In contrast to the numerous studies on the effects of exercise on SCs, literature on exercise-induced changes on macrophages is scarce. Most studies investigating macrophage infiltration in the context of regeneration post-exercise were performed in humans after damaging eccentric exercise (Przybyla et al. 2006; Mahoney et al. 2008; MacNeil et al. 2011), and less is known about the effects of endurance exercise on macrophage infiltration. As infiltration of immune cells is tightly coordinated, the timing of tissue collection is crucial and complicates the experimental setup. In response to resistance exercise, infiltration of CD68+ macrophages is increased after 48 h (Mahoney et al. 2008; MacNeil et al. 2011) and unchanged in the early (3 h) and late (72 h) phases of recovery (Przybyla et al. 2006; Mahoney et al. 2008; MacNeil et al. 2011). The expected switch to CD163+ M2 macrophages is observed after 72 h, i.e., during the later stage of regeneration (Przybyla et al. 2006). In contrast to the infiltration of macrophages seen 48 h post-resistance exercise in humans, endurance exercise had no effect on the number of macrophages at that time point in rats (Kurosaka et al. 2012). Therefore, the timing of macrophage activation, infiltration and polarization depends on the type of exercise, training intensity and most likely other factors that remain to be elucidated.

In skeletal muscle fibers, PGC-1α reduces the expression of pro-inflammatory gene expression by inhibiting the transcriptional activity of the nuclear factor κB (NF-κB; Eisele et al. 2013). Nevertheless, somewhat unexpectedly, sustained overexpression of PGC-1α in muscle increases the number of tissue-resident macrophages in uninjured muscle (Rowe et al. 2014b; Dinulovic et al. 2016a). However, these macrophages have a predominant M2 phenotype (Dinulovic et al. 2016a), indicating that PGC-1α induces an anti-inflammatory environment and may thereby modulate the response to muscle damage. Interestingly, in contrast to the glycolytic phenotype of M1 macrophages, M2 macrophages have a more oxidative phenotype (Galvan-Pena and O'Neill 2014; Kelly and O'Neill 2015), analogous to the metabolic phenotype of muscle overexpressing PGC-1α or PGC-1β. The metabolic shift required for the anti-inflammatory phenotype of macrophages is mediated by PGC-1β (Vats et al. 2006).

Although the mechanisms by which PGC-1α affects macrophage polarization leading to the higher number of M2 macrophages in muscle is unknown, mediators of PGC-1α-dependent macrophage attraction and/or activation have been described (Fig. 1). PGC-1α induces the transcription of secreted phosphoprotein 1 (SPP1), which in turn is involved in the recruitment of macrophages and activation of epithelial cells to ensure functional neovascularization (Rowe et al. 2014b). In the context of acute exercise, PGC-1α controls the expression of the B-type natriuretic peptide (BNP), which promotes macrophage activation in a paracrine manner (Furrer et al. 2017). While M1 macrophage activation is resolved more rapidly in damaged PGC-1α-overexpressing muscles (Dinulovic et al. 2016a), the mechanisms by which muscle PGC-1α affects infiltration and polarization in this context are still unclear.

#### **Exercise-Induced Pre-conditioning for Faster Muscle Regeneration**

As described above, exercise and muscle PGC-1α induce several adaptations such as an increase in the SC pool, remodeling of the SC niche, enhancement of the proliferation and differentiation potential of SCs as well as a higher number of tissue-resident macrophages that could all contribute to an improved regenerative capacity of the muscle. It is conceivable that exercise creates an environment within the muscle and the niche that could prime the muscle for improved regeneration in response to muscle damage. This hypothesis of exercise-induced pre-conditioning is supported by various observations. For example, the altered tissue-resident macrophage population in muscles overexpressing PGC-1α could contribute to the improved SC proliferation and commitment in damaged muscles of these mice, since a variety of cytokines and growth factors that activate SCs are secreted by both muscle and macrophages. M1 macrophages release interleukin 6 (IL-6), insulin-like growth factor 1 (IGF-1) and vascular endothelial growth factor (VEGF), whereas M2 macrophages mainly secrete IGF-1 and VEGF (Wu et al. 2010; Lu et al. 2011; Tonkin et al. 2015). IL-6 and IGF-1 are important for the activation and proliferation of SC (Charge and Rudnicki 2004; Serrano et al. 2008) and may thereby contribute not only to the repair and regeneration but, in the case of resistance training, also the hypertrophic response to exercise. Besides the direct effect of IL-6 on SCs, changes in IL-6 levels may also be involved in the remodeling of the niche, as IL-6 KO mice show lower levels of FN (Serrano et al. 2008). These reduced FN levels contribute to impaired regeneration, which is in line with the FN-dependent decrease in regeneration observed in aging (Lukjanenko et al. 2016). Therefore, the number of macrophages as well as the proportion of M1 and M2 macrophages in a trained muscle could play a role in the pre-conditioning of the muscle.

Of note, several of these factors, such as IL-6, IGF-1 and VEGF, are also myokines that are produced and secreted from muscle after exercise (Schnyder and Handschin 2015). Collectively, the local levels of such signaling molecules could, therefore, affect the activation of both macrophages and SCs. For example, elevation of VEGF stimulates angiogenesis and thereby increases vascularization (Gustafsson 2011; Rowe et al. 2014b). The increase in VEGF is directly regulated by PGC-1α in muscle fibers (Arany et al. 2008; Baresic et al. 2014; Rowe et al. 2014b) and potentially also indirectly through the stimulation of M2 macrophage accumulation (Dinulovic et al. 2016a). The resulting increase in tissue vascularization improves activation of SCs and facilitates infiltration of immune cells.

Besides paracrine effects of muscle and macrophages, exercise-regulated modulation of intrinsic SC properties also influences the proliferation and differentiation potential. For example, isolated SCs of muscles exposed to functional overload fuse better than control SCs (Fujimaki et al. 2016), suggesting that the differentiation potential of SCs is improved by exercise in a manner that is maintained even after removal from the niche. Inactivation of Notch signaling and stimulation of Wnt signaling contribute to improved fusion of trained SCs (Fujimaki et al. 2016), but the exact mechanisms of this priming of SC memory is unclear. Recently, a G0alert state of SCs in the contralateral, non-damaged leg of mice with a pharmacologically induced muscle injury has been proposed to be linked to mammalian target of rapamycin complex 1 (mTORC1) activity (Rodgers et al. 2014). This SC pool exhibits an accelerated proliferative response upon activation. Similar to these observations in SCs, muscles may also have a memory in terms of the post-exercise inflammatory response: in human volunteers, MCP-1 levels as well as CD68+

macrophages were higher two days after a repeated bout compared to the first bout of exercise (Deyhle et al. 2015). While the mechanisms underlying this finding remain enigmatic, similar exacerbated macrophage activation is observed upon elevation of BNP by PGC-1α in exercised muscle (Furrer et al. 2017). Moreover, in muscles overexpressing PGC-1α, a larger area of cardiotoxin-damaged muscle is covered by macrophages and thus undergoing regeneration (Dinulovic et al. 2016a). In addition, after chronic muscle damage by multiple cardiotoxin injections, fiber size was recovered more efficiently and fibrotic tissue was reduced by PGC-1α overexpression (Dinulovic et al. 2016a, b), indicating that exercise- and PGC-1αinduced intrinsic and extrinsic adaptations contributed to enhanced regenerative capacity. Importantly, improved regeneration upon cardiotoxin-induced muscle damage occurred despite strong downregulation of PGC-1α expression (Dinulovic et al. 2016a, b). These data strongly imply a pre-conditioning effect to help trained muscle to cope with damage that includes a broad spectrum of functional modulation of SCs, endothelial cells and macrophages as well as cellular cross-talk that together create an environment that primes the muscle for faster regeneration.

