Another inflammation process in which NETs play an important role is pancreatitis. Acute pancreatitis is an inflammatory disorder of pancreas for which no specific treatment is available. Important risk factors of acute pancreatitis are formation of gallstones and alcohol abuse (Spanier et al., 2008). The most severe cases of the disease are associated with mortality, with acute respiratory distress syndrome being the most frequent cause of death in the early phase of the disease (Pandol et al., 2007). Obstruction of the pancreatic duct causes blockage of pancreatic secretion, which is accompanied by disorders in organelle function of pancreatic acinar cells (Gukovskaya et al., 2016). These disorders promote colocalization of zymogens-containing vesicles and lysosomes leading to formation of co-localization organelles. In co-localized organelles cathepsin B activates trypsinogen to trypsin (Halangk et al., 2000; Van Acker et al., 2002), which mediates acinar cell death and thus causing severe inflammation of the pancreas (Lankisch et al., 2015; Manohar et al., 2017). The destroyed tissue will eventually be replaced by fatty tissue, typical of chronic pancreatitis (Braganza et al., 2011), if the original acute pancreatitis is not resolved (Braganza et al., 2011). Neutrophils infiltrate the pancreatic parenchyma during this acute inflammatory response (Lankisch et al., 2015), and can augment trypsinogen activation via ROS. Both trypsin activation and pancreatic injury were reduced in NADPH oxidasedeficient mice(Gukovskaya et al., 2002). In addition, infiltrating neutrophils aggravate inflammation by releasing NETs in the pancreas and at the sites of systemic injury, namely, the lungs (Merza et al., 2015). NETs incubated in vitro with pancreatic acinar cells led to trypsin activation in these cells, degradation of the NETs by treatment with deoxyribonuclease (DNAse) abolished the trypsin activation, reduced local acinar damage, and systemic inflammation (Merza et al., 2015). Furthermore, neutrophils enter the pancreatic ducts and there they form large deposits of NETs, also known as aggregated NETs (aggNETs). AggNETs in turn obstruct secretory flow and thereby perpetuate inflammation (Leppkes et al., 2016). Therefore, NETs formed after an initial inflammatory stimulus become the activators of further inflammation in the pancreas.

Contrary to the situations mentioned above, Gout is a disease where NETs appear to have a positive effect. Gout is an acute inflammatory reaction originated from precipitation of uric acid in the form of needle-shaped monosodium urate (MSU) crystals (So and Martinon, 2017). Aggregates of MSU crystals known as tophi, induce inflammation in the joints and tissues. Local immune cells such as macrophages and dendritic cells take up the crystals via phagocytosis. The MSU-containing phagosomes then fuse with lysosomes. The low pH in phagolysosomes causes a massive release of sodium and consequently raises intracellular osmolarity, which is balanced by passive water influx through aquaporins. This process dilutes intracellular sodium and potassium concentrations. The low potassium is a trigger for activation of NLRP3 inflammasomes (Schorn et al., 2011). Inflammasome activation by MSU crystals has been considered a response to "danger," since damaged cells release urate and ATP into the environment (Busso and So, 2012). As a consequence of inflammasome activation, proIL-1β is cleaved to release active IL-1β and other pro-inflammatory cytokines (Kingsbury et al., 2011; So and Busso, 2014). Cytokine release then leads to a rapid and dramatic recruitment of neutrophils. Recruitment of neutrophils is further mediated by CXCR2, CXCL-8, CXCL-1, CXCL-2, and CXCL-3 (Terkeltaub et al., 1998). This neutrophil influx is accompanied by the infamously intense clinical symptoms of inflammation during an acute gout attack (So and Martinon, 2017). Due to the intense local inflammation, cytokines produced in large quantities can also enter the circulation, resulting in an acute phase response that can trigger fever and leukocytosis (Maueröder et al., 2015). Continuous recruitment of neutrophils to the site of inflammation results in very high neutrophil densities (Shah et al., 2007). After the neutrophil concentration in the tissue exceeds a certain threshold, NETs begin to aggregate and build aggNETs in which the MSU crystals are embedded in a mesh of DNA and proteins from neutrophil granules. As such, these aggNETs block MSU crystals and also trap and degrade pro-inflammatory mediators by serine proteases attached to the DNA fibers (Schauer et al., 2014). MSU crystal-induced aggNET formation is augmented by release of ATP and lactoferrin from activated neutrophils. The release of ATP during NETs formation is of high importance since extracellular nucleotides initiate antiinflammatory clearance of dead cells by mononuclear phagocytes (Elliott et al., 2009). In addition, lactoferrin on NETs abrogates further recruitment of neutrophils and thus contributes to the anti-inflammatory action of NETs in highly infiltrated tissues (Bournazou et al., 2009). Clearly, in this case NETs have a positive anti-inflammatory effect.

The inflammation processes described before show that NETs are much more than a simple anti-microbial tool and they can be produced in very different conditions of neutrophil activation. However, NETs are a double-edged sword. On the one hand, "bad" NETs are involved in stimulating inflammation, as shown in the obstruction of blood vessels and pancreatic ducts. On the other hand "good" NETs are able to contribute to the resolution of inflammation as shown in gout (Hahn et al., 2016).

In addition, neutrophils primed by microbiota-derived products can form NETs more easily than neutrophils newly released from the bone marrow (Zhang et al., 2015). In contrast, the formation of NETs can be blocked by phagocytosis of small microorganisms via the C-type lectin receptor Dectin-1, which acts as a sensor of microorganism size (Branzk et al., 2014). Dectin-1 downregulates the translocation of neutrophil elastase (NE) to the nucleus. This protease promotes NETosis by degrading histones in the nucleus (Branzk et al., 2014). Also, neutrophils that phagocytose apoptotic cells, lose their capacity to up-regulate β2 integrins and to respond to activating stimuli that induce NETs formation (Manfredi et al., 2015). This could explain in part why in conditions in which phagocytosis of apoptotic cells is compromised, such as SLE and anti-neutrophil cytoplasmic antibody (ANCA)-associated vasculitis, a state of persistent inflammation is observed. These findings imply that heterogeneity in neutrophil function (for example for NETs formation) is also regulated by physiological signals.

It is evident that infection and inflammation can regulate the appearance of neutrophil phenotypes with unique properties. Unfortunately, nowadays we can only (incompletely) describe the function of these neutrophil subtypes. It would become important to characterize these cells at the level of surface markers, functional responses, and transcriptional profiles in order to understand their role in multiple diseases.

### Neutrophil Subpopulations in Cancer

The changes in neutrophil phenotype during cancer are perhaps the most impressive and best studied so far. Neutrophils play important and contradictory roles in cancer development, as reflected by several recent reviews (Sionov et al., 2015; Swierczak et al., 2015; Uribe-Querol and Rosales, 2015; Coffelt et al., 2016; Mishalian et al., 2017). In tumor-bearing mice, the number of circulating neutrophils increases along with tumor progression. Similarly, in patients with advanced cancer counts of neutrophils in blood are also increased. It is not clear how tumors can induce neutrophilia, but a common mechanism seems to be the production by tumors of cytokines that influence granulopoiesis, including G-CSF (McGary et al., 1995), IL-1, and IL-6 (Lechner et al., 2010). The presence of elevated numbers of neutrophils in the circulation is associated with poor outcome in several types of cancers (Schmidt et al., 2005). In addition, the presence of neutrophils in tumors also seems to be an indicator of poor outcome (Sionov et al., 2015). For this reason, the counts of neutrophils in blood in relation to other leukocytes have been suggested as a prognostic value in cancer. Therefore, the neutrophil to lymphocyte ratio (NLR) was introduced as a simple and inexpensive biomarker for many types of cancer (Peng et al., 2015; Faria et al., 2016). In general, the blood NLR is high in patients with more advanced or aggressive cancers (Guthrie et al., 2013), and correlates with poor survival of patients with many solid tumors (Paramanathan et al., 2014; Templeton et al., 2014). Despite the simplicity for using the NLR, it has not been accepted in many clinical settings. One reason for this is that neutrophilia can be the result of elevated granulopoiesis and as a consequence, it is not always a bad sign for cancer progression. Another reason is that neutrophilia does not correlate with poor clinical outcome in all types of cancer. In gastric cancer, for example a high NLR is indicative of positive prognosis (Caruso et al., 2002). This is indicative of the great plasticity neutrophils have. They can directly kill tumor cells and control cancer (Yan et al., 2014), but they can also acquire a pro-tumor phenotype and favor cancer (Fridlender and Albelda, 2012). Therefore, the exact role of neutrophils within the tumor is a controversial matter (Sionov et al., 2015; Uribe-Querol and Rosales, 2015)**.**

### Myeloid-Derived Suppressor Cells (MDSC)

In several types of cancer, not only an increase in the number of neutrophils in blood is observed, but also an increase in immature myeloid cells (Brandau et al., 2013). These immature cells are at various stages of differentiation, accumulate in the spleen of tumor-bearing animals, and present an immunosuppressive phenotype that supports tumor progression (Nagaraj et al., 2010; Raber et al., 2014; Keskinov and Shurin, 2015). For this reason, they were named myeloidderived suppressor cells (MDSC) (Peranzoni et al., 2010). These MDSC are a heterogeneous mixture of cells that can at least be divided into two subgroups: the granulocytic (G-MDSC) and the monocytic (Mo-MDSC) subgroups (Raber et al., 2014). The G-MDSC group resembles neutrophils. Hence, some researchers considerer them to be a bona fide phenotype of neutrophils (Pillay et al., 2013). However, the relationship among these cells is not clear since immature neutrophils do not have immunosuppressive properties (Solito et al., 2017), and neutrophils in the circulation are differentiated cells characterized by a lobulated nucleus (Pillay et al., 2013); while MDSCs are cells with clear immature morphology, including band or myelocyte-like nuclei (Pillay et al., 2013) (**Figures 2**, **4**).

Murine neutrophils are defined as CD11b+ Ly6G+ cells (Daley et al., 2008). Murine G-MDSC are CD11b+ and Ly6G+, thus they are considered neutrophils. In contrast Mo-MDSC express CD11b and Ly6C (Brandau et al., 2013; Keskinov and Shurin, 2015), making them more monocytic- than neutrophillike cells. In humans the problem is more complex, since the Ly6G antigen does not exist. Human mature neutrophils are defined by the phenotype CD14− CD15+ CD16+ CD66b+ (Dumitru et al., 2012). MDSC share all these markers, making it impossible to differentiate these cells from mature neutrophils. An extended panel of six markers (adding CD11b, CD33, and HLA-DR) was used to evaluate human MDSC (Damuzzo et al., 2015). Mo-MDSC were described as CD11b+ CD14+ CD15− CD33+ CD66b+ HLA-DR−/low; while G-MDSC were described as CD11b+ CD14− CD15+ CD33+ CD66b+ HLA-DR− (Brandau et al., 2013; Favaloro et al., 2014; Keskinov and Shurin, 2015). Although, these markers show differences between Mo-MDSC and G-MDSC, they do not allow for clear separation of these cells from neutrophils. At present, it is not possible to distinguish whether MDSC are indeed subpopulations of neutrophils or a separate cell type (Solito et al., 2017),

but an international effort continues to find better ways for identification of these cells by flow cytometry (Mandruzzato et al., 2016).

### Low-Density Neutrophils (LDNs)

An interesting subpopulation of neutrophils is the so-called low-density neutrophils (LDNs). Traditionally, neutrophils are purified by a Ficoll density gradient (Böyum, 1968; García-García et al., 2013) where they appear at the bottom of the tube (high-density fraction), separated from mononuclear cells, which are found at the interphase of plasma and Ficoll (low-density fraction) (**Figure 4**). In contrast, LDNs are found in the lowdensity fraction (Sagiv et al., 2015, 2016) (**Figure 4**). Interestingly, the proportion of LDNs in the low-density fraction increases with tumor growth and progression (Mishalian et al., 2017), and includes cells with mature and immature neutrophil morphology (Sagiv et al., 2015) (**Figure 4**). These LDNs are a subpopulation of neutrophils with characteristics and functions not well described.

Although, LDNs were first reported in the blood of patients with SLE, rheumatoid arthritis, or rheumatic fever (Hacbarth and Kajdacsy-Balla, 1986), they attracted attention only recently because they seem to be associated with cancer (Brandau et al., 2011; Sagiv et al., 2015).  In addition, LDNs have been reported in natural pregnancy (Ssemaganda et al., 2014). During pregnancy, downregulation of T cell functions is required to ensure materno-fetal tolerance. One way to inhibit T cell function is through the enzyme arginase, which depletes L-arginine, an essential amino acid required for proper expression of the T cell receptor CD3 ζ chain and for T cell proliferation (Rodriguez et al., 2004; Raber et al., 2012). Arginase activity is significantly increased in the peripheral blood of pregnant women and also in term placentae (Kropf et al., 2007). The source for arginase in these tissues was identified as LDNs with the phenotype CD15+ CD33+ CD66b+ CD16low (Ssemaganda et al., 2014). This phenotype is suggestive of a activated neutrophil (Fortunati et al., 2009), and is similar to the phenotype of G-MDSC (Favaloro et al., 2014; Keskinov and Shurin, 2015). Thus, situations of chronic inflammation and immunosuppression appear to induce neutrophil diversity. Very little is known about the function of these subpopulations of neutrophils. LDNs from SLE patients were also reported to readily form NETs (Villanueva et al., 2011), and because these NETs presented autoantigens, it has been suggested that LDNs in these patients are responsible for sustaining chronic inflammation leading to autoimmunity (Garcia-Romo et al., 2011; Khandpur et al., 2013). Similarly, it was recently reported that the number of CD66b+ LDNs was markedly elevated in peritoneal cavity after abdominal surgery of gastric cancer (Kanamaru et al., 2018).  These NETs could then assist the clustering and growth of free tumor cells disseminated in the abdomen.

