# **ADVANCES IN SYSTEMS IMMUNOLOGY AND CANCER**

**Topic Editors Masaru Tomita, Masa Tsuchiya and Kumar Selvarajoo**

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**ISSN** 1664-8714 **ISBN** 978-2-88919-313-4 **DOI** 10.3389/978-2-88919-313-4

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# **ADVANCES IN SYSTEMS IMMUNOLOGY AND CANCER**

### Topic Editors:

**Masaru Tomita,** Keio University, Japan **Masa Tsuchiya,** Keio University, Japan **Kumar Selvarajoo,** Keio University, Japan

Fluorescence microscopy image with computational segmentation and annotation of macrophages (adapted from Wenzel et al, in this issue).

Image taken from: Wenzel J, Held C, Palmisano R, Teufel S, David J-P, Wittenberg T and Lang R (2011) Measurement of TLR-induced macrophage spreading by automated image analysis: differential role of Myd88 and MAPK in early and late responses. *Front. Physio*. 2:71. doi: 10.3389/fphys.2011.00071

### Aims and Scope:

The Research Topic is designed to feature the latest innovative and leading-edge research, reviews and opinions on the study of complex and dynamic processes related to the mammalian immune system and cancer. All papers were meticulously selected to present our readers the multidisciplinary approach to tackle the existing challenges faced in these important fields.

From high throughput experimental methodologies to computational and theoretical approaches, the articles are intended to introduce physicists, chemists, computer scientists, biologists and immunologists the idea of systems biology approach to the understanding of mammalian immune system and cancer processes.

Attention was given to works that developed more effective approaches to the treatment of proinflammatory disease and cancer. The strong interdisciplinary focus will discuss biological systems at the level from a few molecules to the entire organism.

Specific focus domain includes:

Innate and adaptive immunity, cancer and cancer stem cell, genomic, proteomic and metabolic analysis, imaging, biophysics of immune and cancer response, computational modeling, non-linear analysis, statistical analysis, translational and disease models

Types of articles:

Viewpoint, commentaries, research letters, research articles, review and methodologies

# Table of Contents

**EDITORIAL** published: 02 July 2014 doi: 10.3389/fphys.2014.00249

## Advances in systems immunology and cancer

### *Kumar Selvarajoo1,2\**

*1 Systems Immunology, Institute for Advanced Biosciences, Keio University, Tsuruoka, Japan*

*2 Systems Biology Program, Graduate School of Media and Governance, Keio University, Fujisawa, Japan*

*\*Correspondence<EMAIL_ADDRESS>
#### *Edited and reviewed by:*

*Raina Robeva, Sweet Briar College, USA*

**Keywords: systems biology, high dimensional data, immunology, cancer, plasticity, computational biology, statistics, nonparametric**

The last two decades have generated numerous studies that show the close link between immune response and cancer progression in the mammalian system. In parallel, we have also witnessed significant progress in systemic approaches, such as high-throughput, multi-dimensional and dynamical analyses, in tackling biological complexities. We took this opportunity to organize a research topic that encompasses the current advances in immunology and cancer. The intention is to emphasize the importance of holistic view, and how such outlook can help shape the future of biological research. In total, our topic consists of 10 articles: five reviews, three research, and two perspectives.

Owens and Naylor introduces the current understanding of cancer heterogeneity and stemness (Owens and Naylor, 2013). They surveyed a depth of recent literature that points to the presence of breast cancer stem cells (CSCs), which are responsible to mediate metastasis and are resistant to both radiationand chemo-therapies. Although the classification of CSCs are currently based on the expression levels of cell surface markers CD44+CD24− and enzyme Aldehyde dehydrogenase (ALDH) activity, they note that the heterogeneity of single cancer cells makes this classification a nontrivial process. Thus, they ask for more mechanistic approaches to elucidate the origins for CSCs, so that more targeted novel therapies can be developed.

In another survey of cancer mechanisms, Catalan et al. discuss the importance in understanding the connection between adipose tissue immunity and cancer (Catalán et al., 2013). They first quote numerous works that showed obesity-related chronic inflammation and, next, mention others that have demonstrated increased levels of immune cells and proinflammatory mediators in the expanded adipose tissue. Finally, they note specific obesityassociated adipokines that can promote tumor growth. Although the mechanistic links between obesity and cancer still remains unclear, more systemic analyses could reveal better hints in the future.

Sangdun Choi and colleagues present a detailed update on the different structures of the crucial innate immune pattern recognition receptors, namely the Toll-like receptors (TLRs) (Manavalan et al., 2011). There are 13 known mammalian TLRs to-date, however, details of TLR12 and 13 is vastly unclear.  They also argue that 3-D molecular simulations can be useful to make predictions on unknown interactions between TLRs and other possible novel interacting partners.

Remaining on the same topic of TLRs, to investigate the differential roles of adaptor molecule MyD88 and MAP kinase activation in early and late immune response, which will influence the spatial movement of macrophages, Wenzel et al. developed an intelligent algorithm for automated image analysis (Wenzel et al., 2011). The novel approach is able to track cell spreading, after ligand stimulation, more accurately and with significant improvement in processing time, compared with manual techniques that are commonly adopted. Their main findings indicate that MyD88 is key for late spreading of macrophages while MAP kinase p38 is crucial for early spreading.

Apart from innate immunity, another important aspect of our immune response is the orchestration of the adaptive immunity. T cells are lymphocytes that are central in the adaptive responses. In order to perform its specialized task, T cells need to differentiate into different lineages for executing distinct responses. In the unstimulated naïve form, T cells exist mainly as two subtypes depending on their surface markers, CD4+, and CD8+ T cells. Ganusov and colleagues reviewed the differentiation lineages taken by CD4+ T cells on encountering MHC class II found on the surface of antigen-presenting cells such as macrophages or dendritic cells (Magombedze et al., 2013). Mainly, they emphasize on the functional plasticity of CD4+ T cells, and argue that understanding this will help treat diseases such as autoimmune diseases and allergic reactions where the elevated activity of differentiated T cells (e.g., T helper 17 or Th17 cells) may be reprogrammed to reach a different attractor state or back to its naïve form that will not be injurious to the host. They acknowledged that computational or mathematical models can be useful for predicting how one could convert a particular T cell subset into another.

A subsequent manuscript by Blair et al. reviews some of the most common mathematical and statistical approaches used for immune and cancer systems biology at different scales of biological modularization (Blair et al., 2012). Next, Hiroi and colleagues present a method to optimize the model parameters where experimental data are either sparse or noisy (Hiroi et al., 2014). They tested their method on well-established data on c-Myc and E2F transcriptional processes. The following article by Oyama and colleagues briefly discusses about recent high-throughput phosphoproteomics research (Kozuka-Hata et al., 2012). They describe the basic terminologies used and also highlight the importance of such methods for the development of large-scale signal transduction models for systemic interpretation of EGF signaling, TLR signaling or any other pathways of interest.

Fitting with the theme of adopting systemic approaches for understanding immune and cancer response is the paper by Campbell et al. (2011). Here, they have studied the distinct roles of CD8+ T cells to the pathogenesis of cancer. Using *in vivo* derived quantitative data of tumor promoting Tag-expressing mice cells encountering CD8+ T cells, they developed a computational model to investigate the interaction pathways. Remarkably, using a simple ordinary differential equation model, the responses of CD8+ T cells to different perturbations *in silico* were consistent with matched experiments. However, from the model, it became clear that the proliferation and decay rates of CD8+ T cells were strongly constrained and hence, Tag-expressing mice cells become tolerant to tumors. Knowing such information *a priori* will surely aid researchers to understand and possibly avoid poor targets for regulating cancer progression.

Finally, we conclude our collection with an interview with a prominent Japanese physicist, Kaneko (2011), who has switched his interest from pure theory to understanding complex living systems. In the article, he describes the reason behind his renewed interest, and the challenges facing theoreticians in biology. In summary, we believe the articles in "Advances in Systems Immunology and Cancer" research topic or e-book will bring continued interests for the development and utility of multidisciplinary approaches to tackle complex diseases.

### **ACKNOWLEDGMENT**

The author thank co-editors Masaru Tomita and Masa Tsuchiya for jointly hosting the research topic.

### **REFERENCES**

dynamic modeling approach. *Front. Physiol.* 2:32. doi: 10.3389/fphys.2011. 00032

**Conflict of Interest Statement:** The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Received: 11 June 2014; accepted: 15 June 2014; published online: 02 July 2014. Citation: Selvarajoo K (2014) Advances in systems immunology and cancer. Front.*

*Physiol. 5:249. doi: 10.3389/fphys.2014.00249 This article was submitted to Systems Biology, a section of the journal Frontiers in Physiology.*

*Copyright © 2014 Selvarajoo. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

## Breast cancer stem cells

### *Thomas W. Owens and Matthew J. Naylor\**

*Discipline of Physiology, School of Medical Sciences and Bosch Institute, The University of Sydney, Sydney, NSW, Australia*

#### *Edited by:*

*Kumar Selvarajoo, Keio University, Japan*

#### *Reviewed by:*

*Zhiqun Zhang, Banyan Biomarkers Inc, USA Guanglong Jiang, Capital Normal University, China*

#### *\*Correspondence:*

*Matthew J. Naylor, Discipline of Physiology, School of Medical Sciences and Bosch Institute, The University of Sydney, Room E212, Anderson Stuart Building (F13), Camperdown, Sydney, NSW 2006, Australia e-mail: matthew.naylor@ sydney.edu.au*

### **INTRODUCTION**

Breast cancer is the leading cause of cancer death in women, causing extensive morbidity and psychological distress to millions globally. Encouragingly, the combination of better screening and treatment programmes have moderately improved the chances of surviving the disease, but there is still much to be done if the many women who are refractory to current therapies are to have a better chance of survival. Over the last decade breast cancer cells with stem-cell-like properties have been identified and characterized. There is now much interest around the role that these breast cancer stem cells (CSCs) have in the disease and whether they provide the key to unlocking new insight into the mechanisms driving breast cancer progression, drug resistance and reoccurrence.

Often described as a caricature of normal tissue development, cancer occurs when the regulation of tissue homeostasis is perturbed, resulting in the evolution of cells with increased growth and survival potential. The breast, like many other organs, is a hierarchically-organized tissue maintained by a series of stem and progenitor cells that have decreasing potency as they differentiate toward terminally-committed epithelial cells. Below, we describe briefly the normal breast epithelial hierarchy, but for comprehensive analyses we recommend (Visvader, 2009; Van Keymeulen et al., 2011; Raouf et al., 2012; Šale et al., 2013).

The breast is composed of a bilayered epithelium comprising two main epithelial cell types; luminal and basal (Watson and Khaled, 2008; Gusterson and Stein, 2012). The luminal cells line the ductal structures that will transport milk to the nipple during lactation. The basal cells surround the luminal cells and are in contact with the surrounding basement membrane that separates the parachyme from the stromal component of the tissue. Mammary stem cells (MaSCs) share cell surface and expression profiles consistent with basal cells and are hence thought to reside within the basal compartment of the gland. Isolated several

Cancer metastasis, resistance to therapies and disease recurrence are significant hurdles to successful treatment of breast cancer. Identifying mechanisms by which cancer spreads, survives treatment regimes and regenerates more aggressive tumors are critical to improving patient survival.  Understanding how CSCs form and how they contribute to the pathology of breast cancer will greatly aid the pursuit of novel therapies targeted at eliminating these cells. This review will summarize what is currently known about the origins of breast CSCs, their role in disease progression and ways in which they may be targeted therapeutically.

 Furthermore, serial transplants have demonstrated that the MSCs can self-renew as well as give rise to the other cell types (Shackleton et al., 2006; Stingl et al., 2006).

Initially thought to be restricted to relatively few cell types (luminal, basal, and stem cells), the repertoire of mammary cell types has expanded over the last few years. Development of lineage-specific markers and *in vitro* functional assays has enabled the isolation of discrete sub-populations of epithelial progenitors (Raouf et al., 2012; Sheta et al., 2012). Using an alternative approach, *in vivo* lineage-tracing has recently identified previously undescribed epithelial cell types (Šale et al., 2013). In the future, these techniques will likely unearth additional levels of complexity in the epithelial cell hierarchy that will no doubt aid our understanding of breast cancer and CSCs. However, when discussing CSCs, it is imperative to highlight that they are distinct from normal stem cells.

### **DEFINING CANCER STEM CELLS**

It is important to clarify that although they share functional similarities to normal stem cells, CSCs are not necessarily derived from stem cells. A CSC is functionally defined by the ability to (1) form a tumor in immunocompromised mice, (2) self-renew shown by tumor formation in secondary mice and (3) "differentiate," i.e., produce cells with non-stem cell characteristics (McDermott and Wicha, 2010).

In certain tissues, new technological advances are enabling CSCs to be studied in their primary setting, without the need for transplantation, however comparable studies have not yet been described in the breast (Chen et al., 2012; Driessens et al., 2012; Schepers et al., 2012).

We have chosen to use the term CSC but we recognize that cells with defining features of CSCs are also referred to as tumorinitiating cells (TICs) and tumor-propagating cells. In the majority of cases, these terms refer to the same functional entity. TICs can also describe the cell from which the cancer originated and CSCs may form long after the tumor was initiated. The cancer cell of origin is discussed in length elsewhere (Visvader, 2011). This review will focus on breast CSCs, their origins, pathological significance and potential therapeutic strategies to tackle them.