#### **Conclusion**

Overexpression of PGC-1α in skeletal muscle ameliorates the symptoms of many different muscle diseases. Similarly, in those pathologies in which patients are exercise tolerant, training confers a beneficial effect on muscle functionality. While many different properties of exercise and PGC-1α most likely contribute to this therapeutic effect, the modulation and orchestration of macrophages and SCs could directly improve repair and regeneration in these diseases. Therefore, a more careful elucidation of the cell autonomous changes and cellular cross-talk that optimize repair and regeneration of damaged muscle tissue after exercise could provide novel targets and avenues to treat a wide variety of different muscle diseases.

**Acknowledgments** The work in our group related to the topic of this review is supported by the Swiss National Science Foundation, the European Research Council (ERC) Consolidator grant 616830-MUSCLE\_NET, Swiss Cancer Research grant KFS-3733-08-2015, the Swiss Society for Research on Muscle Diseases (SSEM), SystemsX.ch and the University of Basel.

#### **References**

regulation of VEGF and angiogenesis by the transcriptional coactivator PGC-1alpha. Nature 451:1008–1012

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# **Skeletal Muscle microRNAs: Roles in Differentiation, Disease and Exercise**

**Rasmus J. O. Sjögren, Magnus H. L. Lindgren Niss, and Anna Krook**

**Abstract** MicroRNAs (miRNA) are a noncoding RNA species that play important roles in the regulation of gene expression. Since miRNAs are able to target multiple genes simultaneously, miRNAs provide a mechanism for efficiently modulating a whole pathway to change or alter the functional properties in a particular target tissue. Ablation of miRNA processing specifically in skeletal muscle results in muscle abnormalities and perinatal death, underscoring that miRNAs control essential processes in skeletal muscle development and function. In this chapter we summarise current knowledge on miRNAs involved in skeletal muscle differentiation, disease and exercise.

#### **Discovery and Biological Role of miRNAs**

Precise control of gene expression and hence transcriptional regulation is a prerequisite for maintaining cellular functions, including growth and metabolic homeostasis. Several epigenetic factors, including DNA methylation, histone modification and miRNAs play important roles in gene expression in multicellular organisms. miR-NAs were first described at the beginning of the 1990s, when it was appreciated that these short single-stranded non-coding RNAs function to post-transcriptionally regulate mRNA abundance and protein translation. Lin-4 and let-7 were the first miRNAs to be identified and found to be involved in the regulation of larval development of *C. elegans* (Lee et al. 1993; Wightman et al. 1993; Reinhart et al. 2000). miRNAs have since been observed in viruses, plants and mammals (Bartel 2009) and found to regulate a wide variety of cellular functions, including proliferation, differentiation and metabolism (He et al. 2007b; Williams et al. 2009a; Massart et al. 2016).

R. J. O. Sjögren

Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden

M. H. L. Lindgren Niss • A. Krook (\*)

Department of Physiology and Pharmacology, Integrative Physiology, Karolinska Institutet, Stockholm, Sweden

e-mail<EMAIL_ADDRESS>
<sup>©</sup> The Author(s) 2017 67

B. Spiegelman (ed.), *Hormones, Metabolism and the Benefits of Exercise*, Research and Perspectives in Endocrine Interactions, https://doi.org/10.1007/978-3-319-72790-5\_6

#### **miRNA Biogenesis, mRNA Interaction and Target Prediction**

miRNAs are expressed in the nucleus as primary-miRNA (pri-miRNA) transcripts, which require processing before becoming functional miRNAs. Pri-miRNAs contain stem-loop structures that are recognized and cleaved by the RNase III endonuclease Drosha to form precursor miRNAs (pre-miRNAs; Lee et al. 2003). The pre-miRNA is then transported to the cytoplasm, where it is subsequently processed by RNase III Dicer into a mature miRNA of approximately 21 nucleotides (Hutvagner et al. 2001). The mature miRNA is then incorporated into the RNA-induced silencing complex (RISC), where mRNA target interaction takes place (Hammond et al. 2000). The canonical model of miRNA-mRNA interaction occurs by perfect or partial pairing at the 5′-end of the miRNA to a complementary site of the 3′ UTR of the target. A sequence of 6–8 nucleotides at the 5′ end of the miRNA, called the seed region, guides the RISC complex to targeted mRNAs. Repression of mRNAs may then occur by inhibition of translation or by degradation of the transcript, ultimately reducing protein abundance of targeted genes (Melo and Melo 2014).

A key challenge in miRNA research to date involves identifying the target genes for each miRNA. The seed is a dominant motif in miRNAs and is, therefore, used in prediction algorithms to identify potential targets. The algorithms are centred on finding complementary sequences in the 3′ UTR of mRNAs. Evolutionary conservation of the complementary sequence is also taken into consideration, and whether there is a supplementary pairing site in the 3′ part of the miRNA (for review, see Bartel 2009). However, at present the prediction algorithms have been only partially accurate in identifying true target sites, yielding both false positive and false negative predictions. When comparing results from silencing and over-expression experiments, only a small percentage of mRNA targets were found to be affected by both (Nicolas et al. 2008). Of these, approximately half had a sequence in the 3′-UTR matching the seed, but none was found by prediction programs (Nicolas et al. 2008). Since miRNAs exert their function both at the level of mRNA abundance and at protein abundance, transcriptomic analysis will miss targets that are regulated only at the protein level. To further add to the complexity of miRNA regulation, one gene may be targeted by several different miRNAs. Additionally, the same miRNA may have several target sites on the same gene (Bartel 2009). One miRNA may even have opposing effects on a target gene in different tissues (Lu et al. 2010; Chuang et al. 2015).

#### **Role of miRNAs in the Regulation of Skeletal Muscle Development**

#### *Skeletal Muscle Development and Regeneration*

Skeletal muscle has been shown to express several RNA species (Gallagher et al. 2010), and dysregulation of skeletal muscle miRNAs has been implicated in a number of different disease states (Williams et al. 2009a; Massart et al. 2016). Skeletal

muscle has a large adaptive potential in response to contractile activity. This plasticity is necessary to modify contractile features and adapt metabolic capacity to new requirements following endurance and/or resistance exercise. Skeletal muscle also has a high regenerative capacity in response to injury or damage. Critical to this plasticity and the regenerative potential of skeletal muscle are myogenic progenitor cells, termed satellite cells (Dumont et al. 2015). In adults, satellite cells are quiescent but they can, in response to injury or increased contractile activity, re-enter the cell cycle and proliferate and thereafter differentiate, fuse, and regenerate myofibres.

