentation, tumours are able to subvert the anti-tumour effects of cytotoxic T lymphocytes, allowing them to proliferate and metastasize to new sites. Figure adapted from [88].

Therapeutic strategies to targeting immune evasion by cancer cells

As noted above, the link between the immune system and control of cancer growth was already established decades ago and became even more relevant due to advances in research that eventually gave birth to cancer immunotherapy as an arm of cancer therapy. The overall goal of cancer immunotherapy is to use the host immune system to eradicate cancer by exploiting specific evasion strategies devised by cancer cells [14]. The field of cancer immunotherapy encompasses many therapeutic strategies, including use of checkpoint inhibitors, oncolytic viruses, adoptive cell therapies, and biologic modifiers such as cytokines and vaccines to augment tumour immunity [69, 70]. Some of these are explained below. The immune checkpoint inhibitors (ICI) are monoclonal antibodies that promote immune-mediated elimination of tumours by interrupting co-inhibitory signalling pathways [71]. The first approved ICI, ipilimumab, prevents T-cell inhibition by targeting CTLA-4 in patients with advanced melanoma [72, 73]. The PD-1 ICIs, pembrolizumab and nivolumab have also shown significant improvements in the treatment of patients with melanoma and non-small cell lung cancer [74-76]. In the same vein, Atezolizumab (anti-PD-L1) was approved in 2016 for the treatment of melanoma, bladder and lung cancers together with triple-negative breast cancer as at March 2019 [77-79]. The clinical efficacies of these ICI as well as others are currently being investigated for other tumours as shown in Table 1 below (see attached document). These therapies are used as single agent or in combination. For example, combined administration of ipilimumab and nivolumab showed at least 80% tumour regression in 50% of patients with advanced melanoma in a phase I clinical trial [80]. Although very efficacious, combination therapy of ICIs or with other conventional therapies can lead to more side effects and toxicities [14]. In addition, although these ICIs have shown improved outcome, only a subset of patients benefit from them with some patients experiencing severe immune-related adverse events due to various local and systemic immune responses [14, 81]. This demonstrate the importance of using predictive biomarkers to identify patients who will eventually benefit from the treatment to avoid any adverse effects. Another type of immunotherapy that has been of keen interest to oncologists and cancer researchers is therapeutic cancer vaccines. By increasing tumour antigen presentation, cancer vaccines can augment the anti-tumour response of the immune system [82]. They are broadly divided into two: autologous and allogenic cancer vaccines [82]. The first FDA-approved autologous cancer vaccine, sipuleucel-T, was used for the treatment of castration-resistant prostate cancer [83]. Here, DCs are collected from the patients and exposed to GM-CSF before being reinjected into the patient’s circulation. Although sipuleucel-T was shown to extend survival of patients in clinical trials, it had no effect on disease progression in the clinical setting [83]. Despite the potential promises of cancer vaccines, there are many hurdles yet to be overcome, including specific tumour-dependent antigen identification, improving their therapeutic efficacies, delivery and enrichment within the tumour relative to normal tissues [70]. The ideal vaccine should be able to trigger DC maturation and subsequent priming and activation of CD8+ T-cells. Why these obstacles are likely to be circumvented, the efficacy of cancer vaccines may still be compromised by the cancer cells’ ability to evade the immune system via downregulation of their antigen presentation pathways. As noted above, this highlights the importance of using predictive biomarkers to identify patients who are likely to benefit from this type of therapy based on how antigenic and immunogenic their tumours are. In addition to the above types of immunotherapies, adoptive T-cell therapy (ACT), which involves isolation and in vitro modification and expansion of patient’s own T-cells and subsequent re-injection into the patient, is also promising [82]. A specific example of ACT is chimeric antigen receptor T-cell therapy (CAR-T) which uses T-cells that have been engineered in vitro to specifically target and eradicate the tumour [69, 82]. Although CAR-T therapy has demonstrated dramatic clinical responses, it is not without problems, such as cytokine release syndrome experienced by patients [69]. CAR-T was first approved for use in children with relapsed B-cell acute lymphoblastic leukemia  in 2017 and later for lymphomas [84, 85]. These and other immunotherapeutic approaches, including use of biologics, such as cytokines, are represented in Table 2 below (see attached document) together with the basic mechanisms, and the major advantages and disadvantages of each therapeutic approach.


The important roles that the immune system plays in eradicating cancer has been the focus of this essay. However, as well as suppressing cancer, some cells of the immune system have been implicated in the development and progression of cancer. By secreting various factors that modulate the tumour microenvironment and due to high intra-tumoural heterogeneity, tumours are able to evade the immune surveillance, grow, form new blood vessels, metastasize and exhibit their clinical manifestations. This essay has attempted to describe majority of the strategies adopted by developing cancer cells and the tumour they eventually form in evading the immune response. The various evasion strategies have been exploited by research scientists in order to boost the immune system. Among the many cancer immunotherapeutic strategies, ICIs have received the most attention as they have so far improved patients’ outcome. Again, the impact of ICIs was also reflected when both Professors James P. Allison and Tasuku Honjo were jointly awarded the Nobel Prize in Medicine or physiology in 2018 for their seminal work that led to our understanding of how the immune system fights off cancer. Although this essay has attempted to show evidence that immune evasion by cancer cells represents an important hallmark for cancer biology, and that developing drugs to boost the immune system has had some clinical benefits, there are still some challenges to be overcome as highlighted under immunotherapeutic strategies against cancer cells above. Despite these obstacles, the potential promises of cancer immunotherapy to people living with cancer is enormous.


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According to the Canadian Cancer Society, nearly 1 in 2 Canadians will be diagnosed with cancer. Despite advances in diagnosis and treatment, cancer continues to be a death sentence for many patients due to relapse, metastasis at distant sites, drug resistance and the toxicities associated with select therapeutic treatment approaches [1, 2]. Studies in the past two decades have identified a subpopulation of cancer cells called cancer stem cells (CSCs) or tumour-initiating cells (TICs) as being solely responsible for tumour initiation, progression, relapse, metastasis and drug resistance [3-6]. Consequently, selective targeting of CSCs within the tumour cell population was initially thought to be a very promising therapeutic strategy to treating cancer [7, 8]. This essay will, therefore, elucidate on the characteristics of CSCs, the challenges and potentials of the proposed CSC therapy, and offer suggestions on the strategies to wholly targeting cancer to prevent relapse, metastasis and drug resistance.

Cancer Stem cells

Two models currently exist to account for tumour growth and the heterogeneity within tumours. The clonal evolution model argues that all cells within a tumour have the capacity to propagate all the various cell types in a tumour mass, and subclonal differences resulting from both genetic and epigenetic changes acquired during development are responsible for the intercellular variations [9]. Conversely, the CSC model suggests that all of the inherent characteristics of tumour, including initiation, heterogeneity, recurrence and metastasis, are being driven by a subpopulation of cells termed cancer stem cells [10, 11]. See Figure 1 below for an illustration of the two models. Although the first evidence of stem-cell like cancer cells was reported as early as 1937 [12], it was not until 1997 that Bonnet and Dick [13] provided indisputable evidence of the existence of a subpopulation of CD34+ CD38- cells in acute myeloid leukemia (AML) patients  that possessed the ability to self-renew, proliferate and differentiate into other cancer cells. CSCs were subsequently identified in a broad spectrum of human solid tumours, including breast (CD44+CD24–/low cells) [14], brain (CD133+ cells) [15], prostate (CD44+CD24cells) [16, 17], among others. As one could imagine, identification of these CSCs was made possible due to specific cell surface markers they were expressing. It is, however, paramount to state that certain markers are not restricted to a particular tumour, and that cancers not expressing the associated markers above have been found to be also tumorigenic. To put this into perspective, CD133- cells in brain tumours have been found to possess high tumorigenic activity [18]. Apart from identifying CSCs using their cell surface markers by fluorescence activated cell sorting (FACS), other methods, including spheroid formation assay [19], side population assay [20] as well as a method based on the enzymatic activity of aldehyde dehydrogenase (ALDH) [21] have been used for the isolation and subsequent evaluation of CSCs.
Cancer stem cell model
Figure 1: A representative scheme of the two models that account for tumour growth and heterogeneity within a given tumour: the cancer stem cell model and the clonal evolution model. Figure adapted from [44].
There are considerable evidence that these CSCs have different cells of origin [22, 23], and that, depending on the cancer types, CSCs may originate from somatic stem cells, partially differentiated progenitor cells or differentiated cells that have acquired stemness through a number of dysregulated mechanisms [24, 25]. Certain signalling pathways have been attributed to the stemness phenotypes of CSCs, including, but not limited to, Wnt, JAK/STAT, Hedgehog, Notch and FAK signalling pathways [26, 27]. Figure 2 depicts a simple schematic representation of these different pathways and some of the targeting strategies that have been proposed to block these signalling pathways. The identification of these stemness propagating pathways, the characterisation of the properties of the CSCs themselves (promoting relapse and drug resistance, for example) as well as the isolation of specific cell surface markers as highlighted above have fuelled interests in designing targeted therapies solely against CSCs. The next section will focus mainly on the limitations and some successes that have characterised this ambitious goal.
Figure 2: Targeted therapies against dysregulated signalling pathways involved in CSCs: This schematic gives a broader knowledge of the different pathways that contribute to the stemness phenotypes of CSCc, including Wnt, JAK/STAT, Hedgehog, Notch and FAK signalling pathways. Figure also shows the subcellular localisation of the each of the signalling pathways, and specifically highlights the targeted therapies that have been designed to combat CSCs by directly inhibiting proteins or enzymes that function to promote the activities of these different signalling pathways. For example, WNT ligands and receptors can be inhibited by ipafricept and vantictumab, respectively. Although these agents are designed to inhibit CSC self-renewal, drug resistance and metastasis mechanisms, they are yet to be clinically demonstrated as being efficacious [27]. Figure adapted from [27].

CSC targeted therapies – a reality or an illusion?

Compelling evidence exists to suggest that targeted therapy has been somewhat successful in the clinic in the eradication of tumours. For example, olaparib, a PARP inhibitor, is used clinically to treat patients with metastatic HER2-negative breast cancers that also have mutations in the BRCA1 or BRCA2 gene [28]. But, as was previously proposed, would designing drugs targeting certain features that drive CSC phenotypes hold the cure to cancer? To answer this question, this section of the essay will discuss some of the limitations of targeting CSCs. First, although it may be possible to design drugs that target certain features of CSCs, it has been suggested that multiple CSC subsets, consisting of undifferentiated cells with different origins, may exist within a tumour. For example, studies suggest the existence of both CD133+ and CD133- CSC subpopulations with different origins [18, 29]. While targeted therapy against CD133+ CSCs may eliminate them, the CD133- subpopulation will be resistant [7]; thus, allowing the CD133- subpopulation to grow, divide and replenish the CSC population. Consequently, some monoclonal antibodies, including Lintuzumab against CD33, have been discontinued, despite showing some modest benefit [7, 30]. Another limitation to targeting CSC is that they share the same cell surface markers (e.g., CD133+) as normal stem cells [31]. Therefore, designing drugs against these markers will not be specific in targeting the CSC population within the tumour. For example, the selective CSC inhibitor, salinomycin, shows increased toxicity to normal CD4+ T cells at concentrations effective against CD4+ T cell leukemia cells [32]. Additionally, CSCs within certain tumours are known to possess a lower proliferative rate, display an elevated level of quiescence [33], and have efficient DNA repair mechanisms [34] compared to other cells within the same tumour. Consequently, these heterogeneous features of CSCs suggest that targeting them with drugs that damage DNA will not have a profound cytostatic effect on them. Compounding this further is the fact that CSCs also possess increased expression of anti-apoptotic proteins [35], ABC transporters (involved in increased efflux of chemotherapeutic agents) [36] and increased level of  ALDH and oxidant scavengers (both function to metabolise chemotherapeutic agents and reactive oxygen species [4]) that ultimately contribute to their resistance to chemo- and radiotherapy. Another hurdle in the CSC therapy is that eliminating CSC may not change disease outcome. This is because new CSCs are likely to be generated via the spontaneous dedifferentiation of non-CSCs due to cellular plasticity [37]. It would, therefore, be beneficial targeting both the differentiated cancer cells and CSCs with combinatorial therapies. Another factor that has limited the use of CSC targeted therapy is similarity in the many genes and signalling pathways that regulate both the stemness pathway and normal stem cells [27]. For example, proper regulation and homeostasis of intestinal stem cells are mediated via Wnt signalling [38, 39]. Hence, there have been concerns about toxicity and off-target effects of targeted therapies against dysregulated proteins in the signalling pathways. Finally, the efficacy of these targeted CSC therapies is not guaranteed due to redundancy in the signalling pathways [27]. To overcome this challenge, recent clinical trials are using high-throughput screening techniques to specifically target CSCs. The afore-mentioned challenges and limitations, therefore, weaken the claim that cancer can be cured by solely targeting CSCs. Despite these hurdles or challenges, the subtle surface marker differences and the dysregulation of the signalling pathways in CSCs have been exploited as potential therapeutic targets [40]. Currently, some of these targeted therapies against CSCs have been investigated in different phases of randomised clinical trials (see table 1 below). For example, the Sonic Hedgehog pathway inhibitor, vismodegib, is currently being used to treat basal cell carcinoma [41] and has been evaluated to treat patients with Hedgehog pathway mutations that lead to medulloblastoma [42]. Majority of these studies are, however, combining with standard-of-care chemotherapy, and none have demonstrated clinical efficacy as single agent inhibitor [7, 27]. The preceding point illustrates the potential of using combination therapy to target both the CSCs and the differentiated tumour [43], thereby overcoming cancer cell heterogeneity and plasticity. Other therapeutic strategies that are currently being exploited in combating CSCs are depicted in Figure 3 below. While efforts have been made to specifically target CSCs with the aim to completely eradicate the tumour mass, as shown by the many targeted monotherapies, there have been no clinically approved single agent therapies against CSCs [27], suggesting that targeting CSCs might not improve clinical outcomes.
Figure 3: Targeted therapies against CSCs. Over the years, more targeted therapies against CSCs have been developed. These therapies have been classified into those that target signalling pathways that promote the CSC phenotypes (green area), targeted therapies against specific surface markers expressed by the CSCs (red area), drugs targeting ABC transporters that are known to be upregulated in CSCs (purple area) and lastly, drugs that inhibit certain growth factors and chemokines that collectively act to promote the CSC phenotypes. Figure adapted from [40].
BSC=best supportive care; carbo=carboplatin; CRC=colorectal cancer; AE= most severe adverse effect; etop=etoposide; GEJ=gastroesophageal junction; gem=gemcitabine; nab-p=nab-paclitaxel; NSCLC=non-small cell lung cancer; SCLC=small cell lung cancer. Table adapted (and modified to reflect current trial status) from [27].

Conclusions and future perspectives

Understanding tumour initiation, progression, and how to combat it has been the focus of cancer research for many decades. While the identification of CSCs and the elucidation of their signalling pathways have paved the way to targeting them, there are considerable challenges yet to be overcome as highlighted above. The idea of solely targeting CSCs is unrealistic given that they (CSCs) share so many characteristics with normal stem cells, together with the high intratumoural heterogeneity within CSCs. While studies evaluating monotherapies against CSCs have shown promise in early phase studies, combining anti-CSC therapies with other traditional or targeted therapies against not just the CSC subpopulation, but the bulk of the tumour represents one approach to eradicating cancers. In addition, there is the need to develop efficacious techniques to selectively target CSCs, while sparing normal stem cells. Finally, overcoming the challenge of CSC therapy resistance using innovative techniques, such as immunotherapy, represents another avenue yet to be investigated.