### **DISCOVERY OF BREAST CANCER STEM CELLS**

Historically, the hematopoietic field has led the way in the identification of stem and progenitor cells and their resulting lineages. The same was true in the CSC field, with the CSC-theory in solid tumors validated only relatively recently (Al-Hajj et al., 2003). Using cell surface markers Al-Hajj and colleagues found that CD44+CD24−*/*low Lin− cells from breast cancer patients were significantly enriched for tumor forming ability in NOD/SCID mice compared with CD44+CD24+ Lin− cells. Moreover, the tumors formed by CD44+CD24−*/*low Lin− cells could be serial passaged (self-renew) and also reproduce the tumor cellular heterogeneity observed in the initial tumor (differentiation).

CD44 is a cell surface receptor for the extracellular matrix molecule hyaluronan, that influences cell behavior by direct signaling/structural roles or by acting as a co-receptor for receptor tyrosine kinases (Ponta et al., 2003). CD24 is a cell surface glycoprotein whose level of expression has become commonly used to isolate distinct cell populations from the normal mammary gland and breast cancer cells.  In the mouse mammary gland, CD24−, CD24low, and CD24high expression levels correspond to populations of nonepithelial, basal and luminal epithelial cells, respectively (Sleeman et al., 2006). Functionally, the epithelial cell populations exhibited differential stem potential in mammary fat pad transplantation assays, with CD24low cells being significantly enriched for mammary gland repopulating capacity.

The combination of CD44 and CD24 expression have been used to successfully enrich for CSCs in both cell line and tumor samples but caution must be exercised. For example, within epithelial populations CD44highCD24− was shown to mark mesenchymal-like cells that formed mammospheres and had an invasive phenotype, but the cells lacked the capacity to produce the heterogeneity of the parental cell line (Sarrio et al., 2012). Therefore, these cells did not meet all the criteria of bona fide CSCs and thus highlight the importance of functionally testing "stemness" rather than assuming that a particular combination of cell surface markers is indicative of a phenotype.

In addition to cell surface markers, other expression-based methods of CSC-enrichment have been developed. Aldehyde dehydrogenase (ALDH) activity has been identified as a method of enriching for normal human breast stem and CSCs (Ginestier et al., 2007). Furthermore, by combining ALDH activity with CD44highCD24− expression, the CSC fraction was refined further compared to either method alone. Interestingly, the ALDH−, CD44highCD24− population was not enriched for CSCs demonstrating that the CD44highCD24− population retains significant heterogeneity.

Separating cell populations based on protein expression profiles of either cell surface markers or ALDH1 requires functional validation of the isolated cells to confirm their capacity as CSCs. Recently, Pece and colleagues developed a novel reciprocal approach of using function to isolate CSCs that were then used to identify new markers. By taking advantage of the stem cell ability to survive in suspension culture combined with slow proliferation rate they isolated stem cells from normal human mammary gland based on retention of a membrane-labeling dye, PKH26 (Pece et al., 2010). Gene expression analysis of the PKH26+ cells revealed a novel set of stem cell markers that the group then used to isolate stem cells from both normal breast and tumor samples (i.e., DNER and DLL1).

Due to the intra- and inter-tumor heterogeneity in cancer, it is possible that CSCs from different tumors have distinct expression profiles. Thus, isolating CSCs by function and detailing their expression profiles may prove extremely valuable where traditional markers fail.

### **ORIGINS OF CANCER STEM CELLS**

The stem cell characteristics of CSCs draw in to question the cell type from which they derive. Two potential models of CSC formation are: (1) the tumor cell of origin had stem cell or progenitor properties, or (2) the tumorigenesis process yields cells distinct from the cells of origin that are capable of reconstituting the tumor (**Figure 1**).

The simple model of hierarchical tissue organization suggests that as cells differentiate along a particular lineage, they lose the potential to give rise to multiple cell types and are therefore less likely to be able to act as CSCs. Normal stem cells already have

many of the properties associated with CSCs. Moreover, the longlived nature of stem cells allows more time for multiple genetic lesions to be acquired. Therefore, it is possible that CSCs originate from tissue stem cells.

Studies demonstrating an increased risk of breast cancer in children exposed to radiation suggest that the cells subject to transformation would be long-lived stem or progenitor cells (Miller et al., 1989; Modan et al., 1989). Much more recently, luminal progenitor cells were identified as the likely cell of origin in *BRCA1* driven tumors (Lim et al., 2009; Molyneux et al., 2010; Proia et al., 2011). Cells displaying the markers of stem cells have also been identified in early DCIS lesions suggesting that possible CSC are present at early stages of tumorigenesis (Pece et al., 2010). If the transformed cell has stem/progenitor properties then it is understandable that this could give rise to CSCs, as well as the non-CSCs that make up the majority of the tumor.

The model in which the cancer cell of origin is responsible for the properties of the CSC would be encouraging when it comes to designing therapies to tackle the disease. If the tumor behaves in a rigid linear hierarchy with relatively few stem cells giving rise to the majority "differentiated" tumor cells then therapies that can kill CSCs or drive them to differentiate would remove the ability of the tumor to regenerate following therapy.

However, cancer is a disease that forms over many years, so even if the original transformation event had occurred in a stemlike cell, the tumor that presents at the clinic is likely to be a much more evolved and heterogeneous entity than a linearlyhierarchical tissue. A linear hierarchy in cancer would also not explain why recurring tumors are resistant to therapy, as successive rounds of tumor growth may be expected to be produce similarly-sensitive progeny. In this sense, it appears that tumors have also evolved mechanisms to be self-sustaining even if their original CSC pool is destroyed, potentially via the generation CSCs cells from non-stem cells.

### **FORMATION OF CSCs FROM NON-CSCs**

A range of breast cancer cell lines are now known to be composed of a heterogeneous mixture of cells. A proportion of the cells act as CSCs by being able to give rise to all the cell types within that line, while the other cells show reduced ability to act as CSCs. There is also suggestion of heterogeneity within the CSC populations themselves (Wong et al., 2012). Significantly, several studies have now demonstrated that cells have the capacity to interconvert between phenotypes.

Breast cancer cell lines SUM159 and SUM149 sorted into stem-like, basal and luminal populations demonstrated the ability to transition between these cell states to maintain the overall heterogeneity of the parental line (Gupta et al., 2011). This stochastic cell state transition enabled purified populations to reconstitute the proportions of the parental cell line within 11 days of sorting (Gupta et al., 2011). Piggott and colleagues used the mammosphere assay to demonstrate that MDA-MB-231, BT474, SKBR3, and MCF7 cells all contained self-renewing mammosphere forming units (MFUs). Interestingly, BT474 cells depleted of MFUs reacquired these progenitor-like cells following 4 weeks in culture (Piggott et al., 2011). *In vitro*, Ca1a, MCF7, Sum159, and MDA-MB-231 breast cancer lines, sorted CD44+CD24+ non-invasive cells could give rise to invasive CD44+CD24− cells (and vice versa), even when initially plated as single cell clones (Meyer et al., 2009).

The generation of CSCs from non-CSCs has been confirmed *in vivo* using transplantation assays. Clones of noninvasive CD44+CD24+ sorted cells from Ca1a, ZR75.1 and MCF7 breast cancer lines transplanted into immunocompromised mice gave rise to molecularly heterogeneous tumors that exhibited local invasion (Meyer et al., 2009). Moreover, the stemlike-depleted basal and luminal populations of SUM159 cells were also able to transition to stem-like cells during tumor formation in NOD/SCID mice. However, it is interesting that the non-stem-like SUM159 populations required co-injection with irradiated parental SUM159 cells for tumor formation to occur. This co-injection requirement suggests that additional factors to those in the homogenous luminal or basal populations are required for conversion to stem-like phenotypes (Gupta et al., 2011).

Recent evidence suggests that the ability of the cancer cells to trans-differentiate is related to the transformation process. Using an inducible Src oncogene to drive transformation of MCF10A cells, CSC-like cells were generated during the transformation process within 16–24 h of Src activation (Iliopoulos et al., 2011). Furthermore, once generated the relative proportion of CSCs was maintained over several weeks in culture. Isolated CSCs readily formed non-CSCs whereas the reciprocal spontaneous conversion did not occur. However, media from CSC was found to drive non-CSCs to form CSCs and this was dependent of IL-6 (Iliopoulos et al., 2011).

Chaffer and colleagues demonstrated that hTERTimmortalized HMECs gave rise to a population of floating cells they term HME-flopcs (Chaffer et al., 2011) CD44low HME-flopcs were able to spontaneously convert to CD44high cells that had stem-like properties. Moreover, transformation of the HME-flopcs with the SV40 and H-ras increased the efficiency with which the conversion to CD44high cells occurred.

Despite the growing evidence of the ability of non-CSCs to produce CSCs it is noteworthy that in the parental populations the proportions of CSCs remains constant over time. Even when sorted into distinct populations, the sorted cells eventually recapitulate the proportions of cells originally present in the parental line. Tumor molecular expression profiles remain constant during disease progression, suggesting a level of stability within a population of tumor cells (Ma et al., 2003; Weigelt et al., 2003). Moreover, similar molecular profiles of primary tumor and metastases suggest ancestors are common rather than genetically distinct (Sorlie, 2004). This supports a hypothesis that perhaps paracrine signals mediate a level of homeostatic control over the proportions of different cell types present within a tumor.

### **CSC AND EPITHELIAL-TO-MESENCHYMAL TRANSITION**

Inter-conversion of CSC and non-CSC (spontaneously or otherwise) means that CSCs do not behave like classical stem cells. The question remains of how CSCs could arise from non-CSCs. Epithelial-to-Mesenchymal transition (EMT) is a natural process that occurs during development and is a method by which cancer cells metastasize during cancer progression (Thiery and Sleeman, 2006). EMT is also thought to be a mechanism by which CSCs form.

Induction of EMT in normal human mammary epithelial (HMLE) cells by expression of Snail, Twist or treatment with TGFβ1 caused the majority of cells to adopt the CD44+CD24low expression profile consistent with CSCs. There was also a significant increase in the number of mammosphere forming cells following EMT (Mani et al., 2008; Morel et al., 2008). In addition to EMT driving cells to acquire stem cell characteristics, naturally occurring stem cell fractions of normal mouse and human mammary epithelium cells as well as human neoplastic samples expressed significant levels of EMT markers (Mani et al., 2008).

The mechanism by which EMT induces CSC formation may involve the transcription factor FOXC2, which was upregulated in immortalized normal human mammary epithelial (HMLE) cells in response to multiple EMT-inducing stimuli (Mani et al., 2007). The CSC-characteristics acquired through EMT were attenuated by suppression of FOXC2 expression (Hollier et al., 2013). Furthermore, FOXC2 was upregulated in CSCenriched populations and expression of FOXC2 in V12H-Rastransformed HMLE cells was sufficient to drive EMT and increase their tumor forming and metastatic potential in transplants (van Vlerken et al., 2013).

The ability of EMT-driving factors to induce CSC formation is likely to be dependent on the cell type in which EMT occurs. Slug is a transcription factor that can drive EMT and its expression is enriched in MaSCs. Exogenous expression of SLUG in luminal progenitor cells was sufficient to drive them to a more stem-like phenotype, whereas SLUG expression in differentiated luminal cells failed to do so (Guo et al., 2012). Interestingly, co-expression of Sox9 with Slug could induce differentiated luminal cells into a stem-like state by activating distinct gene sets. Moreover, Snail, but not Twist could substitute for Slug and cooperate with Sox9 in driving differentiated luminal cells into stem-like cells. Therefore, EMT contributes to, but is not sufficient for the non-stem cell to stem-cell transition and not all EMT-driving factors elicit the same effect (Guo et al., 2012).

Analysis of non-tumorigenic mammary epithelial cell lines (MCF12A, MCF10-2A, and MCF10A) and immortalized Myo1089 cells using EpCAM and CD49f expression levels, identified heterogeneous cell populations. The EpCAM+CD49f+ had an epithelial morphology with an expression profile characteristic of luminal progenitors, while EpCAM−CD49fmed*/*low were fibroblastic in appearance and expressed genes associated with EMT (Twist1/2 and Slug) (Sarrio et al., 2012). Interestingly, although the epithelial (EpCAM+) Myo1089 cells gave rise to mesenchymal-like cells that were more invasive and could form mammospheres, it was the epithelial cells that had higher ALDH1 activity and could recapitulate the heterogeneous cell populations seen in the parental line. Therefore, in this instance EMT was associated with a loss of stem-cell capacity and re-iterates the importance of determining "stemness" functionally (Sarrio et al., 2012).

The reprogramming of cancer cells into CSCs by EMTassociated transcription factors highlights the importance of understanding how transcription factor networks regulate cell fate determination in breast cancer (Kalyuga et al., 2012). The power of transcription factor-mediated cell fate control is most notably demonstrated by the creation of induced pluripotency stem (iPS) cells by the introduction of Oct4, Sox2, c-Myc and Klf4 into differentiated adult cells (Takahashi and Yamanaka, 2006). The same factors that induce pluripotency in normal differentiated cells may also be involved in the formation of CSCs. Nontumorigenic MCF10A cells transduced with Oct4, Sox2, c-Myc, and Klf4 formed iPS-like cells that upon differentiation adopted a CSC phenotype (Nishi et al., 2013). These induced CSC-like-10A cells were largely CD44+CD24low, expressed ALDH1 and had high tumorigenicity *in vivo*. In metastatic breast cancer cells, Klf-4 expression increased the proportions of CD44+CD24low and mammosphere-forming cells (Okuda et al., 2013). Oct4 alone was able to transform primary HMLE cells into cells capable of initiating tumors in xenografts and Oct4 is also thought to be the downstream effector of IL-6 induced CSC formation (Beltran et al., 2011; Kim et al., 2013).