The origin of LDNs remains unclear. Since LDNs are a mixture of cells with segmented or banded nuclei and myelocytelike cells, one thought is that LDNs are immature neutrophils that are released from the bone marrow during chronic inflammation or immunosuppression (Denny et al., 2010; Carmona-Rivera and Kaplan, 2013). Another possibility is that these LDNs are activated neutrophils that have undergone degranulation and therefore they have a reduced density (Rocha et al., 2015; Deng et al., 2016). In mouse models of cancer, these LDNs seem to derive either from immature cells released by the bone marrow (Youn et al., 2008) or from normal-density neutrophils (Sagiv et al., 2015). It is important to notice that these LDNs are still not properly characterized. The immunosuppressive function of these cells has not been directly determined and the transition of mature (normal density) to LDNs appears to involve an increase in volume rather than degranulation (Sagiv et al., 2015). Thus, most likely these LDNs are not activated normal neutrophils. In addition, since SLE patients have chronic inflammation, it is unlikely that their LDNs present immunosuppressive activity. All these possibilities need to be further studied in the future. However, one serious limitation for the characterization of these cells is that there are not specific molecular markers that could distinguish among these possible neutrophils subpopulations.

### Tumor-Associated Neutrophils (TANs)

The phenotypic changes of circulating neutrophils during tumor progression are also related to infiltration of neutrophils into tumors. Unfortunately, the relationship among immunosuppressive cells (MDSC), LDNs, high-density (normal) neutrophils, and tumor-associated neutrophils (TANs) is just beginning to be elucidated (Mishalian et al., 2013; Uribe-Querol and Rosales, 2015; **Figure 5**).

Depending on the phenotype displayed by TANs in tumorbearing mice, they have been classified as N1 or N2 (Fridlender et al., 2009). This classification is analogous to antitumor tumorinfiltrating macrophages (M1) or protumor macrophages (M2) (Galdiero et al., 2013). Murine N1 TANs are proinflammatory and antitumorigenic. In contrast, N2 TANs are protumorigenic (Fridlender et al., 2009). When tumor-bearing mice were treated to inhibit transforming growth factor-beta (TGF-β functions, the CD11b+ Ly6G+ neutrophils recruited to tumors were hypersegmented, more cytotoxic and more proinflammatory (N1). In contrast, in presence of TGF-β, had a protumor (N2) phenotype. Therefore, it seems that TGF-β within the tumor microenvironment induces a population of TANs with a protumor activity (Fridlender et al., 2009). This means that TANs can display an antitumor (N1) phenotype or a pro-tumor (N2) phenotype depending on the tumor microenvironment (Sionov et al., 2015). In addition, in tumor-bearing animals the LDNs increased progressively in circulation, were not cytotoxic, and had reduced expression of cytokines (Sagiv et al., 2015). The authors proposed that these immunosuppressive LDNs (G-MDSC) are the source of N2 TANs (Sagiv et al., 2015) (**Figure 5**). This is consistent with the idea that some of the LDNs are indeed immature neutrophils (Sagiv et al., 2015). Also, in the same murine model mature neutrophils were capable of becoming LDNs upon treatment with TGF-β (Sagiv et al., 2015). This

FIGURE 5 | Neutrophils in the circulation display different phenotypes. Mature (normal) neutrophils (PMN) leave the bone marrow and display the classical pro-inflammatory and anti-tumor properties of these cells. It is thought that these PMN can migrate into tumors and display an anti-tumor (N1) phenotype. In tumor-bearing mice, immature neutrophils, such as band cells, also leave the bone marrow into the circulation. These "low-density" neutrophils include granulocytic myeloid derived suppressor cells (G-MDSC) and neutrophils with immunosuppressive properties. These cells can infiltrate tumors and display pro-tumor (N2) phenotype. Under the influence of transforming growth factor-beta (TGF-β, normal PMN can change into "low-density" neutrophils. The exact origin of recruited neutrophils is not known. Also, it is not clear if N1 cells can change into N2 cells and vice versa under the influence of the tumor microenvironment.

has been interpreted as normal mature neutrophils having the capacity to infiltrate tumors and being cytotoxic, but under the influence of TGF-β, being able to change into N2 cells (Mishalian et al., 2013) (**Figure 5**). Although attractive, this hypothesis requires further evidence to be validated, since TGF-β was able to induce this change in tumor-bearing mice, but it had no effect on tumor-free mice (Sagiv et al., 2015).

It is important to emphasize here that the paradigm of N1 and N2 TANs has been described only in murine models of cancer, and that the nature and function of TANs in the tumor microenvironment remains largely unknown, particularly with human tumors. There are only two reports on isolation of human TANs, but they describe controversial data on their immunosuppression capacity. In one report, TANs were isolated from digested human lung tumors (Eruslanov et al., 2014). These TANs had an activated phenotype (CD62Llow CD54hi) and produced l proinflammatory cytokines. In consequence, these TANs stimulated T cell proliferation and interferon gamma (IFN-γ) production (Eruslanov et al., 2014). In another report, TANs were isolated from a colorectal tumor and found to present a typical neutrophil morphology. These TANs had the phenotype CD45+ Lin− HLADR− CD11b+ CD33+ CD66b+, and were classified as G-MDSC (Wu et al., 2014). These TANs secreted arginase 1 (ARG1) and ROS, and inhibited proliferation of activated autologous T cells and IFN-γ production (Wu et al., 2014). Together these reports suggest that neutrophils are present in three subpopulations in cancer: normal high-density neutrophils, immature LDNs (G-MDSC), and large mature LDNs (**Figure 5**). Early TANs (N1) are not immunosuppressive, but rather stimulate T cell responses (Eruslanov et al., 2014). Latter, the cells acquire an N2 phenotype and become immunosuppressive (Wu et al., 2014) (**Figure 5**).

### Immunogenic Cell Death

Recently, another important role of neutrophils in anticancer therapy has been described. Immunogenic cell death can stimulate neutrophils to utilize cytotoxicity against residual live cancer cells after therapy. The concept of immunosurveillance explains how only the most immunoevasive or highly mutagenic neoplastic cells are able to generate clinically relevant tumors (Dunn et al., 2002; Schreiber et al., 2011). Yet, the immune system is capable of recognizing altered-self molecules in damaged cells through endogenously derived danger signals or alarmins (Bianchi, 2007; Garg et al., 2014). As a consequence of cell death, alarmins, collectively referred to as "damage-associated molecular patterns" (DAMPs), enhance sensing of dying cells by innate immune cells (Zitvogel et al., 2010; Garg et al., 2015b). Release of DAMPs can either be achieved in an unregulated fashion by necrosis (Aaes et al., 2016) or in a regulated fashion by immunogenic cell death, also called immunogenic apoptosis (Kepp et al., 2014; Garg et al., 2015a). Anti-cancer therapyinduced cancer cell death can be subdivided into three distinct types i.e., tolerogenic cell death, inflammatory cell death, and immunogenic cell death. The details of these types of cell death are beyond the scope of the present publication, but the reader is directed to some excellent recent reviews (Green et al., 2009; Garg et al., 2016; Garg and Agostinis, 2017). Briefly, immunogenic cell death is characterized by a defined discharge of DAMPs, type I interferon response, and the production of pathogen responselike chemokines (CXCL1, CCL2, and CXCL10) (Garg et al., 2017b) that together raise the immunogenic potential of dying cancer cells.

Through immunogenic cell death, after some types of anticancer therapy (Galluzzi et al., 2012; Bezu et al., 2015; Garg et al., 2017a), a damaged cancer cell produces specific inflammatory chemokines to recruit neutrophils as first innate immune cells (Kolaczkowska and Kubes, 2013; Garg et al., 2017b). The damaged cancer cells also express two important "eat me" signals on their membranes, namely, phosphatidylserine, and calreticulin. These signals trigger neutrophil phagocytosis of cancer cells and pro-inflammatory stimulation, leading to a change in neutrophil phenotype (Garg et al., 2017b). Neutrophils expressed a mature phenotype characterized by expression of CD86hi MHC-IIhi, and an activated phenotype characterized by IL-6hi IL-1β hi IL-10low expression (Garg et al., 2017b). As a result, neutrophils stimulated by immunogenic cell death showed cytotoxicity against residual live cancer cells (Garg et al., 2017b). Thus, neutrophils interacting with immunogenic apoptotic cells gain a pro-inflammatory profile, culminating into neutrophil dependent cytotoxicity against residual cancer cells.

### CONCLUSION

There is a lot of excitement around the many "new" neutrophil functions just discovered recently. The realization that neutrophils do in fact perform many more functions than just antimicrobial responses, and the fact that neutrophils with different phenotypes have been reported in various tissues and pathological conditions, suggest that indeed different neutrophils exist. However, in most reports the evidence is circumstantial and we are in need for solid experimental proof that the cells described are in fact novel neutrophil subsets. What we have learned for sure so far is that in various pathological conditions, particularly cancer, distinct populations of mature and immature neutrophils are found in circulation. After gradient density centrifugation of blood, the mature high-density (or more properly normal-density) neutrophils mostly represent cells with a pro-inflammatory phenotype, while the LDNs are comprised of immature neutrophils and "activated" mature neutrophils. These cells in turn may migrate to tumors and become, at least in mice, N1 and N2 TANs (**Figure 5**). However, we have to highlight that the actual cell type responsible for the immunosuppressive properties of MDSC remains a mystery.

Many questions remain open, but at least two topics seem to be relevant at the moment. One important topic that needs to be addressed is whether mature neutrophils in circulation can be reprogrammed by external stimuli, or whether defined phenotypes are programmed in the bone marrow and neutrophils exit with a particular phenotypic signature. Evidence suggests that neutrophils are very plastic cells and in consequence the various subtypes described seem to be acquired in the tissues. However, these possibilities need to be formally tested. Another relevant topic is that the many functions described have not been assigned to particular phenotypes of neutrophils. This remains a complex issue, as there are currently no appropriate molecular markers to readily identify these different neutrophil subpopulations. This confusing scenario is the fuel for new and even more exciting research. We expect to learn new tricks from our favorite cell type in the near future.

### AUTHOR CONTRIBUTIONS

The author confirms being the sole contributor of this work and approved it for publication.

### REFERENCES

### FUNDING

Research in the author's laboratory was supported by grant 254434 from Consejo Nacional de Ciencia y Tecnología (CONACyT), Mexico.

### ACKNOWLEDGMENTS

The author thanks Francis Valeria Eliosa García for preparing the list of references, and Dr. Eileen Uribe-Querol for preparing the figures.

stem cells and leukemia cells to avoid phagocytosis. Cell 138, 271–285. doi: 10.1016/j.cell.2009.05.046

from an interim study. Cancer. Immunol. Immunother. 65, 161–169. doi: 10.1007/s00262-015-1782-5

**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.

Copyright © 2018 Rosales. 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) and the copyright owner 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.

# The Role of Inflammation in Age-Related Sarcopenia

#### Sebastiaan Dalle1 , Lenka Rossmeislova2 and Katrien Koppo1 \*

1 Exercise Physiology Research Group, Department of Kinesiology, KU Leuven, Leuven, Belgium, 2 Department for the Study of Obesity and Diabetes, Third Faculty of Medicine, Charles University, Prague, Czechia

Many physiological changes occur with aging. These changes often, directly or indirectly, result in a deterioration of the quality of life and even in a shortening of life expectancy. Besides increased levels of reactive oxygen species, DNA damage and cell apoptosis, another important factor affecting the aging process involves a systemic chronic low-grade inflammation. This condition has already been shown to be interrelated with several (sub)clinical conditions, such as insulin resistance, atherosclerosis and Alzheimer's disease. Recent evidence, however, shows that chronic low-grade inflammation also contributes to the loss of muscle mass, strength and functionality, referred to as sarcopenia, as it affects both muscle protein breakdown and synthesis through several signaling pathways. Classic interventions to counteract age-related muscle wasting mainly focus on resistance training and/or protein supplementation to overcome the anabolic inflexibility from which elderly suffer. Although the elderly benefit from these classic interventions, the therapeutic potential of anti-inflammatory strategies is of great interest, as these might add up to/support the anabolic effect of resistance exercise and/or protein supplementation. In this review, the molecular interaction between inflammation, anabolic sensitivity and muscle protein metabolism in sarcopenic elderly will be addressed.

#### Edited by:

Carlos Rosales, Universidad Nacional Autónoma de México, Mexico

#### Reviewed by:

Gautham Yepuri, University of Fribourg, Switzerland Rui Curi, University of São Paulo, Brazil

#### \*Correspondence:

Katrien Koppo<EMAIL_ADDRESS>
#### Specialty section:

This article was submitted to Integrative Physiology, a section of the journal Frontiers in Physiology

Received: 15 September 2017 Accepted: 29 November 2017 Published: 12 December 2017

#### Citation:

Dalle S, Rossmeislova L and Koppo K (2017) The Role of Inflammation in Age-Related Sarcopenia. Front. Physiol. 8:1045. doi: 10.3389/fphys.2017.01045 Keywords: muscle wasting, muscle protein metabolism, NSAID, sarcopenia, protein supplementation, resistance training, inflammation

## INTRODUCTION

Aging is generally associated with numerous changes that may, directly or indirectly, affect health and/or life span. One of the major problems in the aging population is a progressive loss in skeletal muscle mass, muscle strength, and/or functionality, described as age-related sarcopenia. The elderly suffering from this multifactorial pathological condition are at risk of adverse outcomes such as physical disability, injuries, frailty, social exclusion, hospitalization and eventually an increased mortality (Cruz-Jentoft et al., 2010; Visser and Schaap, 2011). Several strategies to attenuate the loss of muscle mass and other muscle impairments that comes with aging have been developed (Sakuma and Yamaguchi, 2012; Denison et al., 2015). However, none of these have been proven successful to fully reverse the muscle wasting condition. Given the high prevalence of sarcopenia in the aging population and the associated high health care costs, it is of importance to reveal and elucidate the working mechanisms which underlie muscle protein metabolism in the elderly, in order to optimize the classic interventions and/or to develop new ones.