Skeletal muscle development has been studied extensively in vitro through isolation, growth and differentiation of satellite cells and in vivo during embryogenesis (see Fig. 1). Molecular regulation of cell determination to the myogenic lineage and proliferation of these cells involves the paired box transcription factors PAX3 and PAX7 (Braun and Gautel 2011). Pax3 loss-of-function during embryogenesis in mice results in the absence of musculature in the trunk region due to reduced migration of satellite cells (Bober et al. 1994; Tremblay et al. 1998). While Pax7 deletion in animals has little effect on embryogenic muscle formation (Oustanina et al. 2004), it is essential for satellite cell formation and adult myogenesis (Sambasivan et al. 2011). When activated, satellite cells can migrate and re-enter the cell cycle to start proliferation. The processes of satellite cell commitment and differentiation into multinucleated myotubes are controlled by a group of basic helix-loop-helix transcription factors known as myogenic regulatory factors (MRFs). The MRFs consist of four members: myogenic factor 5 (MYF5), myogenic differentiation 1 (MYOD1), myogenin (MYOG), and myogenic regulatory factor 4 (MRF4). These transcription factors promote terminal differentiation by regulating the expression of several myogenic genes (Braun and Gautel 2011). Satellite cells committed to the myogenic program and myogenesis are termed myoblasts, which typically express PAX7 and early MRFs such as MYOD and MYF5. Expression of these early MRFs is crucial for myoblast maturation because a combined Myod and Myf5 loss-offunction results in the absence of skeletal muscle formation during embryogenesis (Rudnicki et al. 1993). MYF5 and MYOD are considered determination factors required for establishment of myogenic identity since they are upstream transcriptional regulators of late MRFs, MYOG and MRF4 (Braun and Gautel 2011). Myoblasts will leave the cell cycle for terminal differentiation by lowering PAX7 and MYF5 expression while inducing the late MRFs. The late MRFs are important for terminal differentiation, and lack of, for example, Myog in mice leads to postnatal lethality due to muscle deficiency (Hasty et al. 1993).

#### *miRNA Regulation of Myogenesis In vitro and In vivo*

miRNAs are important regulators of cell proliferation and differentiation, processes that are critical to proper tissue development and maintenance. The relative expression of miRNAs is tissue dependent, with several miRNAs showing enrichment in skeletal muscle (and heart muscle). These muscle-enriched miRNAs are termed

**Fig. 1** mately 80% confluence (Day 0; white bars) and induced to differentiate for 10 days (Day 10; black bars). RNA was collected and mRNA and miRNA expression was assessed by RT qPCR. (**a**) Expression of the myogenic markers desmin (DES), myogenic factor 5 (MYF5) and myogenin (MYOG) before inducing myoblasts to differentiate and following differentiation into myotubes. (**b**) Expression of a muscle-enriched (miR-1) and a non-muscle-enriched (miR-30c) miRNA before and after differentiation. (**c**) Representative microscope bright-field pictures of cells prior to inducing differentiation (top) and following myotube formation after differentiation (bottom). All data represent average ± SEM, \*p < 0.05 [paired student's t-test (normal distribution of data) or Wilcoxon matched-pairs singed rank test (non-normal distribution of data)] "myomiRs" (Sempere et al. 2004). Some members of the myomiRs are organized in bicistronic clusters with genomic colocalisation of miR-1-1/miR-133a-2, miR-1-2/miR-133a-1 and miR-133b/206. Thus, these miRNAs share similar expression patterns and are induced during skeletal muscle cell differentiation by upstream transcriptional regulation of different MRF members, including MYOD and MYOG (Rao et al. 2006; Sweetman et al. 2008). Besides sharing similar regulation of expression, they also share similar sequences. For example, miR-133a-1/2 (identical sequences) and miR-133b differ by only one single nucleotide outside of the seed region, and miR-1-1/2 (identical sequences) and miR-206 differ in four nucleotides at the 3′ end but share identical seed sequences in humans. Thus, miR-1 and miR-206, and miR-133a and miR-133b, respectively, regulate a set of similar, but not identical, target genes.

The aforementioned myomiRs are all potent regulators of satellite cell differentiation and proliferation. miR-1 and miR-206 are induced upon satellite cell commitment and differentiation, and increased expression promotes differentiation of these cells (Fig. 1; Chen et al. 2006; Kim et al. 2006). miR-1 and miR-206 induce differentiation by targeting several repressors of skeletal muscle cell differentiation, including histone deacetylase 4 (Hdac4); Chen et al. 2006; Williams et al. 2009b), Pax3 (Goljanek-Whysall et al. 2011) and Pax7 (Chen et al. 2010). The effects of miR-133a/b on myogenesis are still debated. It is clear that miR-133, like several other myomiRs, is induced during skeletal muscle differentiation (Chen et al. 2006; Kim et al. 2006), whereas different effects of miR-133 on the myogenic process have been reported. Initial observations have indicated that miR-133 promotes the proliferative state of myoblasts by repressing Serum Response Factor (Srf; Chen et al. 2006). More recent evidence suggests that miR-133 instead functions by inhibiting myoblast proliferation to promote muscle cell differentiation (Zhang et al. 2012; Feng et al. 2013). These effects of miR-133 are due to targeting of proproliferative target genes. Such targets include SP1 transcription factor (Sp1), an upstream regulator of Cyclin D1 expression, and Fibroblast Growth Factor Receptor 1 (Fgfr1) and Protein Phosphatase 2 Catalytic Subunit Alpha (Pp2ac), which are important for regulation of ERK1/2 phosphorylation status (Zhang et al. 2012; Feng et al. 2013). The differences in results noted could indicate that miR-133 can influence both proliferation and differentiation in a context-dependent manner.

While the effects of miR-1 and miR-206 on skeletal muscle cell differentiation in vitro are clear, ablation of miR-1-2 or miR-206 in mice did not result in disturbed skeletal muscle development during embryogenesis in vivo (Zhao et al. 2007; Williams et al. 2009b). Nevertheless, genetic deletion of miR-206 negatively affected post-embryonic regeneration of muscle and neuromuscular connections following injury (Williams et al. 2009b; Liu et al. 2012). Furthermore, miR-206 deletion exacerbated the dystrophic phenotype in a mouse model of muscular dystrophy (Liu et al. 2012). Contrary to these results, deletion of the miR-206/miR-133b cluster was found not to be required for proper muscle regeneration (Boettger et al. 2014). These differences, and the absence of a robust phenotype following myomiR loss-of-function during embryogenesis, could be explained by the large redundancy in this group of myomiRs. Not only are there several genomic copies of

miR-1 and miR-133a, but they also share similar sequences with miR-206 and miR-133b, respectively. Thus, miR-1 and miR-133a could compensate for the loss of miR-206/133b expression (Boettger et al. 2014). In contrast, *D. melanogaster* has only one genomic copy of miR-1 and, in this context, the absence of miR-206 is coincident with severely deformed musculature in miR-1 mutant larvae (Sokol and Ambros 2005), implicating that the presence of several genomic copies, as found in other species, provides system redundancy. Furthermore, a double knockout of miR-133a-1 and miR-133a-2 in mice resulted in cardiomyopathy and lethal defects in approximately 50% of offspring, whereas mice lacking only miR-133a-1 or miR-133a-2 did not show this phenotype (Liu et al. 2008). The miR-133a double knockout mice that survived until adulthood also showed skeletal muscle defects, including mitochondrial dysfunction and centronuclear myopathy of type II fibres (Liu et al. 2011).