Transcription factors mediate changes in gene expression, but the action of transcription factors is also influenced through epigenetic genome modification. Epigenetic regulation of gene expression controls cell fate specification by activating or repressing genes associated with lineage commitment. Epigenetic changes are also associated with cancer progression.

In mammary epithelial cells, repressive and activating histone methylation patterns are associated with changes in gene expression during lineage determination (Pal et al., 2013). CSCs isolated from breast cancer cell lines had elevated levels of the polycomb group protein, EZH2, which catalyses histone methylation (van Vlerken et al., 2013). EZH2 knockdown by siRNA moderately reduced the CSC populations in breast and pancreatic cancer cell lines, inducing a more differentiated pattern of gene expression. Moreover, high EZH2 expression correlates with poor prognosis in breast and prostate cancer (Varambally et al., 2002; Pietersen et al., 2008).

Interestingly, the methylation patterns in mammary epithelial cells alter during pregnancy and also in ovariectomized mice, demonstrating that they are subjected to hormonal control. Furthermore, experiments in isolated epithelial cells suggested that EZH2 is induced by progesterone in a paracrine fashion (Pal et al., 2013). Thus, changes in local tumor environment could alter methylation patterns and facilitate CSC formation in relatively few generations, as it does not require further mutations to occur.

### **FACTORS INFLUENCING CSC FORMATION**

Selective pressure in a genetically unstable environment can drive selection for epigenetic or genetic changes that support survival. Factors that influence this tumor environment include infiltrating cells, hypoxia and chemotherapy, all of which have been linked to CSC development.

Co-culture of SUM159 cells with bone marrow-derived mesenchymal cells induced an expansion of the ALDH1-expressing SUM159 population (Liu et al., 2011). This expansion was due to a chemokine signaling loop between cancer-cell derived IL-6 and CXCL7 produced by ALDH+ mesenchymal cells. Moreover, co-injection of ALDH+ mesenchymal cells with SUM159 cells into NOD/SCID mice accelerated tumor growth and increased the capacity of the SUM159 cells to form secondary tumors following serial passage. Intratibial injection of mesechymal cells demonstrated that they could augment tumor growth and home to the site of breast tumor xenografts (Liu et al., 2011).

The immune response in FVB mice to cells derived from tumors in a Her2/neu transgenic strain caused the outgrowth of Her2-negative tumors. This antigen loss effect was dependent on CD8+ T cells. Her2-negative tumor cells had reduced CD24 levels compared with the parental Her2-positive cells and were more mesenchymal in appearance and expression patterns. Moreover, these CD24−*/*low cells were much more tumorigenic than controls suggesting that the CD8+ T cell-dependent immune response was inducing EMT in the cancer cells to generate CSCs (Santisteban et al., 2009).

#### **HYPOXIA**

As tumors develop, the requirement for oxygen increases, leading to regions of hypoxia. Hypoxia causes activation of hypoxiainducible factors, HIFs, which enable to cells to adapt to the low-oxygen environment. Hypoxic culture conditions (1% O2) induced an increase in the ALDH1+ proportion in breast cancer cell lines (Conley et al., 2012). Moreover, CSCs were enriched in hypoxic regions of tumor xenografts compared with normoxic regions (Conley et al., 2012). Using cycles of hypoxia and reoxygenation to model the tumor microenvironment, Louie and colleagues enriched for populations of MDA-MB-231 and BCM2 cells that were significantly more tumorigenic than the parental lines (Louie et al., 2010). The hypoxia-selected populations also had a greater proportion of CD44+CD24−*/*low cells. The low oxygen levels may influence the progenitor-like state of CSCs, as hypoxia blocked differentiation in MCF10A cells, possibly by maintaining greater levels of histone acetylation (Vaapil et al., 2012).

### **CHEMOTHERAPY**

In addition to CSCs forming as a part of tumor progression, therapeutic intervention may contribute to CSC genesis. Anti-angiogenic agents sunitinib and bevacizumab, which induce hypoxia in tumors, increased the number of CSCs in breast cancer xenografts (Conley et al., 2012). The release of factors by dying tumor cells may also act to augment the CSC pool. Interleukin-8 (IL-8) levels increased in SUM159 breast cancer cells following treatment with chemotherapeutic docetaxel (Ginestier et al., 2010). Interestingly, IL-8 signaling via its receptor CXCR1 on CSCs can expand CSC numbers in breast cancer cell lines (Charafe-Jauffret et al., 2009).

Further to the dying tumor cells releasing CSC-promoting factors, chemotherapy could alter the cells intrinsic mechanisms of preventing EMT. ER can directly suppress the EMT-driver SLUG; therefore anti-estrogen therapies may promote CSC formation by inducing EMT (Ye et al., 2008). Clearly the benefits of anti-estrogen therapies, such as tamoxifen, in prolonging patient survival are unarguable, but it is possible that under certain circumstances, initial anti-estrogen treatment may predispose the patient to recurrence of the disease.

### **PATHOLOGICAL SIGNIFICANCE OF BREAST CANCER STEM CELLS**

#### **TUMOR AGGRESSIVENESS**

Since the discovery of breast CSCs, they have been touted as critical targets for the design of future therapeutics. However, it is important to understand how CSCs influence the pathology of breast cancer so that treatments can be targeted appropriately.

Different subtypes of breast cancer are associated with different prognoses; luminal cancers offer the best chance of longterm survival and basal, claudin-low and Her2-positive cancers offer a much shorter life expectancy. Gene set enrichment analysis demonstrated similarity between the expression profile of stem cells and basal-breast cancers (Pece et al., 2010). The proportion of cells expressing stem-cell markers was approximately 3–4-fold higher in poorly differentiated compared with well-differentiated breast tumors. TAMresistant ER-positive breast cancers are more basal-like, showing reduced E-Cadherin expression, increased CD44 and NFκB expression along with increased motility (Hiscox et al., 2009).

A CSC gene signature from comparative analysis of CD44+CD24− sorted tumor cells and cancer mammospheres showed that this signature was associated with claudin-low breast cancers, suggesting that claudin-low tumors are enriched for CSCs (Creighton et al., 2009). Moreover, the expression profile of the CSC-regulator, FOXC2 was enriched in claudin-low tumors and cell lines (Hollier et al., 2013). Her2 expression has been shown to correlate with ALDH1 expression in human breast cancer. ALDH1 levels also correlated with poor clinical outcome and proved to be an independent prognostic marker (Ginestier et al., 2007; Morimoto et al., 2009). Together, these studies suggest a link between CSCs and the aggressiveness of the disease.

In inflammatory breast cancer (IBC), ALDH1 expression correlated with histological grade but interestingly not with the CD44highCD24− phenotype (Ginestier et al., 2007). This may be due to differences in analyzing CD44 and CD24 expression by immunohistochemistry rather than FACS or that CD44/CD24 may not be suitable markers of CSCs in IBC. A second study using IHC to assess prognostic significance of CD44 and CD24 expression in breast cancer also failed to find a correlation between the CD44highCD24− phenotype and tumor progression, although there was suggestion of a correlation with bone metastasis (Abraham et al., 2005). These discrepancies between FACS and IHC studies could be due to the different techniques employed or other factors, such as the source of the tumor cells being analyzed.

There is accumulating evidence that CSC are involved in the metastatic progression of breast cancer. This is particularly significant given that the majority of cancer deaths are due to secondary lesions that have disseminated from the initial tumor. Immunohistochemistry of breast cancer cells isolated from bone marrow using the CD44highCD24−*/*low phenotype suggests that there may be a much greater proportion of CSCs in metastatic tumors compared with the primary site (Balic et al., 2006). In IBC models, CSCs isolated by ALDH activity were shown to mediate metastasis in both *in vitro* and xenograft studies (Ginestier et al., 2007). Moreover, detection of ALDH+ cells in tumors from IBC patients correlated with both early onset of metastasis and overall decreased survival (Ginestier et al., 2007). CSCs have also been proposed to alter tissue architecture by driving epithelial remodeling. This disruption of normal tissue structure could be another method by which CSCs contribute to metastasis (Parashurama et al., 2012).

#### **CANCER RECURRENCE FOLLOWING THERAPY**

Resistance of CSCs to chemotherapy/radiotherapy is a possible mechanism to explain breast cancer recurrence. CSCs are enriched following neoadjuvant chemotherapy suggesting that CSCs are more resistant to therapy than the bulk of the tumor (Yu et al., 2007; Li et al., 2008). Treatment of both SUM159 and SUM149 cells with chemotherapeutics (paclitaxel or 5-fluorouracil) led to enrichment in the proportion of stem-like cells (Gupta et al., 2011). CSClike MCF7 cells were resistant to several commonly used chemotherapeutics (Adriamycin, Etoposide, 5-Fluorouracil cis-Platinum, and Methotrexate), although they were more sensitive to Taxol (Creighton et al., 2009; Sajithlal et al., 2010).

The association between EMT and CSCs is also relevant to chemo-resistance, as cells undergoing EMT are more resistant to chemotherapeutics (Li et al., 2009). Cells isolated from Her2 antigen loss tumors that had undergone EMT had upregulated expression of protein pumps associated with drug resistance (BCRP and PGP). Accordingly, these cells were protected from chemotherapeutics mitoxantrone and etoposide. The mesenchymal tumor cells also had increased levels of DNA repair enzymes and were resistant to ionizing radiation (Santisteban et al., 2009).

#### **TUMOR MAINTENANCE**

CSCs are often referred to as being responsible for "maintaining" the tumor. In some respects, this maintenance role is an extrapolation of data showing that CSCs can recapitulate tumors of heterogeneous cell types over several passages in immunecompromised mice. Few studies have examined whether elimination of CSCs actually causes spontaneous-regression in the primary setting, which could be expected if the CSCs were maintaining the tumor. Part of the reason for this, is the lack of models in which to test the maintenance of tumors by CSCs.

Seminal lineage tracing experiments in both the skin and intestine demonstrated that during early transformation the tissues retain a cellular hierarchy akin to the normal tissue (Driessens et al., 2012; Schepers et al., 2012). Notably, in contrast to benign skin tumors, squamous cell carcinomas had an increased proportion of CSC, which had reduced propensity to differentiate. These studies demonstrate that CSCs exist early in the tumorigenesis process, but does still not delineate whether these early CSCs are maintaining the tumor. In a mouse model of glioblastoma, Chen and colleagues demonstrated the presence of quiescent CSCs that could expand and re-populate the tumor following chemotherapy with temozxolomide (TMZ). Eradication of these CSCs using a thymidine kinase transgene and ganciclovir (GCV) significantly improved survival. Moreover, the tumors in the GCV treated mice had reduced levels of proliferation and were less invasive suggesting that the CSCs were in indeed maintaining the tumor progression (Chen et al., 2012).

### **THERAPEUTIC TARGETS IN CSCs**

The growing evidence that CSCs contribute to cancer progression and recurrence shows that developing anti-CSC therapies will likely improve chances of long-term survival of cancer patients. A proof of principle for targeting CSCs has been demonstrated in AML where the anti-leukemia drug TDZD-8 selectively killed leukemia stem cells while not affecting normal hematopoietic stem and progenitor cells (Guzman et al., 2007).

Many of the pathways currently under investigation as potential therapeutic targets in CSCs have been shown to regulate normal stem and progenitor cells, so finding methods to selectively target the pathways in cancer will be critical. Two developmental pathways that have received much recent attention as cell fate regulators in the breast are Notch and Wnt (Gu et al., 2013; Meier-Abt et al., 2013; Regan Joseph et al., 2013; Šale et al., 2013). It is therefore not surprising that they may be therapeutic targets in CSCs. In a model of Notch1-driven mammary tumorigenesis, inhibition of Notch signaling induced tumor regression and reduced tumorsphere formation *in vitro* (Simmons et al., 2012). Upregulation of the Notch ligand, Jagged2 in breast cancer cells and bone marrow derived cells in response to hypoxia led to an expansion of CSCs (Xing et al., 2011). Notch 4 activity is increased in breast CSCs and Notch and Wnt signaling were found to mediate radio-resistance in breast progenitor and CSCs (Phillips et al., 2006; Woodward et al., 2007; Harrison et al., 2010). The Wnt co-activator Pygo2 augmented mammosphere formation in MDA-MB-231 breast cancer cells (Chen et al., 2010). Conversely, deletion of pygo2 in MMTV-Wnt1 tumor cells reduced both mammosphere and tumor-forming capacity (Watanabe et al., 2013).

The potential therapeutic benefit of targeting Wnt-signaling was demonstrated by the identification of Salinomycin in a screen for CSC-inhibitors. Salinomycin preferentially eliminated CSCs by inhibiting Wnt signaling and inducing apoptosis Gupta et al., 2009; Fuchs et al., 2009; Lu et al., 2011; Tang et al., 2011. Salinomycin also killed iCSCL-10A cells that were resistant to Taxol and Actinomycin D (Nishi et al., 2013). Another drug that appears efficacious against CSCs is the anti-diabetic drug Metformin. Metformin targets CSC and can act synergistically with chemotherapy drugs to reduce CSC numbers and tumor growth (Hirsch et al., 2009; Vazquez-Martin et al., 2011). Subsequent work demonstrated that Metformin might act by inhibiting nuclear translocation of NF-κB and phosphorylation of STAT3 in CSCs compared with non-CSCs (Hirsch et al., 2013). Metformin may therefore be a candidate to treat TAMresistant ER+ cancers that have been shown to upregulate NF-κB (Hiscox et al., 2009). Significantly, metformin treatment overcame Herceptin™ resistance in a Her2-positive xenograft model (Cufi et al., 2012).