Muscle protein metabolism is carefully regulated by counterbalanced fluctuations in muscle protein breakdown (MPB) and muscle protein synthesis (MPS) (Churchward-Venne et al., 2014).

**83**

In elderly, the balance between MPB and MPS seems to be disturbed, which progressively increases the loss of skeletal muscle mass (Churchward-Venne et al., 2014). Many underlying factors such as hormonal changes, decreased activity, diminished nutrient intake, and neuronal changes were reported in the literature (Dirks et al., 2006; Budui et al., 2015), but lately, the role of inflammation on the regulation of muscle protein metabolism has gained more and more interest among gerontologists. Generally, aging is associated with a chronic state of slightly increased plasma levels of pro-inflammatory mediators, such as tumor necrosis factor α (TNFα), interleukin 6 (IL-6) and C-reactive protein (CRP) (for an overview see **Table 1**). This state is often referred to as a low-grade inflammation (LGI) and is, at least partly, the manifestation of increased numbers of cells leaving the cell cycle and entering the state of cellular senescence. Indeed, senescent cells acquire a Senescence-Associated Secretory Phenotype, which induces the production of pro-inflammatory cytokines (TNFα, IL-6 and an overactivation of NF-κB) (Tchkonia et al., 2013). Moreover, there is a growing interest in the association between the telomere/telomerase system and LGI, as cellular senescence can be triggered by critically short telomeres, representing irreparable DNA damage (Kordinas et al., 2016). Also, there are indications that LGI can directly cause telomere/telomerase dysfunction, enforcing the vicious LGI circle and stimulating an accelerated aging phenotype (Jurk et al., 2014).

Although it has been suggested that inflammatory mediators affect muscle protein metabolism (Frasca and Blomberg, 2016), it is not fully understood to what extent and through which signaling pathways they induce muscle wasting. Populationbased data suggest that circulating concentrations of IL-6 and TNFα are significantly elevated in sarcopenic elderly (Bian et al., 2017) and it was reported that higher IL-6 and CRP levels increase the risk of muscle strength loss (Schaap et al., 2006). In a 10-year longitudinal study in community-dwelling elderly, plasma concentrations of TNFα, IL-6, and IL-1 were shown to be strong predictors of morbidity and mortality in older subjects (Baylis et al., 2013). Furthermore, systemic inflammation was also reported as one of the primary mediators of skeletal muscle wasting in diseases such AIDS, COPD, chronic heart failure and cancer (Sakuma et al., 2015) and it was shown to accelerate aging in general (Jurk et al., 2014). Without pronouncing on causality, these findings suggest that there is a link between inflammatory mediators and muscle mass and function.

This review is particularly concerned with the recent progress that has been made in understanding the role of inflammation in age-related sarcopenia and in muscle protein metabolism in general. In the first sections, pathways of both MPB and MPS will be described and related to inflammation. The following sections will give an overview of the classic approaches (i.e., aerobic/resistance exercise, protein supplementation) that attempt to attenuate age-related sarcopenia. Finally, interventions that target the administration of compounds with anti-inflammatory properties (vitamin D, poly-unsaturated fatty acids, non-steroidal anti-inflammatory drugs) will be discussed as an alternative approach.

### MUSCLE PROTEIN BREAKDOWN AND ITS REGULATION BY INFLAMMATION

As mentioned above, elevated levels of pro-inflammatory plasma cytokines and acute-phase proteins are often observed in sarcopenic elderly. Despite strong correlations between inflammatory markers and the risk for functional decline and mortality in elderly, conclusive evidence on a causative link between these markers and age-related sarcopenia is hard to establish (Reuben et al., 2003; Schaap et al., 2006). Recent evidence, however, suggests that chronic LGI is, at least partially, involved in the onset and/or progression of age-related muscle wasting (Beyer et al., 2012; Argilés et al., 2015). Higher levels of inflammatory markers were reported to negatively affect skeletal muscle metabolism through direct catabolic or indirect mechanisms, such as decreases in growth hormones (Sakuma et al., 2015). Though, the challenge remains to further uncover the specific molecular mechanisms through which inflammation interacts with muscle protein metabolism.

Acute inflammation models are widely applied to study the molecular mechanisms which link inflammation and muscle protein metabolism. In rodents, the effect of lipopolysaccharide injection has been shown to induce muscle catabolism through inflammatory signaling (Schakman et al., 2012). Accordingly, blunting an acute infection-induced inflammation improved muscle performance in elderly (Beyer et al., 2011). However, it should be noted that acute high-grade inflammation has a limited translational potential compared to the LGI present in the aged population.

In general, proteolysis is regulated through four main pathways (Combaret et al., 2009; Fanzani et al., 2012; Bowen et al., 2015). The contribution of each pathway is context- (e.g., stress vs. basal) or tissue- (e.g., brain vs. muscle) dependent (de Oliviera Nunes Teixeira et al., 2012). Their mechanisms are also inter-dependent and might co-regulate proteolysis on a different hierarchical level (Dirks et al., 2006). In the following section, the four main proteolytic pathways (ubiquitin proteasome, calpains, macro autophagy, apoptosis) are briefly discussed, as well as their interaction(s) with inflammatory signaling.

## ATP-Dependent Ubiquitin-Proteasome Pathway

The ATP-dependent ubiquitin-proteasome pathway is responsible for degrading the majority of proteins (80–90%) (Lilienbaum, 2013). The proteolytic core of the 26S complex, a 20S proteasome, degrades ubiquitin-conjugated proteins after binding with a regulatory 19S particle. This conjugation to ubiquitin marks abnormal (misfolded, denaturated) or short-lived proteins for rapid hydrolysis (Rock et al., 1994). Ubiquitination of specific substrates is closely regulated by ubiquitin protein (E3) ligases, such as MuRF1 and MAFbx, specifically in muscle cells (Bodine and Baehr, 2014). Evidence indicates that the expression of both E3 ligases is upregulated in several models of muscle atrophy, such as immobilization, denervation, hind limb unloading, dexamethasone treatment, and IL-1-induced cachexia (Bodine and Baehr, 2014). The role of

TNFα, tumor necrosis factor α; IL-6, interleukin 6; hs-CRP, high-sensitive C-reactive protein.

the ubiquitin-proteasome pathway in sarcopenia, however, is less clear (Bowen et al., 2015; Fan et al., 2016; Wilson et al., 2017). Some studies reported elevated levels of MAFbx and MuRF1 in aged rat muscle (Clavel et al., 2006; Altun et al., 2010), while others observed decreased levels of atrogenes with aging (Erik et al., 2017). Likewise, in human studies, some authors reported no differences in skeletal muscle protease activity between different age groups (Bossola et al., 2008), while others noticed higher mRNA levels of proteins encoding protease components, ubiquitin-like proteins and proteins related to ubiquitin function in elderly compared to younger adults (Welle et al., 2003). Furthermore, elevated levels of the ubiquitin protein in old human muscles mainly occurred in fast-twitch muscles, such as extensor digitorum longus, while no differences were detected in the slow-twitch soleus muscle (Cai et al., 2004). Dissimilarities between studies might therefore be attributed to the studied muscle type, as well as the specific study population, as it was suggested that age-related muscle wasting is mechanistically different from muscle wasting-related confounders such as disuse, bedrest or disease (Erik et al., 2017). A recent review suggested that elevated levels of pro-inflammatory mediators (due to LGI), such as TNFα and IL-6, might upregulate this proteolytic pathway through activation of FOXO3a, which regulates the ubiquitin-proteasome system (Xia et al., 2017). It remains to be elucidated, however, whether low levels of these pro-inflammatory mediators are (on the long-term) sufficient to induce this proteolytic pathway. To conclude, the ubiquitinproteasome pathway is likely to be involved in the regulation of muscle wasting with aging, partially due to its upregulation with inactivity. On the other hand, findings on the association between LGI and the ubiquitin-proteasome pathway are scarce and need to be further elucidated.

### Calpains

Calpains are a family of cysteine proteases with a wide range of cellular calcium-regulated functions. They are responsible for the proteolysis of several substrates, including cytoskeletal and membrane proteins, enzymes and transcription factors (Dargelos et al., 2008). Furthermore, calpains are also involved in apoptosis through two major pathways, triggered by cell death signals (TNF) or endoplasmatic reticulum (ER) stress, respectively (Dargelos et al., 2008). Reactive oxygen species (ROS) accumulation with advanced age is able to upregulate calpain activity directly (Nakashima et al., 2004) or indirectly by inducing Ca2+ release from the ER or oxidation of MAPK-dependent pathways (Kefaloyianni et al., 2006). Although the evidence is scarce, the contribution of calpains to age-related muscle wasting is not excluded. Higher calpain availability and activity, and lower availability of its endogenous inhibitor (calpastatin), were observed in muscles of old (23 months) compared to young rats (3 months) and treatment of dystrophic muscle with calpain inhibitors attenuated muscle degeneration (Dargelos et al., 2007). It was reported that intracellular [Ca2+] were elevated in aged rodent muscle cells, potentially contributing to an increased calpain activation with aging (Andersson et al., 2011). To our knowledge, there are no data which compare calpain acitivity/availability in old vs. young human muscles. Calpains can also promote inflammation through several mechanisms leading to NFκB (pro-inflammatory transcription factor) activation and the production of pro-inflammatory cytokines (Ji et al., 2016). However, it is unclear whether calpains are involved in muscle proteolysis through regulation of inflammation in aged muscles. Therefore, more studies are required to further elaborate the mechanistic association between calpains and age-related muscle wasting in humans.

### (Macro)Autophagy Pathway

The (macro)autophagy pathway is as a housekeeping mechanism responsible for the clearance of dysfunctional organelles and damaged macromolecules (e.g., protein aggregates). The autophagosome, a double-membrane vesicle, encloses macromolecules or organelles for delivery to the lysosome, in which hydrolases stand in for the degradation into recycled "building blocks," e.g., amino acids. The lysosomal protease cathepsin L was shown to be involved in muscle proteolysis upon acute infection (Deval et al., 2001). Both in vitro and in vivo studies reported that elevated levels of the pro-inflammatory cytokine IL-6 is a first mechanism which links inflammation to cathepsin-induced muscle atrophy (Ebisui et al., 1995; Tsujinaka et al., 1995). Whether cathepsin L plays a crucial role in agerelated muscle wasting is less straightforward, as higher levels of cathepsin L were observed in the soleus muscle of old (30 months) vs. young (3 months) rats (Pattison et al., 2003), while no different expression patterns were found in the gastrocnemius muscle of old (30 months) and young (3 months) rats (O'Connell et al., 2007).

Autophagic activity in the fast-twitch plantaris muscle decreases with aging, resulting in insufficient clearance and accumulation of intracellular waste products, such as lipofuscin, protein aggregates and damaged mitochondria (Wohlgemuth et al., 2010). In general, autophagy is accepted to be a promising target to counteract sarcopenia (Fan et al., 2016). Until now, physical exercise and caloric restriction (CR) were proven successful in preventing/attenuating the age-related decrease in autophagic activity. White et al. (2016) reported that long-term voluntary resistance wheel exercise in elderly mice increased basal autophagy (increased LC3III/I ratio, marker of autophagy) compared to sedentary age-matched controls, while MuRF1 and MAFbx remained unaffected. Another study found that a mild CR (8%) positively affected muscle composition, oxidative stress, cell death and autophagy in old rats, suggesting that mild CR might be an applicable intervention to combat age-related sarcopenia, given a sufficiently qualitative energy intake (Wohlgemuth et al., 2010). In human studies, markers of autophagy were downregulated in Duchenne muscular dystrophy patients when compared to healthy controls, suggesting a decreased autophagic activity in these patients (Palma et al., 2012). Although evidence is scarce, Jiao and Demontis (2017) suggested that comparable mechanisms may apply to the development of age-related muscle wasting in elderly.

Autophagy is also involved in the regulation of the production of inflammatory mediators upon acute inflammatory stimuli (e.g., LPS) (Cadwell, 2016). However, to our knowledge, a crosstalk between autophagy and LGI was not yet established in the literature. It is conceivable that inflammation contributes to a dysregulation of the autophagic/mitophagic activity by stimulation of oxidative stress (Li et al., 1999; Suematsu et al., 2003). Nevertheless, it remains to be clarified whether LGI is a sufficiently strong signal for inducing oxidative stress in muscle cells. Furthermore, upregulation of the classic NF-κB signaling is capable of inhibiting autophagy by suppression of autophagyrelated genes (atg5 and beclin 1), but this mechanism was not confirmed yet in old skeletal muscle cells.

To summarize, skeletal muscle autophagy is downregulated with advanced aged and indirectly contributes to muscle wasting due to insufficient clearance of intracellular waste products or damaged organelles, such as mitochondria. An accumulation of damaged mitochondria and cellular waste generally induces oxidative stress, which enforces a pro-inflammatory environment in favor of catabolic processes.

### Apoptosis

Apoptosis is another main player contributing to the onset and progression of sarcopenia. Apoptosis, the process of programmed cell death, generally occurs during development and aging as a homeostatic mechanism to maintain cell populations in tissues (Elmore, 2007). Dirks and Leeuwenburgh (2002) reported higher levels of apoptosis (+50%) (measured by nucleosome fragmentation) in 24 months old rats compared to 6 months young rats. The same research group also looked at the mechanisms through which age-related apoptosis affected muscle wasting in skeletal muscles of old rats (Marzetti et al., 2008). They concluded that muscle weight declined progressively with advancing age, concomitant with increased apoptotic DNA fragmentation. A higher susceptibility of aged type II muscle fibers to TNFα-stimulated apoptotic signaling partially explains the greater loss of fast twitch muscle fibers with aging (Phillips and Leeuwenburgh, 2005).