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Cancer cells are known to proliferate uncontrollably and therefore, have greater demand for energy and a ready supply of the building blocks necessary for the biosynthesis of macromolecules such as nucleotides, proteins and lipids).  This special requirement is important to ensure they duplicate their genome and biomass. To achieve the above target, cancer cells preferentially use aerobic glycolysis, otherwise known as the Warburg effect, over oxidative phosphorylation.  A lot of pathways’ intermediates and key enzymes, together with mutations in genes such as tumour-suppressor genes and proto-oncogenes have been shown to be impaired; and they ultimately contribute to the growth, survival and malignancy of tumours.  In the paper, the relationship between cancer and cellular energy metabolism will be examined. Some of the impairments in glycolytic pathway, Krebs cycle and oxidative phosphorylation that contribute to cancer cell growth, development, progression, survival and malignancy would be critically discussed. Keywords: Cancer; aerobic glycolysis; Warburg effect; pyruvate.


Glycolysis is an anaerobic, sequential, enzymatic and catabolic multi-step process that converts a single glucose molecule into two pyruvates in the cytoplasm coupled with the production of two NADH molecules and a net yield of two molecules of ATP. This ten step pathway was elucidated in the 1940s (Li et al., 2015). Pyruvate, the end-product of glycolysis has several fates within the cell, depending on the energy need of the body. First, in the presence of enough cellular oxygen (or under aerobic conditions), pyruvate is oxidised with loss of carboxyl group as CO2 and the remaining two carbon unit becomes the acetyl group of acetyl coenzyme A (AcetylCoA). This acetyl group is further metabolised in the tricarboxylic acid (TCA) cycle and fully oxidized to yield CO2. This ultimately results in the production of 36 molecules of ATP (Annibaldi and Widmann, 2010).  Second, under condition of low cellular oxygen (as is the case in contracting muscle), otherwise known as anaerobic condition, pyruvate can be reduced to lactate through oxidation of NADH to NAD+ - a process referred to as lactic acid fermentation. Lastly, in microorganisms such as brewer’s yeasts and in certain plant tissues, pyruvate is reduced to ethanol, again with oxidation of NADH to NAD+ (Garrett and Grisham, 2017)

Cancer, on the other hand, can be defined as a heterogeneous group of genetic diseases characterised by unregulated clonal expansion of somatic cells brought about by multiple genetic and epigenetic changes (Evan and Vousden, 2001).  Cancer development and progression in humans involves multi-step processes that usually take place over many decades.  During these processes, the cancer cells acquire multiple allelic mutations in genes such as proto-oncogenes, tumour suppressor (TS) genes and other genes that control cell proliferation (Hahn and Weinberg, 2002).  These allelic mutations lead to the production of dysregulated proteins leading to the activation of oncogenes or the inactivation of TS genes; promoting abnormal regulation of signalling pathways involved in cell cycle regulation, genetic stability, apoptosis and cell differentiation.  This imbalance in cellular regulations drives the process of oncogenesis (Hahn and Weinberg, 2002).

In addition to genetic changes, it is also known that the tumour microenvironment plays key part in the transition from benign to malignant cancer. This is achieved by conferring an adaptive pressure that selects cells for their clonal expansion (Annibaldi and Widmann, 2010). In 2000, Hanahan and Weinberg published a seminal review tagged ‘’the hallmarks of cancer’’ (Hanahan and Weinberg, 2000) which aimed at summarising the promoting features of cancer cells into 6 major hallmarks. These include evading apoptosis, sustained angiogenesis, tissue invasion and metastasis, self-sufficiency in growth signals, insensitivity to anti-growth signals and lastly, limitless replicative potential. A decade later, the authors added two emerging hallmarks: reprogramming energy metabolism and evading immune response, and two enabling traits: genomic instability and mutation, and tumour promoting inflammation (Hanahan and Weinberg, 2011; Yousef and Carmen, 2017). The impact of Hanahan and Weinberg hallmarks of cancer I & II have been overwhelming as they serve as blueprints for understanding core traits of cancer.

This piece of work seeks to understand the relationship between cancer and high rate of glycolysis observed in tumours. It will elucidate on the additional hallmarks of cancer mentioned above, i.e. reprogramming energy metabolism in cancer cell by pointing out to key experiments pertaining, and that serve as proof, to this phenomenon.  It also aims at understanding the mechanism or proffer answers to the question of why there is a high rate of glycolysis in cancer cells.

The Warburg effect (Aerobic glycolysis)

Earlier work on cancer metabolism was pioneered by the biochemist and Nobel Award winner Otto Warburg (Justus et al., 2015). He proposed a direct association between mitochondrial impairment and cancer development by hypothesising that a defect in mitochondria respiration was responsible for the development of cancer (Warburg, 1956; Weinhouse et al., 1956). This postulation was motivated by his observation that cancer cells showed an increased rate of glycolysis when compared to normal cells, even in the presence of oxygen, which would normally inhibit glycolysis – a phenomenon known as Pasteur effect (inhibition of glycolysis in the presence of oxygen) (Warburg, 1956). As pointed out in the introduction above, one would expect pyruvate to be diverted to the Tricarboxylic Acid – TCA – cycle and then oxidative phosphorylation, but this is not the case in cancer cell metabolism.  Cancer cells would rather by-pass this cellular norm and instead, increase the rate of breakdown of sugar via glycolysis.  Recall that the process of oxidative phosphorylation occurs in the mitochondrial matrix and that pyruvate must be transported into the matrix via specific pyruvate transporters (example being the mitochondrial pyruvate carrier – MPC – complex) after pyruvate must have been delivered to the intermembrane compartment from the outer mitochondrial membrane via the action of a voltage-gated porin complex (Michael, 2017).  However, this is not the case in cancer cells. It preferentially adopts the process to glycolysis to process its energy. The increased rate of glycolysis in the cytoplasm would have led Warburg to conclude that the mitochondria was defective in cancer cells, and this defect could, in effect, contribute to cancer development. A defective mitochondrion would mean that cancer cells are unable to carry out oxidative phosphorylation to produce more energy in the form of ATP, and consequently, cells must increase the rate of glycolysis to get enough energy to meet up the increasing biomass observed in cancer.

Although it remains true, in most cases, that cancer cells exhibit a higher rate of glycolysis, the idea that mitochondrial defect was responsible for cancer development no longer holds true (Koppenol et al., 2011). It has now been proven that cancer cells have active and functional mitochondrial, contrary to Warburg’s theory (Ju et al., 2014; Xu et al., 2015)

Aerobic glycolysis in cancer

Increase in biomass and replication of the genome prior to cell division to create two daughter cells are key features of proliferating cells. The cell must therefore, generate enough energy and synthesise biomolecules at a sufficient rate to meet the demands for proliferation. Proliferation in cancer cells is always on the high rate and unregulated.  For developing tumours to survive, it needs to alter energy metabolism and nutrient uptake to favour its malignant growth. This alteration in energy metabolism and nutrient uptake led to the observation of Otto Warburg that cancer cells preferentially use glycolysis over mitochondrial oxidative phosphorylation for glucose-dependent ATP production even in the presence of ample oxygen, a phenomenon known as the ‘’Warburg effect’’ or aerobic glycolysis (Jones and Thompson, 2009; Warburg, 1956)

Cancer development has been previously linked to an increase in lactate production when compared to normal cells, even in the presence of ample oxygen which should ideally inhibit glycolysis (a concept known as ‘’pasteur effect’’). This peculiar feature of cancer cells has been confirmed by many subsequent studies (Warburg et al., 1926). Interestingly, this effect is not peculiar to cancer cells alone, as normal cells such as proliferating lymphocytes also show enhanced glycolysis (Greiner et al., 1994). The massive lactate production as a survival feature of cancer cells is necessary to regenerate, via lactate dehydrogenase, NAD+ needed to sustain glycolysis.

The paradigm of a purely ‘’glycolytic’’ cancer cells has been consistently challenged. Recent research shows that some glioma, hepatoma and breast cancer cell lines possess functional mitochondria and equally source their ATP mainly from oxidative phosphorylation (Kashiwaya et al., 1994). Interestingly, some cancer cells can switch between fermentation and oxidative metabolism, depending on the absence or presence of glucose and the environmental conditions. It is therefore, imperative to understand the mechanism by which cancer cells can reversibly regulate their energy metabolism. Such a situation is the glucose-induced suppression of respiration and oxidative phosphorylation (Crabtree, 1929; Diaz-Ruiz et al., 2009). This is referred to as the ‘’crabtree effect’’ and is usually short-term and reversible.

Molecular mechanisms that support the high rate of glycolysis in cancer cells.

This section will concentrate on some of the processes and/or mechanisms that contribute or support the fact that there is a high rate of glycolysis in cancer cells. This will be discussed in the following sections.

 The need to generate sufficient biosynthetic precursors

Cancer cells face the challenges of how to provide the requisite bioenergy and biosynthetic precursors to meet up with the ever-increasing genome and biomass. It is therefore, imperative for cancer cells to devise strategies to ensure the constant supply of these precursors, and this it achieves through aerobic glycolysis. It can be inferred here that glycolysis provides precursors for several biosynthetic pathways, and may provide a range of precursors required for RNA and DNA synthesis, via the Pentose Phosphate Pathway (PPP); and for certain amino acids and glycerol for lipids. This could support the need for the Warburg effect observed in cancer cells. This could also explain why cancer cells employ glycolysis for energy uptake, despite the low ATP production involved when compared to oxidative phosphorylation that yields tremendous amount of ATP. To buttress the hypothesis of a possible upregulation of the PPP mentioned above, Deberardinis and Tong together with their teams have reported that the non-oxidative branch of the PPP appears to be the main source of ribose-5-phosphate in tumour cells (Deberardinis et al., 2008; Tong et al., 2009). This, in effect, supports the need for increased glycolysis as ribose-5-phosphate is a key precursor for nucleotide biosynthesis.

Overexpression of glucose transporters

Commonly, substrate supply is the controlling step of a metabolic pathway for some cancer cell lines (Kashiwaya et al., 1994; Rodríguez-Enríquez et al., 2009). Accordingly, the overexpression of glucose transporters has been reported as one of the main determinants of the Warburg effect (Yamamoto et al., 1990). Glucose derivatives such as 18Fluoro-deoxyglucose (FDG) is used as a tracer in positron emission tomography (FDG-PET) scan for imaging uptake of glucose in tissues in vivo (Czernin and Phelps, 2002). Enhanced glucose uptake visualised by FDG-PET correlates with poor prognosis and higher metabolic potential in many tumour types.

The high affinity glucose transporters (Glut 1 and 3) have been shown to be overexpressed in cancer cell lines (Macheda et al., 2005) and their inhibition in vitro has been shown to impair growth of tumours in cells (Cao et al., 2007). This is a clear evidence of their direct involvement in tumour growth. Overexpression of glucose transporters in tumour cells indicates that there is an increased uptake of glucose into the cell and hence, an increase in the rate of glycolysis to metabolise the excess glucose. As a result, glucose transport could be a suitable target for pharmacological anti-tumour agents.

Increased activity (or downregulation) of glycolytic pathway enzymes

The high rate of glycolysis may also be explained by the increased activity of glycolytic pathway enzyme. To prove this, Pelicano et al. has shown that each of the enzymes involved in the glycolytic pathway is overexpressed or downregulated in several cancer cell lines (Pelicano et al., 2006). Other reports have supported the fact above, pointing out that each of the enzymes in the pathway shows a several-fold increase in activity compared to their normal counterparts (Marín-Hernández et al., 2006). It is important to note here that only the rate limiting enzymes in the pathway would have significant impact for the quantitative increase in glycolysis. As a result, only hexokinase (HK, especially HKII isoform), phosphofructokinase (PFK) and pyruvate kinase (PK) may be implicated, and their regulation and expression pattern change in some tumours – see figure 1 below (Diaz-Ruiz et al., 2011).  For example, the activity of PFK is tightly regulated according to the energy state in a cell – inhibited when the cell no longer requires ATP. However, in cancer cells like leukemia and lymphoma, the L and P isoforms of PFK are predominant. Surprisingly, the allosteric properties of these isoforms allow the maximal activity of the enzyme even in low energy demand condition (they respond less effectively to their inhibitors – citrate and ATP) while they are highly activated by lower concentrations of fructose-2, 6-bisphosphate (Vora et al., 1985; Vora et al., 1980). The above points also support the observation of a high rate of glycolysis in cancer cells as the rate-limiting enzymes are found to be overexpressed and/or upregulated as the case may be. Fig. 1 summarises some of the enzymes that are overexpressed, upregulated and/or downregulated in tumours. It also highlights the role of the enzymes in either the glycolytic pathway or the TCA cycle.

The pyruvate crossroad

Pyruvate is located at the intersection between two of the main catabolic pathways of the cell: glycolysis and Krebs cycle (see figure 1 above). Pyruvate can be transported into the mitochondrial matrix and be converted to acetyl-coA (via the action of pyruvate dehygrogenase) which subsequently enters the Krebs cycle to yield reducing equivalents to be later used by respiratory chains to drive oxidative phosphorylation (Diaz-Ruiz et al., 2011). On the other hand, the metabolite can reside within the cytosol and be reduced by lactate dehydrogenase to lactate.

Under aerobic glycolysis which predominates in cancer cells, pyruvate is said to be inefficiently metabolised by mitochondrion and subsequently deviating the metabolic flux into lactate production (Diaz-Ruiz et al., 2011). Three events have been suggested to be responsible for this:
  • The restriction of pyruvate transport into mitochondrial matrix
  • The inhibition of the pyruvate dehydrogenase complex
  • The over-activation of lactate dehydrogenase
All three events promote aerobic glycolysis and therefore, favour the development of tumours. The last two events will be elaborated further here.  First, using pyruvate dehydrogenase (PDH) complex as a case study, PDH catalyses the oxidative decarboxylation of pyruvate to produce acetyl-coA, a key intermediate in Krebs cycle (see figure 1 above). Its activity is tightly controlled according to the energy need of the cell. It is inhibited when the cell’s energy store is high, and vice versa (Strumiło, 2005). Two key enzymes covalently and reversibly regulate the activity of PDH – PDH kinases (pdhk) phosphorylate and thus inactivate it whereas PDH phosphatases (PDP) revert this inactivation (Strumiło, 2005).  The activity of pdhk1, an isozyme of pdhk has been shown to be upregulated by c-Myc and hypoxia-inducible factor-1a (HIF-1a) in cancer cells, which lead to inhibition of mitochondrial respiration in cancer cells (Saunier et al., 2016). Again, the transcription of pdhk3, another isozyme of pdhk has been reported to be induced by HIF-1a (Saunier et al., 2016). Taken together, these data suggest that the phosphorylation of PDH is an important factor for the formation and/or progression of tumour.

Lastly, using lactate dehydrogenase (LDH) as a case study.  Pyruvate is reduced in the cell cytosol by LDH in order to keep a constant supply of NAD+. The latter is required to drive glycolysis. See figure 2 below for the chemical reaction catalysed by LDH. This enzyme has been found to be overexpressed in a variety of cancer cell lines (Goldman et al., 1964) and the disruption of its expression stimulates respiration and decreases tumour cell viability in hypoxic conditions (Fantin et al., 2006; Le et al., 2010). Over-expression of the M-subunit of LDH homo- or hetero-tetramer has been detected in several human tumours (Fantin et al., 2006).

Krebs Cyle and Oxidative phosphorylation defects or weakening:

Defects in the Krebs cycle and oxidative phosphorylation pathways could also be responsible for the increased glycolysis rate observed in tumour cells. Research has confirmed high citrate efflux in mitochondria isolated from a hepatoma cell line (Parlo and Coleman, 1984, 1986). The leaked citrate is oxidised by isocitrate dehydrogenase to produce NADPH which is required for lipid synthesis – as a consequence of an impaired Krebs cycle. However, a study contrary to this report has been published. The only difference between the two research results is that a different cell line was used in the latter. The synthesised lipids are key to building up the membranes of the fast-growing cancer cells. Again, mutations in succinate dehydrogenase, which participates in the mitochondrial respiratory chain as complex II commonly occur in phaeochromocytomas and paragangliomas; suggesting that this TCA enzyme may contribute to tumour growth (Gottlieb and Tomlinson, 2005). Another example is the mutation observed in isocitrate dehydrogenase, which is known to be implicated in adult cases of glioblastoma and seems to have a major role in the development of the tumour by a gain-of-function effect (Dang et al., 2009; Yan et al., 2009). The above findings suggest that a defect in the mitochondrial Krebs cycle enzymes could also be responsible for the switch to glycolysis as characterised by cancer cells. Furthermore, there is also the influence of various cancer-associated mutations, with many having considerable impacts on metabolism (Potter et al., 2016).