Cell surface receptors make attractive targets for therapeutic design, as they are accessible to drugs. The growth factor receptor PDGFR-β was shown to lie downstream of FOXC2 in cells induced to undergo EMT and both proteins were expressed in CSC-enriched populations of SUM159 and HMLER cells (Hollier et al., 2013). The PDGFR-β inhibitor sunitinib reduced tumor growth and metastasis of FOXC2-expressing tumor cells (Hollier et al., 2013). Thus, sunitinib may be effective to combat CSC that arise as a result of EMT. FGF-receptor 2 (FGFR2) was enriched in CSC isolated from a MMTV-PyMT mouse breast cancer model (Kim et al., 2013). Moreover, FGFR2-expressing human tumor cells were more tumorigenic than FGFR2-negative cells in the xenograft experiments. Treatment with the FGFR inhibitor, TKI258, reduced the proportion of CSCs in MMTV-PyMT-driven tumors and delayed tumor growth (Kim et al., 2013).

The enrichment of CSCs that occurs under certain conditions, suggests that CSCs are capable of increasing their numbers by symmetric division. Blocking this mechanism of CSC expansion may slow tumor progression and allow more successful elimination of the CSC pool. By restoring p53 function in Her2 over-expressing cells, asymmetric cell division in the CSCs was restored leading to reduced tumor formation (Cicalese et al., 2009). Hedgehog (Hh) signaling via Bmi1 increased the frequency of mammosphere forming cells and this effect was reversed using the Hh inihibitor cyclopamine (Liu et al., 2006). Suppression of cFLIP eliminated CSCs in response to TRAIL, reducing formation of primary tumors in transplant models and almost completely preventing metastasis (Piggott et al., 2011). cFLIP suppression also reduced MFU-enrichment following passage of mammospheres, suggesting symmetric CSC division was compromised.

The plasticity of tumor cells is another hurdle that needs to overcome in order to prevent *de novo* CSC formation from non-CSCs. By blocking Activin/Nodal signaling, the ability of CD44+CD24+ (non-stem) cells to give rise to CD44+CD24low (CSC) progeny was also blocked (Meyer et al., 2009).

Therapeutic ablation of specific cell populations is likely to only provide temporary relief from tumor progression. Moreover, as some therapies appear to support CSC production, it will be necessary to tackle cancer in a multi-pronged

### **REFERENCES**

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approach, targeting both CSC and non-CSCs.  Combining GCV and TMZ to target both CSCs and non-CSCs significantly reduced the tumor burden compared with GCV treatment alone (Chen et al., 2012). Unfortunately, the outgrowth of cells that had suppressed the TK transgene precluded the authors from determining if there was a significant benefit to overall survival.

A problem with current cancer therapies is that they have been tested, selected and approved based on the ability to reduce tumor size without testing the effect on CSCs. Therefore, in addition to developing drugs that target CSCs it will be necessary to develop new assays focused on being able to detect changes in CSCs function that alone may not necessarily cause a reduction in tumor size. The efficacy of CSC-targeted therapeutics could also be determined by examining cancer recurrence in patients treated with combined drug regimes.

### **SUMMARY**

There is now little doubt that cancer cells with the properties of stem cells exist within heterogeneous populations and that these CSCs have tumor-forming capacity. However, the role that these cells have in the formation and progression of the tumor in the primary setting is still unclear and will require suitable models to be developed for this to be delineated. The mechanisms of CSCs formation will require particular attention if they are to be successfully eliminated from patients. Finally, new assays that can detect the efficacy of targeting CSCs are essential if CSC-therapies are to make it to the clinic.

### **ACKNOWLEDGMENTS**

Financial support was provided by the Cancer Council NSW, National Breast Cancer Foundation of Australia, National Health and Medical Research Council of Australia and Prostate Cancer Foundation of Australia.

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**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Received: 29 May 2013; accepted: 03 August 2013; published online: 27 August 2013.*

*Citation: Owens TW and Naylor MJ (2013) Breast cancer stem cells. Front.* *Physiol. 4:225. doi: 10.3389/fphys. 2013.00225*

*This article was submitted to Systems Biology, a section of the journal Frontiers in Physiology.*

*Copyright © 2013 Owens and Naylor. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

### *Victoria Catalán1,2\*, Javier Gómez-Ambrosi 1,2, Amaia Rodríguez 1,2 and Gema Frühbeck1,2,3*

*1 Metabolic Research Laboratory, Clínica Universidad de Navarra, Pamplona, Spain*

*2 CIBER Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Pamplona, Spain*

*3 Department of Endocrinology and Nutrition, Clínica Universidad de Navarra, Pamplona, Spain*

#### *Edited by:*

*Masa Tsuchiya, Keio University, Japan*

#### *Reviewed by:*

*Sudipto Saha, Bose Institute, India Satyaprakash Nayak, Pfizer Inc., USA*

#### *\*Correspondence:*

*Victoria Catalán, Metabolic Research Laboratory, Clínica Universidad de Navarra, Avda. Pío XII, 36, 31008 Pamplona, Spain e-mail<EMAIL_ADDRESS>
Inflammation and altered immune response are important components of obesity and contribute greatly to the promotion of obesity-related metabolic complications, especially cancer development. Adipose tissue expansion is associated with increased infiltration of various types of immune cells from both the innate and adaptive immune systems. Thus, adipocytes and infiltrating immune cells secrete pro-inflammatory adipokines and cytokines providing a microenvironment favorable for tumor growth. Accumulation of B and T cells in adipose tissue precedes macrophage infiltration causing a chronic low-grade inflammation. Phenotypic switching toward M1 macrophages and Th1 T cells constitutes an important mechanism described in the obese state correlating with increased tumor growth risk. Other possible synergic mechanisms causing a dysfunctional adipose tissue include fatty acid-induced inflammation, oxidative stress, endoplasmic reticulum stress, and hypoxia. Recent investigations have started to unravel the intricacy of the cross-talk between tumor cell/immune cell/adipocyte. In this sense, future therapies should take into account the combination of anti-inflammatory approaches that target the tumor microenvironment with more sophisticated and selective anti-tumoral drugs.

**Keywords: adipose tissue, inflammation, immune cells, adipokines, angiogenesis, hypoxia, macrophages, tumor growth**

### **INTRODUCTION**

The incidence of obesity and its associated disorders is increasing at an accelerating and alarming rate worldwide (Flegal et al., 2012; Frühbeck et al., 2013). Relative to normal weight, obesity is associated with significantly higher all-cause mortality (Frühbeck, 2010; Flegal et al., 2013). Body mass index (BMI) represents the most used diagnostic tool in the current classification system of obesity, frequently used as an indicator of body fat percentage (BF). The controversy in studies (Hughes, 2013) arises in part because a wide variety of BMI cutoffs for normal weight has been applied to correlate with mortality which can yield quite diverse findings. Furthermore, in spite of its wide use, BMI is only a surrogate measure of body fat and does not provide an accurate measure of body composition (Frühbeck, 2012; Gómez-Ambrosi et al., 2012).

Results from epidemiological studies indicate that overweight and obesity contribute to the increased incidence and/or death from quite diverse types of cancers, including colon, breast (in postmenopausal women), endometrium, kidney (renal cell), esophagus (adenocarcinoma), stomach, pancreas, gallbladder and liver, among others (Calle and Kaaks, 2004). The mechanisms linking excess of adiposity and cancer are unclear but the obesityassociated low-grade chronic inflammation is widely accepted as an important factor in cancer pathogenesis (Catalán et al., 2011d; Hursting and Dunlap, 2013). Chronic hyperinsulinaemia as well as the alterations in the production of peptide and steroid hormones associated to obesity are other postulated mechanisms involved in cancer development (Calle and Thun, 2004). Particular attention is placed on the pro-inflammatory microenvironment associated with the obese state (Catalán et al., 2011d; Ribeiro et al., 2012; Hursting and Dunlap, 2013), specifically highlighting the involvement of obesity-associated hormones/growth factors in the cross-talk between macrophages, adipocytes, and epithelial cells in many cancers. Among the various pathophysiological mechanisms postulated to explain the link between obesity and cancer, the dysfunctional adipose tissue may be a unifying and underlying factor (van Kruijsdijk et al., 2009). Understanding the contribution of obesity to growth factor signaling and chronic inflammation provides mechanistic targets for disrupting the obesity-cancer link (Harvey et al., 2011).

In this regard, obesity prevention is a major part of several evidence-based cancer prevention guidelines (Kushi et al., 2012). Recent studies exploring the effect of weight loss, suggest that severe caloric restriction in humans may confer protection against invasive breast cancer (Michels and Ekbom, 2004). This protective effect includes reductions in the initiation and progression of spontaneous tumors in several tissues (Longo and Fontana, 2010). Moreover, the association between obesity and cancer is consistent with data from animal models showing that caloric restriction decreases spontaneous and carcinogen-induced tumor incidence (Dunn et al., 1997; Yun et al., 2013). Both bariatric surgery and short-term intentional weight loss have been shown to improve insulin sensitivity and inflammatory state, which have been postulated to contribute to the relationship between obesity and cancer (Sjöström et al., 2007; Cummings et al., 2012).

### **THE IMPORTANCE OF OBESITY-INDUCED CHRONIC INFLAMMATION**

Adipocytes, the principal cellular component of adipose tissue, are surrounded by connective tissue comprising macrophages, fibroblasts, preadipocytes, and various cell types included in the stromovascular fraction (Hausman et al., 2001; Nishimura et al., 2007; Cinti, 2012). Although adipocytes have been considered primarily as fat-storage depots, in recent years, it has become clear that together with other metabolically active organs, adipose tissue is a dynamic endocrine system key in the regulation of whole body energy homeostasis (Frühbeck et al., 2001a; Ahima, 2006; Sáinz et al., 2009). Indeed, mature adipocytes are involved in endocrine, paracrine, and autocrine regulatory processes (Ahima and Flier, 2000) through the secretion of large number of cytokines, hormones and other inflammatory markers, collectively termed adipokines (Lago et al., 2007, 2009; Lancha et al., 2012). In addition to playing key roles in the regulation of the lipid and glucose homeostasis, adipokines modify physiological processes, such as hematopoiesis, reproduction, and feeding behavior, being also involved in the genesis of the multiple pathologies associated with an increased fat mass including cancer development (Rajala and Scherer, 2003). However, adipose tissue not only secretes adipokines but also functions as a target of these pro-inflammatory mediators, expressing a wide variety of receptors for cytokines, chemokines, complement factors, and growth factors (Frühbeck, 2006a,b; Schäffler and Schölmerich, 2010).

The connection between inflammation and diabetes was suggested more than a century ago (Williamson, 1901), but the evidence that inflammation is an important mediator in the development of insulin resistance came recently. It was described that the administration of tumor necrosis factor-α (TNF-α) led to increased serum glucose concentrations (Feingold et al., 1989). The first study that established the concept of obesity-induced adipose tissue inflammation was conducted by Hotamisligil et al. (1993), demonstrating that the pro-inflammatory cytokine TNFα mediate insulin resistance in many experimental models of obesity. Importantly, the development of adipose tissue has been associated with increased plasma levels of well-known inflammatory and acute phase proteins such as C-reactive protein, interleukin (IL)-6, IL-8, serum amyloid A (SAA) and monocyte chemotactic protein (MCP)-1 in patients and different animal models of obesity (Frühbeck et al., 1995; Wellen and Hotamisligil, 2003; Frühbeck, 2005; Gómez-Ambrosi et al., 2006; Kahn et al., 2006b; Kim et al., 2006; Catalán et al., 2007, 2008), whereas production of the anti-inflammatory and insulinsensitizing adipokine adiponectin is reduced with increasing body weight (Kadowaki et al., 2006). In obesity, the activation of the c-Jun N-terminal kinase (JNK) and nuclear factor κB (NF-κB) transduction signals is key in the inflammation process of adipose tissue and these pathways could interact with insulin signaling via serine/threonine inhibitory phosphorylation of IRS (Bastard et al., 2006; Gil et al., 2007). Genetic or pharmacological manipulations of these different effectors of the inflammatory response modulate insulin sensitivity in different animal models.

Recent data suggest that stromovascular cells also contribute to the secretion of inflammatory adipokines. In this sense, the infiltration of adipose tissue by immune cells is a feature of obesity, with adipose tissue macrophage (ATM) accumulation being directly proportional to measures of adiposity in both mice and humans (Weisberg et al., 2003). This evidences a role of adipose tissue as part of the innate immune system.