In skeletal muscle, two main routes are described to induce apoptosis, i.e., an internal and an external pathway. In the (mitochondrial-dependent) internal pathway, nonreceptor-mediated stimuli in the cytosol, such as elevated ROS and Ca2+ levels, can directly act upon mitochondrial homeostasis, increasing their permeability (Nitahara et al., 1998). Consequently, pro-apoptotic proteins are released from the intermembrane space into the cytosol. In the external pathway, ligands (such as TNFα) bind to their respective receptor and initiate the recruitment of adaptor proteins, eventually resulting in a death-induced caspase-cascade. As TNFα protein levels are generally elevated in both circulation and skeletal muscle in elderly (Greiwe et al., 2001), this pathway might also play a role in age-related muscle wasting. On the other hand, no apoptosis of muscle cells was observed in inflammatory myopathies, which questions the role of this pathway in age-related LGI (Migheli et al., 1997). Furthermore, it is not fully clear which apoptotic pathway is the main player in age-related muscle wasting. Future research should further reveal the contribution of each apoptotic pathway in order to develop targeted anti-apoptotic strategies. Similar to autophagy, exercise and mild CR were found to be effective in attenuating apoptosis with aging (Dirks and Leeuwenburgh, 2004; Phillips and Leeuwenburgh, 2005; Wohlgemuth et al., 2010). Despite the extensively studied association between age-related muscle wasting and apoptosis in rodents, evidence emerging from human samples are currently lacking to our knowledge. Therefore, human data studying the role of apoptosis in sarcopenia are required in order to see whether the mechanisms/observations are comparable to rodent models. As concluded by Marzetti and Bernabei (2012), targeting apoptosis might be an effective intervention to counteract age-related muscle wasting.

To conclude, several pathways are involved in the regulation of MPB in sarcopenia. However, it should be noted that possible other pathways might be active and remain to be confirmed in sarcopenic human muscles. These pathways contribute to muscle wasting to a certain extent, which depends on the unique context of each muscle fiber, i.e., fiber type, (im)mobility, oxidative damage, etc. Until now, no studies focused on the relative contribution of each proteolytic pathway. Therefore, future research should elucidate the contribution of each proteolytic pathway, taking into account the specific research sample/population/condition, in order to allow the development of effective and specific strategies, which target one or more proteolytic mechanisms.

### MUSCLE PROTEIN SYNTHESIS AND ITS REGULATION BY INFLAMMATION

Besides its putative role in MPB, inflammation negatively affects MPS. Already in 1984, Klasing and Austic (1984) established that high-grade inflammatory challenges, such as E. coli injection, decreased the in vivo MPS in broilers. Also, a more prolonged exposure to a high-grade inflammatory milieu, e.g., sepsis, reduced MPS, through decreased activation of the mTORC1 signaling pathway, and hence muscle mass, as reviewed by Frost and Lang (2011). The mechanistic link between low-grade inflammation and the downregulation of aged-related MPS, however, is less understood.

Muscle protein metabolism is the result of a balance between an increased post-prandial whole body protein synthesis and a decreased post-absorptive protein synthesis (Dardevet et al., 2012). This regulation is mainly orchestrated by the mTORC1 signaling pathway that integrates environmental stimuli to control cellular growth (Deldicque et al., 2005). In the muscle, many mTORC1 stimuli are described, including amino acids (leucine in particular), insulin, hormones released during mechanical stimuli as well as contractions per se, while energy deprivation and stress/hypoxia inhibit mTORC1 activity. Therefore, combined resistance exercise and protein supplementation has been proven effective in stimulating muscle growth in adults (Churchward-venne et al., 2012). Under basal conditions, MPS does not appear to be compromised in elderly, but its response to physiologic stimuli, such as amino acids, exercise or insulin (rather as a permissive mediator) is blunted (Dardevet et al., 2012; Drummond et al., 2012; Haran et al., 2012). This phenomenon is called "anabolic resistance" and implicates the need for higher intakes of amino acids and/or higher loads of resistance exercise, which is difficult to meet in a frail population that generally suffers from a loss of appetite (Rennie, 2009; Wysokinski et al., 2015).

### Protein Intake As Anabolic Signal Animal Studies

Inflammation is an important underlying factor that contributes to the insensitivity to anabolic signals. The research group of Dardevet confirmed the interference of inflammatory background (NF-κB, TNFα, IL-6 etc.) with anabolic signaling in several animal studies. They observed that rats which developed LGI at 25 months were unresponsive, in their MPS, to food intake, while in rats without LGI MPS was significantly increased (Balage et al., 2010). Another study revealed that rats, whose LGI was attenuated by ibuprofen (NSAID) treatment, exhibited a restored protein anabolism at post-prandial state (Rieu et al., 2009). Similarly, the supplementation of an antioxidant mixture during 7 week, which reduced LGI, was effective in improving the anabolic response to leucine in skeletal muscles of aged rats (Marzani et al., 2008). In contrast, Mayot et al. (2008) observed no differences in MPS between aged-matched 24 months old LGI and no-LGI rats. However, the authors did not specifically mention when MPS was measured. Previous studies showed differences in post-prandial MPS with similar post-absorbative MPS between LGI and no-LGI aged rats (Rieu et al., 2009; Balage et al., 2010).

### Human Studies

To our knowledge, only two human studies looked at the association between LGI and meal-induced stimulation of MPS. A study executed by the research group of Dardevet found that elderly with LGI (assessed by increased CRP levels) had no different post-absorptive or post-prandial MPS, compared to elderly without LGI (Buffière et al., 2015). The authors suggested that other pro-inflammatory cytokines (TNFα, IL-1/6) might be more suitable to establish the association between LGI and anabolic resistance. Furthermore, the post-protein bolus time interval during which MPS is measured was 5 h in this study, while other studies recommend a shorter period of 1.5–2 h (Bohé et al., 2001; Norton et al., 2009; Atherton et al., 2010; Wilson et al., 2011). Similarly, it was found that ibuprofen (1,800 mg.d−1 ) administration during 1 week in elderly with LGI did not affect MPS in response to a whey protein bolus when compared to placebo (Dideriksen et al., 2016). However, this might be due to the lack of efficacy of a short NSAID administration period of 1 week, demonstrated by the lack of decrease in CRP levels in this group. Furthermore, it is possible that the anabolic effect of anti-inflammatory strategies (e.g., NSAIDs) might partially occur via a decreased MPB rather than an increased MPS. Future human trials are warranted in order to reproduce the findings of earlier animal studies in which the association between LGI and anabolic resistance was established (Marzani et al., 2008; Rieu et al., 2009; Balage et al., 2010). The studies should take into account the exposure time to LGI, the treatment duration, applied LGI biomarker(s), sufficiently large sample sizes, and the post-prandial time interval to assess MPS.

### Exercise As Anabolic Signal

Besides the effect of inflammation on the anabolic response to food/protein intake, its effect on the anabolic response to exercise is broadly studied. These findings might also contribute to our understanding of why some elderly gain less muscle mass with relatively identical exercise stimuli compared to younger adults. Interesting to note is that NSAID administration in younger adults vs. elderly results into different adaptations to exercise training. In younger adults, studies which looked at the acute effects of NSAID intake on the muscle response to a single bout of resistance exercise observed a decrease in MPS compared to placebo (Trappe et al., 2002) and an attenuated satellite cell proliferation up to 8 days after resistance exercise (Mikkelsen et al., 2009) or endurance exercise (Mackey et al., 2007). Also, when long-term (8 week) resistance training was combined with a daily high-dose (1,200 mg.d−1 ) of ibuprofen intake, muscle strength and muscle hypertrophic adaptations to resistance training were impaired compared to a control condition (Lilja et al., 2017). In contrast, a moderate dose of ibuprofen (400 mg.d−1 ) was not sufficient to negatively affect muscle strength and hypertrophy following 6 week resistance training (Krentz et al., 2008). Similar to the results observed in younger adults, a study performed in young rats reported that ibuprofen administration (∼20 mg.kg−1 .d−1 ) during chronic overload by synergist ablation for 14 days inhibited muscle hypertrophy with ∼50% (Soltow et al., 2006). The authors, however, were not sure whether the inhibition of hypertrophy by the NSAID was caused by its interference with the regeneration process (decreased satellite cell activity), an attenuation of MPS, or both. A downregulated MPS response to resistance exercise with NSAID intake could be ascribed to a blunted prostaglandin F2α (PGF2α) increase, which normally stimulates skeletal MPS (Rodemann and Goldberg, 1982).

Contrary to the younger adults, long-term resistance training combined with NSAID intake, unexpectedly induced additional gains in muscle mass and/or muscle strength in elderly (Trappe et al., 2011) and osteoarthritis patients (Petersen et al., 2011). Trappe et al. (2011) speculated that COX inhibition, an important mechanism of action of NSAIDs, might have a relatively stronger inhibitory effect on MPB compared to MPS in elderly. Besides blunting PGF2α, it was also shown that COX inhibition might suppress protein degradation by reducing intramuscular production of prostaglandin E2 (PGE2), eventually resulting in a higher net protein balance (Rodemann and Goldberg, 1982). It cannot be excluded that the effect of NSAID administration on training adaptations is partly related to its pain-relieving feature, as it was reported that osteoarthritis patients were able to produce higher maximal strengths after 12 week of ibuprofen (1,200 mg.d−1 ) administration (Petersen et al., 2011).

In general, it is important to interpret findings concerning the role of inflammation in muscle protein metabolism upon exercise with caution, taking into account the study population (adults vs. elderly with LGI/patient populations), type of exercise/muscle adaptation (endurance vs. resistance exercise vs. muscle damage following severe resistance exercise), and the time span of evaluation of muscle effects (immediately following exercise vs. long-term effects). Furthermore, studies should focus on mechanisms through which MPS is affected by LGI. Currently, many studies do not or only superficially report findings concerning the COX or prostaglandin pathways.

### SATELLITE CELLS AND THEIR REGULATION BY INFLAMMATION

Satellite cells are adults muscle stem cells which play an important role in muscle growth and repair (Yin et al., 2013). Under basal conditions, satellite cells remain sublaminal in a quiescent state. Upon muscle damage, satellite cells exit their quiescent state, start to proliferate through the sarcolemma and fuse with existing muscle fibers. These processes are accompanied by specific expression patterns of myogenic regulator factor (MRF) genes and protein levels. Adult quiescent satellite cells express Pax7, while Myf5 and/or MyoD expression is rapidly upregulated following satellite cell activation, both regulated by Pax 7 (Cornelison and Wold, 1997; Rudnicki et al., 2008).

The lower regenerative potential of aged muscles can be explained by a deterioration in satellite cell differentiation and a reduced Pax7 pool of myogenic stem cells (Collins et al., 2007; Bernet et al., 2014). Furthermore, recent evidence suggests that many aged satellite cells switch from the quiescent state to an irreversible senescence state, and fail to activate and expand upon injury (Sousa-Victor et al., 2014). The reduced satellite cell function with aging might be due to altered systemic factors which affect satellite cell activity and differentiation (Conboy et al., 2005), such as altered Notch signaling in muscles, altered circulating levels of protein growth differentiation factor 11, reduced levels of IGF-1, increased inflammation and proinflammatory cytokines (Harridge, 2003; Degens, 2010; Jang et al., 2011; Sinha et al., 2014). It was shown that TNFα, in particular, reduces the expression of MyoD and myogenin in myoblasts and destabilizes MyoD in regenerating mice muscles (Szalay et al., 1997; Langen et al., 2004). Therefore, it can be hypothesized that some effects of LGI on muscle protein metabolism are mediated through changes in satellite cell function, as TNFα levels affect MRF expression.

### CLASSIC APPROACHES TARGETING AGE-RELATED SARCOPENIA

Many studies currently focus on developing strategies to combat age-related sarcopenia. In this regard, life style interventions are of great interest due to their relatively straightforward applicability. These interventions can be subdivided in two main categories, i.e., exercise and nutrition. Both aerobic and resistance exercise, as well as the supplementation of amino acids/proteins, vitamin D (vit D) and polyunsaturated fatty acids (PUFAs), have ergogenic implications on the regulation of muscle mass in elderly. In addition, aerobic exercise, and the supplementation of vit D or PUFAs showed interesting interactions with the modulation of inflammation, and might therefore decrease LGI and subsequently reduce muscle wasting in elderly.

### Aerobic Exercise

Aerobic exercise has been suggested as a meaningful strategy to combat age-related sarcopenia. Besides its effects on cardiovascular fitness and endurance, aerobic exercise is a potent inducer of muscle size and strength (Konopka and Harber, 2014). Recent evidence indicates that endurance exercise-induced increases in muscle size and strength are due to an increased MPS, similarly in elderly and young adults (Harber et al., 2012; Ozaki et al., 2013; Konopka and Harber, 2014). Some studies concluded that gains in muscle growth, induced by aerobic exercise, are comparable to the hypertrophic response observed following resistance training, and that endurance exercise can be considered an effective countermeasure for muscle loss with advancing age (Konopka and Harber, 2014).

The mechanisms, which underlie these positive effects on the muscle hypertrophic response, are diverse. Firstly, an upregulation of the transcription of genes involved in mitochondrial biogenesis, such as CAMK, AMPK, and PGC1α (Iolascon et al., 2014), increases the muscle mitochondrial content and function. This eventually results in an attenuated production of mtROS and thus oxidative stress, regarded as a potential mediator in skeletal muscle loss with aging (Jackson, 2016). Secondly, aerobic exercise training also has an impact on MPB, as basal FOXO3a (upstream of MAFbx and MuRF1) and myostatin (inhibitor of MPS upstream of Akt-mTORC1) mRNA levels were reduced in elderly following a 12 week training program, with concomitant muscle hypertrophy (Konopka et al., 2010). Another indirect mechanism involves the partial alleviation of age-related insulin resistance, which contributes to the age-related anabolic resistance to protein supplementation (Dickinson et al., 2014; Konopka and Harber, 2014). Finally, aerobic exercise also exhibits antiinflammatory capacities (Montero-Fernandez and Serra-Rexach, 2013). Transient elevations of IL-6, released in response to acute exercise, might stimulate in the long-term the expression of antiinflammatory mediators (such as IL-1 receptor antagonist and IL-10) and downregulate the expression of pro-inflammatory mediators (such as TNFα and IL-1β) (Pedersen et al., 2007). Together, these findings indicate that long-term aerobic training is effective to overcome anabolic resistance and decrease MPB, exhibiting a protective effect on muscle wasting.