Skeletal muscle-specific Dicer ablation in mice results in reduced musclespecific miRNA expression and perinatal death, abnormal muscle morphology and reduced skeletal muscle mass (O'Rourke et al. 2007), indicating that proper miRNA maturation is essential for overall muscle function and development. Conditional knockout of Dicer in satellite cells in adult mice results in satellite cells exiting quiescence and entering the cell cycle, leading to severely impaired regeneration following skeletal muscle injury (Cheung et al. 2012). Collectively, evidence points towards a central function of miRNAs, not only for normal embryonic skeletal muscle biogenesis but also for adult muscle function and repair.

Not only is the expression of several myomiRs altered in response to satellite cell expansion and differentiation, but also several non-muscle enriched miRNAs are temporally regulated in expression during muscle cell differentiation (see Fig. 1). Several studies have determined miRNA expression profiles during differentiation in vitro and during myogenesis in vivo (Chen et al. 2011; Cheung et al. 2012; Koning et al. 2012; Dmitriev et al. 2013; Sjögren et al. 2015). We identified 44 miR-NAs, including the canonical myomiRs, to be altered during primary human skeletal muscle cell differentiation (Sjögren et al. 2015), comparable to the 60 miRNAs altered in a similar study (Dmitriev et al. 2013). Nevertheless, these miRNAs might not influence either the proliferation or differentiation process but may be involved in other phenotypic changes associated with muscle cell differentiation, such as metabolism, hypertrophy or mitochondrial function. Examples of non-muscleenriched miRNAs identified to have altered expression and directly affecting proliferation or differentiation of skeletal muscle cells include, for example, miR-26a (Dey et al. 2012), miR-30 (Fig. 1; Guess et al. 2015) and miR-199a-5p (Alexander et al. 2013). Future studies are required to determine the targets and effects on skeletal muscle cell proliferation and differentiation, both in vivo and in vitro, of several of the miRNAs that have altered expression during satellite cell activation and differentiation.

#### *Skeletal Muscle miRNA in Metabolic Disease*

Approximately half of the total body mass in healthy individuals consists of skeletal muscle, representing a substantial part of whole-body metabolism (DeFronzo and Tripathy 2009). Skeletal muscle also represents a primary target for insulin-mediated glucose disposal, and skeletal muscle insulin resistance is a characteristic feature in type 2 diabetes and in conditions of impaired glucose metabolism transport activity (Krook et al. 2000; Ryder et al. 2000). Furthermore, skeletal muscle function is crucial for proper posturing and locomotion patterns, and disruption in metabolic or motile functionality may greatly decrease an individual's quality of life. While there is good evidence that miRNAs play a key role in the regulation of muscle growth and differentiation, dysregulation of skeletal muscle miRNA expression in disease states has been less well studied, in particular in human cohorts.

Several miRNAs were found to be downregulated in response to a 3-h euglycaemic-hyperinsulinaemic clamp in human skeletal muscle, including miR-1 and miR-133a (Granjon et al. 2009). Expression profiles of skeletal muscle miR-NAs have been reported in a number of different rodent models of obesity and diabetes, including Goto-Kakizaki (GK) rats, a non-obese spontaneous T2D model (He et al. 2007a; Huang et al. 2009; Herrera et al. 2010), rats rendered diabetic by a combination of high fat diet and a low dose of streptozotocin (Karolina et al. 2011), as well as mice fed a high fat diet (Chen et al. 2012; Mohamed et al. 2014). These different models share skeletal muscle insulin resistance as a key characteristic. When miRNA profiles from patients with type 2 diabetes were compared against these different animal models, eight upregulated miRNAs found in type 2 diabetic patients were also found to be upregulated in at least one of the rodent arrays (for review and analysis, see Massart et al. 2016). Similarly, 16 of the downregulated miRNAs were recapitulated in rodent studies. Interestingly, two thirds of the commonly regulated miRNAs have been identified in mice rendered insulin resistant in response to a high fat diet (Massart et al. 2016). However, different miRNAs showed altered expression depending on the model. For example, miR-99a and miR-100, which were downregulated in skeletal muscle from patients with type 2 diabetes and in mice fed a high fat diet, were upregulated in rats on a high fat diet rendered diabetic with streptozotocin, suggesting that the aetiology of the insulin resistance influenced the miRNA expression signature. As yet, only a few of these miRNAs have been studied in vitro and/or in vivo in relation to metabolic disease, and their targets remain poorly characterized.

#### **Skeletal Muscle miRNAs in Response to Exercise**

Skeletal muscle responds rapidly to exercise, and even a single bout of acute exercise is sufficient to induce changes in gene expression. In response to many bouts of acute exercise, i.e., exercise training, skeletal muscle adapts by altering protein production, which impacts the functional as well as the metabolic properties of the exercised muscle. The inter-individual variability in skeletal muscle hypertrophic response to resistance exercise has been attributed to variations in the ability of satellite cell mobilization to proliferate and subsequent fuse with existing muscle fibres (Petrella et al. 2006, 2008). As detailed above, several miRNAs regulate skeletal muscle satellite cell quiescence, activation and differentiation, indicating potential roles for these miRNAs in skeletal muscle hypertrophy.

Identification of pathways and molecular mechanisms regulating skeletal muscle exercise adaptation has been a key focus from a number of different angles; from probing understanding of athletic performance to alterations relevant for metabolic disease. Different modes of exercise training lead to different adaptive responses; for example, strength training leads to more hypertrophic responses whereas aerobic exercise increases endurance. These differences are also mirrored in the different intracellular signalling pathways that are activated (for review, see Egan and Zierath 2013). Several lines of evidence suggest an important role for miRNAs in skeletal muscle development and hypertrophy (for review see Zacharewicz et al. 2013; Kovanda et al. 2014).

Several human studies have reported that short exercise bouts/training duration lead to increased skeletal muscle expression of miR-1 (Nielsen et al. 2010; Russell et al. 2013), whereas longer endurance training protocols lead to miR-1 downregulation (Nielsen et al. 2010; Drummond et al. 2011; Keller et al. 2011). Rodent studies are in general agreement regarding regulation of myomiRs in response to exercise (for review see Zacharewicz et al. 2013). In contrast, although several of the classical myomiRs have been noted to respond to exercise, a clear consensus has yet to emerge regarding which other miRNAs are regulated in response to exercise. These inconsistencies in the literature may be due to species differences as well as differences in the exercise protocols used, but they may even depend on different RNA extraction protocols (for more in depth discussion see Zacharewicz et al. 2013).

#### **miRNA Regulation of Skeletal Muscle Atrophy and During States of Muscle Mass Loss**

Skeletal muscle is the largest organ in adult mammals and muscle function is critical to metabolic homeostasis and health across the whole life span. Loss of skeletal muscle mass (atrophy) and function, also referred to as sarcopenia, occurs with aging (Cartee et al. 2016). Muscle loss is also noted in obese individuals, a phenomenon known as sarcopenic obesity, and is associated with lowered skeletal muscle insulin sensitivity. In addition, individuals with cancer, HIV-AIDS, chronic heart failure, chronic obstructive pulmonary disease, renal failure, rheumatoid arthritis, and osteoarthritis can all experience a dramatic loss of muscle mass (Wolfe 2006). Thus understanding the regulation of skeletal muscle mass and function is of clinical relevance.