### **ADIPOSE TISSUE INFLAMMATION, A MICROENVIRONMENT FOR TUMORIGENESIS**

Analogously to adipose tissue, the tumor microenvironment is composed by multiple cell types including epithelial cells, fibroblasts, mast cells, and cells of the innate and adaptive immune system that favor a pro-inflammatory and pro-tumorigenic environment (Harvey et al., 2011). These inflammatory cells secrete cytokines, growth factors, metalloproteinases, and reactive oxygen species, which can induce DNA damage and chromosomal instability, thereby favoring carcinogenesis (Khasawneh et al., 2009). The abundance of leukocytes in neoplasic tissue was crucial to establish the link between chronic inflammation and cancer development (Virchow, 1863). Now, inflammation is a well-known hallmark of cancer, and growing evidence continues to indicate that chronic inflammation is associated with increased cancer risk (Aggarwal and Gehlot, 2009).

The expanded adipose tissue constitutes an important initiator of the microenvironment favorable for tumor development (Catalán et al., 2011d) due to its ability to produce and secrete inflammatory cytokines by adipocytes or infiltrating macrophages (Xu et al., 2003). Noteworthy, novel adipokines [lipocalin-2 (LCN-2), osteopontin (OPN) and YKL40] related to inflammation and insulin resistance with emerging roles in tumor development have been recently described to be increased in adipose tissue from patients with colon cancer (Catalán et al., 2011d).

In this line, periprostatic adipose tissue of obese subjects shows a dysregulated expression of genes encoding molecules involved in inflammatory processes including antigen presentation, B cell development, and T helper cell differentiation. Moreover, subjects with prostate cancer display an altered profile of genes with great impact on immunity and inflammation in their periprostatic adipose tissue (Ribeiro et al., 2012). The up-regulation of complement factor H and its receptor in periprostatic adipose tissue from patients with prostate cancer has been also described, suggesting an inhibitory modulation of the complement activity in prostate tumor cells and evasion to attack. Other altered molecules include the B lymphocyte antigen CD20 encoded by the *MS4A1* gene with a functional role in B-cell activation and *FFAR2* that encodes a protein reported to modulate the differentiation and/or activation of leukocytes (Ribeiro et al., 2012). This observation highlights the bi-directional interactions between periprostatic adipose tissue and tumor cells, which influence adipose tissue function and may influence prostate cancer progression inducing an environment favorable to cancer progression.

Clusters of enlarged adipocytes become distant from the vasculature in expanding adipose tissue leading to local areas of hypoxia and eventually necrosis. The reduction in oxygen pressure associated with adipose tissue hypoxia is considered to underlie the inflammatory response (Trayhurn et al., 2008; Ye, 2009; Trayhurn, 2013). The master regulator of oxygen homeostasis is the hypoxia-inducible factor (HIF)-1α. HIF-1α is increased in the adipose tissue of obese patients and its expression is reduced after surgery-induced weight loss (Cancello et al., 2005). It is well-documented that HIF-1α also influences both the innate and the adaptive immunity regulating functions of myeloid cells, neutrophils, macrophages, mast cells, dendritic cells, natural killer cells and lymphocytes (Eltzschig and Carmeliet, 2011). Similarly to what takes place in tumor tissue, adipose tissue hypoxia is related to the presence of macrophages, which migrate to the hypoxic regions and alter their expression profile increasing inflammatory events (Fujisaka et al., 2013). Hypoxia activation is a critical microenvironmental factor during tumor progression with oxygen concentrations in solid tumors being frequently reduced compared with normal tissues (Semenza, 2003; Jiang et al., 2011). HIF-1α and HIF-2α are overexpressed in certain solid tumors (Zhong et al., 1999; Talks et al., 2000), with these elevated levels being associated with cancer-related death in specific tumoral types of the brain (oligodendroglioma), breast, cervix, oropharynx, ovary, and uterus (endometrial) (Semenza, 2003). HIF-2α is also strongly expressed by subsets of tumor-associated macrophages, sometimes in the absence of expression in any tumor cell (Talks et al., 2000). Overall, hypoxia has effects on the function of adipocytes and appears to be an important factor in adipose tissue dysfunction in obesity increasing the risk of cancer development.

Moreover, hypoxia is a primary physiological signal for angiogenesis (growth of blood vessels) in both physiological and pathological conditions. Angiogenesis is a physiological response that regulates adipogenesis representing a hallmark of tumor growth (Hanahan and Folkman, 1996; Carmeliet and Jain, 2000; Cao, 2007). Adipocytes seem regulate angiogenesis both by cell to cell contact and by adipokine secretion (Cao, 2007; Lemoine et al., 2013). In this regard, many cytokines produced by adipose tissue show angiogenic activities such as leptin, TNF-α, IL-6, IL-8, vascular endothelial growth factor (VEGF) and tumor growth factor β (TGF-β) (Ferrara and Kerbel, 2005; Ye, 2009; Gómez-Ambrosi et al., 2010).

The blocking of tumor angiogenesis as an anticancer strategy has shown desirable results across multiple tumor types (Folkman, 1971; Schneider et al., 2012). The standard chemotherapy usually results in partial or total resistance after different cycles of treatment (Kerbel, 1997). Based on the hypothesis that endothelial cells have a normal complement of chromosomes and a relative genetic stability, the use of inhibitors of angiogenesis may avoid acquired drug resistance (Kerbel, 1997). Current pharmacotherapeutic options for treating obesity and related metabolic disorders remain limited and ineffective. Emerging evidence shows that modulation of angiogenesis is a possible therapeutic intervention to impair the development of obesity by regulating the growth and remodeling of the adipose tissue vasculature (Rupnick et al., 2002; Cao, 2010). Adipose tissue growth is angiogenesis-dependent (Rupnick et al., 2002) and the modulation of angiogenesis appears to have the potential to impair the development of obesity (Lijnen, 2008). Studies in mice have shown that the administration of anti-angiogenic agents prevents diet-induced or genetic obesity (Brakenhielm et al., 2004a). Genetically obese mice treated with different angiogenesis inhibitors such as TNP-470, angiostatin, endostatin, Bay-129566, a matrix metalloproteinase inhibitor, or thalidomide showed reduced body and adipose tissue weights as well as increased apoptosis in the adipose tissue compared with control mice (Rupnick et al., 2002). In this regard, targeting a proapoptotic peptide to prohibitin in the adipose vasculature caused ablation of white fat in both, diet-induced and age-related obesity (Kolonin et al., 2004). Recently, the antiangiogenic treatment blocking VEGFR2 by antibodies but not of VEGFR1 has been described to limit adipose tissue expansion (Tam et al., 2009). To evaluate the effects of the different antiangiogenic agents characterized in the cancer field in obesity models *in vivo* may be an attractive target to limit adipose tissue expansion. However, a too strong inhibition of adipose tissue expansion by impairing angiogenesis may lead to ectopic lipid storage, increased inflammation, and further deterioration of systemic insulin sensitivity (Sun et al., 2012; Lemoine et al., 2013). Moreover, adipose tissue development is a multifactorial process and it is unlikely that a single angiogenesis inhibitor will allow reduction of obesity without associated side effects (Lijnen, 2008). Thus, blocking the capacity for angiogenesis may have different outcomes, depending on the stage of obesity.

### **IMMUNE CELL TYPES PRESENT IN EXPANDED ADIPOSE TISSUE**

In cases of severe obesity, adipose tissue can constitute up to 50–60% of the total body mass being the expanded adipose tissue a largely uncharacterized immunological organ with distinct subpopulations of cells of the immune system (Kanneganti and Dixit, 2012). Furthermore, excess of body fat is accompanied by altered immune cell function and different expression profile of genes related to immunity in obese human subjects compared with healthy-weight individuals (Gómez-Ambrosi et al., 2004). Discrepancies in leukocyte number, subset, and activity of monocytes between lean and obese individuals have been reported (Nieman et al., 1999). Adipose tissue has been shown to exhibit a dynamic infiltration by innate and adaptive cells during the onset of insulin resistance and diet-induced obesity (Duffaut et al., 2009). The observation of infiltrated macrophages in the adipose tissue of obese patients prompted an increased interest in the interplay between immune cells and metabolism. Recent studies have revealed a growing list of immune cell types (including macrophages, lymphocytes, mast cells, eosinophils neutrophils and foam cells) that infiltrate adipose tissue and have potential roles in insulin resistance (Olefsky and Glass, 2010; Dalmas et al., 2011; Wu et al., 2011; Shapiro et al., 2013) (**Figure 1**).

The role of adaptive immune cells in obesity-induced adipose tissue inflammation has been less characterized than that of innate immune cells. Based on studies in mouse models, lymphocyte infiltration in adipose tissue might occur in a chronological sequence. B and T lymphocytes are recruited during early

obesity-induced inflammation by preadipocytes or chemotactic adipokines like CCL5, CXCL5, CXCL12, or CCL20. Furthermore, the cytokines derived from Th lymphocytes reportedly modulate macrophage phenotype switching, which is directly linked to insulin resistance (Sell et al., 2012).

To explain the chronological order of how immune cells infiltrate adipose tissue in obesity, it has been proposed that T cells may stimulate preadipocytes to induce the recruitment of macrophages via chemotactic factors such as MCP-1, shedding new light on the importance of chemotaxis in this scenario (Kintscher et al., 2008).

### **INNATE IMMUNE SYSTEM IN ADIPOSE TISSUE**

Macrophages and monocytes are representative of the innate immune system and represent a large proportion of the stromovascular cell fraction in adipose tissue. Several cell types of the innate immune system are involved in the development of adipose tissue inflammation and the most studied cell type among these is the ATM (Kalupahana et al., 2012). Neutrophils and mast cells, also members of the innate immune system have been also implicated in promoting inflammation and insulin resistance during obesity, whereas eosinophils and myeloid-derived suppressor cells have been suggested to play a protective role (Wu and Van Kaer, 2013).

### **MONOCYTES AND MACROPHAGES IN ADIPOSE TISSUE**

The majority of macrophages found in the adipose tissue of diet-induced obese mice are originated from blood monocytes (Weisberg et al., 2003; Dalmas et al., 2011). Monocytes are a heterogeneous cell population that differ in their migration and cell fate properties (Saha and Geissmann, 2011). The phenotype of macrophages depends on the subset of monocytes upon arrival at target tissues being probably determined by the local microenvironment (Dalmas et al., 2011). The number of resident macrophages present in adipose tissue was found to correlate positively with obesity in various mouse models and in human adipose tissue (Weisberg et al., 2003; Xu et al., 2003). Thus, it is possible to speculate that macrophages might be involved in the growth of the fat mass in a similar manner to that described in tumors (Curat et al., 2004).

Based on their cytokine profile secretion and cell surface markers, ATMs are classified into two main types: the "classical" macrophages named M1 in contrast to the "alternatively activated" M2. M1 macrophages are the first line of defense against intracellular pathogens with high microbicidal activity and are classically stimulated by interferon (IFN)-γ or by lipopolysaccharide (LPS). M1 induce the secretion of inflammatory cytokines (IL-1, IL-6, TNF-α, MCP1) and reactive oxygen species, and nitric oxide (NO) through the stimulation of inducible NO synthase (iNOS) (Lumeng et al., 2008). Alternative activation, resulting from induction by the Th2 cytokines interleukin IL-4 and IL-13 (Gordon, 2003) is associated with tissue repair and humoral immunity producing immunosuppressive factors, such as IL-10, IL-1Ra, and arginase (Gordon and Taylor, 2005). Obesity induces a phenotypic switch from an anti-inflammatory M2 polarized state to a pro-inflammatory M1 state (Lumeng et al., 2007). The importance of the M1/M2 ratio has been reported in macrophage-specific *Pparg-*deficient mice that show impaired alternative macrophage activation, increased development of obesity and adipose tissue inflammation as well as glucose intolerance (Odegaard et al., 2007). The identification of the signaling pathways that control macrophage polarization in expanding adipose tissue remains a challenging issue. In this sense, it has been described that the local hypoxia in expanding adipose tissue may promote the M2 to M1 switching (Ye and McGuinness, 2013). Moreover, a recent study in *Trib1*-deficient mice has shown a severe reduction of M2-like macrophages in adipose tissue highlighting the contribution of Trib1 for adipose tissue homeostasis by controlling the differentiation of tissue-resident M2-like macrophages (Satoh et al., 2013).

### **INVOLVEMENT OF NEUTROPHILS, EOSINOPHILS, AND MAST CELLS IN OBESITY**

The notion that a transient "acute inflammatory infiltrate" precedes the "chronic inflammatory infiltrate" in obesity and that neutrophils play a key role (Wagner and Roth, 2000) producing chemokines and cytokines, thereby facilitating macrophage infiltration has been proposed (Talukdar et al., 2012). In this line, adipose tissue neutrophils could have a role in initiating the inflammatory cascade in response to obesity based on the fact that mice fed with a high-fat diet show an increase in neutrophil recruitment to adipose tissue peaking at 3–7 days and subsiding thereafter (Elgazar-Carmon et al., 2008). The treatment of hepatocytes with neutrophil elastase causes cellular insulin resistance while deletion of neutrophil elastase in obese mice leads to reduced inflammation (Talukdar et al., 2012).

Although eosinophils are associated with allergic diseases and helmintic infections (Rothenberg and Hogan, 2006), the biologic role of these cells in adipose tissue remains incompletely defined (Maizels and Allen, 2011). It has been shown that eosinophils are the main source of IL-4 and IL-13 in white adipose tissues of mice, and, in their absence, M2 macrophages are greatly attenuated (Wu et al., 2011). Moreover, in the absence of eosinophils, mice which were fed a high-fat diet develop increased body fat and insulin resistance (Wu et al., 2011). The promotion of eosinophil responses can protect against metabolic syndrome (Wu et al., 2011).