### Resistance Exercise

Despite these promising findings, resistance exercise is generally accepted as the most effective approach to induce muscle hypertrophy in both young and old adults, as the relative gain in muscle size in response to resistance training was found to be similar in adult (<65 years) and young individuals (Narici et al., 2004; Slivka et al., 2008; Raue et al., 2009).

The hypertrophic response following resistance training is mediated through several mechanisms. The mechanical stimuli per se and growth factors released in response to mechanical stimuli are both able to independently activate the mTORC1 pathway. The activation of mTORC1 by growth factors (insulinlike growth factor, mechano growth factor) occurs via the "classic" PI3K/Akt signaling (Bodine et al., 2001). Mechanical stimuli have a more acute effect, which is, contrarily, not always dependent on PI3K/Akt signaling (Hornberger et al., 2004). It was suggested that mechanical signals can activate the mTORC1 pathway after they are converted in biological responses. The proposed mediators of these stimuli are phosphatidic acid, which is highly produced in response to stretching and activates p70S6K, and integrins, which connect the extracellular matrix to the muscle cell membrane (Zanchi and Lancha, 2008).

Despite positive findings in adults (<65 years), Peterson et al. (2011) reported less pronounced gains in lean body mass in response to resistance exercise in elderly. This indicates that the myocellular response to resistance training is blunted in advanced age (Narici et al., 2004; Slivka et al., 2008; Raue et al., 2009). The precise mechanism(s), underlying the development of this anabolic insensitivity in elderly, are not fully understood yet. It is likely, however, that the limited activation of the mTORC1 axis following an acute bout of exercise in elderly is an important contributor to this observation (Kumar et al., 2009; Fry et al., 2011). Accordingly, one study found an impaired downregulation of REDD1 (potent inhibitor of mTORC1) mRNA in elderly following resistance exercise (Greig et al., 2011). Nevertheless, it is conceivable that also inflammation is somehow involved in the blunted adaptation to resistance exercise, since the effect of resistance training on gains in muscle mass and muscle strength was reinforced when combined with NSAID administration in elderly (Trappe et al., 2011).

### Protein Supplementation

Among the nutritional interventions, supplementation with proteins/amino acids (leucine in particular) is widely applied to treat muscle loss in elderly. Proteins stimulate MPS, and are therefore generally expected to attenuate muscle wasting. The effects of protein supplementation on MPS are mediated through several mechanisms. Firstly, protein synthesis is induced by cell swelling per se (Lang et al., 1993). This swelling can be obtained through an increased cellular osmolarity, due to Na+-dependent transport into the cell or accumulation of metabolites, such as glutamate. Secondly, mTORC1 regulates protein translation by controlling the phosphorylation of its downstream targets p70S6K and 4E-BP1 (Laplante and Sabatini, 2012). Protein supplementation induces the mTORC1 activation via two independent ways. Amino acids can be directly sensed by GTPases, amino acid transporters and receptors, which transmit the signal to mTORC1 by different signaling pathways (Zheng et al., 2016). This cascade occurs without modulation of PI3K and its downstream effector Akt. Additionally, branched chain amino acids induce the release of insulin, which act upon the PI3K-Akt-mTORC1 axis. Due to its vasodilatory effect, insulin also facilitates the uptake of amino acids from the blood circulation by the skeletal muscle cells. It is accepted that the availability of insulin plays a rather permissive effect, as it was shown that only ∼10% of the post-prandial protein anabolism was dependent on insulin signaling, while ∼90% was related to the increased amino acid levels (Volpi et al., 1996). Furthermore, Abdulla et al. (2016) reported that, in healthy adults, the positive effects of insulin per se on MPS became significant, only when the amino acid delivery to the skeletal muscle increased. At fixed amino acids concentrations, supraphysiological levels of insulin did not induce further increments in MPS (Greenhaff et al., 2008). To summarize, the beneficial effect of physiological hyperinsulinemia per se on MPS can only be observed as long as it concomitantly increases amino acid delivery and availability to the muscle (Fujita et al., 2006).

Among the amino acids, the essential amino acids (EAAs) (e.g., leucine) are the most effective to stimulate MPS (Stipanuk, 2007). Along with its stimulatory effect, leucine in particular also decreases MPB (Buse and Reid, 1975). Another mechanism ascribed to leucine involves its capacity to reverse insulin insensitivity in elderly. The precise mechanism(s) of the insulin sensitizing effects of long-term leucine supplementation are not fully elucidated yet, but may act through leucineinduced decreased adiposity, hepatic glucose production, hepatic steatosis, and adipose tissue inflammation (Zhang et al., 2007; Macotela et al., 2011; Binder et al., 2013). Insulin resistance, which often occurs with aging, is an important contributor to the pathophysiology of age-related sarcopenia, as this negatively affects mTORC1 signaling (Cleasby et al., 2016). Therefore, interventions that might positively affect the insulin sensitivity, are very likely to increase MPS (Børsheim et al., 2006; Solerte et al., 2008). Recently, a downstream metabolite of leucine, βhydroxy β-methylbutyrate (HMB), gained much interest due to its broad ergogenic effects on muscle protein metabolism. HMB was shown to stimulate mTORC1 and the proliferation and differentiation of satellite cells (Cruz-Jentoft, 2017). Besides its anabolic actions, HMB was shown to exert anti-catabolic actions through the attenuation of proteasomal-mediated proteolysis and mitochondrial-mediated myonuclear apoptosis (Smith et al., 2005; Kovarik et al., 2010; Hao et al., 2011; Wilkinson et al., 2013).

Similar to resistance exercise, many elderly suffer from a blunted anabolic response to EAA intake. Therefore, it is generally suggested that larger doses may, at least partially, compensate for the blunted anabolic sensitivity (Paddon-Jones et al., 2004; Cuthbertson et al., 2005; Katsanos et al., 2006; Moore et al., 2015). Recent evidence-based recommendations state that 1.2–1.5 g.kg−1 BW should be ingested on a daily base by elderly, while the RDA for young adults is 0.8 g.kg−1 BW (Deer and Volpi, 2015; Devries and Phillips, 2015; Nowson and O'Connell, 2015; Moore et al., 2017). Furthermore, an intake of 25–30 g high quality proteins (∼10 g EAAs) each meal has been proven to maximally stimulate MPS in elderly for 24 h (Deer and Volpi, 2015; Devries and Phillips, 2015; Nowson and O'Connell, 2015).

Despite the well-established positive findings of acute protein/leucine supplementation on the stimulation of MPS, the effectiveness of long-term protein supplementation as a treatment for age-related sarcopenia is more equivocal. On the one hand, a recent meta-analysis demonstrated the beneficial effects of leucine supplementation on MPS in elderly (be it without changes in lean body mass or lean leg mass) (Xu et al., 2015), while other studies failed to show a consistent effect of protein/leucine supplementation on muscle mass, strength and/or function (Bonnefoy et al., 2003; Verhoeven et al., 2009; Tieland et al., 2012b). The review of Hickson (2015) concluded that both whole-protein and EAA supplementation failed to show consistent effects on muscle mass, strength or function. Accordingly, it was suggested that, despite its acute anabolic effects, leucine supplementation has no beneficial effects on skeletal muscle mass or function on the long-term in muscle wasting conditions (Ham et al., 2014). In contrast to leucine, studies that focused on HMB yielded more consistent results (Hickson, 2015; Wu et al., 2015). Consequently, HMB might be more effective for the preservation of muscle mass, strength and functionality in elderly. However, more research is required to further elucidate the efficacy of the long-term intake of amino acids and their metabolites to combat age-related sarcopenia. In addition, it might be of great interest to develop alternative treatments that sensitize the skeletal muscle to leucine and anabolic stimuli in general, such as anti-inflammatory strategies.

## Combined Protein Supplementation and Resistance Exercise

Since both protein supplementation and resistance exercise are suggested to induce muscle hypertrophy by stimulating the mTORC1 signaling pathway, it is not surprising that their combined effect was broadly researched. In healthy adults, evidence indicates that the combined intervention induces additional gains in muscle mass and muscle strength, when compared to resistance training as such (Morton et al., 2017). In elderly, however, the findings are less conclusive, as many studies reported no superiority of resistance exercise with protein supplementation compared to resistance exercise as such (Fiatarone et al., 1994; Godard et al., 2002; Candow et al., 2006; Kukuljan et al., 2009; Verdijk et al., 2009; Denison et al., 2015; Thomas et al., 2016). Denison et al. (2015) suggested that an additional effect due to protein supplementation was mainly to be expected in subjects with low basal protein intakes, whereas those with adequate basal intakes would benefit less from additional protein supplementation. Since adequate protein intake is often problematic in frail and institutionalized elderly, it is of importance to not only emphasize on protein supplementation during resistance training, but also to ensure a sufficient basal protein intake in these subpopulations (Tieland et al., 2012a; Thomas et al., 2016).

### INFLAMMATION-REDUCING APPROACHES TARGETING AGE-RELATED SARCOPENIA

As stated earlier, inflammation is closely involved in both the blunted anabolic response and increased catabolic processes in elderly. In the following section, nutritional strategies that attenuate muscle wasting in elderly, partially regulated through anti-inflammatory mechanisms, will be discussed. **Figure 1** gives an overview of the mechanisms through which LGI may indirectly affect age-related muscle wasting.

## Vitamin D Supplementation

Historically, vit D supplementation has been broadly applied in elderly, due to its well established effects on calcium (Ca2+) and bone homeostasis. It was shown that supplementation of vit D, preferably together with Ca2+, increases bone mineral density and decreases the risk of osteoporotic fractures in elderly. More recently, vit D has also been shown to play a regulatory role in metabolic pathways implicated in muscle wasting, and in the regulation of the immune system. It seems that vit D has beneficial effects on muscle strength (Muir and Montero-odasso, 2011; Beaudart et al., 2014), however, this only seems to apply to vit D-deficient elderly (Janssen et al., 2002; Stockton et al., 2011). In contrast, muscle mass seems not to be increased by vit D supplementation in elderly (Beaudart et al., 2014). Since the prevalence of vit D deficiency is very high among elderly (up to 42% in the U.S.), its supplementation should be recognized as an important intervention in elderly (Forrest and Stuhldreher, 2011).

The mechanisms through which vit D affects muscle cell functioning can be broadly divided into two categories, the genomic and non-genomic effects. For the genomic effects, binding of vit D to its nuclear receptor, a transcription factor, is required. This will result in changes in gene transcription, e.g., stimulation of cell proliferation and terminal differentiation, among others, both important in muscle development and growth (Boland, 1986; Ceglia and Harris, 2013). Indeed, decreases in muscle functioning related to vit D signaling with aging, can be mainly ascribed to the decreased vit D receptor expression in old skeletal muscle cells (Bischoff-Ferrari et al., 2004). Salles et al. (2013) also showed that vit D positively affected the expression of proteins involved in the insulin-AktmTORC1 pathway, along with an increased MPS in C2C12 muscle cells. These findings were recently confirmed in vivo, as vit D supplementation restored the blunted anabolic response in vit D-deficient old rats (Chanet et al., 2017). To our knowledge, these mechanisms were not yet studied in humans. The nongenomic "rapid" effects are mediated through the membranebound vit D receptor. Firstly, this regulates the influx of Ca2+ and inorganic phosphate in skeletal muscle cells, important for the regulation of muscle contractions and for the production of energy-rich phosphate groups, respectively. A second nongenomic effect involves the activation of protein kinase C. This kinase plays a crucial role in the regulation of MPS by mediating the anabolic signaling of both insulin (in an IRS1 and PI3K-dependent way) and leucine on protein synthesis (Selless and Boland, 1991; Mendez et al., 1997; Yagasaki et al., 2003).

As mentioned earlier, vit D also modulates the immune system and might therefore be an interesting target to combat LGI. Apart from muscle cells, the vit D receptor is expressed by cells which play a key role in immunity, such as macrophages and lymphocytes. Binding of vit D to its receptor in macrophages inhibits NF-κB activity (a transcriptional key regulator in the upregulation of pro-inflammatory mediators), and thereby attenuates TNFα production (Cohen-lahav et al., 2006). The same NF-κB-dependent inhibition of cytokine secretion through vit D signaling is present in lymphocytes (Calton et al., 2015). In murine models, vit D signaling also stimulates the production of lymphoid cell lineages with anti-inflammatory properties, such as Treg cells (Tian et al., 2015). Another mechanism involves a direct induction of relevant genes by vit D in specific liver cells, resulting in an elevated production of insulin-like growth factor-1 (IGF-1), which has previously been shown to engage in antiinflammatory actions (Bellini et al., 2011; Yu et al., 2012; Wang et al., 2017). Furthermore, IGF-1 is also engaged as a stimulator of cell growth and proliferation through activation of the Akt signaling pathway.

Compared to rodent studies, findings from human interventions with vit D supplementation are very hard to interpret. Most human studies, which link vit D signaling to anti-inflammatory actions, focused on several diseases, each with their specific inflammatory context, which results in ambiguous evidence. Consequently, there is a need for human trials, which study the effects of vit D supplementation in elderly with limited comorbidities. In general, it can be concluded that vit D supplementation might be useful in elderly, as they belong to a population that is prone to vit D deficiency due to reduced exposure to sunlight, low oral intake of vit D, intestinal malabsorption, and decreased vit D hydroxylase activity in the kidneys.