Three members of the myomiR family, miR-208a, miR-208b and miR-499, are encoded and expressed from sequences embedded within heart- and skeletal muscleenriched myosin genes, MYH6, MYH7 and MYH7B, respectively (van Rooij et al. 2009). miR-208a is exclusively expressed in heart muscle, whereas miR-208b and miR-499 are expressed both in heart muscle and in type I (oxidative) skeletal muscle fibres. Loss of function of both miR-208b and miR-499 in mice results in loss of type I fibres and is associated with increased expression of miR-208b and miR-499 pro-atrophic target genes, including purine rich element binding protein beta (Purb) and SRY-box containing gene 6 (Sox6; van Rooij et al. 2009). Furthermore, four weeks of hind limb suspension, resulting in severe skeletal muscle atrophy, reduces expression of miR-208b and miR-499 in rat skeletal muscle (McCarthy et al. 2009). In humans with skeletal muscle loss due to spinal cord injury, expression of miR-208b and miR-499-5p is reduced and inversely related to expression of myostatin, a critical regulator of skeletal muscle mass (Boon et al. 2015). These miRNAs also participate in a transcriptional network together with estrogen-related receptors (ERRs) and members of the peroxisome proliferator activated receptor (PPAR) family to coordinate the regulation of skeletal muscle energy metabolism and fibre type specification (Gan et al. 2013).

Muscular dystrophy is characterized by muscle atrophy and weakness. A large microarray-based study found expression of 185 miRNAs to be altered in skeletal muscle in at least one of 10 common muscular disorders, including Duchenne muscular dystrophy (DMD; Eisenberg et al. 2007). Several miRNAs have been noted to be dysregulated both in human Duchenne muscular dystrophy and in a mouse model of the disease (Greco et al. 2009). Examples of dysregulated miRNAs include miR-206, which was upregulated in states of muscular dystrophy (McCarthy et al. 2007; Greco et al. 2009), potentially due to constant regenerative processes in dystrophic skeletal muscle. However, as mentioned above, mice lacking miR-206 have a muscle dystrophic phenotype (Liu et al. 2012), underscoring the complexity of miRNA regulation. In the case of muscular dystrophy, a number of miRNAs has been proposed to have possible therapeutic potential. For example, overexpression of miR-486, another muscle-enriched miRNA with decreased expression in muscle dystrophy (Eisenberg et al. 2007), improved muscle function in a mouse model of muscle dystrophy (Alexander et al. 2014). Another example is miR-431, where transgenic miR-431 mice with muscular dystrophy have improved skeletal muscle fatigability and force generation, potentially due to improved muscle regeneration and enhanced muscle cell differentiation capacity in vitro (Wu et al. 2015).

miRNAs are also potent regulators of signalling pathways known to regulate muscle atrophy. Two critical regulators of skeletal muscle atrophy are F-box Protein 32 [FBXO32; also known as Muscle atrophy F-box (MAFbx)] and Tripartite Motif Containing 63 [TRIM63; also known as Muscle RING finger 1 (MuRF1)], which regulate protein degradation through regulation of ubiquitination. The family of Forkhead Box O (FOXO) transcription factors, especially FOXO1 and FOXO3, are strong upstream regulators of both these genes during skeletal muscle atrophy (Glass 2010). FOXO1 activity was reduced following miR-486 overexpression in skeletal muscle cells in vitro, both through decreased FOXO1 expression and through increased FOXO1 phosphorylation (inhibitory) through direct miR-486targeting of Phosphatase and Tensin Homolog (PTEN), a negative regulator of FOXO1-upstream kinase AKT (Xu et al. 2012). miR-486 overexpression also reduced induction of FOXO1 targets MAFbx and MuRF-1 protein expression in primary mouse muscle cells following treatment with dexamethasone, a potent inducer of muscle atrophy (Xu et al. 2012). In addition, overexpression of miR-486 protected against skeletal muscle loss in a mouse model of chronic kidney disease (Xu et al. 2012). Furthermore, miR-486 inhibition in vitro slightly reduced myotube diameter, and inhibition in vivo reduced the cross-sectional area of muscle fibres, confirming the effects of miR-486 on regulation of skeletal muscle mass (Hitachi et al. 2014). MAFbx and MuRF1 are also directly under miRNA-dependent regulation by two different miRNAs, miR-23a and miR-23b (Wada et al. 2011). miR-23a overexpression either in vitro or with a transgene in mouse skeletal muscle protected against dexamethasone-induced muscle atrophy (Wada et al. 2011). Cellular miR-23a is decreased in dexamethasone-induced skeletal muscle atrophy, potentially through a mechanism including enhanced exosomal release of miR-23a (Hudson et al. 2014).

miRNA expression has also been assessed following age or sarcopenia-induced skeletal muscle loss (Drummond et al. 2011; Rivas et al. 2014; Zacharewicz et al. 2014). Nevertheless, the overlap of miRNA signatures is small between these studies, potentially due to the small number of biological replicates in each study or due to different platforms to assess miRNA expression. Further studies will be needed to determine the impact of miRNA expression and regulation of target genes in human sarcopenia and muscle loss with ageing.

#### **Conclusions**

miRNAs are critical regulators of skeletal muscle function and play important roles in maintaining muscle function and regulating adaptation to different situations, including muscle use and disuse, as well as in different disease states. While the role of some of the classical myomiRs is increasingly appreciated and well understood, the challenge for the field lies in understanding not only the precise regulation of different miRNA species but also in dissecting and understanding the precise targets regulated by each miRNA or combination of miRNAs. Unravelling these molecular signatures could facilitate targeted and tissue-specific miRNA interventions in different disease states affecting skeletal muscle function and metabolism.

#### **References**

Alexander MS, Kawahara G, Motohashi N, Casar JC, Eisenberg I, Myers JA, Gasperini MJ, Estrella EA, Kho AT, Mitsuhashi S, Shapiro F, Kang PB, Kunkel LM (2013) MicroRNA-199a is induced in dystrophic muscle and affects WNT signaling, cell proliferation, and myogenic differentiation. Cell Death Differ 20:1194–1208

**Open Access** This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

# **Tryptophan-Kynurenine Metabolites in Exercise and Mental Health**

**Paula Valente-Silva and Jorge Lira Ruas**

**Abstract** In our efforts to identify molecular mediators of the benefits of exercise to human health, we have uncovered a biochemical pathway in skeletal muscle that positively impacts mental health. This mechanism is activated by endurance training and controlled by the transcriptional coactivator PGC-1α1, which induces transcription of several kynurenine aminotransferase (KAT) genes in muscle. KAT enzymes catabolize the neuroinflammatory tryptophan metabolite kynurenine, which can accumulate in the brain and lead to alterations associated with stressinduced depression (among other psychiatric diseases). Here, we discuss our findings in the context of what is known about the kynurenine pathway of tryptophan degradation and how its many metabolites can directly affect the brain. These findings provide a mechanism for how physical exercise can improve mental health and offers potential therapeutic targets for future antidepressant medications.