Both a decrease in ADP translocation to the mitochondrial matrix as well as the inhibition of the ATP synthase have been reported as highlighted in figure 1 above (Lee and Yoon, 2015; Sonveaux et al., 2008).  Both scenarios would restrict ATP production in mitochondria, thereby forcing the cell to rely mostly on glycolysis-derived ATP. It is worth noting that several studies have shown that other cancer cell lines possess fully functional mitochondria which is contrary to the Warburg effect (Guppy et al., 2002; Pasdois et al., 2003; Rodríguez-Enríquez et al., 2006)

Another interesting feature of cancer cell energy metabolism is their extensive consumption of glutamine, which is the most abundant amino acid in mammals (Kovacević and Morris, 1972). Glutaminolysis has been reported to increase in cancer cell lines (Matsuno and Hirai, 1989). Glutamine is involved in numerous anabolic pathways (such as nucleic acid) and can be degraded in the Krebs cycle thereby generating ATP through both substrate level and oxidative phosphorylation (Kovacević and Morris, 1972; Reitzer et al., 1979).

Although pyruvate, via the action of pyruvate carboxylase, could have also supplied oxaloacetate to Krebs cycle, this enzyme is reported to be suppressed in certain cancers (liver, brain and breast) (Deberardinis et al., 2008). Again, there is also the influence of various cancer-associated mutations with many having considerable impacts on metabolism (Potter et al., 2016)


The aim of cancer cells is to ensure they by-pass the body’s defence or regulatory mechanisms to promote their own survival.  To survive and grow, and in addition to the hallmarks of cancers discussed, altered energy metabolism is one strategy cancer cells utilise to acquire enough energy and precursors to duplicate their genome and biomass.

Cancer cells utilise the concept of aerobic glycolysis to ensure that they have sufficient energy and biosynthetic precursors to ensure they develop, progress and survive the cell’s surveillance mechanism.  They achieve this by employing a variety of survival mechanisms, among which include the overexpression of glucose transporters, up- or downregulation or overexpression of key enzymes in glycolysis, Krebs cycle and/or oxidative phosphorylation pathways, in addition to activating proto-oncogenes and inactivating tumour-suppressor genes.

This paper has highlighted the relationship between cancer and high rate of glycolysis (observed in tumours) and discussed some of the various mechanisms cancer cells employ to achieve this. Some of the key pathways or mechanisms discussed above, including increased expression of glucose transporters, over-activation of lactate dehydrogenase, key enzymes implicated in the Krebs cycle could serve as targets for drug design.


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Despite the huge interest in cancer research in recent times, it continues to be one of the major genetic diseases that contributes to human morbidity and mortality.  As a result, there has been the need to understand the genetic basis of cancer at the molecular level to give way for the screening and design of novel therapies to correct or alleviate the molecular alterations that frequently occur in cancers.  One of these molecular alterations has been identified in the oncogene, ErbB2.  This gene is significantly amplified and its protein overexpressed in 20-30% of breast cancer and this is associated with a more aggressive tumour and poor prognosis.  Knowledge of the critical role this oncogene plays in both normal and tumour cells have enabled researchers to develop clinically useful antibody therapy and small molecule compounds targeting its extracellular receptor domain and the intracellular tyrosine kinase domain, respectively.

Key words: Her2/ErbB2; gene amplification; breast cancer; therapy.


Cancer can be defined as a heterogeneous group of genetic diseases characterised by unregulated clonal expansion of somatic cells brought about by multiple genetic and epigenetic changes [1].  Cancer development and progression in humans involves multi-step processes that usually take place over many decades.  During these processes, the cancer cells acquire multiple allelic mutations in genes such as proto-oncogenes, tumour suppressor (TS) genes and other genes that control cell proliferation [2].  These allelic mutations lead to the production of dysregulated proteins leading to the activation of oncogenes or the inactivation of TS genes; promoting abnormal regulation of signalling pathways involved in cell cycle regulation, genetic stability, apoptosis and cell differentiation.  This imbalance in cellular regulations drives the process of oncogenesis [2].

This essay aims to explore one of these molecular alterations: gene amplification of the proto-oncogene ERBB2, its roles in cancer development and how it has contributed to improve cancer treatment.  Although other types of cancers will be highlighted, the essay will focus on the role of ErbB2 in breast cancer (BC) development and treatment, and this will be discussed under the following headings.

Target identification

ErbB2 (also referred to as Her2, c-erbB2 or neu) is a 185KDa proto-oncogene that belongs to the transmembrane receptor tyrosine kinase family, of which there are other three receptor types, including epithelial growth factor (EGF) receptor 1 (Her1), Her3 and Her4 [3].  The ERBB2 gene is located in chromosome 17q11.2-12 [4], and its product is a glycoprotein comprising an N-terminal extracellular domain (ECD), a single 23 amino-acid transmembrane domain, and an intracellular tyrosine kinase domain [3].  The structure of the ECD reveals why ErbB2 is an orphan receptor (as opposed to others) as the ligand binding site is occluded by the direct contact between domains I and III of the ECD [5].

Binding of growth factors such as EGF or neuregulin to their cognate receptors, and subsequent heterodimerisation with their preferred dimerisation partner (ErbB2) regulates downstream processes such as cell growth, differentiation, and apoptosis via auto-phosphorylation or trans-phosphorylation of the tyrosines in their C-terminal domain [3].  The above processes are coordinated through at least three different pathways: phosphatidylinositol 3-kinase (PI3K), mitogen-activated protein kinase MAPK, and phospholipase C-γ as illustrated in Figure 1 below [3].  Although usually membrane localised, ErbB2 has been reported to translocate to the nucleus where it acts as a transcription factor for COX2 [6] and STAT3 [7] genes – key genes that promote tumourigenesis - suggesting a role for ErbB2 in transcription deregulation in tumours.

ErbB2 gene amplification and overexpression has been identified in 15-30% of human breast cancers [8], which is predictive of a more aggressive tumour with poor prognosis [3, 5, 9].  Other amplified ErbB2-associated malignancies include ovarian, gastric, and colon cancers [9]. In an attempt to understand the mechanisms of ERBB2 gene amplification, Marotta et al. (2012) proposed that a common copy-number breakpoint in a duplicated segment associated with the Keratin-associated protein (KRTAP) gene was the cause of ErbB2 gene amplification in primary BC due to this loci’s fragility.  In addition, Li et al. [10] showed that a macroH2A1.2 interacts with Her2 to induce Her2 transcription, and hence, increased cell proliferation and tumourigenicity in SKBR-3 breast cancer cells.  The amplified region in Her2 is located within chromosomes as homogeneously staining region [11], and telomeric deletions have been shown to be common in ERBB2 [12] indicating the involvement of DNA breaks in ERBB2 amplification.

Methods such as fluorescence in situ hybridisation (FISH), immunohistochemical (IHC) and immunocytochemical analyses, and PCR have been used to estimate and validate HER2 gene amplification and protein overexpression in breast cancer cells [13].  Results from this study show that FISH is the method of choice for Her2 status tests, and may be employed to confirm the results obtained from other methods above.  Moreover, Jonathan et al. [14] and Yeh, et al. [15] used cDNA microarray and array Comparative Genomic Hybridisation, respectively to show that ERBB2 gene was amplified and overexpressed in breast cancer.

Target validation

Identification of ERBB2 as the gene overexpressed in cancer enabled scientists to demonstrate that it does play a critical role in breast cancer development and progression.  To effectively target this molecular defect, the role of ErbB2 overexpression should be investigated and validated.  To achieve this, several functional studies have been carried out on ErbB2-induced cancer cells.  To validate this, Vaughn et al. [16] used antisense DNA to specifically downregulate overexpressed ErbB2 in breast cancer cells.  This caused a shift in cell cycle profile, with a significant time in the G1-phase.  In a similar study, siRNA was used to silence the Her2/neu gene, resulting in the induction of apoptosis and inhibition of proliferation in SKBR3 BC cell lines [17].  Similarly, transformation of primary murine mammary epithelial cells with ErbB2 overexpression results in the destabilisation of the cdk inhibitor p27 and concomitant increased expression of cyclin D1 leading to increased proliferation [18].  Results from these independent experiments show that ErbB2 overexpression is associated with tumourigenesis, and could serve as potential therapeutic target in breast cancer (and other cancer type where ErbB2 is overexpressed).

Drug discovery

Knowledge of the genetic basis of cancer at the molecular level has contributed to the development of novel targeted therapies [19].  Drugs targeting the ErbB2 receptor selectively attack tumour cells, thereby discriminating, to some extent, against normal cells – which is the ultimate aim of cancer therapy [19].  Drugs that target two domains: ECD and the cytoplasmic tyrosine kinase domain of the ErbB2 receptor have been developed [19].

Drebin et al. [20] first used monoclonal antibodies (mAbs) to specifically target and immunoprecipitate the p185/her2/neu from a DNA donor rat neuroblastoma.  Further to this was the finding that specific mAbs (such as mAb7.16.4) could actually induce the reversible downregulation of ErbB2 receptors, leading to a growth inhibition in athymic mice overexpressing the receptor [21, 22].  These studies paved way for the use of mAbs for the treatment of human malignancies and further validated oncogenes as targets for therapeutic agents [19].  Sequel to these developments, recombinant humanised Antibody rhumAb4D5 was engineered, and later renamed trastuzumab or Herceptin (developed by Genetech Inc.) and was approved for BC treatment by the FDA in 1998 [19, 23].

Herceptin targets domain IV of the ECD of ErbB2 and studies have shown that when combined with standard chemotherapy, it reduces the risk of cancer recurrence as compared with chemotherapy alone as indicated in Figure 2 below [24-26].  Herceptin mechanisms of action have been proposed as follows:
  1. ErbB2 internalisation and degradation via the activity of ubiquitin ligase c-Cbl [27].
  2. Antibody dependent cellular cytotoxicity (ADCC) via the recruitment of natural killer cells [28]
  3. Inhibition of the MAPK and PI3K/Akt pathways leading to cell growth suppression, cell cycle arrest and PTEN (a negative regulator of PI3K/Akt and STAT-3) overexpression by interfering with ErbB2 dimerisation [29]
Pertuzumab is another mAb that binds to domain II of the ECD, hindering ErbB2 interactions with cognate receptors; and also mediates ADCC [30, 31].  Lapatinib is a tyrosine kinase inhibitor that causes potent inhibition of both EGFR and ErbB2 activation, hence indirectly inhibiting the activity of downstream effectors MAPK and Akt in breast cancer cells in vivo and in vitro [19].

Clinical application

Clinical application of trastuzumab and lapatinib is limited since only about 20% of BC show ErbB2 overexpression.  Therefore, diagnostic tests such as FISH, IHC are performed to detect gene amplifications [32].  Adjuvant therapies that include trastuzumab and chemotherapy are now seen as the standard of care for patients having ErbB2-induced BC [24, 25].  Recently in a clinical trial, it was shown that trastuzumab plus lapatinib was effective in the treatment of metastatic breast cancer than lapatinib alone, again showing the benefit of combined therapy in the treatment of BC [33].

Although there has been great success in the use of trastuzumab in combination with other drugs, it is presented with resistance by the cancer cells and with some potential side effects [34].  This resistance could be inherent or acquired and it has been shown that approximately 70% of patients develop secondary resistance [34].  Some tumour suppressor proteins and oncogenes, including PTEN and src have been implicated to play critical roles in the mechanisms of primary or secondary resistance [34].  Several mechanisms of resistance in BC have been proposed including alterations in Her2 receptor complex, downstream activation of PI3K signalling pathway, alteration in other receptors such as insulin-like growth factor receptor-1 and Met receptor [32, 34].  This is further summarised in table 1 below.


In conclusion, cancer treatments have received great attention in recent times due to the molecular understanding of the genetic basis of cancer development and progression and the recent completion of the sequencing of the human genome.  Identification of ErbB2 as the protein overexpressed in some breast cancer patients have paved way for the development of novel therapies for the treatment of breast cancer.  Although these novel therapies have been clinically useful, more future research is expected to overcome the challenge of drug resistance and other potential side effects associated with them.


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Biological membranes are selective barriers that separate cells from the environment, and also partition intracellular organelles.  They are important in the selective transport of biological molecules such as ions, glucose, carbon dioxide in and out of the cell and its organelles (Brown, 1996). Biomembranes, are in common, made up of lipid, protein and sugar molecules, with lipids forming the highest proportion (Saiz et al., 2002).  Phospholipid, a component of the lipid molecule, gives membranes their amphipathic property - which forms the primary structure of all biomembranes.  Other components of the lipid moiety of membranes include cholesterols and glycolipids that are involved in the regulation of membrane permeability and cell-cell communication, respectively (Crockett, 1998; Malhotra, 2012).  Membrane proteins are also a structural component of the biomembranes.  They play a number of crucial roles within biomembranes, ranging from  binding to extracellular molecules such as ions, solutes and signalling proteins (receptor function), to membrane attachment of cytoskeletal proteins and their involvement also in the intracellular signalling pathway mechanism (Lodish et al., 2013). For example, the transmembrane protein, cystic fibrosis transmembrane-conductance regulator (CFTR), that normally localises in the apical cell membrane of epithelial cells is an ion channel involved in the transport of chloride ions across the epithelial membrane (Riordan, 2008).  In addition to selectively transport molecules into and out of the cell, biological membranes are important cellular component that function to compartmentalise organelles, protect the cell from environmental damages, synthesise proteins and energy, and to form a cell-cell communication link (Fagone and Jackowski, 2009).  This shows the physiological relevance of membranes, and why it's important that we study their interactions with other micro- and macro-molecules.

A defect in the functioning of one or more of these membrane components might lead to a membrane or cellular disease.  Knowledge of the different cellular mechanisms by which these diseased conditions results is necessary for the proper understanding of how drugs could be used to treat or alleviate the symptoms of the disease (Howell et al., 2006; Ashrafuzzaman and Tuszynski, 2013).  Presently, there is no general consensus of a membrane-based disease classification due to the fact that many pathways are implicated for a particular disease (Ashrafuzzaman and Tuszynski, 2013).  For example, a defect in membrane transport could be attributed to malfunctioning in several factors, including membrane proteins, lipid bilayers and others factors.  Despite this classification problem, Petit-Zeman and Ashrafuzzaman and his colleague argued that there are two classification of membrane-based diseases: defects in cytoskeletal components and alteration of membrane lipid composition, each of which affect membrane function and trafficking, respectively (Petit-Zeman, 2004; Ashrafuzzaman and Tuszynski, 2013).  Hyaline membrane disease, Alzheimer's disease, cystic fibrosis (CF), Duchenne muscular dystrophy (DMD), Hermansky-pudlack syndrome and Neimann-pick disease type C have all been identified as being caused by abnormalities in membrane functioning (Ashrafuzzaman and Tuszynski, 2013).

This essay aims to describe the molecular pathology of two of these diseases: CF and DMD to emphasise the roles membranes play in the cell.  It will also suggest some recent treatment options available.