Mast cells, like macrophages, are inflammatory cells, but the exact mechanisms of mast cells in the pathogenesis of obesity are not fully understood. In this regard, increased mast cells in adipose tissue from obese subjects compared with those of lean subjects have been reported. Obese subjects also had significantly higher tryptase concentrations in their serum than lean individuals. Mast cells may contribute to inflammation through the secretion of IL-6 and IFN-γ (Stienstra et al., 2011). Moreover, mast cell number is related to fibrosis, macrophage inflammation and endothelial activation of adipose tissue in human obesity (Divoux et al., 2012). These observations suggest a possible association between mast cells and obesity-associated inflammation (Liu et al., 2009; Zhang and Shi, 2012).

### **ADAPTIVE IMMUNE SYSTEM IN ADIPOSE TISSUE**

Recent advances in the field of adipose tissue biology reveal a prominent role of different types of lymphocytes (T-lymphocytes, B-lymphocytes, and natural-killer cells) in adipose tissue inflammation depending on the obese state in parallel to macrophages (Sell and Eckel, 2010).

### **T-LYMPHOCYTES IN ADIPOSE TISSUE**

CD4+ T cells along with CD8+ T cells constitute the majority of T-lymphocytes. Experimental data suggest that T-lymphocytes might play a role in the development of insulin resistance during obesity. In this sense, T-lymphocytes are described in visceral and subcutaneous adipose tissue of obese mice and humans (Bornstein et al., 2000) but the role of different subtypes of lymphocytes, CD4+, and CD8+ cells, in adipose tissue inflammation remains largely unexplored. The increase in the number of T cells in adipose tissue from diet-induced obesity mice is genderdependent, with higher numbers of T cells in obese males than in females or lean males (Wu et al., 2007). Based on studies in mouse models, lymphocyte infiltration in adipose tissue might occur in a chronological sequence with T lymphocytes being recruited during early obesity-induced inflammation by chemokines like RANTES, a T-cell specific chemokine also known as CCL5 (Sell et al., 2012). In this regard, the expression of RANTES and its respective receptor CCR5 in visceral adipose tissue of morbidly obese patients have been described (Wu et al., 2007).

CD4+ T cells are crucial in achieving a regulated effective immune response to pathogens. In adipose tissue, CD4+ T cells are mainly classified into the classical T-helper 1 (Th1) and T-helper 2 (Th2) although new subsets have been identified including T-helper 17 (Th17), induced T-regulatory cells (iTreg), and the regulatory type 1 cells (Tr1), among others (Luckheeram et al., 2012). The roles for CD4+ T lymphocytes in adipose tissue are related to the regulation of body weight, adipocyte hypertrophy, insulin-resistance, and glucose tolerance. Thus, CD4+ cells are key in the control of disease progression in diet-induced obesity (Winer et al., 2009). Th1 cells show a pro-inflammatory profile, secreting IFN-γ, which elicits the production of macrophage mediators, induces leukocyte adhesion molecules and chemokines, as well as increases antigenpresenting capacity by macrophages and endothelial cells (Geng and Hansson, 1992; Tellides et al., 2000). Interestingly, T cells extracted from fat tissue of obese mice and stimulated *in vitro* produced higher amounts of IFN-γ than those extracted from lean animals. This finding suggests that obesity primes T cells from adipose tissue toward a Th1 switch (Rocha et al., 2008). Winer *et al*. (Winer et al., 2009) reported that the increase of CD4+ T cells with obesity in mice is largely due to the accumulation of IFNγ produced by Th1 cells. The elevated levels of IFNγ also contribute to the classical activation of adipose tissue macrophages, resulting in increased inflammation in adipose tissue. On the other hand, Th2 are anti-inflammatory cells and are a source of IL-4 and IL-13. In this regard, T cells may orchestrate an inflammatory cascade, depending on the set of cytokines they predominantly produce (Hansson and Libby, 2006). A dramatic increase in the number of Th1 cells has been described in dietinduced obesity states, whereas the number of Th2 cells remained unchanged (Sell and Eckel, 2010).

T regulatory (Treg) cells are a small subset of T lymphocytes constituting normally 5–20% of the CD4+ compartment. Tregs are critical in the defense against inappropriate immune responses such as inflammation and tumorigenesis (Sakaguchi et al., 2008) because they control the behavior of other T cell populations and influence the activities of the innate immune system cells (Maloy et al., 2003). Treg cells regulate the activities of macrophages and adipocytes probably secreting IL-10, given their association with improved insulin sensitivity in both rodents and humans (Scarpelli et al., 2006). It has been recently described that the accumulation of Tregs in visceral adipose tissue is mediated by the nuclear receptor peroxisome proliferator-activated receptor (PPAR)-γ (Cipolletta et al., 2012). PPAR-γ tended to impose the transcriptional characteristics of visceral adipose tissue Tregs on naïve CD4+ T cells (Cipolletta et al., 2012). Tregs may be regulated by local hypoxia, increased adipocyte death and adipocyte stress (Feuerer et al., 2009). The diminished Treg cells in obesity could promote the infiltration of macrophages in adipose tissue and, thereby, increase the production of inflammatory cytokines.

CD8+ T cells are involved in the initiation and propagation of inflammatory cascades in obese adipose tissue (Nishimura et al., 2009). CD8+ cells are required for adipose tissue inflammation and have major roles in macrophage differentiation, activation and migration (Nishimura et al., 2009). A study in mice reported mainly CD8+ lymphocyte infiltration in hypoxic areas of epididymal adipose tissue in mice fed a high-fat diet, whereas the numbers of CD4+ and regulatory T cells were reduced (Rausch et al., 2008). The infiltration by CD8+ T cells precedes the recruitment of macrophages. Indeed, immunological and genetic depletion of CD8+ T cells lowered macrophage infiltration and adipose tissue inflammation as well as ameliorated systemic insulin resistance (Rausch et al., 2008). Another study also demonstrates an early T lymphocyte infiltration during the development of insulin resistance in a mouse model of high fat diet-induced obesity as well as a correlation of T cells with waist circumference in diabetic patients (Kintscher et al., 2008), highlighting the association of insulin resistance with adipose tissue lymphocyte infiltration. Oppositely, most of these cells were CD4+ with only a few CD8+ cells.

Recent studies have focused on another regulatory T cell subset, natural killer T (NKT) cells, in the development of obesity-associated inflammation and comorbidities (Lukens and Kanneganti, 2012; Lynch et al., 2012). NKT cells are abundant in metabolically active organs such as liver and adipose tissue (Emoto and Kaufmann, 2003; Lynch et al., 2009) and show the capacity to produce a variety of both pro- and anti-inflammatory cytokines (Wu and Van Kaer, 2013). NKT cells exert their effects in the development of inflammation and metabolic diseases in response to nutritional lipid excess (Wu and Van Kaer, 2013).

### **B-LYMPHOCYTE ACCUMULATION IN DYSFUNCTIONAL ADIPOSE TISSUE**

A fundamental pathogenic role for B cells in the development of metabolic abnormalities has been described (Winer et al., 2011; DeFuria et al., 2013). In mice, B-lymphocytes accumulate in adipose tissue before T cells, shortly after the initiation of a high-fat diet (Duffaut et al., 2009). The early recruitment of B cells promotes T cell activation and pro-inflammatory cytokine production, which potentiates M1 macrophage polarization and insulin resistance (Winer et al., 2011).

Moreover, an impaired function of toll-like receptors in B cells from patients with T2D that increases inflammation by the elevation of pro-inflammatory IL-8 and lack of antiinflammatory/protective IL-10 production has been described (Jagannathan et al., 2010).

### **ADIPOKINE DYSREGULATION AND CANCER**

A growing body of evidence suggests that the inflammatory milieu of the obese state is linked to the development of cancer through different mechanisms (Grivennikov et al., 2010). Infiltrating immune cells in adipose tissue regulates the local immune response, inducing increased levels of pro-inflammatory cytokines and adipokines and providing a major link to the obesity-associated tumor development (van Kruijsdijk et al., 2009). Critical molecules involved in the promotion of tumor cell proliferation include inflammatory transcription factors [such as NF-κB and signal transducer and activator of transcription 3 (STAT3)], adipokines (leptin and adiponectin) as well as inflammatory cytokines and enzymes (TNF-α, IL-6, MCP-1, SAA) and matrix metalloproteases (Gómez-Ambrosi et al., 2006; Aggarwal, 2009). Among all these molecules, perhaps the transcription factor NF-κB is the central mediator of inflammation (Aggarwal, 2004).

Leptin, the product of the *ob* gene, is an adipocyte-derived hormone that is a central mediator in regulating body weight by signaling the size of the adipose tissue mass (Zhang et al., 1994). Leptin levels are closely correlated with adiposity in obese rodents and humans (Maffei et al., 1995; Frühbeck et al., 1998, 2001b; Muruzábal et al., 2002). Subsequent studies have suggested that this hormone may be linked to the increased incidence of cancer in obesity (Khandekar et al., 2011). Leptin has attracted attention due to its potential function as an antiapoptotic, mitogenic, proangiogenic, and prometastatic agent, as observed in numerous *in vitro* studies (Frühbeck, 2006a,b; Park et al., 2011). Circulating levels of leptin have been investigated to determine the correlation with cancer and progressive disease. A strong association between leptin levels and colorectal and endometrial cancer has been reported (Petridou et al., 2002; Koda et al., 2007a). However, the findings of clinical studies of the relationship between leptin and breast cancer are inconsistent (van Kruijsdijk et al., 2009). Interestingly, many colorectal, breast, and endometrial cancers overexpress the leptin receptor OB-R (Koda et al., 2007a,b). Leptin produced by adjacent adipose tissue might promote the growth of colorectal cancer enhancing the proliferation of colon cancer cells although other factors released by adipocytes are also likely to be involved in the process. It suggests that the presence of tumor-associated adipose tissue represents an important microenvironmental influence (Amemori et al., 2007; Vansaun, 2013).

It has now been extensively documented that adiponectin expression is inversely correlated with obesity (Scherer et al., 1995; Hu et al., 1996). Adiponectin may influence cancer risk through its well-recognized effects on insulin resistance, but it is also plausible that adiponectin acts on tumor cells directly (Yamauchi et al., 2001; Barb et al., 2007). Interestingly, several cancer cell types express the adiponectin receptors AdipoR1 and AdipoR2 that may mediate the inhibitory effects of adiponectin on cellular proliferation (Kim et al., 2010). Epidemiologic studies show that low levels of adiponectin have an inverse association with the risk for the development of multiple cancers as well as advanced progression of disease (Wei et al., 2005; Barb et al., 2007; Bao et al., 2013).  Adiponectin also inhibits prostate and colon cancer cell growth (Bub et al., 2006). In a mouse tumor model, adiponectin markedly induced a cascade activation of caspase−8, −9, and −3, which leads to cell death inhibiting primary tumor growth (Brakenhielm et al., 2004b).

TNF-α, a cytokine originally identified as mediating endotoxin-induced tumor necrosis (Carswell et al., 1975), has been shown to be involved in the development of a number of cancers through the promotion of vessel growth and tumor destruction by direct cytotoxicity angiogenesis (Leibovich et al., 1987) as well as the metastatic potential of circulating tumor cells (Orosz et al., 1993). However, although TNF-α is the most potent activator of NF-κB, elevated levels of TNF-α in tissue or serum are not very common in cancer patients (Aggarwal and Gehlot, 2009). The increased circulating levels of TNF-α of both obese rodents and obese humans, suggest a possible link between obesity and tumorigenesis (Khandekar et al., 2011). In this regard, obesity-promoted hepatocellular carcinoma development was dependent on increased production of the cytokines TNF-α and IL-6, which cause hepatic inflammation and activation of the oncogenic transcription factor STAT3 (Park et al., 2010). Diet-induced obesity produces an elevation in colonic TNF-α giving rise to a number of alterations including the dysregulation of the Wnt signaling pathway, with an important involvement in colorectal cancer (Liu et al., 2012).

Another pro-inflammatory molecule produced in adipose tissue is IL-6. The circulating levels of IL-6 are higher in subjects with obesity-related insulin resistance (Kern et al., 2001). IL-6 is a pleiotropic cytokine with a significant role in growth and differentiation (Ghosh and Ashcraft, 2013) that signals to the nucleus through STAT3, an oncoprotein that is activated in many human cancers and transformed cell lines (Bromberg et al., 1999). Interestingly, STAT3 is activated by leptin (Vaisse et al., 1996) and probably may have a role in the pro-tumorigenic effects of this adipokine. Moreover, different studies indicate that serum IL-6 levels are a negative indicator of the development of breast cancer in overweight or obese patients with prominent insulin resistance (Gonullu et al., 2005; Knupfer and Preiss, 2007).

MCP-1 is a member of the CC chemokine superfamily (Panee, 2012) that plays a crucial role in recruitment and activation of monocytes during acute inflammation and angiogenesis (Charo and Taubman, 2004). Circulating levels of MCP-1 are generally increased in obese patients compared to lean controls (Catalán et al., 2007). Gene expression levels in adipose tissue follow the same trend, being higher in the visceral and subcutaneous adipose tissue of obese patients compared to lean volunteers (Huber et al., 2008). There is emerging evidence that MCP-1 induces tumor cell proliferation via activation of the phosphatidylinositol 3-kinase/protein kinase B (PI3K/Akt) pathway in various cancer types (Loberg et al., 2006). Moreover, MCP-1 promotes cancer tumorigenesis indirectly via its effects on macrophage infiltration (Walter et al., 1991). It has been described that MCP-1 is highly expressed by breast tumor cells and has causative roles in breast malignancy and metastasis (Soria and Ben-Baruch, 2008). The pleiotropic roles of CCL2 in the development of cancer are mediated through its receptor, CCR2 (Lu et al., 2007).