#### **Introduction**

The many benefits of physical exercise to human health are widely recognized. Depending on their type, intensity, duration, and frequency, different exercise modalities can be used to improve diverse physiological parameters (Hawley et al. 2014). These include cardiovascular fitness, strength, energy metabolism, and resistance to fatigue, among others. Importantly, exercise training can be used as a prophylactic or therapeutic intervention for a variety of pathologies ranging from obesity and diabetes to cancer and mental health disorders (Cooper et al. 2017; Hawley 2004). Indeed, maintaining an active lifestyle continues to be the best way to promote healthy longevity. However, the molecular mechanisms that allow the human body to adapt to exercise challenges are still poorly understood. This is true

P. Valente-Silva • J. L. Ruas (\*)

Department of Physiology and Pharmacology, Molecular and Cellular Exercise Physiology, Karolinska Institutet, Stockholm, Sweden e-mail<EMAIL_ADDRESS>
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for individual tissues and organs but even more so for the network of inter-organ communication events that coordinates whole-body adaptation to the multitude of stimuli that exercise training entails. Skeletal muscle plays a central role in this process and has been the most studied tissue in this context. From this work, we have learned valuable information about key players in the regulation of muscle function, such as the peroxisome proliferator-activated receptor (PPAR)-γ coactivator-1α (PGC-1α) proteins (Correia et al. 2015). This still expanding family of transcriptional coactivators is composed of several splicing variants with different biological activities and discrete regulation, important for the many effects of physical exercise (Martínez-Redondo et al. 2015). By understanding these mechanisms, we have also started to elucidate how myokines (muscle-derived factors with local and/or distal effects) communicate to the rest of the body the changes in skeletal muscle elicited by exercise (Giudice and Taylor 2017). For example, exercise training can reduce the levels of several mediators of chronic low-grade inflammation (Handschin and Spiegelman 2008), which increase with sedentary habits. This kind of sustained, unresolved, low-grade sterile inflammation has been linked to the etiology of many diseases such as diabetes, cancer, and depression. In this context, the discovery that trained muscle can actively participate in the catabolism of neurotoxic tryptophan metabolites with known deleterious effects on mental health (Agudelo et al. 2014) has added another layer of complexity to the many functions of exercised muscle.

#### **The PGC-1α Family of Transcriptional Coactivators in Skeletal Muscle**

Although our understanding of the mechanisms that regulate skeletal muscle adaptation to different exercise challenges remains incomplete, PGC-1α coactivators have been shown to play important roles in this process. These proteins are expressed in energy-demanding tissues such as heart, skeletal muscle, adipose tissue, and brain (Correia et al. 2015). Interestingly, the PGC-1α gene can be transcribed from alternative promoters and its transcripts can be spliced to generate several PGC-1α variants with different biological activities (Martínez-Redondo et al. 2015; Ruas et al. 2012). The expression of PGC-1α1 [the founding member of the family (Puigserver et al. 1998)] is increased by aerobic exercise and regulates genes involved in mitochondrial biogenesis, adaptive thermogenesis, lipid and glucose homeostasis, and fiber-type switching, among others (Correia et al. 2015). For these reasons, reduced PGC-1α1 expression in different tissues has been linked to obesity, diabetes, sarcopenia, and neurodegeneration. Conversely, it has been shown that sustained PGC-1α1 expression in mouse skeletal muscle has several beneficial effects. PGC-1α4 is induced by resistance exercise training and promotes skeletal muscle growth and strength. Importantly, transgenic animals with elevated PGC-1α4 levels in skeletal muscle show increased exercise performance and resistance to atrophy and to cancer-induced cachexia (Ruas et al. 2012). Of the many other PGC-1α isoforms (Martínez-Redondo et al. 2015), PGC-1α2 and α3 have been shown to be involved in alternative splicing of their target genes (Martinez-Redondo et al. 2016), but their main biological roles in skeletal muscle remain unknown.

In addition to helping us understand local adaptations in skeletal muscle, PGC-1α coactivators have been used as tools to investigate the distal actions of exerciseinduced myokines. This research has been greatly helped by the use of mouse genetic models with tissue-specific gain or loss of PGC-1α function developed to mimic the physiological and pathophysiological situations associated with altered PGC-1α expression (Rowe and Arany 2014). From these efforts, PGCs have been found to regulate the expression of several myokines involved in inflammation (Handschin and Spiegelman 2008), angiogenesis (VEGF; Arany et al. 2008; Chinsomboon et al. 2009), non-shivering thermogenesis (Fndc5/Irisin, Meteorinlike 1, and β-aminoisobutyric acid; Boström et al. 2012; Rao et al. 2014; Roberts et al. 2014), and muscle mass regulation (Myostatin; Ruas et al. 2012). Similarly, the observation that muscle-specific PGC-1α1 transgenic mice (MKC-PGC-1α; Lin et al. 2002) are resistant to developing depressive-like behaviors when exposed to chronic mild stress resulted in the identification of skeletal muscle as an important site for kynurenine (Kyn) detoxification (Agudelo et al. 2014). Kyn and some of its metabolites are known neurotoxic compounds linked to mental health disorders, such as depression and schizophrenia (Cervenka et al. 2017).

#### **The Kyn Pathway of Tryptophan Degradation**

Tryptophan (TRP) is an essential amino acid that cannot be synthesized by the human body and must be acquired from diet. TRP is taken up through the large neutral amino acid transporter (LAT) isoforms 1 to 4, which are also responsible for the uptake of other amino acids such as phenylalanine, tyrosine, and cysteine, and others. However, with the exception of the blood-brain barrier (BBB), LATs have sufficient capacity to avoid significant competition between TRP and other amino acids. A large fraction of plasma TRP circulates bound to albumin and is thus not available for cellular uptake and usage, since only free TRP is taken up by LATs. Under normal circumstances, only about 1% of absorbed TRP is used in protein synthesis, 4–5% is used in the production of the neurotransmitter serotonin and the hormone melatonin, and about 95% is catabolized by the Kyn pathway of TRP degradation (KP). The KP generates NAD+ as an end-product (mainly in liver and kidney; Houtkooper et al. 2010) and a collection of intermediary metabolites with diverse biological activities (Fig. 1), which have been mainly studied in the context of psychiatric disease.

There are two isozymes that catalyze the first step of TRP degradation: tryptophan 2,3-dioxygenase (TDO) and indoleamine 2,3-dioxygenase (IDO). TDO is specific for TRP and has a more restricted expression pattern (highest in the liver; Yu et al. 2016), whereas IDO is more ubiquitous and metabolizes any compound with

**Fig. 1** The kynurenine pathway of tryptophan degradation. *Tph* tryptophan hydroxylase, *IDO* indoleamine 2,3-dioxygenase, *TDO* tryptophan 2,3-dioxygenase, *L-Kyn* L-kynurenine, *AA* Anthranilic acid, *KMO* Kynurenine 3-Monooxygenase, *KATs* Kynurenine amino-transferases, *3HAO* 3-Hydroxyanthranilate 3,4-Dioxygenase, *PA* Picolinic acid, *Quin* Quinolinic acid, *QPRT* Quinolinate phosphoribosyltransferase, *NAD+* Nicotinamide adenine dinucleotide, *NAPRT1* Nicotinate phosphoribosyltransferase

an indol ring structure. The expression and activity of TDO are regulated by cues that include tryptophan, glucocorticoid, and estrogen levels (Yu et al. 2016) and are inhibited under pro-inflammatory conditions. Under those conditions, liver TDO ceases to be the main isozyme responsible for TRP metabolism (normally accounting for 90%), and the extra-hepatic IDO takes on a more prominent role. This shift in TRP metabolism causes an increase of Kyn produced by immune cells that express high levels of IDO in an attempt to control the inflammatory environment. Indeed, as pro-inflammatory cytokines stimulate IDO activity, rising Kyn levels activate the aryl hydrocarbon receptor (AhR) in discrete immune populations and reduce the activity of natural killer cells (NKT), dendritic cells (DC), and T-cells while allowing for Treg proliferation. The net result of this process is an increase in immune tolerance. In line with this, some tumor cells express high IDO/TDO levels as a strategy for escaping the immune system (Platten et al. 2015). Importantly, not all cells possess the complete enzymatic machinery to fully convert TRP to NAD+. In general, the metabolites generated by the KP depend on which enzymes are expressed in each cell type. For example, Kyn aminotransferases (KATs), of which there are four genes in the human genome, are responsible for metabolizing Kyn to kynurenic acid (Kyna) and 3-hydroxykynurenine (3-HK) to xanthurenic acid (XA) (Fig. 1). This is particularly important in the brain, as both Kyna or XA have neuroprotective properties as opposed to several of the downstream metabolites of the KP.