Duchenne Muscular Dystrophy

Duchenne muscular dystrophy (DMD) is a lethal X-linked recessive neuromuscular disease that frequently affects 1 in 3500 liveborn males (Hayes et al., 2008). Anderson et al. (2002) earlier reported that the disease is genetically inherited, and is the second most occurring inherited disease.  Clinical manifestations in affected people include a progressive lost of smooth, cardiac and skeletal muscles from around 3 years of age (Hayes et al., 2008).  Afflicted patients die at adolescent as a result of prolonged muscle weakness, coupled with respiratory insufficiency.

A major cause of the disease has been linked to the absence (or insufficiency) of a cytoskeletal protein called dystrophin, encoded by the dystrophin gene (Kristen and Kay, 2004).  The dystrophin gene is said to be the largest gene of all the human genes, comprising an estimated 2.6 million base pairs of DNA that encodes 79 exons (Kristen and Kay, 2004; Walmsley et al., 2010).  Defective (or loss) of dystrophin protein is as a result of insertion or deletion mutation in about 60% of most cases, whereas about 40% of point mutation in the same gene has been attributed to the loss-of-function mutation of the dystrophin gene (Kristen and Kay, 2004).  Walmsley et al. (2010) added that the axons at position 45-53 are ''hotspot'' for deletion-type frameshift mutation.  Research shows that dystrophin gene is also expressed in other tissues apart from cardiac and skeletal tissues.  Muntoni et al. (2003) in a review article stated that isoforms of the dystrophin gene is expressed in the brain and the retina, and that mutation in these isoforms, the brain isoform, for example, causes mental retardation and low intelligence quotient in the affected individuals.  The brain isoforms are called Dp140 and Dp71 (D’Angelo et al., 2011).  Consequently, it's been theorised that there is a link between DMD and impairments of the central nervous system (CNS) caused by mutation of the dystrophin gene.  Insights into these DMD/CNS impairments have come from studies in an mdx mouse model and in Golden Retriever Muscular Dystrophy (gmrd) dogs, both deficient in dystrophin protein (Campbell, 1995; Kristen and Kay, 2004).  Becker Muscular Dystrophy is another form of muscular dystrophy, though with  a milder phenotype as a result of insignificant level of synthesised dystrophin at the sarcolemma or a small length of the protein, or both (Campbell, 1995).  Other types of muscular dystrophies that have been identified and studied are congenital muscular dystrophy (CMD), fukuyama-type congenital MD, limb-girdle MD and finally, severe childhood autosomal recessive muscular dystrophy (SCARMD) that affects both males and females at the same rate (Campbell, 1995).

This 427 kDa dystrophin protein has 4 functional domains that are structurally different: a cysteine-rich domain, an N-terminal domain, a C-terminal coiled coil rod (containing repeats that are spectrin-like) (Lapidos et al., 2004).  Dystrophin is associated with other membrane-localised glycoproteins via at least two of its four domains.  Its N- and C-terminal domains each bind F-actin and the dystrophin-associated protein complex (DAPC, which comprise proteins such as sarcoglycans, dystroglycans, integrins, caveolin, biglycans, and sarcospan), respectively at the sarcolemma (Kristen and Kay, 2004).  As depicted in figure 1 below, the extracellular protein (α-dystroglycan), via its interaction with the G domain of merosin (laminin-2 in figure 1), connects the sarcolemma to the extracellular matrix, a process that depends on the presence of Ca+2 (Campbell, 1995).  Again, the DAP-complex interacts with the α-dystroglycan and hence, links it to the sarcolemma and finally, the N- and C-terminal domains of dystrophin associate the subsarcolemma cytoskeleton to the membrane by interacting with F-actin and the DAP-complex (Fig. 1) (Campbell, 1995).  The carboxyl terminal of dystrophin also directly binds dystrobrevin, and syntrophin triplets, however, only two subunits are shown in the illustration below.

The DAP-complex is vital as it stabilises the sarcolemma during periods of muscle contractions and relaxations (Ehmsen et al., 2002).  Ehmsen and colleagues further stated that that DAPC does this by forming a link between the N-terminal bound actin and the extracellular matrix.  It's also reported that the DAPC plays an important part in cell signalling by interacting with Ca+2-bound calmodulin, neuronal nitric oxide synthase (nNOS) and Grb2 (Rando, 2001).  These membrane interactions by these transmembrane and membrane-associated proteins are illustrated and summarised in figure 1 below.

Pathophysiology Of DMD: The mechanism underlying the pathogenesis of the disease is the absence (or insufficient synthesis) of the dystrophin protein as a result of frameshift mutations (Muntoni et al., 2003) that lead to the absence of the C-terminal and cysteine-rich domain of the dystrophin protein (Campbell, 1995).  The absence of the dystrophin protein causes a disruption of the DAP-complex (and therefore, affects costameric formation), and consequently, there is a damage to fibre and a rapid increase in membrane permeability to ions, and other substances (Kristen and Kay, 2004; Deconinck and Dan, 2007).  Initially, there is an improved regeneration of muscle cells, however, progressive damage by necrosis leads to degeneration of the muscles (Perkins and Davies, 2012).  Deconinck and Dan (2007) in a review article further added that other secondary events that occur in response to a disruption of the DAP-complex include muscle degeneration, impaired vascular adaptation, apoptosis, high intracellular Ca+2 levels and fibrosis.  Diagnosis of the condition is often done by measuring the level of serum creatine kinase - a marker of necrosis in the muscle - which always increase rapidly in DMD patients (Perkins and Davies, 2012).  A number of techniques have been employed in the study of the mutations that lead to DMD; these include techniques such as multiplex PCR, quantitative PCR, multiplex amplifiable probe hybridization and studies using model animals such as mdx mice and gmrd dogs (Muntoni et al., 2003).

Treatments For DMD: A incisive knowledge of the pathogenesis of DMD would avail scientists and drug researchers the opportunity to develop drugs or techniques to manage the clinical symptoms of this devastating disease (Deconinck and Dan, 2007).  Although there is currently no effective cure for DMD (Deconinck and Dan, 2007), DMD gene therapy holds a promising therapeutic treatment for the afflicted patients (Duan, 2011).  In fact, the introduction a an autosomal homologue of dystrophin gene called utrophin witnessed the localisation of the DAP-complex to the sarcolemma and subsequently restores effective muscle contractions and relaxations in mdx mice (Kristen and Kay, 2004).  Duan (2011) added that challenges facing the gene therapy approach to treating DMD are the design of a vector to contain the large size of the dystrophin gene and any subsequent immune response that might results.  Other therapeutic approach include the use of protease inhibitors to regulate the DAPC pathway, increasing alternative gene expression, use of aminoglycans and chimaeraplasts and antisense oligonucleotides (Kristen and Kay, 2004).   Analysing the points above, it can be said that studies in model animals and a knowledge of the causative mechanisms, coupled with the identification of the gene responsible for the dystrophin protein have helped scientists in the search for possible treatment options.

Cystic Fibrosis

Another membrane-related disease that, compared to DMD, affects males (but also females) is cystic fibrosis (CF). It is described as an autosomal recessive inherited disease that frequently populate among the Caucasian descent, of which 1 in 25 people are carriers and 1 in 2500 are affected also (Davidson and Porteous, 1998).  it's estimated that CF affects about 70,000 individuals worldwide, albeit no cure for the condition despite intensive research in the field (Ramsey et al., 2011).  However, insights into research and therapeutic approach to treating CF patients have improved the CF median survival rate from 11 years to 37 years (Shane and Graeme, 2008; Davis, 2011).  The disease is caused by defect in a single gene located on chromosome 7 called ''cystic fibrosis transmembrane conductance regulator'', CFTR gene for short, which encodes a 1480 amino acids sequence called CFTR protein (William and Bruce, 2006).  Oxford University GeneMedicine (2012) states that this 250,000 base pair CFTR gene was identified in 1989 using the technique of Restriction Fragment Length Polymorphism, RFLP analysis.  six different classes of mutations that impair the normal function of this chloride-selective channel protein have been identified (Wilschanski, 2013) and are presented in the table below.

The CF Mutation Database, which is a repository that collects and stores mutations associated with the CF gene currently holds 1938 mutations associated with the CFTR gene (CF Mutation Database, 2011; Wilschanski, 2013).  These mutations have given rise to the classification identified in the above table.  However, the most common CF mutation, in about 70-90% of CF cases, is the ΔF508 mutation (William and Bruce, 2006).  This mutation results from a phenylalanine deletion in exon 10 (of the 27 exons) in the CFTR gene which codes for the foremost nucleotide binding domain (NBD) of the CFTR protein (Wilschanski, 2013).

The CFTR protein is a member of the ABC transporter superfamily that is primarily expressed in epithelial cells of the kidney, pancreas, heart, intestine, vas deferens, sweat glands and lungs (William and Bruce, 2006).  It can therefore, be concluded that it's a multi-organ protein, and any defects in it could become generalised.  It's composed of 12 membrane-spanning alpha-helices that is mainly involved in the selective transport of chloride ions (Tector and Hartl, 1999); 2 NBDs that bind and hydrolyse ATP and are also involved in the regulation of channel opening and closing; and finally, a regulatory (R) domain that binds protein kinase A and protein kinase C to activate the channel (William and Bruce, 2006).  It is this opening and closing that mediates the transepithelial movement of salt and water across the apical cell membrane in organs where CFTR protein plays physiological roles.  Moreover, the CFTR protein is important in the regulation of other ions such as Na+ (via ENAC channels) and bicarbonate ions (Cohen and Prince, 2012).  The CFTR protein associated with the cell membrane is shown in figure II below, together with other proteins that interact to bring about the physiological role of CFTR in the cell.

Pathophysiology Of CF: The class II mutation in delta-F508 of the CFTR gene product is identified by the ER Quality Control (QC) machinery as a misfolded protein and is targeted for proteasomal degradation, and so the protein never reaches the epithelial cell membrane where it functions normally as a selective chloride channel (Riordan et al., 2001; William and Bruce, 2006; Cohen and Prince, 2012).  Lodish et al (2013, Pg. 642) added that the three bp phenylalanine deletion stop the usual transport of the CFTR protein to the apical cell membrane by preventing its loading into COPII coated vesicles that usually bud off from the ER to the Golgi apparatus.  However, small quantity of the proteins that escape the ER QC machinery are able to reach the cell surface, but are less active and are quickly degraded from the cell surface (Davis, 2011).  This is in contrast with wild-type CFTR protein that are sufficiently retained in the apical cell membrane before being targeted for degradation (Davis, 2011).  Some CFTR gene mutations encode full-length CFTR proteins that are processed normally in the ER, but are defective in ion-channel activity (Rowe et al., 2005).  For example, G551D mutation in table 1 above.

The absence (or insufficient quantity) of CFTR channels at the apical cell membrane brings about a complex, multi-organ symptoms that are characteristic of CF phenotype, although the severity vary among the affected organs (Wilschanski, 2013).  Due to the disruption of chloride transport and dysregulation of other ion channels in epithelial tissues of the lungs, mucus hypersecretion and dehydration of the lungs develop in affected individuals (Davidson and Porteous, 1998).  As a result, mucocilliary clearance of the mucus in the lungs is impaired, and therefore, pathogens such as P. aeruginosa, S. aereus, Burkolderia apacia and H. influenza find this microenvironment suitable to live and infect the host's lungs (Rowe et al., 2005).  Colonisation by these pathogens and continuous influx of neutrophils lead to a more severe or chronic inflammatory response (Davidson and Porteous, 1998). Subsequently, the affected patient develops respiratory problems (including airway and submucosal gland obstructions), a major cause of death in affected people, and which also determine the quality of life lived by CF patients (Cohen and Prince, 2012).  As a multi-organ disease, some patients also suffer chronic fibrosis of the pancreatic duct and infertility in affected male individuals (Rowe et al., 2005).  Again, there is high salt concentration in the sweat of CF patients because of the dysfunction effects that CFTR mutations have on the regulation of ENAC channels (Rowe et al., 2005). Rowe et al. (2005) also added that the transepithelial potential difference across the sweat gland is higher in CF patients than it's in unaffected people.

Newborn screening, and genetic, lung function and sweat tests and sputum cultures are some of the diagnostic measures currently in  use to detect CF in affected persons (NHLBI (National Heart Lung and Blood Institute), 2011)).

Treatments for CF: Insights into the molecular and genetic basis of CF have open ways for new therapeutic approaches to be developed.  Like DMD, gene therapy approach for treating CF patients have received much public and research interest ever since the first CF clinical trials in 1993 (Burney and Davies, 2012).  Topical administration of gene targeting agents (GTA) to epithelial cells of the lungs have been the aim of recent clinical trials (Griesenbach et al., 2002; Burney and Davies, 2012).  However, Griesenbach, Burney and their colleagues reported that this approach has been faced with challenges owing to immune response and some extracellular physical barriers.  Again, certain pharmacological agents have been exploited for the treatment of CF.  For example, on January 31, 2012, it was announced that FDA approved the use of Kaledeco (Ivacaftor), that works by increasing the open state probability of CFTR channels in certain CFTR mutations such as G551D and delta-F508 (Song, 2012).  Other drugs such as PTC124 (ataluren), and Vx-809 are both in their phase-III clinical trial (CF Foundation, 2013).  PTC124 and VX-809 each work by producing a full-length CFTR protein (thereby correcting the premature-stop codon mutation of class I) and by targeting class II delta-F508 CFTR protein to the apical cell surface, respectively (Shane and Graeme, 2008).  Other therapeutic approaches aimed at correcting defects in CF and improving the performance of the lungs and at eradicating bacteria and mucus clogging in the lungs and control of inflammatory responses include the use of pulmozymes, nebulised salt solution, and an anti-ineffective drug called tobramycin (Shane and Graeme, 2008).


The important physiological roles played by membranes in normal cellular functioning and diseases have been given much in-depth touch in this essay.  Some diseases associated with these membrane dysfunctions have also been elucidated, with particular emphasis on CF and DMD.  In all, model organisms have been of immense help in the study of the disease mechanisms and how modern treatment options could be employed to alleviate the symptoms or to completely eradicate the disease.  It’s therefore, important to understand disease mechanisms, factors involved, and the many tissues and organs that it affects in order to search for the best possible treatment options.



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Muscles are necessary for the proper functioning and coordination of the entire human body.  They aid in the movement of the body parts by their contraction and subsequent relaxation processes using ATP.  In fact, the primary function of muscles is to respond to body stimulus by generating force or movement (Boron and Boulpaep, 2009: 237).  They are classified based on their morphology, functions and locations into skeletal, cardiac and smooth muscle cells.  These different types of muscle cells perform many specific, important roles within the body including maintaining posture, heat generation, and aiding movement (Pearson Higher Education, 2011).  Due to the identifying features of the types of muscle cells, there exist differences and similarities in appearance, functions and locations which this essay aims to elucidate on.


On the basis of structure, location and function, Sinauer Associates (2009) argued that skeletal muscle cells appear cross-striated when observed under the light microscope whereas smooth muscle cells are non-striated.  These striations are due to the arrangement of a repeating unit called sarcomere.  Vanderbilt University Medical Centre (2011) further stated that skeletal muscle cells are syncytial, that is, multinucleated; cylindrical shaped and 1 to 40 mm long compared to smooth muscle cells that have centrally placed nucleus, spindle, (fusiform) shaped and approximately 20 µm long in blood vessels.  The multinucleated cells of skeletal muscle cells are formed as result of fusion of myoblasts and they aid maximal contraction whereas smooth muscle cells do not fuse during development, but form gap junctions that aid in cell-to-cell communication (UWA, 2009). Moreover, skeletal muscle cells are attached to bones and found in the abdominal walls, pelvis, etc and aid in the movement of skeleton.  In contrast, smooth muscle cells are found in hollow organs such as blood vessels and are responsible for the passage of, for example, substances through the blood vessels (Sinauer Associates, 2009).  Again, the contraction of smooth muscle cells are under involuntary control mediated by the autonomous nervous system and various hormones whereas skeletal muscle cells’ contraction are voluntary and are mediated by the somatic nervous system (Seifter, et at, 2005: 94-97).