Novel adipokines involved in obesity-associated inflammation have emerged as important players of tumor growth (Catalán et al., 2011d). OPN is a secreted glycoprotein expressed by different cellular types (Brown et al., 1992). Recently, several studies have highlighted the expression of OPN in adipose tissue of both humans and mice and its involvement in obesity and obesityassociated T2D promoting inflammation and the accumulation of macrophages in adipose tissue (Gómez-Ambrosi et al., 2007; Nomiyama et al., 2007). High OPN expression in the primary tumor is associated with early metastasis and poor outcome in human breast and other cancers (Denhardt et al., 2001). LCN-2 also known as neutrophil gelatinase associated lipocalin is a component of the innate immune system with a key role in the acute-phase response to infection (Flo et al., 2004). Increased levels of LCN-2 in visceral adipose tissue in human obesity and a relationship with pro-inflammatory markers has also been described (Catalán et al., 2009, 2013). In addition to inhibiting invasion and metastasis, LCN-2 also appears to be a negative regulator of angiogenesis in cancer cells (Chakraborty et al., 2012). Tenascin-C (TNC) is an extracellular matrix glycoprotein specifically induced during acute inflammation and persistently expressed in chronic inflammation (Chiquet-Ehrismann and Chiquet, 2003; Udalova et al., 2011). Increased expression of TNC has been described in most solid cancers, playing important roles in enhancing proliferation, invasion and angiogenesis during tumorigenesis and metastasis (Midwood and Orend, 2009; Midwood et al., 2011). In this line, elevated expression levels of TNC have been found in visceral adipose tissue in obesity with a tight association of genes being involved in maintaining the chronic inflammatory response associated to obesity (Catalán et al., 2011c). YKL-40 is another adipokine involved in inflammation and cancer cell proliferation. YKL-40 is a growth factor with elevated gene and protein expression levels in visceral adipose tissue in human obesity-associated T2D (Catalán et al., 2011b). Moreover, circulating levels of this cytokine are described as an obesity-independent marker of T2D (Nielsen et al., 2008). On the other hand, elevated levels of YKL-40 were found in patients with different types of solid tumors, including several types of

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### **CONCLUSIONS**

The prevalence of obesity has risen steadily for the past several decades. Excess of adiposity is associated with increased death rates for all cancers combined and for cancers at multiple specific sites with the strongest evidence for endometrial cancer, postmenopausal breast cancer, colon cancer, renal cell carcinoma of the kidney, liver, gallbladder, esophageal, and pancreatic cancer. The mechanisms linking obesity and cancer are unclear but lowgrade chronic inflammation, dysregulation of growth signaling pathways, chronic hyperinsulinemia, and hypoxia associated to obesity are widely accepted as important factors in cancer pathogenesis. Particular attention is placed on the pro-inflammatory environment associated with the obese state, specifically highlighting the involvement of infiltrated immune cells into adipose tissue. In this sense, the understanding of the regulatory mechanisms that lead to polarization of macrophages or lympocytes in adipose tissue toward a pro-inflammatory phenotype will provide new ways to control adipose tissue inflammation (**Figure 2**). A better understanding of the mechanistic links between obesity and cancer will help to identify intervention targets and strategies to avoid the pro-tumorigenic effects of obesity.

### **ACKNOWLEDGMENTS**

This work was supported by Fondo de Investigación Sanitaria (FIS) PI11/02681, PI12/00515 from the Spanish Instituto de Salud Carlos III and by the Department of Health (48/2011 and 58/2011) of the Gobierno de Navarra of Spain.

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**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Received: 11 June 2013; paper pending published: 17 July 2013; accepted: 12 September 2013; published online: 02 October 2013.*

*Citation: Catalán V, Gómez-Ambrosi J, Rodríguez A and Frühbeck G (2013) Adipose tissue immunity and cancer. Front. Physiol. 4:275. doi: 10.3389/fphys. 2013.00275*

*This article was submitted to Systems Biology, a section of the journal Frontiers in Physiology.*

*Copyright © 2013 Catálan, Gómez-Ambrosi, Rodríguez and Frühbeck. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

# Similar structures but different roles – an updated perspective onTLR structures

### *Balachandran Manavalan, Shaherin Basith and Sangdun Choi\**

*Department of Molecular Science and Technology, Ajou University, Suwon, South Korea*

#### *Edited by:*

*Masa Tsuchiya, Keio University, Japan*

#### *Reviewed by:*

*Vladimir N. Uversky, University of South Florida, USA Tiandi Wei, Shandong University, China*

#### *\*Correspondence:*

*Sangdun Choi, Department of Molecular Science and Technology, Ajou University, Suwon 443-749, South Korea. e-mail<EMAIL_ADDRESS>
Toll-like receptors (TLRs) are pattern recognition receptors that recognize conserved structures in pathogens, trigger innate immune responses, and prime antigen-specific adaptive immunity. Elucidation of crystal structures of TLRs interacting with their ligands such as TLR1-2 with triacylated lipopeptide, TLR2-6 with diacylated lipopeptide, TLR4–MD-2 with LPS, and TLR3 with double-stranded RNA (dsRNA) have enabled an understanding of the initiation of TLR signaling. Agonistic ligands such as LPS, dsRNA, and lipopeptides induce "m" shaped TLR dimers in which C-termini converge at the center. Such central convergence is necessary to bring the two intracellular receptor TIR domains closer together and promote their dimerization, which serves as an essential step in downstream signaling. In this review, we summarize TLR ECD structures that have been reported to date with special emphasis on ligand recognition and activation mechanism.

**Keywords: innate immunity, ligand, myeloid differentiation factor 88,Toll-like receptor**

### **INTRODUCTION**

The Toll-like receptor (TLR) protein family plays an important role in the innate immune system by recognizing common structural patterns in diverse microbial molecules (Gay and Gangloff, 2007). TLRs are type I transmembrane glycoproteins characterized by the presence of an extracellular domain (ectodomain; ECD) containing leucine rich repeats (LRRs), which is primarily responsible for mediating ligand recognition, followed by a single transmembrane helix and an intracellular Toll-like/interleukin-1 (IL-1) receptor (TIR) domain that is responsible for downstream signaling. To date, 10 and 12 functional TLRs have been identified in humans and mice, respectively. TLR1-9 is conserved in both species; however, mouse TLR10 is not functional because of a retrovirus insertion, and TLR11-13 have been lost from the human genome (Kawai and Akira, 2010). "Toll" was first identified as a protein important in the early stages of development in *Drosophila*. Later, it was discovered that Toll signals to Dorsal (like mammalian NF-κB) and is involved in the coordination of antifungal and antibacterial responses (Rosetto et al., 1995; Lemaitre et al., 1996).

The TLR family can be largely divided into two subgroups, extracellular and intracellular, depending on their cellular localization. TLR1, 2, 4, 5, 6, and 10 are largely localized on the cell surface to recognize PAMPs. Conversely, TLR3, 7, 8, and 9 are localized in intracellular organelles such as endosomal/lysosomal compartments and the endoplasmic reticulum (ER). Among the TLRs, the ligand (lipopolysaccharide; LPS) of TLR4 was first identified by genetic studies (Lemaitre et al., 1996). Lipopeptides or lipoproteins are recognized by TLR2 in complex with TLR1 or 6, while viral double-stranded RNA (dsRNA) is recognized by TLR3, flagellin is recognized by TLR5, single-stranded RNA is recognized by TLR7 and 8, and host- or pathogen-derived DNA is recognized by TLR9. In addition to known pathogen/microbial derived

ligands, TLR also recognizes the endogenous ligands (produced by stressed or damaged cells) and synthetic ligands listed in **Table 1**.

The common mechanism of TLR signaling is that interaction of an agonist with the ECD either induces the formation of a receptor dimer, or changes the conformation of a pre-existing dimer (Latz et al., 2007; Zhu et al., 2009) in such a way that it brings two intracellular TIR domains of the TLRs to interact physically. This simple rearrangement serves as a nucleating act for the recruitment of downstream signaling adapter proteins (Jin and Lee, 2008). Signaling cascades via the intracellular TIR domains are mediated by specific adaptor molecules such as Myd88 (Myeloid differentiation factor 88), Mal (Myd88 adaptor like), TRIF (TIR domain containing adaptor inducing interferon-β), and TRAM (TRIF related adaptor molecule). These adaptor proteins also contain TIR domains that mediate TIR–TIR interactions between TLR receptors, receptor–adaptor, and adaptor–adaptor interactions that are critical for signaling (Palsson-Mcdermott and O'Neill, 2007). In general, intracellular TIR domain of adaptor proteins are composed of approximately 160 amino acid residues and the primary sequences of TIR domains are characterized by three conserved sequence boxes designated Box 1, 2, and 3. Box 1 is considered to be the signature sequence of the family, whereas boxes 2 and 3 contain functionally important residues involved in signaling (Carpenter and O'Neill, 2009). These processes result in the formation of a large multimer complex, or "signaling platform," that propagates downstream signaling, eventually leading to changes in the expression of several hundred primary immune response genes. However, the architecture of the TLR signaling complexes is poorly understood at this time due to a lack of reliable methods to study such interactions as well as the inherent weaknesses of individual inter- and intra-protein interactions in transitory complexes.

#### **Table 1 | Toll-like receptors and their principal ligands.**

*(Continued)*

#### **Table 1 | Continued**

Structural studies of TLR–ligand complexes have been an attractive area of research that has enabled a better understanding of the structure based activation of innate immunity. Such information is essential for the development of adjuvants that specifically bind to TLR ECD and activate its signaling and also in the development of anti-inflammatory drugs that block TLR mediated signaling. To date, five TLR–ligand structures (TLR1–TLR2–Pam3CSK4, TLR2–TLR6–Pam2CSK4, TLR4– MD-2–Eritoran, TLR4–MD-2–LPS, and TLR3–dsRNA) have been determined (Jin et al., 2007;Kim et al., 2007b; Liu et al., 2008;Kang et al., 2009; Park et al., 2009). Currently, these solved atomic models can be used as templates to predict the structures of other unknown TLRs. In this review article, we discuss how similar structures of TLR ECD LRRs have evolved to bind a wide array of different ligands and their activation mechanism.

### **GENERAL STRUCTURE OF TLR ECDs**

The ECD of TLR members contains multiple blocks of LRR, which are protected by cysteine rich regions to form cap-like structures at the LRR-N- and -C-terminal ends. The C-terminal capping structure of TLRs is connected to the cytoplasmic TIR domain via a single transmembrane α helix. Individual LRR module (approximately 20–30 amino acid residues long) consists of conserved "LxxLxLxxNxL" motifs and a variable region (**Figure 1A**). The conserved leucine residue in these motifs can be substituted by other hydrophobic amino acids (Matsushima et al., 2007). The asparagine residues that are also present in the motif form continuous H-bonds with the backbone carbonyl group of neighboring strands throughout the entire protein, resulting in an asparagine ladder. These conserved asparagine residues are important in maintaining the overall shape of the ECD, which can also be replaced by other residues such as cysteine, threonine, or serine, which are able to form H-bonds (Kajava et al., 1995; Kobe and Deisenhofer, 1995; Bell et al., 2003). The variable "*x*"

residues present in the motif are exposed to the solvent. Among them, only few residues are involved in ligand recognition. The "LxxLxLxxNxL" motifs located in the inner concave surfaces of the horseshoe-like structure form parallel β-strands, whereas the variable region forms a convex surface generated by α helices, βturns, and loop structures (**Figure 1A**). LRR proteins are present in a very large and diverse group of proteins and have been found to be involved in a wide variety of physiological functions including immune responses, signal transduction, cell cycle regulation, enzyme regulation, and transcriptional regulation (Buchanan and Gay, 1996; Dolan et al., 2007).

The crystallization of some LRR proteins, including TLRs, has proven to be very difficult. This problem was overcome by the introduction of a new method known as the "hybrid LRR technique" (Jin et al., 2007; Kim et al., 2007a,b; Kang et al., 2009; Park et al., 2009). Hagfish variable lymphocyte receptors (VLRs) were chosen as fusion partners, and the TLR and VLR were fused at their conserved LxxLxLxxNxL motifs. Interestingly, the TLR–VLR hybrid demonstrated that the structure and function of the fusion proteins were not altered. Some hybrids fail to form soluble proteins due to the atomic collisions or the exposed hydrophobic core at the fusion sites. However, hybrids that produced soluble proteins formed stable heterodimers and possibly bound with ligands that were used for the crystallographic studies (Jin et al., 2007;Kim et al., 2007a,b; Kang et al., 2009; Park et al., 2009).