### N-3 Polyunsaturated Fatty Acids

N-3 poly unsaturated fatty acids (PUFAs) are characterized by a double bond at the third carbon from the methyl end of the carbon chain. Besides their role as a structural component, i.e., in membranes, PUFAs function in several cellular processes as regulatory or signaling molecules. In elderly, long-term n-3 PUFA supplementation increases muscle volume and muscle strength (Smith et al., 2015). Furthermore, n-3 PUFA/fish oil supplementation has additional beneficial effects on resistance training-induced muscle functionality in elderly women (Rodacki et al., 2012; Da Boit et al., 2017), though without changes in muscle mass (Da Boit et al., 2017). The anabolic effects of n-3 PUFAs can be ascribed to an increased MPS, mainly regulated through activation of the mTORC1 signaling pathway, as observed in both animal (Gingras et al., 2007; Wei et al., 2013) and human (Smith et al., 2011; Yoshino et al., 2016) studies. A possible link can be found with an increased insulin sensitivity, as it was shown that PUFA supplementation leads to increased phosphorylation of the insulin receptor and its downstream signaling (Kamolrat and Gray, 2013; Wei et al., 2013). Furthermore, enhanced insulin signaling induces vasodilation, which increases amino acid availability (Smith, 2016).

Besides their anabolic capacities, n-3 PUFAs possess wellestablished anti-inflammatory properties. Once transported into the cell, PUFAs are either stored, oxidized or incorporated into cellular membranes. PUFAs, released from the cell membranes, may form prostanoids, which are involved in direct inflammatory actions or regulate the production of other mediators such as inflammatory cytokines (Williams et al., 2000). When consuming a typical western diet, rather high rates of the n-6 PUFA arachidonic acid (ARA) are integrated in the membrane phospholipids of inflammatory cells, while proportions of the n-3 PUFA eicosapentaenoic acid (EPA) or docosahexaenoic acid (DHA) are low. Therefore, ARA is often the main substrate for the formation of prostanoid synthesis (Bagga et al., 2003). After mobilization from the membrane phospholipids by the enzyme phospholipase A2, ARA binds the cyclooxygenase enzymes (COX-1/2) from which downstream intermediates are further metabolized to finally synthesize pro-inflammatory prostanoids. When more n-3 PUFAs are integrated in the diet, less n-6 PUFAderived pro-inflammatory prostanoids will be formed.

Early findings indicate the association between muscle protein metabolism and a certain subclass of the prostanoids, i.e., prostaglandins (Rodemann and Goldberg, 1982; Rodemann et al., 1982). The research group of Goldberg observed that PGE2, produced by muscle upon ARA incubation, consistently stimulated protein degradation (+22%), while no significant changes in MPS were observed. PGF2α, derived from ARA, contrarily, had no effect on MPB but increased MPS. These findings are of great interest from an aging perspective, as an increased LGI was related to an overexpression of COX-2, among others, eventually evoking a pro-inflammatory status (e.g., elevated levels of PGE2) (Chung et al., 2006). More recently, Standley et al. (2013) suggested that COX-induced expression of PGE2 increased the expression of IL-6 and MuRF-1, which are both related to MPB and might therefore explain the mechanism through which PGE2 is linked to muscle proteolysis.

Despite promising associations between n-3 PUFAs and muscle metabolism, this area of research needs to be further explored. Animal studies revealed that the EPA was effective in attenuating the ubiquitin-proteasome pathway in several muscle wasting conditions, such as fasting, cachexia and arthritis (Whitehouse and Tisdale, 2001; Whitehouse et al., 2001; Castillero et al., 2009). Furthermore, in obese adults, with elevated inflammatory mediators (e.g., IL-6), n-3 PUFAs were effective in alleviating systemic inflammation (Itariu et al., 2012; Allaire et al., 2016). It would be of interest to expand this knowledge to elderly suffering from LGI in order to develop strategies that support/optimize the current interventions. Recently, a paper confirmed that n-3 PUFA supplementation in elderly downregulated pathways related to calpain- and ubiquitin-mediated proteolysis in skeletal muscle (Yoshino et al., 2016). This shows the anti-proteolytic potential of n-3 PUFAs in age-related muscle wasting.

### The COX-Pathway and Muscle Metabolism

There seems to be an interesting link between inflammation and muscle protein metabolism. A lower MPS was reported in old rats suffering from LGI (Balage et al., 2010) and their MPS was increased when the LGI was blunted by NSAID administration (Marzani et al., 2008; Rieu et al., 2009). Interestingly, LGI also impaired muscle protein anabolism in response to resistance training in elderly (Trappe et al., 2002). As mentioned before, these effects might be related to certain intermediates of the COX pathway, i.e., prostaglandins. COX-inhibiting interventions through NSAID administration might therefore be effective in improving muscle protein metabolism and thus for the treatment of muscle wasting (Greig et al., 2009). This idea was further strengthened by the cross-sectional study of Landi et al. (2013), which investigated the relationship between NSAID use and sarcopenia in community-dwelling elderly (>80 years). In this study only 9% of the subjects that chronically used NSAIDs were affected by sarcopenia, compared to 32% in the non-user group (Landi et al., 2013). Although it tantalizing to promote daily consumption of NSAIDs to overcome the age-related muscle anabolic resistance, the associated risks, such as gastric, cardiovascular and hepato-renal adverse events, cannot be denied (Greig et al., 2009). In this perspective, other anti-inflammatory compounds might be of great interest in the search for a physiological-holistic approach to combat age-related sarcopenia. It should be noted though that antiinflammatory compounds, inhibiting COX-2 and concomitant PG synthesis, might negatively affect skeletal muscle regeneration and hypertrophy. Some studies reported that COX-2 inhibition resulted in a decreased size of regenerating muscle fibers following injury and negatively affected ex vivo satellite cell proliferation, differentiation and fusion (Bondesen et al., 2004; Mendias et al., 2004). Furthermore, COX-2 inhibition decreased intramuscular macrophage accumulation and cell proliferation, and the consequent hypertrophic response in a synergist ablation model (Novak et al., 2009). Future studies should clarify whether these inhibitory effects on muscle regeneration also apply to sarcopenic elderly, and/or whether nutritional antiinflammatory strategies can enforce the positive effects of classic strategies.

### CONCLUSION

Plural mechanisms were shown to contribute to the etiology and/or progression of muscle wasting with advancing age. Somehow, many of these mechanisms interfere with inflammatory mediators. However, further research is required to determine through which mechanisms inflammation directly or indirectly affects MPB and MPS with aging. Classic interventions such as protein supplementation and resistance exercise are generally accepted to be the most appropriate to positively affect muscle protein metabolism in elderly. However, not all studies univocally support the effectiveness of these strategies for long-term treatment of age-related muscle wasting. Elderly, and very old/frail seniors in particular, might benefit from a strategy which primarily focusses on alleviating their muscle insensitivity to anabolic stimuli. In this regard, the treatment of LGI in these elderly might play an important role. Given the limited applicability of NSAIDs, other (non-pharmaceutical) approaches to attenuate LGI should gain more attention. Additionally, future research should also focus on possible interactions with the "classical" anabolic

### REFERENCES

treatments, in order to develop a holistic approach, that takes into account the personal capabilities and experiences of the old individual.

### AUTHOR CONTRIBUTIONS

Substantial contributions to the conception or design of the work: SD, LR, KK. Drafting the work or revising it critically for important intellectual content: SD, LR, KK. Final approval of the version to be submitted: SD, LR, KK. All authors agree to be accountable for all aspects of the work.

### FUNDING

LR is supported by a grant from the Ministry of Health of the Czech Republic (AZV 16-29182A).

### ACKNOWLEDGMENTS

The authors thank Prof. Dr. Vladimir Stich for his critical review during the preparation of this article.

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as countermeasures to offset sarcopenia. BioFactors 40, 199–205. doi: 10.1002/biof.1138

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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.

Copyright © 2017 Dalle, Rossmeislova and Koppo. 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.

# "Thinking" vs. "Talking": Differential Autocrine Inflammatory Networks in Isolated Primary Hepatic Stellate Cells and Hepatocytes under Hypoxic Stress

Yoram Vodovotz 1, 2, 3, Richard L. Simmons 1 , Chandrashekhar R. Gandhi 1†, Derek Barclay 1 , Bahiyyah S. Jefferson1 , Chao Huang1 , Rami Namas 1 , Fayten el-Dehaibi 1 , Qi Mi 1 , Timothy R. Billiar 1, 2, 3 and Ruben Zamora1, 2, 3 \*

*1 Department of Surgery, University of Pittsburgh, Pittsburgh, PA, United States, 2 Center for Inflammation and Regenerative Modeling, McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA, United States, 3 Pittsburgh Liver Research Center, University of Pittsburgh, Pittsburgh, PA, United States*

### Edited by:

*Enrique Hernandez-Lemus, National Institute of Genomic Medicine, Mexico*

#### Reviewed by:

*Savneet Kaur, Institute of Liver and Biliary Sciences, India David Sharkey, University of Adelaide, Australia*

> \*Correspondence: *Ruben Zamora<EMAIL_ADDRESS>
† Present Address: *Chandrashekhar R. Gandhi, Department of Surgery, University of Cincinnati, OH, United States*

#### Specialty section:

*This article was submitted to Integrative Physiology, a section of the journal Frontiers in Physiology*

Received: *11 September 2017* Accepted: *14 December 2017* Published: *22 December 2017*

#### Citation:

*Vodovotz Y, Simmons RL, Gandhi CR, Barclay D, Jefferson BS, Huang C, Namas R, el-Dehaibi F, Mi Q, Billiar TR and Zamora R (2017) "Thinking" vs. "Talking": Differential Autocrine Inflammatory Networks in Isolated Primary Hepatic Stellate Cells and Hepatocytes under Hypoxic Stress. Front. Physiol. 8:1104. doi: 10.3389/fphys.2017.01104* We hypothesized that isolated primary mouse hepatic stellate cells (HSC) and hepatocytes (HC) would elaborate different inflammatory responses to hypoxia with or without reoxygenation. We further hypothesized that intracellular information processing ("thinking") differs from extracellular information transfer ("talking") in each of these two liver cell types. Finally, we hypothesized that the complexity of these autocrine responses might only be defined in the absence of other non-parenchymal cells or trafficking leukocytes. Accordingly, we assayed 19 inflammatory mediators in the cell culture media (CCM) and whole cell lysates (WCLs) of HSC and HC during hypoxia with and without reoxygenation. We applied a unique set of statistical and data-driven modeling techniques including Two-Way ANOVA, hierarchical clustering, Principal Component Analysis (PCA) and Network Analysis to define the inflammatory responses of these isolated cells to stress. HSC, under hypoxic and reoxygenation stresses, both expressed and secreted larger quantities of nearly all inflammatory mediators as compared to HC. These differential responses allowed for segregation of HSC from HC by hierarchical clustering. PCA suggested, and network analysis supported, the hypothesis that above a certain threshold of cellular stress, the inflammatory response becomes focused on a limited number of functions in both HSC and HC, but with distinct characteristics in each cell type. Network analysis of separate extracellular and intracellular inflammatory responses, as well as analysis of the combined data, also suggested the presence of more complex inflammatory "talking" (but not "thinking") networks in HSC than in HC. This combined network analysis also suggested an interplay between intracellular and extracellular mediators in HSC under more conditions than that observed in HC, though both cell types exhibited a qualitatively similar phenotype under hypoxia/reoxygenation. Our results thus suggest that a stepwise series of computational and statistical analyses may help decipher how cells respond to environmental stresses, both within the cell and in its secretory products, even in the absence of cooperation from other cells in the liver.

Keywords: liver, hepatocyte, stellate cell, cytokines, systems biology, hypoxia

### INTRODUCTION

In settings of stress, such as liver ischemia/reperfusion, hemorrhagic shock after trauma, and drug-induced liver injury, endogenous mediators released from liver cells are known to initiate and regulate sterile liver inflammation (Peitzman et al., 1995; Vodovotz et al., 2004; Sun et al., 2017; Woolbright and Jaeschke, 2017). There are two major types of cells in the liver: parenchymal cells [hepatocytes (HC)] and non-parenchymal cells [sinusoidal endothelial cells, phagocytic Kupffer cells, and hepatic stellate cells (HSC)]. Whereas 70–85% of the normal adult murine liver volume is occupied by HC (Kmiec, 2001 ´ ), Kupffer cells—which comprise approximately 35% of the nonparenchymal liver cells in normal adult mice (Bilzer et al., 2006)—play a central role in responses to early reperfusion injury following hypoxia/reoxygenation due to their macrophage-like properties (Bilzer and Gerbes, 2000). Studies over the past several decades have focused predominantly on the stress responses of HC, Kupffer cells (a heterogeneous cell population) and trafficking innate immune cells (de Groot, 1992; Peitzman et al., 1995; West and Wilson, 1996). Lesser attention has been paid to sinusoidal or vascular endothelial cells and HSC and the inherent autocrine complexity of the cellular components of the normal liver under stressful conditions have rarely been the subject of investigation.

Various studies have focused on the responses of HC cell lines to hypoxia/reoxygenation. For example, early studies showed that human HepG2 hepatoma cells express interferonγ (IFNγ), Tumor Necrosis Factor-α (TNF-α), Transforming Growth Factor-β1 (TGFβ1), Macrophage Colony-Stimulating Factor (M-CSF), Oncostatin-M (OSM), Intercellular Adhesion Molecule (ICAM-1), Interleukin 4 (IL-4), IL-5, IL-7, IL-10, IL-11, IL-12, and IL-6 receptor (IL-6R), while the expression of IL-1β, IL-2, IL-3, IL-6, CD40 ligand and IL-2R genes was not detected (Stonans et al., 1999). Normal HC seem to have a much narrower spectrum of response: for example, primary human HC were shown to release IFNγ, IL-12p40, IL-12p70, IL-17A, and IL-10 following exposure to hepatotoxic drugs (Ogese et al., 2017). We recently used LuminexTM technology coupled with computational analyses to study the in vitro response of mouse HC to hypoxia (Ziraldo et al., 2013). We found that many inflammatory mediators were changed significantly in both normoxic and hypoxic cultures, and MCP-1 was identified as central node in the inflammatory networks of HC and as an inducer of IL-6; segregating trauma patients based on their co-expression of MCP-1 and IL-6 allowed us to suggest MCP-1 as a potential biomarker for clinical outcomes in trauma/hemorrhagic shock (Ziraldo et al., 2013).