On the basis of excitation-contraction coupling, Reece (2009) argued that whereas skeletal muscle cells have T-tubules, smooth muscle cells possess a structure called caveolae that does similar function.  Raff (2003) added that the absence of T-tubules in smooth muscle cells is due to their large surface-area-to-volume ratio for the influx of Ca+2.  Raff (2003) further added that the ca+2 bind to a structure called calmodulin that activate a myosin light chain kinase, which then phosphorylate a protein on myosin head leading to the hydrolysis of ATP and therefore, formation of cross-bridge as illustrated in figure 1 below.  In contrast, Ca+2 bind to troponin C on skeletal muscle to uncover the myosin binding site.

Sabir and Usher-Smith (2008) added that whereas troponin covers the mysosin binding site on actin, a atructure termed caldesmone covers that of the smooth muscle cells.   Again, Raff (2003) added that smooth muscle cells lack sarcomere therefore, its actin are attached to structures called dense bodies that are interconnected by intermediate filament rather than Z-line in the sarcomere of skeletal muscle cells.  Additionally, the ratio of actin to myosin is 14:1 compared to the 2:1 in skeletal muscle cells.  This gives more room for the myosin and actin cross-bridges in smooth muscles and therefore, a much greater shortening of its length compared to skeletal muscles.  This is particularly important in urinary bladder in changing its lumen diameter from an expanded state to entirely zero (Reece, 2009).  Reece (2009) further stated that during excitation-contraction coupling, contraction is much slower in smooth muscles than in skeletal muscles due to the slow activity of ATPase.  Therefore, energy requirement is smaller in smooth muscles than in skeletal muscle cells.  This is necessary for prolonged tonic contraction for hollow organs that work all day long.

In spite of all these differences between skeletal and smooth muscle cells, there are some similarities as well.


One of the similarities between skeletal and smooth muscle cells is that both have myofilaments: thick filaments as myosin; and thin filaments as actin and tropomyosion that help to bring about their contraction by the sliding filament mechanism, in which actin slides over myosin leading to the formation of cross-bridge in both muscle cells which in turn shortens the cells and therefore aid in the movement of body parts and blood vessels repectively (Seifter et al, 2005).  Raff (2003) added that the contraction is facilitated by the release of Ca+2 due to the arrival of an action potential in both.  Therefore, the contractions of both muscle cells are triggered by an action potential.  Reece (2009) again stated that the myosin binding sites on actin of both skeletal and smooth muscle cells are covered by some regulatory proteins as stated earlier in the absence of Ca+2.  Again, the contractions of both muscle cells use up energy by the hydrolysis of ATP.  More importantly, both muscle cells have similar evolutionary history as they were both myoblasts in some stages in their development (UWA, 2009).  Other similarities between skeletal and smooth muscle cells are that both have important organelles such as nucleus, mitochondria (for ATP production), sarcoplasmic reticulum, sarcolemma and are controlled by the nervous system (Seifter et al, 2005).

In conclusion, the difference and similarities between skeletal and smooth muscle cells are as a result of their varying structures, locations, and functions.  The activity of the proteins: troponin and caldesmone as regulatory proteins are important for the proper contraction to be exhibited in both muscles.  Again, it is clear from the analysis above that Ca+2 is an invaluable substance within our body systems, as it is required for the contraction of both smooth and skeletal muscle cells.  Therefore, a deficiency of calcium may result into malfunctioning of some body parts.  Finally, energy in the form of ATP is particularly important for the functioning of the body as argued above.



1. Introduction

The production of cyclic AMP is mediated by adenylyl cylclases, that are, in turn, controlled by the heterotrimeric G-proteins referred to as Gs (for stimulatory) and Gi (for inhibitory) (Morris, et al., 1994).  Both are subclasses of the alpha subunit of G-proteins.  There is substantial evidence that one of the inhibitory isoforms, called Guanine Nucleotide Binding-protein G(I) subunit alpha-2, α-Gi-2 can regulate the cellular function of adenylate cyclase, AC (Li et al, 2005).  In addition, many studies have shown that PKC activation resulted in the regulation of AC within the cell (Morris et al., 1994).  Therefore, the inhibitory effect imposed on AC by α-Gi-2 can be prevented by the action of PKC.  PKC does this by phosphorylating specific residues of the α-Gi-2 protein.  Several chemical reagents have been used to study or simulate this cellular mechanism.  One such reagents is phorbol ester (TPA) that specifically phosphorylate serine residues of α-Gi-2 (Bushfield et al., 1991).  Another example is the tumour promoter and phosphatase inhibitor, okadaic acid that is particularly important for the increased phosphorylation state of α-Gi-2 (Bushfield et al., 1991).  Finally, the non-hydrolysable analogue of GTP, GTPγS is useful in the selective inhibition of adenylate cyclase via its action on α-Gi-2 (Bushfield et al., 1991).

In this experiment, we had employed various bioinformatics and proteomics tools and knowledge in the search to identify a protein (in this case, α-Gi-2) and a potential phosphorylation site within its amino-acid sequence.  In recent years, identification of protein sequences has been made easier with the introduction of the principle of mass spectrometry (MS).  In addition, the fast growth of genomic and protein databases has had great positive impact in the search for protein sequences and homology among proteins (Baldwin, 2004).  MS-based identification of proteins can be achieved via two ways: sequence-specific peptide fragmentation or peptide mass fingerprinting (PMF).  The standard experimental method to identify proteins involves separation by 1-D or 2-D gel electrophoresis (or other separation techniques), followed by excision and digestion of the protein with specific endopeptidases such as trypsin to generate peptides (Thiede et al., 2005).  Next, the peptide masses are measured using the MALDI-TOF - a fast, robust and accurate technique.  These masses are then compared against set of masses predicted for each protein in the databases and a score is then assigned to each match that ranks the quality of the matches (Aebersold and Goodlett, 2001).  Examples of these databases include MASCOT, MS-Seq., NCBI, UniProt, amongst others.  Figure 1.1 below illustrates various procedures taken to identify proteins by MS.  MS is also employed for the detection and identification of post-translational modifications (PTMs) such as phosphorylation of proteins.  Neutral ion loss technique is particularly useful to detect loss of phosphate group of 98Da from specific amino-acids due to a process known as β-elimination (Wu and Liao, 2007).


2. Methods

2.1. Assay on inhibition of forskolin stimulation of adenylyl cyclase

To assay for the effect of GTPγS on adenylyl cyclase of rat hepatocytes, the following experiment was set up.  Three different protocols were set up.  In the first protocol, the rat hepatocytes were pre-incubated with okadaic acid for 10 mins.  10 µl buffer A (without TPA-phorbol ester) was then added to the mixture and incubated for a further 10 mins.  At the point, 10 µl buffer B, containing GTPγS was added to the mixture and incubated for another 10 mins.  Finally, 10 µl buffer C containing forskolin, an adenylyl cyclase activator, was added and further incubated for another 10 minutes, after which the CAMP level was measured.  This experiment was carried out at 37oC.  The above procedure was repeated for the second protocol, but buffer A now contained TPA.  To confirm the loss of ability of GTPγS to inhibit forskolin stimulation of adenyl cylcase after being exposed to TPA, a control protocol was done by replicating the first experiment.  However, buffer A and B now had no TPA and GTPγS contained in them, respectively.  These series of procedures were carefully controlled to ensure that they each received the same treatment - same volume of buffer and within the same time frame.

2.2. 1D SDS-PAGE Experiment

After CAMP level measurement, we performed a 1D SDS-PAGE experiment on the samples and found a novel band.  We hypothesised that the band had appeared as a result of phosphorylation.  Consequently, we excised the band from the gel and performed a trypsin digest overnight to produce peptides of the protein sequence for MS analysis.  The MALDI-TOF technique was used to measure the masses of the peptides.  To confirm any modifications done on the protein we employ the proteomic technique: neutral ion loss.

2.3. Peptide Mass Fingerprinting with MASCOT

The peptide masses were transferred into the PMF search program MASCOT.  The organism was specified to be ''rattus'' and the database was set to SwissProt and trypsin as the enzyme used to digest the protein.  We allowed up to 1-miss cleavage.  All other settings were left to default.  Results from the database search are shown in  figures 3.2 to 3.5 below.

2.4. MS/MS Analysis using MS-Seq.:

To confirm the identity of the protein, we run an MS/MS analysis of three peptides with their b- and y-ions for the parent ions (all expressed in m/z).  Here the taxonomy was again set to Rattus Norvegicus to minimise the search results and trypsin, being the cleavage enzyme.  Other settings were left to default.  Results of the search are shown below in figure 3.6.


2.5. Identification of Post-translational modification (PTM) within the protein

To identify any PTM within the protein, the Neutral Ion Loss technique was used. We speculated that the protein must have been phosphorylated since neutral ion loss

  revealed two ions which have been modified.  To identify the phospho-peptide, we retrieved the fasta sequence of the protein from NCBI protein database using the protein's accession number.  We then carry out an MS-digest search for the predicted phospho-peptide using the two product ion values.  Results from this search are shown in figure 3.7 below.

3. Results

The results of the experiment and the data obtained from the different databases are shown below.  Tables 3.1-3 and fig. 3.1 are results of the actual experiment giving information on the peptide masses, neutral ion loss studies, MS/MS analysis of ions of three peptides, and finally, levels of CAMP formed as a result of interaction of the different ligands on rat hepatocytes.  Figures 3.2-6 are results that were obtained from the different databases used to identify the protein and to test the hypothesis that the novel band was as a result of the phosphorylation of the protein.  Fig. 3.8 represents the 3-D structure of the protein.

MS/MS analysis of ions
Parent ionm/z
b ionsy ions
1 (1413.8312)229.11831284.789
2 (1053.5034)261.1598940.4193
3 (1393.7434)277.11831246.675
Table 3.1: MS/MS analysis of three peptide ions showing the m/z ratio of the b- and y-ions of the peptides.
Neutral ion Loss
Parent ion (m/z)Product ion (m/z)
Table 3.2: Neutral ion loss of parent and product ions to reveal which peptide, and hence amino acid was phosphorylated
Peptide Mass (m/z)
Table 3.3:  Peptide masses of the different peptide generated using MALDI-TOF technique.


4. Discussions

In this experiment, we had been able to prove that GTPγS has an inhibitory effect on AC, and that okadaic acid increases the phosphorylation state of α-Gi-2 by inhibiting phosphatases as earlier reported by Bushfield and colleagues (Bushfield et al., 1991).  As depicted in figure 3.1 above, the percentage of CAMP levels decreases significantly on addition of GTPγS prior to adding TPA showing that TPA activates PKC that, in return, phosphorylates serine residues of α-Gi-2 whereas GTPγS inhibits AC which, upon activation, leads to increased CAMP levels in the cell.

To confirm that our hypothesis that the novel band observed on the 1-D SDS-PAGE was as a result of phosphorylation of a protein,  we used different protein databases including MASCOT to identify the protein as Guanine Nucleotide Binding-protein G(i) subunit alpha-2 (also known as adenylate cyclase inhibiting G alpha protein and abbreviated α-Gi-2; GNAI2_RAT in rats) using the peptide masses of the protein as query terms (Fig. 3.2; 3.5).  From the search we obtained a score of 72 (above the significant limit value) and confirmed that the protein was from rat hepatocytes by the taxonomy classification.  Out of 17 peptides masses as query terms, the MASCOT database returned 10 mass value matches with a protein sequence coverage of 27% (Fig. 3.4).  Again, the molecular mass of the protein is given as 40473 Da with a pI of 5.3 and a sequence of 355 amino-acids (see fig. 3.4).  Other proteins returned from our search were rejected as they had lower score number and some with high expect value (see fig. 3.3).  There were some missed cleavages by the trypsin enzyme as shown in figure 3.5 above.

To further confirm that identity of the protein, we did an MS/MS search for the m/z ratio of b- and y-ions of three peptides (Table 3.1 above).  Parent ion 2 and 3 did not match any peptide when the MS/seq was run.  However, results from the MS-Digest search results gave a match to parent ion 2 with the peptide sequence ''LFDSICNNK'' between the amino-acid position 250 and 258.  Parent ion 1 also had a similar result with the peptide sequence ''EVKLLLLGAGESGK'' between positions 33 and 46.  In contrast, parent ion 3 failed to return any peptides. From the MS/seq search we got the accession number of the protein to be to be ''P04897'' (see fig. 3.5).

MS-Digest results and search for the peptide with the product ion 1585.9523 pointed to the sequence ''LLLLGAGESGKSTIVK'' at position 36-51. In contrast, the second product ion did not link to any peptides in the databases.  Searches using phosphosite program shows that the protein has a potential phosphorylation site at the amino-acid position 44 on serine (see fig.3.7 above).  However, it's not phosphorylated.  The protein is phosphorylated at this position in other species (humans and mouse) (Chu et al., 2010).  From the sequence above, another potential phosphorylation site is Threonine at position 48, but it's not phosphorylated in any of the three species.  However, it's phosphorylated at other amino-acid position such as position 6 on serine and five other positions on either serine or threonine.  As a result, the protein has been modified post-translationally, but at other positions other than Ser44.  Further studies are needed to confirm that the protein was actually phosphorylated on Ser44 or Thr48 as indicated by the neutral ion loss studies.  Morris et al. (1991) demonstrated that Ser144 is a site of phosphorylation by PKC, and that on high concentration of CAMP, Ser207 is also phosphorylated.  Morris and colleagues further stated that the phosphorylation of α-Gi-2 by PKC leads to the inactivation of the protein and hence, an increase in the number of activated AC.  Consequently, this brings about increased levels of the second messenger, CAMP that further results in the activation of CAMP-dependent protein kinase and finally, phosphorylation of effector protein-targets such as transcription factors (Shemarova, 2009).  Regulators of G-protein signalling-12 and -14 contain sites that interact with α-Gi-2 in a way to regulate the rate of exchange of GDP for GTP (Kimple et al., 2001).

In conclusion, we had been able to assay for the effects of different ligands on the phosphorylation and dephosphorylation of Guanine Nucleotide binding-protein G(I) subunit alpha-2 and the effects that this has on the activation state of Adenylate cyclase and the levels of CAMP in the cell.  We have shown that data obtained from mass spectrometry could actually explain the results we obtained, and how growth in the development of these databases has helped in protein sequence identity of any post-translational modifications on proteins.



  1. Aebersold, R. and Goodlett, D.R. (2001) 'Mass Spectrometry in Proteomics', Chemical Reviews, 101(2), pp. 269-296.
  2. Baldwin, M.A. (2004) 'Protein Identification by Mass Spectrometry: Issues to be Considered', Molecular & Cellular Proteomics, 3(1), pp. 1-9.
  3. Bushfield, M., Lavan, B.E. and Houslay, M.D. (1991) 'Okadaic acid identifies a phosphorylation/dephosphorylation cycle controlling the inhibitory guanine-nucleotide-binding regulatory protein Gi2. ', J., 317-321(Part-2), p. 274.
  4. Chu, J., Zheng, H., Zhang, Y., Loh, H.H. and Law, P.-Y. (2010) 'Agonist-dependent μ-opioid receptor signaling can lead to heterologous desensitization', Cellular Signalling, 22(4), pp. 684-696.
  5. Kimple, R.J., De Vries, L., Tronchère, H., Behe, C.I., Morris, R.A., Farquhar, M.G. and Siderovski, D.P. (2001) 'RGS12 and RGS14 GoLoco Motifs Are GαiInteraction Sites with Guanine Nucleotide Dissociation Inhibitor Activity', Journal of Biological Chemistry, 276(31), pp. 29275-29281.
  6. Li, Y., Hashim, S. and Anand-Srivastava, M.B. (2005) 'Angiotensin II-evoked enhanced expression of RGS2 attenuates Gi-mediated adenylyl cyclase signaling in A10 cells', Cardiovascular Research, 66(3), pp. 503-511.
  7. Morris, N.J., Bushfield, M., Lavan, B.E. and Houslay, M.D. (1994) 'Multi-site phosphorylation of the inhibitory guanine nucleotide regulatory protein Gi-2 occurs in intact rat hepatocytes.', J., 301(Part-3), pp. 693-702.
  8. Shemarova, I.V. (2009) 'cAMP-dependent signal pathways in unicellular eukaryotes', Critical Reviews in Microbiology, 35(1), pp. 23-42.
  9. Thiede, B., Höhenwarter, W., Krah, A., Mattow, J., Schmid, M., Schmidt, F. and Jungblut, P.R. (2005) 'Peptide mass fingerprinting', Methods, 35(3), pp. 237-247.
  10. Wu, H.Y. and Liao, P. (2008) 'Analysis of Protein Phosphorylation Using Mass Spectrometry', Chang Gung Med J, 31, pp. 217-227.