The LRR protein family can be classified into seven subfamilies based on their sequence and structural patterns. TLR belongs to the typical subfamily of the LRR superfamily (Kobe and Kajava, 2001; Matsushima et al., 2007). Each LRR region consists of 24 amino acid residues, possesses the conserved motif, xLxxLxxLxLxxNxLxxLPxxxFx, and displays a unique horseshoe shape structure (**Figure 1B**). LRR modules of TLR1, 2, 4, and 6, but not TLR3, have been shown to deviate from their conformation and length when compared with other typical members (Kim et al.,

2007b; Jin and Lee, 2008; Kang et al., 2009; Park et al., 2009). These four TLRs have major structural changes in their central β-sheets; hence, their LRR domains can be divided into an N-terminal, central, and C-terminal domain, respectively (**Figure 1C**). The central domain of TLR1, 2, 4, 6, and 10 lacks an asparagine ladder, which is primarily responsible for the stabilization of the horseshoelike structure. Furthermore, this broken asparagine ladder leads to unusual structural distortions. LRR modules of the central domain differ considerably in the number of residues, varying from 20 to 33. However, the LRR modules present in the majority of LRR proteins are of uniform length (Kajava et al., 1995; Kobe and Deisenhofer, 1995; Matsushima et al., 2007). LRR subfamilies with shorter LRR modules encompass loops in the convex surface, and those containing longer LRR modules have bulkier α helices. It should be noted that helices require more space than loops; therefore, subfamilies with α helices have smaller radii than those with loops that generate enough space in the convex region (Jin and Lee, 2008; Kang and Lee, 2011). This anomaly explains the structural conformation variations of TLR receptors and the ability of the receptor to bind with diverse ligands as well as co-receptors.

### **CRYSTALLOGRAPHIC STRUCTURES OF TLR ECD WITH THEIR LIGANDS**

To date, five crystallographic structures of the TLR ECDs and their ligand complexes have been reported. Of those, four were complexed with agonistic ligands and the remaining one was complexed with a co-receptor and an antagonistic ligand. These structures provide evidence about how pattern recognition receptors (PRRs) recognize patterns present in the ligands. Additionally, these studies suggest that ECD activation mechanisms are also common among all TLR receptor family members.

### **TLR2 COMPLEXES**

Toll-like receptor-2 heterodimerizes with TLR1 or 6 to recognize multiple PAMPs of fungi, Gram-positive pathogens and mycobacteria (Kawai and Akira, 2010). TLR2 recognizes lipopeptides that are anchored to the bacterial membrane by lipid chains covalently attached to N-terminal cysteine (Hantke and Braun, 1973). Lipopeptides from Gram-negative bacteria have three lipid chains. Two of these are attached to the glycerol through an ester bond, which is in turn connected to the sulfur atom of the N-terminal cysteine. The third lipid chain is connected to the amino terminal via amide bonds. Lipopeptides from Gram-positive bacteria or mycoplasma have only two lipid chains and lack the amidelinked lipid chain (Muhlradt et al., 1997; Shibata et al., 2000). Synthetic lipopeptide analogs (Pam2CSK4,Pam3CSK4) containing a di- or tri-acylated cysteine group mimic the pro-inflammatory properties of the lipoproteins, which confirms that acylated Nterminal cysteine is the primary motif responsible for stimulating the immune response. Furthermore, TLR2 receptor also recognizes other ligands such as lipoteichoic acid, lipomannan, peptidoglycan, zymosan, and phenol-soluble modulin (Zahringer et al., 2008).

### **TLR1–TLR2–TRIACYLATED LIPOPEPTIDE COMPLEX**

The crystal structure of TLR2 in association with TLR1 and a synthetic triacylated lipopeptide, Pam3CSK4, has been determined (Jin et al., 2007). Indeed, this is the first crystal structure of a TLR dimer resulting from the binding of agonists, which further explains the ligand-induced dimerization. In this structure, the ECD of TLR2 and 1 form an "m" shaped heterodimer, with the two N-terminals extending in the opposite direction and the Cterminals converging in the middle region (**Figure 2A**). Pam3CSK4 consists of three lipid chains,two of those insert into the hydrophobic pocket of TLR2 and the remaining one inserts into a narrow hydrophobic channel of TLR1 (**Figure 2B**). Apart from the acyl chain binding, the head groups of Pam3CSK4 also interact with TLRs 1 and 2. In particular, TLRs form H-bonds with glycerol and peptide backbone and also form hydrophobic interactions with sulfur atoms. The ligand-binding pockets of TLR1 and 2 are located at the junction of the central and C-terminal domains, indicating the importance of structural transition in the formation of ligand-binding pockets. The ligand binding in the convex surface of TLR2/1 was found to be quite unusual because most ligand-binding sites on LRR proteins that have been identified were found to be present on the concave surfaces (Kobe and Deisenhofer, 1995). The ligand bound complex of TLR1 and 2 is stabilized by non-covalent forces such as H-bonding, hydrophobic interactions and ionic interactions at the interface near the ligand-binding

pocket. It is worth noting that TLR1 P315L polymorphic variation has been reported to interfere with TLR1 signaling (Omueti et al., 2007). In fact, this P315 residue is located at the TLR1/2 dimer interface, highlighting the importance of P315 in TLR1 and 2 heterodimerization. Moreover, species-specific lipoproteins response has also been observed (Grabiec et al., 2004). Lipopeptides with shorter lipid chains act as more potent activator in mouse than human TLR2. This phenomenon is mainly due to the structural variations observed in the TLR2 pocket (Jin et al., 2007).

### **TLR2–TLR6–DIACYLATED LIPOPEPTIDE COMPLEX**

The crystal structure of TLR2 in association with TLR6 and a synthetic diacylated lipopeptide Pam2CSK4 has been determined (Kang et al., 2009). In this structure, the ECD of TLR2 and 6 form an "m" shaped heterodimer, with the two N-terminals extending in the opposite direction and the two C-terminal ends converging in the middle region (**Figure 2C**). The dimeric arrangement of TLR2/6 is similar to TLR2/1 complex. However, TLR1 and 6 contain important structural differences in their ligand-binding sites and dimerization interface. In TLR6, the side chains of two phenylalanine (F343 and F365) residues block the lipid-binding pocket, leading to a pocket that is less than half the length of the TLR1 (**Figure 2D**). This structural feature provides selectivity for diacylated over triacylated lipopeptides, as confirmed by the mutation studies of these phenylalanine residues to the corresponding amino acids of TLR1 that rendered TLR6 fully responsive not only to diacyl but also to triacylated lipopeptides. In

the TLR2/6 complex, two-ester bound lipid chains of Pam2CSK4 are inserted into a hydrophobic pocket in TLR2 that is located between the LRR11 and 12 loops. Whereas, F319 located in the LRR11 loop of TLR6, forms an H-bond with the peptide bond of the ligand. Such an H-bond network is absent in the TLR2–TLR1– Pam3CSK4 structure. Moreover, TLR2-6 heterodimerization is primarily mediated by surface exposed residues of LRR11-14 modules. In the TLR2-1 complex, the amide bound lipid chain plays an important role in bridging the two TLRs. Although Pam2CSK4 lacks these amide bound chains, it still forms a dimer, primarily through hydrophobic and hydrophilic interactions of their surface exposed residues between the two TLRs. This area of hydrophobic interaction is 80% larger than in the TLR1/2 complex, suggesting that this surface interaction together with the H-bond between LRR11 and the ligand drives the heterodimerization of TLR6.

### **TLR2–LPTA**

During the course of TLR2–TLR6–diacylated lipopeptide complex determination, TLR2 in complex with two non-peptide ligands, *Streptococcus pneumonia* lipoteichoic acid (pnLTA) and PE-DTPA (1,2-dimyristoyl-sn-glycero-3-phosphoethanolamine-*N*-diethylenetriaminepentaacetic acid), has been determined (Kang et al., 2009). PE-DTPA is a synthetic derivative of phospholipid in which metal coordinating DTPA is attached to the ethanolamine head group. In the monomeric TLR2–pnLTA structure, the overall horseshoe-shaped structure of TLR2 and the ligand-binding pocket remain unchanged. When compared with TLR2-6–Pam2CSK4,the position of the sugar head group of LTA of the TLR2–pnLTA complex displaces upward by ∼5.2 Å and rotated by 110 Å toward the lateral surface of the ECD. Moreover, the hydrogen donor and acceptor atoms in the sugar head group of pnLTA have a different arrangement than the lipopeptides. Hence, it is not possible to form an H-bonding network. Due to the shift, TLR1 or TLR6 cannot approach TLR2 to form heterodimers. In the TLR2–PE-DTPA structure, the acyl chain and head group arrangements are similar to those of TLR2–pnLTA. When compared with TLR2-6 lipopeptide complexes, the head group of PE-DTPA is shifted ∼4.3 Å. This structural shift primarily occurs due to a lack of proper H-bonding between the ligand head group and the TLRs, as well as to repulsion of the hydrophilic oxygen atom of the ligand, whose corresponding position in lipopeptide contains sulfur that forms a hydrophobic interaction with TLRs. These complexes (pnLTA and PE-DTPA) have little or no ability to activate TLR2 because of the structural shift in ligand head groups, which strongly suggests that the ligand/lipopeptide head group plays an important role in TLR2 activation via heterodimerization. A large proportion of TLR2 ligands are lipopeptides that can bind to the TLR2 hydrophobic pocket, but some TLR2 ligands including peptidoglycan, hyaluronic acid, teichoic acid, and zymosan do not contain this hydrophobic region (**Table 1**). Hence, the interaction of these ligands with TLR2 might use different binding sites. Further crystallographic or modeling studies are required to clarify the exact binding sites of non-lipid ligands and to verify whether these bindings induce the formation of similar heterodimeric structures such as TLR1-2 or TLR2-6.

### **TLR3–dsRNA COMPLEX**

Toll-like receptor-3 has been shown to recognize dsRNA produced during viral replication (Alexopoulou et al., 2001). The first TLR3 structure was identified independently by two different groups (Bell et al., 2005; Choe et al., 2005). Both groups have shown that the LRR region of TLR3 displays a heavily glycosylated horseshoe-shaped solenoid structure. Choe et al. (2005), postulated that dsRNA might bind at the convex surface because this region is a glycan-free face, which enables dsRNA to bind to the positively charged residues of the TLR ECD. However, Bell et al. (2005) suggested that the nucleotide binding site is located in the concave surface. This is likely due to the fact that during crystallization, two sulfate molecules from the crystallization medium stably bound to residues in LRRs 12 and 20, and these two LRRs contain large insertions. As the sulfate ions share the same atomic arrangement as phosphate groups, those present in the dsRNA backbone might be able to bind to one or both of the sulfate binding sites. Hence, each group prediction differs in the dsRNA binding sites and it was not clear how TLR3 specifically recognizes dsRNA and initiates signaling. However, the recently solved crystal structure of mTLR3 bound to dsRNA explains how this is accomplished (Liu et al., 2008). TLR3 ECD exists as a monomer in solution and the dimerization only occurs upon ligand binding. In the structure, dsRNA interacts with both the N- and C-terminal sites on the lateral side of the convex surface of the TLR3 ECD (**Figure 3A**). The N-terminal interaction sites are composed of LRRNT and LRR1-3 modules, whereas the C-terminal site is composed of LRR19-21 modules. The dsRNA in the complex retains a typical A-DNA like structure, in which the ribose phosphate backbone and the position of the grooves are the major determinants in binding (**Figure 3B**). The mTLR3–ECD interacts with the sugar phosphate backbones, but not with individual bases, which accounts for the lack of any particular nucleotide specificity in binding (Alexopoulou et al., 2001; Leonard et al., 2008). This feature would prevent the viruses from escaping detection by mutation (Botos et al., 2011). Moreover, the identified structure reveals the possible reasons for the inability of TLR3 to recognize dsDNA. The helical structure of dsDNA is the B form, whereas dsRNA is present in A form. The B form helical structure would not be structurally compatible with the two terminal binding sites on the TLR3–ECD. Moreover, several H-bonds were observed between TLR3–ECD and the 2- -OH groups of dsRNA that is missing in dsDNA.

The TLR3–TLR3 interaction site located near the LRRCT occupies only a small portion, demonstrating that ligand–protein interaction as the major driving force behind TLR3 dimerization. The ligand interaction sites (two TLR3 ECD N-terminal regions) are separated by about 120 Å, thus showing why only 40–50 base pairs are sufficient for the stabilized binding of dsRNA to TLR3 (Leonard et al., 2008). However, there have also been study reports of dsRNA of substantially less than 40 base pairs being able to initiate TLR signaling (Kariko et al., 2004; Kleinman et al., 2008). This raises the possibility that the N-terminal interaction site is not essential for efficient TLR3 signal induction in some experimental conditions. Moreover, mutation studies

have identified functional amino acid residues in three different regions (N-terminal, C-terminal, and dimerization region) of the TLR3 ECD. H539E and N541A mutation in the C-terminal, H39A/E and H60A/E mutation in the N-terminal region, and D648A, T679A, and P680L in the dimerization region leads to a loss of TLR3 activity (Bell et al., 2006; Ranjith-Kumar et al., 2007; Fukuda et al., 2008; Wang et al., 2010). Although hydrophobic interactions play a crucial role in binding of lipopeptides to TLR1/2 and 2/6, the TLR3 interaction with dsRNA mainly involves electrostatic interactions and H-bonds. Despite these differences in ligand interactions, the ligand-induced dimers of TLR3, TLR2-6, and TLR1-2 adopt a similar fold, the "m" shaped dimer, in which the two C-termini of the TLR ECDs are in proximity, thereby bringing the two TIR domains together on the cytoplasmic side and providing a scaffold for the recruitment of adaptor proteins and subsequent initiation of further downstream signaling.

### **TLR4–MD-2-AGONIST/ANTAGONIST COMPLEX**