HSC represent only 5-8% of the total number of liver cells (Geerts, 2001). The inflammatory responses of this cell type to hypoxia/reoxygenation are less well studied, though HSC are considered important in the pathogenesis of liver fibrosis (de Oliveira da Silva et al., 2017); furthermore, protection of the liver cells from ischemia/reperfusion injury in HSC-depleted mice indicates that HSC are major contributors to liver damage (Stewart et al., 2014). DNA microarray analyses have shown that hypoxia regulates the expression of genes that may alter the sensitivity of HSC to chemotaxins and other stimuli, and regulates the expression of genes important for angiogenesis and collagen synthesis (Copple et al., 2011). Furthermore, in primary HSC, bacterial lipopolysaccharide (LPS) strongly upregulated numerous CC and CXC chemokines as well as IL-17F (Harvey et al., 2013). Other studies showed that HSC can express a number of other cytokines and chemokines such as Eotaxin, IFNγ, IL-6, IL-8, and IL-10 (Berardis et al., 2014).

Most studies of the effects of environmental stress are carried out in vivo, in which the interaction of various cell types within an organ cannot be discerned from one another. To our knowledge, no studies have utilized pure hepatic cell cultures to examine the autocrine effects, which themselves reflect complexity of response even in the absence of interaction with neighboring cell types. In addition, most studies explore the repertoire of mediator secretion ("talking") by stressed cells, without questioning the internal reactions that take place within the cell ("thinking"). The interpretation of such complex responses require a baseline of information about the inherent responsiveness of purified cells. In addition, mere description of the secretory output of a cell under stress does not adequately address the complexity of even the simplest of experimental systems. For this reason, and given the close proximity of HSC and HC (one HSC contacting two HC on either side of the central vein), this study was designed to compare the intracellular and secretory (autocrine) responses of pure HC and HSC in culture to hypoxic stress and reoxygenation using a unique stepwise series of statistical and data driven modeling techniques previously used to define dynamic molecular networks in both experimental and clinical trauma and acute systemic inflammation (Mi et al., 2011; Ziraldo et al., 2011, 2013; Zaaqoq et al., 2014; Almahmoud et al., 2015; Abboud et al., 2016; Namas et al., 2016). These approaches, including Two-Way ANOVA, hierarchical clustering, Principal Component Analysis (PCA) and Network Analysis were chosen to unmask the complexity of even simple cellular systems. Our results suggest that intracellular inflammatory networks and principal characteristics ("thinking") differ in several important respects from those in the conditioned medium ("talking") of both HSC and HC. Our results further suggest that a stepwise series of computational and statistical analyses may help decipher how cells respond to inflammatory stresses.

### MATERIALS AND METHODS

### Materials

Williams Medium E, penicillin, streptomycin, L-glutamine, and HEPES were purchased from Invitrogen (Carlsbad, CA). Insulin (Humulin R ) was purchased from Eli Lilly (Indianapolis, IN), and calf serum was obtained from HyClone Laboratories (Logan,

**Abbreviations:** GM-CSF, Granulocyte-Macrophage Colony Stimulating Factor; IFN, Interferon; IL, interleukin; IP-10, IFN-γ-inducible Protein of 10 kDa/CXCL10; KC, Keratinocyte-derived Cytokine/CXCL1; MCP-1, Monocyte Chemotactic Protein-1/CCL2; MIG, Monokine induced by γ-interferon/CXCL9; MIP-1α, Macrophage Inflammatory Protein-1α (MIP-1α/CCL3α); TNF-α, Tumor Necrosis Factor-α; VEGF, Vascular Endothelial Growth Factor; PCA, Principal Component Analysis.

UT). Tissue culture dishes were from Corning Glass Works (Corning, NY).

### Liver Cell Isolation and Culture

This study was carried out in accordance with the recommendations of the National Institutes of Health. The protocol was approved by the Institutional Animal Care and Use Committee of the University of Pittsburgh. The liver cell isolation and culture procedures were as follows:

### Analysis of Inflammatory Mediators

Mouse inflammatory mediators were measured using a Luminex R 100 IS apparatus (Luminex, Austin, TX) and the BioSource 20-plexTMmouse cytokine bead kit (BioSource-Invitrogen, San Diego, CA) as per manufacturer's specifications. The antibody bead kit included: Basic fibroblast growth factor (FGF basic), Granulocyte-Macrophage Colony-Stimulating Factor (GM-CSF), Interferon-γ (IFN-γ), Interleukin (IL)- 1α, IL-1β, IL-2, IL-4, IL-5, IL-6, IL-10, IL-12 (both p40 and p70 subunits), IL-13, IL-17A, Interferon-γ-inducible Protein 10 (IP-10/CXCL10), Keratinocyte-derived Cytokine (KC/CXCL1), Monocyte Chemoattractant Protein-1 (MCP-1/CCL2), Monokine induced by Interferon-γ (MIG/CXCL9), Macrophage Inflammatory Protein-1α (MIP-1α/CCL3), Tumor Necrosis Factor-α (TNF-α), and Vascular Endothelial Growth Factor (VEGF).

### Statistical and Computational Analyses

We applied a series of statistical and data-driven modeling techniques aimed at discovering principal drivers, interconnected networks, and potential key regulatory nodes of inflammation in HSC vs. HC following increasing levels of cellular stress. We applied these methods in a stepwise fashion based on our concept of the way liver cells respond to inflammatory stimuli (**Figure 1**, adapted from Namas et al., 2015). Importantly, we also sought to address intracellular information processing ("thinking") as well as extracellular inflammatory mediator production ("talking") carried out by each cell type individually, as well as inferring how these two processes may interact in each cell type. In brief, we hypothesize that low-level stresses associated with cell culture, as well as more stressful stimuli such as hypoxia with or without reoxygenation, will cause liver cells to produce a panoply of inflammatory mediators in order to restore homeostasis. We hypothesize that this dynamic production of inflammatory mediators takes the form of inflammatory networks, and thus can be elucidated using network inference algorithms. As these early inflammatory responses progress, and in the face of continued hypoxia or the onset of reoxygenation, these networks coalesce around an intermediate, core set of inflammation-associated cellular functions, which we hypothesize can be inferred via PCA and related methods. These intermediate processes evolve to form a differential set of inflammatory responses characterized by distinct mediators that characterize the autocrine response of each liver cell type. These differential responses can be discerned both by standard statistical approaches such as Twoway ANOVA as well as data-driven modeling tools such as hierarchical clustering (**Figure 1**). Accordingly, we followed the stepwise series of analyses detailed below:

1) Two-Way Analysis of Variance (ANOVA) was carried out to analyze the changes in inflammatory mediators in HC vs. HSC in both CCM and WCL, using SigmaPlot (Systat Software, San Jose, CA) as indicated.

each component, depicted as a stacked bar graph. This gives a measure of a given inflammatory mediator's contribution to the overall variance of the system. The mediators with the largest scores are the ones which contributed most to the variance of the process being studied (Mi et al., 2011).

4) Network Analysis was carried out to define the central inflammatory network nodes as a function of cell type (i.e., HC vs. HSC), experimental condition, and spatial localization (lysate vs. conditioned medium) using a modified version of our previously published algorithm for Dynamic Network Analysis (Mi et al., 2011; Ziraldo et al., 2013; Zamora et al., 2016). Connections between inflammatory mediators were created if the Pearson correlation coefficient between any two nodes (inflammatory mediators) was greater or equal to a threshold of 0.95, as indicated. The "network complexity" for each experimental condition was calculated using the following formula: Sum (N1 + N2 +. . .+ Nn)/n-1, where N represents the number of connections for each mediator and n is the total number of mediators analyzed. The total number of connections represents the sum of the number of connections in a given experimental sub-group.

### RESULTS

In the present study, we sought to compare, in a systematic fashion, the inflammatory responses of freshly isolated HC or HSC to hypoxia and hypoxia/reoxygenation. We reasoned that distinct statistical and computational analyses would elucidate different steps in the intracellular and extracellular responses of these cells to stress, shown schematically in **Figure 1** (see Materials and Methods for details). In the context of that schematic, we worked backwards from the most directly apparent/final responses (assessed by Two-Way ANOVA and hierarchical clustering) to the intermediate responses (assessed by PCA), and then to discerning the most proximal responses to stress (assessed by network analysis). Another key goal of our studies was to utilize computational modeling to assess the interactions between intracellular information processing ("thinking") vs. extracellular information transfer ("talking") in both HC and HSC. For that reason, we examined both the wholecell lysates (WCL) and the cell culture media (CCM) in each cell type.

### Differential Production and Release of Inflammatory Mediators in HSC vs. HC in Response to Hypoxia and Hypoxia/Reoxygenation

The production of inflammatory mediators in both CCM and WCL under the three experimental conditions, along with the corresponding P-values for **c**omparison of the changes in HSC vs. HC by Two-Way ANOVA, can be found in **Supplementary Figure 1**. Key examples of inflammatory mediators whose intracellular and extracellular levels are significantly different between HSC and HC are shown in **Figure 2**. In general, on a pg protein per mg total cell protein basis, HSC both produced and secreted much larger quantities of inflammatory mediators than the rather indolent response of HC. To identify those inflammatory mediators that showed similar expression or secretion behavior in each cell type, these patterns were then compared and grouped using hierarchical clustering. The resulting dendrograms showed a clear cluster separation between HC and HSC both in the CCM (**Figure 3A**) and WCL (**Figure 3B**) under the three experimental conditions studied. From this analysis, we infer that there is more heterogeneity in the intracellular ("thinking") vs. the extracellular ("talking") levels of inflammatory mediators in both cell types.

### Hypoxia and Hypoxia/Reoxygenation Induce a Differential Response in Hsc vs. HC Inferred from Principal Component Analysis (PCA)

Next, we sought to define an intermediate, core set of inflammation-associated characteristics of HSC and HC in response to increasing levels of stress, which we hypothesize represent different inflammation-related functions that ultimately become manifest in the expression and secretion of the defined subset of inflammatory mediators studied. To do so, we utilized PCA (**Figure 1**), a technique (or variants thereof) used by multiple investigators to delineate the core characteristics of a multivariate biological response (Janes et al., 2005; Janes and Yaffe, 2006; Mi et al., 2011; Nieman et al., 2012; Azhar et al., 2013; Ziraldo et al., 2013). We assessed several properties of the inflammatory responses under control, hypoxia, and hypoxia/reoxygenation: (1) the total number of mediators contributing to the total variance (i.e., mediators with sums of eigenvalues > 0; (2) the number of inferred principal components (represented as stacked bars), which we hypothesize represent distinct characteristics or inflammation-related functions; and (3) the sums of the eigenvalues (amplitude) for each experimental condition, which we hypothesize represents the magnitude of the inflammatory response to the indicated level of stress. The number of mediators and number of principal components expressed by each cell type under each condition is shown in **Figure 4**. An overall comparison of the inflammatory mediators showed that the inflammatory response of HSC was clearly different from that in HC both in CCM (**Figure 4A**) and WCL (**Figure 4B**) under each experimental condition. But certain similarities were seen. The major similarity was that the amplitude of the response was greatest when both HSC and HC were subjected to hypoxia/reoxygenation. Moreover, in general, the number of mediators involved as well as the inferred functions (number of principal components) in both WCL ("thinking") and CCM ("talking") were lowest in both HSC and HC subjected to hypoxia/reoxygenation as compared to control or hypoxia. A notable exception was the "thinking" response in HC WCL (**Figure 4B**), in which all three experimental conditions were characterized by two principal components, and the overall number of mediators was least in the hypoxia setting. Together, these analyses suggest that above a certain threshold of cellular stress, the inflammatory response generally becomes focused on a limited number of functions and associated mediators at a high intensity of response.

### Network Analysis Revealed the Presence of Distinct Autocrine Inflammatory Networks in HSC vs. HC

The final analysis we carried out was aimed at discovering the initial networks of inflammation induced under control conditions, hypoxia, or hypoxia/reoxygenation (**Figure 1**). We have previously employed Dynamic Network Analysis (DyNA) to define the dynamic interconnections among inflammatory mediators in a mouse model of trauma/hemorrhagic shock (Mi et al., 2011), in isolated mouse HC subjected to hypoxic stress (Ziraldo et al., 2013), and to define the dynamics of systemic inflammation after trauma (Abboud et al., 2016; Namas et al., 2016) or during acute liver failure (Zamora et al., 2016). For the current analyses, we used a modified version of this method, in which data from a single experimental condition (rather than a time interval) were used.

This network analysis suggested the presence of more complex extracellular inflammatory networks in HSC than in HC under all experimental conditions (**Figures 5A,B** and **Supplementary Figure 2A**) but roughly similarly complex intracellular networks (**Figures 5C,D** and **Supplementary Figure 2B**). Despite this difference in network complexity, multiple, shared inflammatory mediators were involved in both "thinking" and "talking" in HSC and HC. Secreted mediators shared by networks in both HSC and HC under control conditions included VEGF, MCP-1, KC, and IL-17A. Intracellularly under control conditions, only MIG and IL-4 were shared by HSC and HC networks. Under hypoxia, intracellular HSC and HC networks shared the mediators TNF-α, KC, MIG, and MCP-1, while intracellular networks only shared the mediators MIP-1α and IP-10. Under hypoxia/reoxygenation,

HC from C57BL/6 mice were cultured under 21% O2 for 6 h (control), hypoxia (1% O2) for 6 h or hypoxia (6 h) followed by reoxygenation for 18 h. Inflammatory mediators were measured by LuminexTMin both cell culture media and whole cell lysate as described in *Materials and Methods.* Box plots show the levels of IL-17A, IP-10, KC, MIP-1α, and TNF-α, where the line within the box marks the median and the lower and upper boundaries represent the 25th and 75th percentiles, respectively (\**P* < 0.001, HSC vs. HC, analyzed by Two-Way ANOVA).

secreted HSC and HC networks shared the greatest number of mediators: IL-1α, IL-2, IL-4, IL-6, IL-17A, MIG, KC, IP-10, GM-CSF, VEGF, and TNF-α; in contrast, shared intracellular mediators included TNF-α, MIG, MCP-1, IL-1α, and IL-17A. Control experiments comparing single network analysis of both isolated HC and HSC cultured under 21% O2 for 24 h confirmed

the presence of more complex extracellular inflammatory networks in HSC than in HC under both control and hypoxia/reoxygenation ( **Supplementary Figure 3A**) and similar complex intracellular networks after hypoxia/reoxygenation (**Supplementary Figure 3B**).