Conventional cancer treatment often involves the use of chemotherapeutic agents which have some clinical limitations due to strong side-effects and cellular toxicity associated with their use [1].  Therefore, the search for new therapeutic approaches to treat primary and metastatic cancer is on the increase.  The application of tumour-colonising, facultative anaerobic bacteria that selectively grow in, and kill necrotic and viable malignant tissues is a novel alternative treatment approach[2].  The facultative anaerobe, Salmonella typhimurium possesses these unique characteristics and has been in the forefront of modern research into cancer therapy.

The primary purpose of this study was to genetically modify S. typhimurium to create strains that will preferentially target primary and metastatic tumours as an improvement on previous works done in the field.  To achieve this, we have exploited some of the features of the bacterium, including its selective growth in anaerobic conditions, virulence and mode of infection, and those of the tumours such as specific genes that induce aberrant tumour growth [2-4].  Conditions such as extensive necrosis and impaired circulation which are characteristics of tumours permit the growth of anaerobic bacteria.  To create attenuated strains of S. typhimurium, we have deleted genes encoding the PhoP/PhoQ two-component system proteins [4] and deprived the bacteria of the amino-acid Leucine and arginine for selective growth in tumours, and not normal cells [2].

In the study, we have created three attenuated strains of S. typhimurium with the primary aim to selectively target tumourigenic, but not normal cells.  The first strain called the A-1 is auxotrophic for the amino-acids leucine and arginine, and is therefore, dependent on neoplastic tissues to survive locally.  When injected intravenously or intratumourally into PC-3 human prostrate cancer (expressing GFP in the nucleus and RFP in the cytoplasm) implanted into nude mice, the A-1 strain selectively grew in PC-3 prostrate cancer tumours [2].

The second strain was an improvement on the A-1 strain.  The A-1 strain was reisolated from the tumours and genetically modified further to increase its tumour targeting potential.  The new strain called A1-R was administered to human metastastic cancer [3].  The cancer cells were also GFP- and RFP-labelled as in the A-1 strain to enable real-time visualisation of apoptosis and bacterial targeting using fluorescence microscopy.

The third strain was designed to deliver gene-specific, plasmid-based   shRNA of the signal transducer and activator of transcription 3 (stat-3) to the prostrate tumours (in C57BL6 nude mice) by means of siRNA gene targeting therapy [4].

15 days after treatment with strains A-1 and A1-R, examinations of the liver, spleen, lung, kidney show no presence of S. typhimurium, but they continued to grow and replicate in tumours and consequently, progressively regressed tumourigenic growth.  When injected intratumourally, the metastases (targeted by A1-R strain) and prostrate cancer tumours (targeted by A-1) were completely eradicated by day 21 [2, 3]. Similar results were obtained for the phoP/PhoQ deletion strain that delivered a plasmid-based shRNA to prostrate tumours.  The results show that the recombinant strain carrying a Psi-Stat3 greatly reduced tumour proliferation and number of metastasis in organs, and prolonged the life time of prostrate tumour-grafted C57BL6 mice compared to the control that carried a scrambled siRNA of stat-3 [4].

The overall results indicate that attenuated strains of salmonella typhimurium can be effectively used in the treatment and cure for cancer.  Their selectivity shows that primary and metastatic cancers can be preferentially targeted with genetically modified anaerobic bacteria without the need of toxic chemotherapy [3].  These results are intriguing and promising as they contrast previous experiments that had aimed to treat cancer via the same gene therapy approach.  This is because the strains of bacteria used in previous studies were confined to necrotic areas of tumours whereas the bacteria strains used in this research grew in both viable and necrotic tumours, accounting for their exceptional antitumour efficacy [2, 3].

It’s therefore, glaring from the results that the application of certain genetically modified strains of bacteria in the therapeutic cure for primary and metastatic cancers is promising and could be useful in the clinical treatment of cancer.  However, more research is needed to ensure that the approach is safe for treatment of cancer in humans before a lab bench to hospital bed treatment could be achieved.



  1. Leschner, S., et al., Identification of tumor-specific Salmonella Typhimurium promoters and their regulatory logic. Nucleic Acids Research, 2012. 40(7): p. 2984-2994.
  2. Zhao, M., et al., Tumor-targeting bacterial therapy with amino acid auxotrophs of GFP-expressing Salmonella typhimurium. Proceedings of the National Academy of Sciences of the United States of America, 2005. 102(3): p. 755-760.
  3. Hayashi, K., et al., Cancer metastasis directly eradicated by targeted therapy with a modified Salmonella typhimurium. Journal of Cellular Biochemistry, 2009. 106(6): p. 992-998.
  4. Zhang, L., et al., Intratumoral Delivery and Suppression of Prostate Tumor Growth by Attenuated Salmonella enterica serovar typhimurium Carrying Plasmid-Based Small Interfering RNAs. Cancer Research, 2007. 67(12): p. 5859-5864.


Biological processes such as transcription, translation, and DNA repair all depend on the specific interactions between proteins and DNA.  These DNA-protein interactions are necessary for the growth, development and survival of organisms in the three domains of life, and perturbations that affect these complex interactions account for the numerous diseases, including cancers in humans [1, 2].  These DNA-binding proteins may function as structural proteins, enzymes, transcription factors and co-factors [2]. Owing to these invaluable roles, It is therefore of utmost importance that we study the mechanisms by which DNA-binding proteins specifically interact with their target DNA (and RNA) to bring about changes in gene expressions or regulation or cellular events [2].

Several techniques have been developed for the study of DNA-protein interactions, which have helped in the elucidation of the mechanisms of complex formation between DNAs and proteins.  These techniques include in vitro methods such as electrophoretic mobility shift assay (EMSA), footprinting assays, phage-display and proximity ligation assay; and in vivo methods such as DNA adenine methyltransferase identification and chromatin immunoprecipitation, as well as in silico tools [2].  The various approaches above have their advantages and drawbacks, and are designed to assay specific interaction parameters [2].

This essay will elaborate on the EMSA approach: describe the general principles including the methods, factors affecting resolution of complex, applications, limitations and draw reasonable conclusions.

EMSA: Definition and Basic Principles

This technique, also called Gel Retardation Assay, is a rapid, simple, efficient and highly sensitive method used for the study of DNA-protein interactions [5].  EMSA is widely used for the identification of DNA-binding proteins (qualitative use), and to determine the binding affinities, stoichiometry, kinetics (quantitative use), and conformational changes of the interactions between DNAs and proteins [1, 5]. It has been particularly useful in the study of how transcription factors bind – either as repressors or activators – to specific promoter regions in DNA to regulate gene expression [1].  This in vitro technique relies on the principle that the complex formed between protein and its target site on DNA will migrate slowly towards the positive bottom end in a gel matrix compared to unbound (or free) DNA, thereby causing a shift that can be easily detected after running the samples on a polyacrylamide or agarose gel [2, 5]. The popularity and versatility of EMSA amongst researchers is attributed to its numerous unique features when compared to other methods. These features are explained below:
  • High sensitivity: The technique is very sensitive since low concentrations (0.1mM) and sample volumes (20 uL) can be used to give detectable bands when analysed by autoradiography or with ethidium bromide [1].
  • Wide range of samples: A variety of nucleic acids (in size and structures) and proteins (including heavy complexes) can be used to assay the complex interactions [1].
  • Again, there is the choice of using either crude protein extracts or purified recombinant proteins. The latter is very useful when the researcher is interested in characterising DNA-binding proteins contained in a nuclear cell extract [1].

Methods Involved in EMSA

Five basic steps are usually carried out in a conventional EMSA protocol [1, 4].  An overview of the steps is sequentially listed below.
  • Preparation of purified or crude protein sample, and
  • Preparation of nucleic acid: either radioactively labelled (usually with 32P or occasionally with biotin, fluorophores or digoxigenin) or unlabelled. The sources of DNA could be from cloned DNA (50-400bp) or synthetic oligonucleotide (20-70 nucleotides; [2]).
  • Binding reactions: considerations are made for buffer conditions and use of additives to reduce non-specific binding of protein to DNA sequence [1]. For example, studies on the human recombinase rad51 and the E. coli CAP protein show that they require ATP and cAMP, respectively for efficient binding to DNA [6, 7].
  • Non-denaturing gel electrophoresis to separate free nucleic acid from preformed complex.
  • Lastly, detection of the outcome, usually with ethidium bromide for unlabelled DNA or by autoradiography for labelled DNA. These steps are as depicted in Fig. 1 below.

Factors Affecting Resolution of Complex

The inherent properties of the protein-nucleic acid complex act to affect their relative mobility (RL, defined as observed distance migrated divided by expected distance) through the gel and the resolution of the complex [8].  These properties include the ratio of the relative mass of the two components that form the complex, charge alteration, and the conformational changes in DNA upon protein binding [8].  Other factors such as gel matrix compositions and concentrations, incubation temperature are external factors that also affect the resolution of the complex [8].  In addition, non-specific DNA-binding proteins (in crude cell extracts) also affect the mobility and hence, an EMSA result outcome [1].  To circumvent the effect of non-specific DNA-binding proteins, non-specific DNAs such as salmon sperm, synthetic poly(dI:dC) are included in the assay so that the non-specific proteins can rather bind to the sites on them [5].  Two of the above factors are explained further in the following paragraph. As would be expected, the relative mobility of the protein-DNA complex decreases as the protein size increases as has been studied for the transcriptional regulator, GCN4 but an increase in the length of DNA exhibits the opposite effect [8].  The latter scenario can be observed when Lac repressor binds to operator DNA (of between 80-500 bp).  In this case, the RL value rises from 0.5 to 0.68 [9].   It can therefore be inferred that it is the individual mass of the protein and DNA fragment that determines the RL rather than the absolute mass of the complex.  Moreover, concentrations and compositions of gels also influence the results obtained from an EMSA [8].  A tiny pore diameter and one that allows the migration of both free and bound DNA complex across the gel is chosen.  Polyacrylamide gel pore size averages about 50-200 A0 for an acrylamide concentration of between 4 and 10%; and this causes an intense frictional drag on the complex as they migrate across the gel [8].  On the other hand, the pore diameters of agarose gels are wider compared to those of polyacrylamide gels and are therefore, only suitable to resolve large protein masses [8].  Despite these differences in pore sizes, the two gel types are used to resolve complexes.

EMSA Applications

In addition to its use for the study of the associations between nucleic acids and proteins, the technique is also useful in the study of parameters such as binding constants and conformational changes in the DNA molecules brought about by protein binding [1].
  1. Study of conformational changes in DNA: The interactions between proteins and its cognate sites on DNA may result in bending of the DNA molecule.  This alteration in structure leads to a reduced relative mobility; and the degree of curvature is dependent on the binding angle and the position of the bend relative to the ends of the DNA [8]. A lower relative mobility is more pronounced if the bend is in the middle of the DNA fragment [8].  Variants of the EMSA method such as circular permutation assay (identifies presence of a bend, see fig. 2 below), and helical phase assay (determines bend direction) have been developed to study the effects of protein-induced bending on DNA [10]. The formal variant, for example, has been used to show that proteins such as TFIIIA, c-AMP activating protein (CAP) and TATA binding protein (TBP) induce bends in DNA [3, 11].  Hence, EMSA method can be used to identify which proteins induce bending in DNA upon binding.
  2. Binding Constants: EMSA can also be used to measure the kinetic and thermodynamic parameters of the interactions between proteins and DNAs.  Consider the chemical reaction [5]: If a relatively strong interaction occurs between DNA and protein, then Ka > Kd, and therefore, two bands are observed: complex PD and free DNA (fig. 3). In contrast, weak interactions means that Kd > Ka, and therefore, a fainter band (corresponding to complex PD) and a more intense smear are produced [5].  Although the interactions in the second scenario are weak, complex stability is maintained through molecular sequestration and by ‘’cage effect’’ (prevents dissociated proteins from escaping; [5]).  The binding constant can then be extrapolated by measuring the amount of complex formed relative to protein concentration at equilibrium [1].  An example is illustrated in fig. 3 below for the association of small delta protein with DNA [1].
  3. Monitoring protein-DNA complex formation from crude cell extracts: One of the many advantages of EMSA is that both purified proteins and crude cell extract proteins can be used to bind DNA and monitor complex formation [2]. Although there is the problem of non-specific DNA binding when crude cell extract is used, this can be easily overcome by using shorter length of DNA fragment (to limit the binding sites) or by using an EMSA variant called supershift assay in which target-specific antibodies are used to reduce the relative mobility of the complex in gel [5]. Hence, a secondary mobility shift is produced with bound DNA and protein of interest. On the other hand, purified recombinant proteins give accurate and useful data. Parameters such as binding constant and binding affinity and effects of factors such as divalent metals can be easily obtained [5].

    Despite its numerous applications, EMSA is faced with some challenges. First, the complex formed are not always at chemical equilibrium when run on a gel which could result into wrong interpretation of, for example, the molecular weight of the complex formed [5]. Second, EMSA results provide little information about where the protein specifically binds to on the DNA sequence. This information can be readily obtained from the chemical footprinting assay [1, 5, 8]. However, deletion mutations of the DNA fragment have been reported that identifies the binding sequence using EMSA technique [12]. Finally, kinetic studies are limited due to the significant amount of time needed to prepare the sample mix, and then run it on the gel [1].


Electrophoretic mobility shift assay has yielded useful information on the mechanisms by which certain proteins specifically bind to DNA sequences and whether or not these interactions induce any conformational changes in DNA.  The technique has also allowed for the study of quantitative parameters such as the kinetics of the interactions between DNAs and proteins; and to identify which proteins bind to a particular DNA sequence from a crude cell extract.  Although of importance, EMSA is faced with some limitations and further work to overcome these would be valuable in molecular biology studies.


  1. Alves, C. and C. Cunha, Gel Electrophoresis - Advanced Techniques. Electrophoretic Mobility Shift Assay: Analyzing Protein - Nucleic Acid Interactions, ed. M. Sameh. 2012: InTech. 500.
  2. Dey, B., et al., DNA–protein interactions: methods for detection and analysis. Molecular and Cellular Biochemistry, 2012. 365(1-2): p. 279-299.
  3. Schroth, G.P., J.M. Gottesfeld, and E.M. Bradbury, TFIIIA induced DNA bending: effect of low ionic strength electrophoresis buffer conditions. Nucleic Acids Research, 1991. 19(3): p. 511-516.
  4. Yang, V.W., Eukaryotic Transcription Factors: Identification, Characterization and Functions. The Journal of Nutrition, 1998. 128(11): p. 2045-2051.
  5. Gaudreault, M., et al., Electrophoretic Mobility Shift Assays for the Analysis of DNA-Protein Interactions, in DNA-Protein Interactions, B. Leblanc and T. Moss, Editors. 2009, Humana Press. p. 15-35.
  6. Chi, P., et al., Roles of ATP binding and ATP hydrolysis in human Rad51 recombinase function. DNA Repair, 2006. 5(3): p. 381-391.
  7. Fried, M.G. and D.M. Crothers, Equilibrium studies of the cyclic AMP receptor protein-DNA interaction. Journal of Molecular Biology, 1984. 172(3): p. 241-262.
  8. Lane, D., P. Prentki, and M. Chandler, Use of gel retardation to analyze protein-nucleic acid interactions. Microbiological Reviews, 1992. 56(4): p. 509-528.
  9. Fried, M.G., Measurement of protein-DNA interaction parameters by electrophoresis mobility shift assay. ELECTROPHORESIS, 1989. 10(5-6): p. 366-376.
  10. Kahn, J., Methods for Analyzing DNA Bending, in DNA Topoisomerase Protocols, M.-A. Bjornsti and N. Osheroff, Editors. 1999, Humana Press. p. 109-123.
  11. Wu, H.-M. and D.M. Crothers, The locus of sequence-directed and protein-induced DNA bending. Nature, 1984. 308(5959): p. 509-513.
  12. Pongubala, J.M.R. and M.L. Atchison, PU.1 can participate in an active enhancer complex without its transcriptional activation domain. Proceedings of the National Academy of Sciences, 1997. 94(1): p. 127-132.