#### **KP Metabolites and Mental Health**

The link between the KP and central nervous system (CNS) toxicity has been appreciated for a long time (Lapin 1978). However, it was only in the early 1980s that quinolinic acid (QA) was shown to be a NMDAR agonist (Stone and Perkins 1981), thus providing the first mechanism for KP-induced CNS toxicity. The majority of Kyn reaches the brain from the periphery, as it can easily cross the BBB. However, Kyn can also be produced locally from TRP by astrocytes and microglial cells (Guillemin et al. 2001; Stone and Darlington 2002). Interestingly, although Kyn accumulation in the CNS is consistently associated with neuroinflammation and negative outcomes, its precise mechanism of action remains elusive. In fact, activation of astrocytic AhR (for which Kyn is an agonist) has been shown to be antiinflammatory in the context of multiple sclerosis (Rothhammer et al. 2016). To date, KP-associated toxicity is mostly understood as the balance between the excitotoxic actions of QA and the neuroprotective effect of Kyna as a NMDAR and α7-nicotinic acetylcholine receptor (α7-nAChR) antagonist. Microglial cells can produce QA whereas astrocytes preferentially generate Kyna (although they can produce QA from extracellular 3-HK, which increases with neuroinflammation). Interestingly, elevated Kyna levels have been found in the cerebrospinal fluid of schizophrenic patients (Linderholm et al. 2012). Conversely, reducing brain Kyna levels in preclinical studies (using a KAT2 inhibitor) resulted in improved cognitive function during chemically induced challenges to working and spatial memory tasks (Kozak et al. 2014). In this situation, the pathological consequences of high Kyna concentrations are likely to manifest due to hypoglutamatergic function. 3-HK induces neuronal apoptosis through free-radical generation (Okuda et al. 1998; Polyzos and Ketelhuth 2015). Several metabolites of the pathway have also been linked to neurodegenerative diseases such as Alzheimer's disease, Huntington's disease, Parkinson's disease, among others.

#### **Crosstalk Between Physical Exercise and KP Metabolites**

The importance of TRP in exercise has been extensively investigated (Newsholme and Blomstrand 2006), not only due to its critical role in protein synthesis but also in efforts to understand the development of central fatigue and the interplay between diet, exercise, and mood disorders. For example, the increase in circulating free fatty acids that occurs during endurance exercise displaces TRP from albumin and

increases free TRP levels, resulting in higher TRP uptake and metabolism in different tissues, with direct impact on the KP. Accordingly, an increase in free TRP plasma levels is followed by an increase in TRP and some of its metabolites in several brain regions associated with the control of mood and fatigue (Blomstrand et al. 1989; Newsholme and Blomstrand 2006).

High TRP uptake during exercise could lead to higher NAD+ production, which is indeed increased in tissues such as the liver (which is able to fully catabolize TRP). However, the risk of accumulating KP metabolites that can cross the BBB (e.g., Kyn and 3-HK) could have detrimental effects. Recently, it has been shown that exercise increases the expression of KAT enzymes in skeletal muscle (Agudelo et al. 2014; Schlittler et al. 2016), promoting the conversion of Kyn into Kyna, which does not cross the BBB (Agudelo et al. 2014). Peripheral Kyn catabolism prevents its accumulation in the brain and the associated deleterious effects. This is particularly relevant in the context of stress-induced depression, which is characterized by high Kyn levels. This work has provided a mechanism for how physical exercise can improve mental health. Exercise training activates muscle KAT expression and Kyn detoxification by inducing the expression of PGC-1α1 and the PPARα/δ transcription factors, which offers potential therapeutic targets for future antidepressant medications. Interestingly, elevated NAD+ levels could further promote this mechanism, as NAD+ is a positive regulator of Sirtuin1, which in turn activates PGC-1α1 through lysine deacetylation (Rodgers et al. 2005). Sirtuins have multiple effects on the regulation of cellular energy metabolism, metabolic enzymes and oxidative stress responses in mitochondria (Cantó et al. 2015). Therefore, alterations in the KP may ultimately affect skeletal muscle metabolism and function through multiple concurrent mechanisms.

#### **Future Perspectives and Considerations**

TRP is without doubt an important player in many critical functions of a living organism. In addition to being a building block for protein synthesis, TRP plays a role in mental health, energy homeostasis, and immune regulation. It is exciting to see research in these different areas becoming more and more interdisciplinary.

Although we have focused here mainly on post-absorptive TRP metabolism, this amino acid can be sequestered by gut microbiota and converted to tryptamine (Williams et al. 2014) or other indole compounds such as indole-3-propionic acid and indole-3-aldehyde (Rothhammer et al. 2016). Indol compounds serve as important signals between bacteria and between bacteria and their host. Indols are also AhR agonists involved in the local immunomodulation of host tolerance responses and, distally, in the regulation of CNS inflammation (Hubbard et al. 2015). It is therefore critical to consider the effect of the microbiome on TRP metabolism and host physiology. For example, germ-free (GF) mice show reduced anxiety-like behavior, and this phenotype is reversed after colonization. This observation suggests that gut-derived molecules can modulate mood and possibly cognition, and it opens interesting challenges in the discovery of novel pathways of gut-brain communication (Heijtz et al. 2011). Physical exercise also has an impact on gut microbiome composition, but it remains to be explored if this directly impacts on gut TRP metabolism.

The KP is highly conserved evolutionarily, which allows for its study in simpler organisms to dissect its many functions. For example, Kyna has been shown to be involved in *C. elegans* feeding behavior in a NMDAR-dependent mechanism (Lemieux et al. 2015). It will be interesting to continue exploring the role of KP metabolites as possible mediators of inter-organ communication, which could integrate nutrition, metabolism, and immune responses, with all the physiological and pathophysiological implications this could have.

**Acknowledgments** The authors wish to acknowledge members of the Ruas lab for critical reading of the manuscript and funding from the Swedish Research Council, the Novo Nordisk Foundation (Denmark), Karolinska Institutet, The Lars Hierta Memorial Foundation, The Strategic Research Program (SRP) in Diabetes, and The SRP in Regenerative Medicine at Karolinska Institutet.

 Cell Metab 19:96–108

**Open Access** This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

# **The Role of FNDC5/Irisin in the Nervous System and as a Mediator for Beneficial Effects of Exercise on the Brain**

#### **Mohammad Rashedul Islam, Michael F. Young, and Christiane D. Wrann**

**Abstract** Exercise can improve cognitive function and the outcome of neurodegenerative diseases like Alzheimer's disease. This effect has been linked to the increased expression of brain-derived neurotrophic factor (BDNF). However, the underlying molecular mechanisms driving the elevation of this neurotrophin remain unknown. Recently, we have reported a PGC-1α-FNDC5/irisin pathway that is activated by exercise in the hippocampus in mice and induces a neuroprotective gene program, including *Bdnf*. This review will focus on FNDC5 and its secreted form "irisin," a newly discovered myokine, its role in the nervous system and its therapeutic potential. In addition, we will briefly discuss the role of other exerciseinduced myokines in positive brain effects.