Based on PCA (**Figure 4**), we hypothesized that network interactions would lead to a reduced set of principal mediators and inflammatory functions in both CCM and WCL of HSC as they transition from hypoxia to hypoxia + reoxygenation. In support of this hypothesis, the networks observed under hypoxia/reoxygenation in HSC CCM as well as WCL exhibited multiple anti-correlated nodes (indicated in red; **Figures 5A,C**). We interpret this finding to mean that both intracellular and extracellular HSC mediators induced in the setting of hypoxia followed by reoxygenation regulate each other in a negative fashion that leads ultimately to the evolution of a reduced number of inflammatory functions relative to hypoxia alone. In the extracellular milieu of HSC, the chemokine MIG; the cytokines TNF-α, IL-6, and IL-17A; and the hypoxia-inducible growth factor VEGF appear to coordinate some of these downregulatory responses (**Figure 5A**). VEGF, TNF-α, MCP-1, and IL-17A also appear to play a more limited negative intracellular role (**Figure 5B**).

The foregoing analyses suggested that there may be an interplay between intracellular responses ("thinking") and extracellular responses ("talking") in each cell type. We therefore next sought to use network analysis to gain insights into the autocrine information flow within each cell population. Accordingly, we repeated the network analysis, but this time using both CCM and WCL data together for HSC vs. HC (**Figure 6**). Surprisingly, this analysis suggested the presence of multiple negative interactions in control HSC. This analysis also inferred the presence of a large number of negative interactions in both HSC and HC exposed to hypoxia/reoxygenation (**Figure 6A**,**Table 1**). Network analysis also demonstrated a higher complexity of autocrine networks involving both intracellular ("thinking") and extracellular ("talking") in HSC as compared to HC (**Figure 6B**), similar to the results obtained separately in CCM and WCL (**Figures 5B,D**).

We next assessed the flow of information from the intracellular (WCL) to extracellular milieu (CCM) in HSC vs. HC in greater detail (**Figure 6** and **Supplementary Table 1**) Under control conditions, inflammatory networks in HC were comprised solely of extracellular mediators (IL-1β, IL-13, IL-17A, MCP-1, KC, VEGF, and TNF-α). In contrast, the combined network analysis inferred multiple positive and negative connections among a large number of intracellular and extracellular inflammatory mediators in HSC, suggesting a much more robust "thinking" and "talking" interplay in this cell type at baseline. Under hypoxia alone, both HSC and HC exhibited only positive, extracellular inflammatory connectivity, though the number of connections was higher in

HSC (a large number of mediators) as compared to HC (only TNF-α, IL-17A, KC, MCP-1, and MIG). Interestingly, when subjected to hypoxia/reoxygenation, both HSC and HC exhibited a large number of positive and negative connections in the intracellular as well as extracellular compartments. This suggests a major adaptation to stress in HC, while this phenotype in HSC remained qualitatively similar to that observed under control conditions.

## DISCUSSION

We report for the first time a detailed multiplex analysis of inflammatory mediators in isolated HSC and HC cultured under hypoxic conditions with and without reoxygenation, which suggests the presence of distinct, endogenous autocrine networks in isolated HSC and HC. The other cellular and structural components of the liver (vascular and sinusoidal endothelial cells, Kupffer cells, fibroblasts, biliary ducts, etc.) were excluded deliberately from these experiments in order to study the complexity of the autocrine responses of these two cellular components without the contribution of inflammatory mediators from contiguous cell types. Our datadriven computational work flow (see conceptual summary in **Figure 1**) was designed to elucidate how early inflammatory networks can lead to an intermediate set of responses that manifest ultimately in a generally restricted number of inflammatory mediators/functions that distinguish the responses of HSC from those of HC. Based on this work flow, we show that both cell types respond to hypoxic stress by releasing a somewhat similar, but not identical, repertoire of cytokines and chemokines in vitro. The similarities suggest the possible presence of a mutually supportive autocrine environment in the intact liver in vivo. The hypoxic stimulus evokes a much greater change in the secreted cellular inflammatory network of HSC than HC, indicating that the secretory machine in HC might be either more limited at baseline or disrupted following stress. Furthermore, the responses of HSC to hypoxia followed by reoxygenation are more vigorous and form more interactive networks than responses to hypoxia alone.

A significant role for HSC in regulating hepatic inflammatory and immunological responses by altering expression of numerous relevant genes has already been shown in LPSstimulated rat HSC (Harvey et al., 2013). We have also shown previously that HSC produce numerous inflammatory mediators constitutively, expression of which is increased upon stimulation by LPS (Uemura and Gandhi, 2001; Thirunavukkarasu et al., 2005) (Thirunavukkarasu et al., 2008; Jameel et al., 2010; Harvey et al., 2013; Dangi et al., 2016). Furthermore, activation of unstimulated HSC stimulate DNA synthesis in HC (Uemura and Gandhi, 2001), and the mediators produced by LPS-stimulated HSC induce autophagy in HC as a survival mechanism but also cause apoptosis of a subset of cells (Dangi et al., 2016). We also

reported a comprehensive analysis of LPS-induced expression of inflammatory mediators in HSC (Harvey et al., 2013). In the setting of hypoxia, we found that HSC-depleted mice are protected from ischemia/reperfusion injury, suggesting that under ischemic/hypoxic condition HSC release mediators that are injurious to HC (Stewart et al., 2014). Our analyses in the current study showed the presence of more complex autocrine inflammatory networks in HSC as compared to HC, which supports the notion that these two cell types not only coexist but also provide a microenvironment for each other in which HSC play the "messenger" role as opposed more of a "responder" role for HC. Experimental testing of this inference is part of ongoing and future experiments that will include other liver cells as well.

Our results suggest a complex interplay among chemokines (e.g., MCP-1, KC, MIG, and IP-10), cytokines (e.g., TNF-α and IL-17A, among many others), and growth factors (e.g., VEGF) in the response to hypoxia ± reoxygenation even in the absence of the contribution of other liver cells. Some of these cytokines have been reported to be induced by stress in HC and HSC previously (Kmiec´, 2001; Brenner et al., 2013; Rani et al., 2017). Our computational analysis, however, elucidate a novel dimension of information regarding how HSC and HC "think" and "talk": as stress increases, intracellular and extracellular information transfer appears to result in a reduction of inflammationrelated functions, a process we hypothesize is driven through negative network interactions among key inflammatory mediators.

Finally, we are aware of the limitations in translating these computational analyses in vivo. Under physiological conditions O2 is a major effector of metabolic zonation and plays a major role in the liver. When compared to other somatic cells, liver cells are relatively hypoxic, both in the periportal

level 0.95) for each experimental condition combining the data in CCM and WCL (A). Black and red arrows represent positive and negative connections, respectively.

TABLE 1 | Total number of connections from Network Analysis.

(B) Compares the combined network complexity in HSC vs. HC.

*Freshly isolated hepatic stellate cells (HSC) and hepatocytes (HC) from C57BL/6 mice were cultured under 21% O*2 *for 6 h (control), hypoxia (1% O*2*) for 6 h or hypoxia (6 h) followed by reoxygenation for 18 h. Inflammatory mediators released into cell culture media (CCM) and in whole cell lysate (WCL) were measured by LuminexTMand Network Analysis was performed as described in Materials and Methods (see Results in* Figure 6A*). Table shows the total number of positive (black arrows, see* Figure 6A*) and negative connections (red arrows, see* Figure 6A*) for each experimental condition in both cell types in CCM* + *WCL (stringency level 0.95).*

(zone 1, 9–11% O2) and the perivenous region (zone 3, 5– 7% O2) (Jungermann and Kietzmann, 2000). Changes in this complex O2 gradient, as well as in the production of hormones and enzymes, affect the response of the liver in a number of pathophysiological processes. Thus, we agree that virtually all routine tissue culture experiments involving liver cells, such as the control conditions described herein are normally performed under relatively hyperoxic conditions that may not reflect in vivo physiology (Sullivan et al., 2006). Notably, the complex O2 gradient in the liver is very difficult to duplicate using isolated cells in vitro. Furthermore, it is also difficult to identify (and quantify) the presence and activity of specific inflammatory or parenchymal cells during specific time periods, as is the identification of potential crosstalk among dynamic networks in vivo. Although it is very likely, whether or not these inflammatory networks change in both composition and complexity when HC and HSC are in close proximity, as it happens in vivo, has not yet been reported and it represents another interesting element in the complex relationship between liver cells. To elucidate this and other of our hypotheses will require a more detailed analysis of data obtained from co-cultures of HC with HSC both alone and in combination with other relevant cells in the liver such as Kupffer cells and endothelial cells. Future studies will be directed to address these limitations, specifically we have ongoing studies in primary mouse HC as well as in plasma and whole liver in a mouse model of ischemia/reperfusion.

In summary, our systems-based analysis suggests the presence of an inflammatory homeostatic environment in resting isolated liver cells, which is disrupted under stress such as hypoxia and hypoxia followed by reoxygenation. The vigorous production of secreted inflammatory mediators from HSC and the relative indolent response from isolated HC suggest that HSC act as resident transformers of external stimuli or blood-borne for other less responsive cells within the complex cellular structure of the liver. The relevance of these findings in vivo warrants further investigation.

### AUTHOR CONTRIBUTIONS

YV participated in computational and statistical analysis, data interpretation, and writing. RS participated in data interpretation and writing. CG participated in data interpretation and writing. DB participated in Luminex analysis. BJ participated in cell culture experiments (HC). Fe-D participated in data analysis. QM participated in statistical and computational analysis. CH participated in cell culture experiments (HSC). TB participated in data interpretation and writing. RZ participated in study design, computational and statistical analysis, data interpretation, and writing. RN participated in cell culture experiments (HSC).

## FUNDING

This work was supported by NIH grant RO1-GM107231.

### SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fphys. 2017.01104/full#supplementary-material

Supplementary Figure 1 | Production of inflammatory mediators in mouse hepatic stellate cells (HSC) and hepatocytes (HC). Freshly isolated HSC and HC from C57BL/6 mice were cultured under 21% O2 for 6 h (control), hypoxia (1% O2) for 6 h or hypoxia (6 h) followed by reoxygenation for 18 h. Inflammatory mediators were measured by LuminexTM in both cell culture media and whole cell lysate as described in *Materials and Methods*. Results shown represent mean ± SEM (∗*P* < 0.05, HSC vs. HC, analyzed by Two-Way ANOVA).

Supplementary Figure 2 | Network Analysis of inflammatory mediators in mouse hepatic stellate cells (HSC) and hepatocytes (HC). Freshly isolated HSC and HC from C57BL/6 mice were cultured under 21% O2 for 6 h (control), hypoxia (1% O2) for 6 h or hypoxia (6 h) followed by reoxygenation for 18 h. Inflammatory mediators released into cell culture media (CCM) and in whole cell lysate (WCL)

### REFERENCES

were measured by LuminexTM and Network Analysis was performed as described in *Materials and Methods*. Figure shows the networks (stringency level 0.95) for each experimental condition in both cell types in CCM (A) and WCL (B). Black and red arrows represent positive and negative connections, respectively.

Supplementary Figure 3 | Network Analysis of inflammatory mediators in mouse hepatic stellate cells (HSC) and hepatocytes (HC). Freshly isolated HSC and HC from C57BL/6 mice were cultured under 21% O2 for 24 h (control) or hypoxia (6 h) followed by reoxygenation for 18 h (as in Supplementary Table 1). Inflammatory mediators released into cell culture media (CCM) and in whole cell lysate (WCL) were measured by LuminexTM and Network Analysis was performed as described in *Materials and Methods*. Figure shows the networks (stringency level 0.95) for each experimental condition in both cell types in CCM (A) and WCL (B). Black and red arrows represent positive and negative connections, respectively.

Supplementary Table 1 | Network Analysis of inflammatory mediators using the combined data of both the cell culture media (CCM) and whole cell lysate (WCL) in mouse hepatic stellate cells (HSC) and hepatocytes (HC). Freshly isolated HSC and HC from C57BL/6 mice were cultured under 21% O2 for 6 h (control), hypoxia (1% O2) for 6 h or hypoxia (6 h) followed by reoxygenation for 18 h. Inflammatory mediators released into cell culture media (CCM) and in whole cell lysate (WCL) were measured by Luminex® and Network Analysis was performed as described in *Materials and Methods*. The figure shows the detailed connectivity (stringency level 0.95) for each experimental condition combining the data in CCM and WCL.

reversion as cellular connections that modulate liver fibrosis. Cell Biol. Int. 41, 946–959. doi: 10.1002/cbin.10790

**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.

Copyright © 2017 Vodovotz, Simmons, Gandhi, Barclay, Jefferson, Huang, Namas, el-Dehaibi, Mi, Billiar and Zamora. 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.

# Coactivation of TLR2 and TLR8 in Primary Human Monocytes Triggers a Distinct Inflammatory Signaling Response

#### Edited by:

Enrique Hernandez-Lemus, National Institute of Genomic Medicine, Mexico

#### Reviewed by:

Kevin Woollard, Imperial College London, United Kingdom Roland Lang, Universitätsklinikum Erlangen, Germany

#### \*Correspondence:

Richard K. Kandasamy<EMAIL_ADDRESS>Bjarte Bergstrøm<EMAIL_ADDRESS>†These authors have contributed

equally to this work.