Database search for similar or homologous sequences of proteins and DNA has been on the increase in recent times as more new proteins are being discovered and the genome sequence of many organism are being deposited in databases [1].  Database online programs such as BLAST, UNIPROT, Pfam are widely used for this purpose.  In this exercise, these tools have been used to identify SinR protein, its biochemical function and 3-D structure, and its cellular and phylogenetic distribution using information from the protein sequence.  These properties of SinR will be focus of this essay.

Database search methods

Blast search querying protein database at the NCBI website was performed with the 111 protein sequence for the identification of the unknown protein.  The following parameters helped in the identification of the unknown protein: maximum score of 225, 100% query cover, 100% identity and a lowest E-value of 1e-76, indicating that the search result was not a mere chance.  PROSITE, UNIPROT, Pfam and SMART databases were used to identify the functional domains, phylogenetic and cellular distribution of the protein using the provided CDS.  The PDB ID (IB0N) was used to access the structure of the protein in the PDB database.

Cellular distribution and biochemical function of SinR

The 111 amino acid sequence is the tetrameric protein SinR that functions as a master regulator of sporulation and biofilm formation in B. subtilis [2].  This 12.8 KDa protein represses the transcription, and hence expression of major genes such as Spo0A (controls SinI production), espA-O (for exopolysaccharide production) and yqxm-sipW-tasA (encodes for secreted, amyloid-like protein component of the matrix) that regulate the developmental pathways of B. subtilis during period of starvation or environmental changes [2, 3].   SinR binds to pyrimidine-rich sequence, 5′-GTTCTYT-3′, with Y being any pyrimidine base.  It, however, prefers binding to inverted repeat sequences [2].   This sequence specific repressive activity of SinR is relieved by its antagonist, SinI via a SinR-SinI complex at the oligomerisation domain of SinR when sufficient amount of the antagonist is present [2]. This then leads to the dissociation of the SinR tetramer from the promoter regions of the DNA, and a simultaneous formation of a SinR-SinI heterodimeric  complex [4].  As a result of the inhibition of SinR repressive activity, the genes encoding specific proteins that promote biofilm formation are expressed.

SinR is constitutively expressed in B. subtilis whereas the levels of SinI expression are on the increase only during sporulation and biofilm formation as [SinI] it’s under the control of a phosphorylated form of the transcription factor, Spo0A [2].  SinR is therefore, the master regulator that decides whether this bacterium forms biofilms or not.  Other proteins are also found to interact with SinR, including SlrA and SlrR proteins.  The slrR is positioned directly adjacent to espA within the chromosome of B. subtilis, and hence is negatively regulated by SinR [2].  Inhibition of SinR by SinI activates the expression of SlrR protein encoded by slrR.  SlrR, in turn, forms complex with SinR inhibiting the expression of genes encoding autolysin and flagellar biosynthesis, thus preventing cell separation and motility, respectively [2, 3].  On the other hand, SlrA forms complexes with SlrR and binds to the promoter regions of esp and yqxM operons thereby preventing SinR binding to these promoters and hence, induce the expression of the genes required for biofilm formation [2, 5].  Figure 1 below summarises these intricate protein-protein interactions that control the fate of B. subtilis in response to external factors.

Recent Research by Winkelman et al. (2013) shows that RemA, a DNA binding protein binds to the promoters upstream of eps and tapA-sipW-tasA operons, and activates the expression of these genes by competitively displacing SinR from these promoter regions.  Therefore, it can be concluded that SinR interacts with many proteins in B. subtilis to regulate the expression of the genes involved in sporulation and biofilm formation.


Phylogenetic distribution of SinR

SinR protein is cytoplasmically located in the gram-positive bacterium, bacillus subtilis and it belongs to the Helix-turn-Helix motif transcription factor family of proteins (see figure 2A below) [2].  This HTH motif aids this family of proteins to insert into the major groove of DNA, and leading to their binding specificity [6].  The DNA binding domain (DBD) of SinR shows a structural similarity to those of the repressor proteins Cl and Cro of bacteriophage 434 (PDB ID 3CRO; see figure 2B) [2, 4].  In addition, functional similarities also exist between these repressor proteins and SinR as indicated by their repressive activities in the developmental pathways of phage and B. Subtilis, respectively [4].  These structural and functional similarities may suggest an evolutionary trend between sporulation and prophage induction, in order to adapt to the changing environment.  It is possible that there might have been horizontal gene transfer from bacteriophage to B. subtilis as evidenced by the presence of prophage in the genome of the bacterium [4]

3-D structure of SinR

The structure of SinR solved by X-ray diffraction technique indicates two distinct, approximately equal size domains termed DNA binding domain and oligomerisation domain at the N terminus and C-terminus of the protein, respectively [4].  The structure was solved from the B. subtilis strain 168.  Each of these domains has specific cellular activities. Again, tetramerisation takes place between the four chains of the oligomerisation domain of SinR by a dimer of dimmers interaction [3].  Lewis et al. (1998) solved the 3-D structure of SinR in complex with its antagonist, SinI (figure 1).  The DBD of SinR is arranged as a 5-alpha helical motif formed by residues 1-69.  The HTH motif, reminiscent of DBD of transcription factors, is formed by residues 17-36, represented in yellow in figure 1. The two helical hook oligomerisation domain of SinR is formed by residues 70-110 – this domain is also homologous to SinI protein [4].  It is reported that the complex formed between DNA and SinR is made more stable in the presence of zinc which binds to, and induce kinks in the DNA [6].   However, there is no presence of a zinc finger in the amino acid sequence of SinR.


Database search has been useful in identifying SinR from sequence information.  Through online database search tools, some of the proteins that SinR is known to interact with has been deduced, together with the 3-D structure and phylogenetic distribution of SinR.  It has been shown that the master regulator, SinR serves important role in the developmental pathways of B. subtilis by interacting with other proteins, and also with DNA.  


  • Yang, J.-M. and C.-H. Tung, Protein structure database search and evolutionary classification. Nucleic Acids Research, 2006. 34(13): p. 3646-3659.
  • Newman, J.A., C. Rodrigues, and R.J. Lewis, Molecular Basis of the Activity of SinR Protein, the Master Regulator of Biofilm Formation in Bacillus subtilis. Journal of Biological Chemistry, 2013. 288(15): p. 10766-10778.
  • Colledge, V.L., et al., Structure and Organisation of SinR, the Master Regulator of Biofilm Formation in Bacillus subtilis. Journal of Molecular Biology, 2011. 411(3): p. 597-613.
  • Lewis, R.J., et al., An evolutionary link between sporulation and prophage induction in the structure of a repressor:anti-repressor complex. Journal of Molecular Biology, 1998. 283(5): p. 907-912.
  • Kobayashi, K., SlrR/SlrA controls the initiation of biofilm formation in Bacillus subtilis. Molecular Microbiology, 2008. 69(6): p. 1399-1410.
  • Cervin, M.A., et al., The Bacillus subtilis regulator SinR inhibits spoIIG promoter transcription in vitro without displacing RNA polymerase. Nucleic Acids Research, 1998. 26(16): p. 3806-3812.
  • Winkelman, J.T., Bree, A.C., Bate, A.R., Eichenberger, P., Gourse, R.L. and Kearns, D.B. (2013) 'RemA is a DNA-binding protein that activates biofilm matrix gene expression in Bacillus subtilis', Molecular Microbiology, 88(5), pp. 984-997.


Lysozyme was purified from the egg-white using the Cation Exchange Chromatography method.  The Concentration and activity of the extracted lysozyme and the egg-white was determined through Bradford assay and use of Micrococcus lysodeikticus as a substrate to assay for the enzyme activity.  The presence of lysozyme in the purified fractions was confirmed and its molecular weight determined  by the use of SDS-PAGE technique and finally, some pre-purified lysozyme was crystallised.  The outcome of the result shows that lysozyme has enzyme activity on the bacterium, Micrococcus lysodeikticus and also,  that other protein, e.g avidin, also elute with lysozyme at pH8.0.


1. Introduction

Egg-white contains, amongst several component proteins, about 3.5% of the enzyme, lysozyme (Guerin et al., 2005).  Lysozyme, also called mucopeptide N-acetylmuramoylhydrolase, is an anti-microbial enzyme that lyses the cell walls of bacteria (mostly gram positive bacteria) by cleaving the B-1, 4 linkages of N-acetylglucosamine and muramic acid (Li-Chan et al., 1986).  In addition, it has the ability to inactivate viruses; prevent anti-inflammatory response and as an antalgic agent (Guerin et al., 2005).  It's composed of 129 amino acids, having four disulphide bridges, a molecular weight of 14.6kDa and works best at a broad range of pH (6.0 to 9.0) (Research by Nawi, 2006).  It's extracted on a large scale from egg-white by the techniques of ion-exchange chromatography and salting out with a purity range of 90 - 95% (L-Chan et al., 1986).  It's property of having high basic residues in its amino acid sequence makes it suitable to be extracted by the techniques of Cation exchange chromatography and then by salting out with varying concentrations of NaCl (Guerin et al, 2005).  It's reported that lysozyme was the first enzyme to have its 3-dimensional structure determined by X-ray diffraction method (Vocadlo et al., 2001). This experiment is aimed at probing some of the properties of lysozyme.  The objective was to purify lysozyme from egg-white, assay its activity and determine its purity by the method of SDS-PAGE.  We have also aimed to grow some lysozyme crystal.  We have hypothesised that lysozyme can lyse the cell walls of some bacteria, and we hope to use some molecular biology technique to assay this function of lysozyme and test its purity.

2. Materials and Methods

2.1 Materials:

The following materials had been supplied and used for this experiment:
  • AKTA purifier,
  • spectrophotometer,
  • autoradiography equipment,
  • Computer with CPU,
  • and other lab equipment including pipettes, eppendorf tubes, falcon tubes, cuvettes, etc.
The following solutions and reagents were supplied and used during the course of the practical:
  • Buffer A containing 0.1M glycine/NaOH at pH 8.0
  • Buffer B containing 0.1M glycine/NaOH at pH 8.0 and 1M NaCl
  • 20ml filtered egg-white diluted 5-fold with 0.1M glycine/NaoH buffer at pH 8.0
  • Bradford Assay Solution
  • 20ml suspension of Micrococcus lysodeikticus
  • 20mg/ml of pre-purified lysozyme
  • Crystallisation solution (30% w/v PEG, MME 5000, 1.0 M NaCl, 0.05 M Sodium Acetate pH 4.6)
  • 4X Laemmli loading dye (contains SDS) and molecular weight marker (MWM)

 2.2 Methods:

2.2.1  Lysozyme Purification
The lysozyme protein was purified using ion exchange chromatography method to separate the lysozyme from other constituent proteins in the egg-white.  The pH was kept constant at pH 8.0 and was performed at room temperature. 1mL of the 20ml filtered egg-white (5-fold with 0.1M glycine/NaoH buffer at pH 8.0) was initially aliquoted and put in a clean eppendorf tube, labelled F0  for use in the protein assay.  Next, the position 2 tube from the AKTA, initially placed in buffer A ( containing 0.1M glycine/NaOH at pH 8.0), was inserted into the remaining 19 ml egg-white in a falcon tube. Then ends of the brown ''waste'' tubes were put into an empty falcon tube.  This tube was held in position as the experiment proceeded.  The SP-Sepharose column of the AKTA was already pre-equilibrated.  Initially, the AKTA settings were set to run manually with the following parameters: method base to ml; concentration to 0%B; gradient to off; flow rate to 2ml/min.; fraction base to ml; fraction size to 0ml; fraction limit to 0.6MPa; injection valve position to load; and buffer valve position to 2.  After these settings, the AKTA was made to start whilst the waste was being collected.  Upon the sample being fully loaded, the AKTA was pulsed and the P2 valve returned to buffer A in the falcon tube and then the AKTA was unpaused and allowed to run until almost all the buffer A was used up to wash the sample in the pump system.  Care was taken to ensure that the sample was not completely empty to avoid air being loaded onto the column.  Again, the absorbance reading on the computer was noted as the sample was being loaded. After the sample was completely washed by buffer A, the system was paused, and the following parameters were changed: buffer valve position was changed from 2 to 1 and then the ''continue button'' was hit to allow loading buffer onto the column.  As the buffer washed the column, and the baseline was reached, we started to elute the bound protein using the step elution method. At this stage,  the ''fraction size'' was changed from 0ml to 1.5ml and then ''concentration'' from 0% to 20% of buffer B (.1M glycine/NaOH at pH 8.0) -used to elute the protein.  At this concentration,  the purified protein fraction started to collect in  the 1.5ml eppendorf tubes in the fraction collector carousel.  This step was allowed to continue until the baseline was flattened again, at which stage, the process was repeated for 60% and 100% of B.  For each of the buffer percentages, we collected 10, 8 and 7 fractions respectively which were due for analysis using Bradford assay.  The computer screen result for this experiment is shown in fig. 3.1 in the result section below.
2.2.2 Protein Identification
We had used Bradford assay to confirm the presence or absence of proteins (and in what amount) in each of our 25 fractions.  We ensured to note the colour change, from brown to blue (if any), that occurred upon reacting our fractions with the Bradford assay solution.  First, we zeroed the absorbance at 595nm on the spectrophotometer by aliquoting 100 μl of buffer A and mixed it with 1ml of the bradford reagent in a semi-micro cuvette.  We then aliquoted 100 μl of our protein fraction into a new eppendorf tube labelled 1, and then mixed it with 1ml Bradford reagent, and read the absorbance at 595nm in a semi-micro cuvette.  This step was the major change to the experiment procedure, as we were initially supposed to use 20 μl of buffer A in each case, rather than 100 μl.  This process was then repeated for the remaining 24 fractions, and for the ''waste'' and the F0.  Lastly, Bovine Serum Standard Curve (see fig. 3.3) was used to calculate the concentration (in mg/ml) of each 5 of the selected fractions we thought contained the desired protein of interest given that they had changed colour to blue and that they had the highest peak as measured by the absorbance reading.  The result from this experiment is represented in table 3.1 in the result section below.
2.2.3. Protein Activity Assays
Here, we had used a suspension of the bacterium, Micrococcus lysodeikticus to measure the activity of the selected fractions based on results from the Bradford assay above.  As before, we zeroed the absorbance reading, but this time with no cuvette in the spectrophotometer and at 450nm.  This was followed by aliquoting 2ml of the suspension into a UV-vis cuvette and placed into the spectrophotometer, and the absorbance noted at 450nm.  Then 400 μl of our selected highest peak fraction (labelled peak 1) was mixed with the 2ml bacterial suspension in the cuvette and then placed in the machine.  The decline in absorbance was noted and recorded at a minute interval for 3 minutes.  The procedure was repeated for the other four selected fractions together with the ''waste'' solution and the F0.   Results from this assay is presented in tables 3.2 and 3.3 in the result section below.
2.2.4. Polyacrilamide Gel Electrophoresis
To determine the molecular weight of proteins contained in the fractions, we analysed the fractions on a gel by aliquoting 5 μl of 4X Laemmli loading dye containing SDS, mixed with 15 μl of each fraction of selected peaks.  This procedure was also repeated for the ''waste'' solution.  SDS buffer was added to the plate before loading the protein fraction.  To load the wells on the gel electrophoresis equipment, 15 μl of Molecular Weight Marker was loaded on to the first well, and then the  20 μl of the waste, and 20 μl of our fractions in the order waste, peaks 1-5.  The lid was then attached and connected to a power source at the following reading 240 Volts for 45 minutes.  As the time elapsed, the glass plate was removed and the gel placed in enough coomassie stain for 20 minutes to allow bands to form on the gel and then destained in ''destain'' for a further 20 minutes.  The gel was then analysed for the presence of protein bands using an electrophoresis apparatus.  Gel photograph showing results of this experiment is displayed in fig. 3.4 in the result section below.
2.2.5. Lysozyme Crystallisation
To crystallise a pre-purified lysozyme, 1ml crystallisation solution was pipetted into one well of a pre-greased tissue-culture tray.  Then 2 μl of a pre-purified lysozyme (at a concentration of 20mg/ml) was aliquoted onto a clean coverslip.  Care was taken not to touch the middle of the coverslip to avoid any contamination.  Next, 2 μl of the well solution from the tissue-culture tray was mixed gently with the 2 μl sample of pre-purified lysozyme, avoiding air-bubble in the mixture.  After the mixture, the coverslip was, with great care, inverted over the well containing the crystallisation solution and pressed down against the vacuum grease to get an air-tight seal.  The tray was then allowed for hours, to be analysed using a light microscope. See figures 3.5 and 3.6 for the demonstration model and our result.

3. Results

The results obtained at the end of the experiment are shown below.  From fig. 3.2 below, at the start of the chromatography experiment, there was a quick rise in the UV reading on the computer screen.  Later, this plateaued and then dropped off to the baseline (supposedly 0) as buffer A washed through the column.  As the different percentage concentrations of buffer B was added, the UV reading quickly went up and down to the baseline (an indicator showing that the protein was completely eluted).  Again, the concentration and conductance curves had risen and flattened as the concentration of buffer B was increased, as represented in fig. 3.1 and 3.2 above.  When buffer 2 (containing 0.1M glycine/NaoH at pH8.0 and 1M NaCl) was added, the bound lysozyme started to elute and was collected in fractions and then assayed for the amount of protein present in them.  Some of the fractions had changed colour when probed for the presence of protein in them using the Bradford reagent, with some giving a high absorbance reading as well.   The presence or absence of protein is indicated in table 3.1 with the corresponding fraction.  The enzyme activity of lysozyme was investigated by reacting it with a bacterial suspension.  The results of the protein activity assay are indicated in tables 3.2 and 3.3.  As expected, the absorbance decreased along a time course as the protein fraction was being added to the bacterial suspension.  The F0 had shown a rapid decrease in absorbance within the 3 minutes it was being experimented. Fig. 3.3 is the BSA standard curve used to calculate the approximate concentration (in mg/ml) of the protein fractions.  Fig. 3.4 is the gel photograph of the electrophoresis experiment showing different bands of proteins of proteins that eluted during  the separation process.  Again, fig. 3.5 and 3.6 are the model and the group result respectively, of the crystallisation experiment.

Protein Fraction Identification

Protein Fraction

% Buffer B ConcentrationAbsorbance readingColour Change
Table 3.1:  This shows that different protein fraction extracted together with the absorbance reading at 595nm (column 1).  Column 2 shows the eluting buffer concentrations used to elute the fractions whereas the fourth column relates to the change in colour upon addition of the Bradford reagent from brown to blue.  The (-) indicates absence of protein in the fraction, the (+/-) shows that there was little or no colour changes in the fraction whereas the number of  (+) indicates the strength of blue colour seen.  The highlighted rows are the ones we have used for peaks based on the highest to the lowest number of absorbance reading and the positive colour change.

  Time course of Protein Activity on Micrococcus lysodeikticus
FractionTime (minute)
Peak 11.1791.1171.1041.086
Peak 21.1390.990.690.484
Peak 31.21.0570.8040.652
Peak 41.1771.0120.760.595
Peak 51.1620.950.6040.427
Table 3.2:  This table shows the protein activity of the peaks together with the F0 and the waste on the bacterial suspension in a defined time interval of 1 minute for 3 minutes.

Protein Activity Assay
FractionVolume of Fraction used (ml)Protein Concentration of Fraction (mg/ml)Total Protein In Fraction (mg)Percentage of Total Protein (%)Decline in Absorbance (nm)Activity UnitsPercentage of Total Activity Unit (%)
Peak 10.40.2090.08454.90.0626244.9
Peak 20.40.1610.06441.80.149149107.9
Peak 30.40.0760.0319.60.143143103.6
Peak 40.40.0670.02717.60.165165119.6
Peak 50.40.0630.02516.30.212212153.6
Table 3.3: This table shows a comprehensive data of the results from the activity assay.  Column 3 data was derived from BSA standard curve as indicated in fig. 3.3 below.  The total protein in fraction (column 4) was calculated by multiplying the corresponding values in column 2 by those in column 3.  The percentage of total protein (column 5) was calculated using the value of the F0 total protein as a standard and calculating the values for the peaks as relative to the F0 total protein concentration.  Again the decline in absorbance (column 6) is a result of the difference between the values under 0 and 1 minute in table 3.2.  Furthermore, the Activity Unit (column 7) was calculated by dividing the corresponding values in 6 by the absorbance rate of 0.001 per minute.  Finally, the percentage of total Activity Unit  (column 8) was derived by considering F0 as a standard and calculating the percentages of the peaks and waste relative to the F0 value from the Activity Unit in Column 7.

4. Discussion

  Cation exchange chromatography has been used in this experiment to isolate the protein of interest, lysozyme from the vast majority of proteins present in egg-white as lysozyme is a positively charged protein with a very high isoelectric point at pH 11.0 and would preferentially bind to negatively charged resin at pH8.0, a pH where other proteins will not bind to the resin (Guerin et al., 2005).  These unbound proteins were recovered as ''waste'' during the experiment.  Washing column with different concentrations of positively charged NaCl caused for the elution of the protein fractions as the Na+ ions in NaCl competes with the proteins for the binding site on the resin.  However, it was observed that a high concentration (at 100%) of the elution buffer did not elute any further protein from the column as indicated by the flattened nature of the absorbance at baseline.  This accounted for the (-) sign and lower absorbance readings  observed for the 100% Buffer B in table 3.1 above.  Upon loading the sample onto the column, it was reported that UV absorbance increased dramatically above the baseline, an indication that proteins were already being collecting in the column.  This was not observed in our own experiment due to the faultiness of the AKTA machine we had used.  Our UV trace was relatively constant all through this experiment, this is shown in fig. 3.1 above.  To confirm the presence or absence of protein in the fractions we tested all of our fractions as the UV traces on the computer screen were not good enough for a guide of which peaks contained proteins.

The ''waste'' and F0 fractions in table 1 had shown strong change in colour from brown to blue (as indicated by the ++ sign) to reflect the amount of proteins present in them.    One major change made during the Bradford assay was using 100 ul of the protein fractions instead of the intended 20 ul.  This step was taken to better the absorbance reading since the Bradford reagent initially used did not have any effect of colour change on the protein fractions.  This was later balanced by dividing the calculated protein concentration (from the BSA standard curve) by 5.

Analysing the enzyme activity of lysozyme on Micrococcus lysodeikticus, we found out that the activity unit of peak 1 (the fraction with the highest protein concentration) was lower compared to those of the other peaks (see table 3.2 above).  This was a striking outcome, which after much insightful thought, we found why that was the case.  We did this by analysing the gel photograph and found that the band for peak 1 was higher up in the gel.  This suggests that peak 1 fraction did not contain the lysozyme.  The activity Units of peak 2 up to peak 5 had either increased or decreased due to inconsistency in the absorbance reading, amount of protein present in the fraction, improper mixture of the peak fraction/bacterial suspension complex  and also due to the number of active sites being occupied by Micrococcus lysodeikticus in one minute.   As expected, the ''waste'' and F0 had shown a low and high unit of activity respectively (Table 3.3).  This is because the ''waste'' contained little or no lysozyme to act on the bacterial suspension whereas the F0 contained a significant amount of unpurified lysozyme.  The percentage of total activity unit of peaks 2-5 is greater than that of the F0 (100%) due to fact the they contained purified lysozyme (and therefore, no contaminants) with a much higher enzyme activity.

The gel photograph (fig. 4) shows that peaks 2 -5 contained our protein of interest, lysozyme as indicated by the four equal size bands at the bottom of the gel.  When measured, they were approximately corresponded to the molecular weight of lysozyme, which is ≈14.6 kDa (Vocadlo et al., 2005).  Peak 1 (on lane 3) has a high molecular weight of around 68 kDa.  This is the approximate molecular weight of a biotin bound protein in egg white called avidin, which is positively charged at pH8.0 - the pH at which lysozyme is also positively charged (Rothemund, 2002; Guerin et al., 2005).  Therefore, from the polyacrylamide gel electrophoresis, it was confirmed that our purified proteins were lysozyme and avidin.

Comparing the crystal patterns in figures 5 and 6, they seem not to look alike, as also indicated by the diameter of the crystals.  Some of the model crystals of the lysozyme in fig. 5 look larger compared to the one we had for our group.  This disparity is caused by the complications involved in the growth of protein crystals as a result of factors such as temperature, pH, and the concentration of the protein under investigation, coupled with errors that may have occurred during growth of the crystal (Hodgson et al, 2008; Dong et al., 1997)

The questions associated with this practical are answered next, with answers to each question being addressed in a paragraph.

A DNA binding protein is positively charged so as to form an electrostatic interaction with the negatively charged phosphate group of the sugar-phosphate backbone of DNA. This non-covalent interaction would help stabilise the protein-DNA complex during the process of transcription, replication, recombination and repair of DNA (McKee and Mckee, 2011)

The UV absorption had increased whilst loading the egg-white sample to indicate that proteins of the egg-white were being detected as they collect in the column by the optical unit of the AKTA.

Before starting to elute the protein, it's a good idea to allow the UV to reach the baseline as this is when all the proteins and/or ions that are not bound to the column have been completely washed away.  Therefore, the proteins can then be eluted with confidence that there is little or no contaminant present in the column to elute with the pure, bound proteins.

According to GE lifeScience (2010) in their book ''strategies for Protein Purification'' : In gradient elution, the composition of the eluent is continuously (either increasing or decreasing) modified until a stage is stage where the bound protein can dissociate from the column  whereas in step elution, the eluent composition is changed stepwise.  Therefore, step elution seems to be faster than gradient elution, however, optimisation of the process is more likely to be achieved with the gradient method as step elution could carry contaminants along with the protein of interest.  Again for step elution, it uses simple equipment and consume less buffer compared to gradient elution.  Finally, gradient elution allows for the separation of components with a wider range of properties and peak tailing is reduced, compared to step elution which is more discrete.

To calculate the molarity of NaCl present at the different percentages of B:  the molarity of NaCl contained in the elution buffer is 1M.  Therefore, at 20% of the buffer, the molarity of NaCl is 0.2M; at 60% it is 0.6M, and finally at 100% it is 1M.  This calculation is done by multiplying the percentage result by the original 1M present in the buffer.

Each peak on the chromatogram represents a separate component (of the sample being assayed)or charged ions passing through the detector.

Egg-laying hens have recently been used for the large-scale production of pharmaceutical products.  These hens have, in some ways, been genetically modified to enable suit this role.  Lillico et al (2007) have identified some of the advantages of these hens as bioreactors to produce such proteins as reduced financial input for production compared to other bioreactors, coupled with the speed involved in the setup of the production.  Again, Lillico et al (2005) in a reviewed paper have added that the therapeutic protein could be easily purified from egg-white due to their stability.  Another advantage of using transgenic eggs is linked to the fact that the recovered proteins are thought to be glycosylated, and therefore, are functional and would not lead to any antibody response when used pharmaceutically.   (Lillico et al, 2005; Mahdi, 2011).

Lysozyme is an antimicrobial agent that target the cell walls of gram positive bacteria.  In the experimental assay where lysozyme was mixed with the bacterial suspension, Micrococcus lysodeikticus, it was observed that the absorbance reading decreased as the mixture was retained.  The bacteria acted as substrate for the enzyme activity of lysozyme, which decrease over time as indicated by the absorbance reading.  Lysozyme, using its active site,  does this by cleaving the B-1, 4 linkages of N-acetylglucosamine and muramic acid in the peptidoglygan cell wall of Micrococcus lysodeikticus.  The absorbance decreased as the number of bacterial cells were reducing with time.  Therefore, the basis of the protein activity assay was to determine the protein activity, on Micrococcus lysodeikticus, of each of the fractions to provide an idea of which fraction actually contain lysozyme.  This was further verified from the gel electrophoresis result.

SDS is a commonly used surfactant for the estimation of the molecular weight of proteins.  It causes the lysozyme to denature (unfold) and therefore, introducing distortion in the secondary and tertiary structure of the protein.  It does this by disrupting the non-covalent bond interactions present in the lysozyme and therefore, the 3-dimensional structure of the protein is lost in the process (Bhuyan, 2009).

Our group purification had worked perfectly well as we were able to see four bands of equal size and a band, partly of different size on the gel photograph.  Peak 1 which had the highest protein concentration showed a lower activity unit  compared to other four peaks (Table 3.3).  This raised the question of ''why was that so''.   After analysis of peak 1 on the gel, it appeared that peak 1 occupied the top part of the gel, and so had the highest molecular weight, and hence contained a different protein (we suggested the protein to be avidin, with a molecular weight of about 68 kDa) other than lysozyme (fig. 3.4) (Rothemund, 2002).  This accounted for the lower activity unit observed in peak 1.  All other peaks had similar activity units and had equal size of bands on the gel.  The ''waste'' lane had no band present, suggesting the absence of lysozyme, as indicated by the lower activity unit as shown in table 3.3.  We have failed to load our F0 onto the gel, and therefore, unable to decide on the whole protein contents of the egg-white.

If I were to do the experiment again, I would probably run the gel for a longer time, say 1 hour or more, to allow the proteins to separate.  Similarly, I would allow more time for the the bands to form in coomassie stain and more time for it to destain in de-stain. In addition, I would have to boil the mixture in the presence of beta-mercaptuethanol before loading them onto the gel, this would impose further denaturation on the protein.  Again, I would use the method of MS-MS spectroscopy or peptide mass fingerprinting to identify the protein or determine the amino-acid sequence of the protein rather than SDS-PAGE, to confirm that the fractions actually contained lysozyme. In addition, I would ensure that I prevent air bubbles in the chromatography column.  Finally, I would have to repeat many times the absorbance readings for each fraction and calculate their means.  This would represent a true reading of the spectrophotometer.

To purify a protein with an isoelectric point of 3, I would adopt the method of isoelectric focussing where the protein would elute at a certain pH equal to its isoelectric point.

Given a molecular weight of 14,600 Da and a mass concentration of 20mg/ml, the molar concentration can be calculated as follows:

5. Conclusion

In conclusion, our aims and objectives for this experiment has been a success. This is because, after purification of the lysozyme, and then its activity assayed on the bacterial suspension, it was found that lysozyme exhibited antimicrobial activity on the bacteria.   Cation exchange chromatography has proven useful to purify and extract lysozyme from egg-white.  The experiment has demonstrated the activity of the purified lysozyme on bacteria and has shown that a certain pH, some proteins elute with lysozyme.  It has also shown that the crystal structure of lysozyme can be grown and studied.


References and Bibliography

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