#### **Introduction**

Exercise, especially endurance exercise, is known to have beneficial effects on brain health and cognitive function (Cotman et al. 2007; Mattson 2012; Voss et al. 2013). This improvement in cognitive function with exercise has been most prominently observed in the aging population (Colcombe and Kramer 2003). Exercise has also been reported to ameliorate outcomes in neurological diseases such as depression, epilepsy, stroke, and Alzheimer's and Parkinson's disease (Ahlskog 2011; Arida et al. 2008; Buchman et al. 2012; Russo-Neustadt et al. 1999; Zhang et al. 2012). The effects of exercise on the brain are most apparent in the hippocampus and the dentate gyrus, a part of the brain involved in learning and memory. Specific beneficial effects of exercise in the brain have been reported to include increases in the size of, and blood flow to, the hippocampus in humans and morphological changes in dendrites and dendritic spines, increased synapse plasticity and, importantly, de novo neurogenesis in the dentate gyrus in various mouse models of exercise (Cotman

M. R. Islam • M. F. Young • C. D. Wrann (\*)

Massachussetts General Hospital and Harvard Medical School, Charlestown, MA, USA e-mail<EMAIL_ADDRESS>
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et al. 2007; Mattson 2012). De novo neurogenesis in the adult brain is observed in only two areas; the dentate gyrus of the hippocampus is one of them. Exercise is one of the few known stimuli of this de novo neurogenesis (Kobilo et al. 2011).

One important molecular mediator of these beneficial responses in the brain to exercise is the induction of neurotrophins/growth factors, most notably brainderived neurotrophic factor (BDNF). In animal models, BDNF is induced in various regions of the brain with exercise, most robustly in the hippocampus (Cotman et al. 2007). BDNF promotes many aspects of brain development, including neuronal cell survival, differentiation, migration, dendritic arborization, synaptogenesis and plasticity (Greenberg et al. 2009; Park and Poo 2013). In addition, BDNF is essential for synaptic plasticity, hippocampal function, and learning (Kuipers and Bramham 2006). Highlighting the relevance of BDNF in humans, individuals carrying the Val66Met mutation in the *Bdnf* gene exhibit decreased secretion of BDNF, decreased volume of specific brain regions, deficits in episodic memory function and increased anxiety and depression (Egan et al. 2003; Hariri et al. 2003). Blocking BDNF signaling with anti-TrkB antibodies attenuates the exercise-induced improvement of acquisition and retention of a spatial learning task, as well as the exercise-induced expression of synaptic proteins (Vaynman et al. 2004, 2006). However, the underlying mechanism that induces BDNF in exercise remains incompletely understood.

We recently described a role for the newly discovered "exercise-hormone," FNDC5 (Bostrom et al. 2012), and its secreted form, "irisin," in the protective effects of exercise on the brain. *Fndc5* expression is induced by exercise in the hippocampus in mice; it can, in turn, activate BDNF and other neuroprotective genes (Wrann et al. 2013). Importantly, peripheral delivery of FNDC5 to the liver via adenoviral vectors, resulting in elevated blood irisin, induces expression of *Bdnf* and other neuroprotective genes in the hippocampus. These data indicate that either irisin itself can cross the blood-brain-barrier to induce gene expression changes or irisin induces a "factor x" that can, which has significant implications for irisin as a novel therapeutic target. This review will examine previous literature about FNDC5/ irisin as well as its therapeutic potential for treating neurodegenerative disease.

#### **Discovery of FNDC5/Irisin**

In 2002, two groups independently cloned a novel gene termed either PeP or, alternatively, Frcp2, that contained a fibronectin type III (FNIII) domain; it is now named FNDC5 (Ferrer-Martinez et al. 2002; Teufel et al. 2002). Recently, our group identified FNDC5 as a PGC-1α-dependent myokine that is secreted from muscle during exercise and induces some major metabolic benefits of exercise (Bostrom et al. 2012).

FNDC5 is a glycosylated type I membrane protein. It contains a N-terminal signal peptide [amino acid (aa) 1-28], a FNIII domain (aa 33–124), a transmembrane domain (aa 150–170), and a cytoplasmic tail (aa 171–209) (www.uniporot.org) (Fig. 1). The secreted form of FNDC5 contains 112 amino acids (aa 29-140), named irisin, and is generated by proteolytic cleavage and is released into the circulation.

**Fig. 1** Analysis of Irisin Peptides by Mass Spectrometry. (**a**) Scheme of the murine FNDC5 protein structure (top) and murine irisin protein structure (bottom). *SP* signal peptide, *H* hydrophobic domain, *C* cytoplasmic domain. (**b**) Murine FNDC5 amino acid sequence with corresponding domains colored. The irisin sequence is underlined

The protease/sheddase responsible for that cleavage has not yet been identified. Irisin has been crystallized and its structure has been solved (Schumacher et al. 2013). Interestingly, the FNIII-like domain shows an unusual confirmation, with a continuous intersubunit beta-sheet dimer, that has not been previously described for any other FNIII protein. Subsequent biochemical experiments confirmed the existence of irisin (bacterial recombinant) as a homodimer.

#### **Irisin in Humans**

Irisin is a highly conserved polypeptide across mammals and is, in fact, 100% identical in mice and humans (Bostrom et al. 2012). Such a high degree of conservation is often the result of evolutionary pressure to conserve function. Interestingly, the human FNDC5 has an atypical start of translation, ATA in place of ATG, as compared to mouse Fndc5. While it is now known that a few percent of eukaryotic mRNAs begin translation with non-ATG start codons (Ingolia et al. 2011; Ivanov et al. 2011; Peabody 1989) and are often associated with regulation on the translational level (Chang and Wang 2004; Starck et al. 2012), recent reports (Albrecht et al. 2015; Raschke et al. 2013) have argued that this ATA codon in human FNDC5 was a "null mutation" or a "myth" and, as a result, human irisin would not be produced. Furthermore, many reports from other groups measuring irisin in humans by Western blot or ELISA have suggested results to be artefacts of poor antibody specificity (Albrecht et al. 2015; Erickson 2013; Raschke et al. 2013), even though an earlier study had detected irisin circulating in human plasma using mass spectrometry, an unbiased method independent of the quality of existing antibodies (Jedrychowski et al. 2015; Lee et al. 2014). To identify and quantify irisin in human plasma, we used targeted mass spectrometry with control peptides enriched with stable isotopes as internal standards. This precise, state-of-the-art method demonstrated that human irisin was mainly translated from its non-canonical ATA start codon (Jedrychowski et al. 2015). In addition, it showed that, in sedentary individuals, irisin circulated at ~3.6 ng/ml and that it was significantly increased in individuals undergoing aerobic interval training. This study determined that, at the atomic level, human irisin exists, and is regulated by certain forms of aerobic exercise.

#### **FNDC5/Irisin in Exercise**

FNDC5/irisin was first described as an exercise-induced myokine by Bostrom et al. in 2012, where they observed upregulation of *Fndc5* gene expression in skeletal muscle and increased serum irisin levels after prolonged endurance exercise in both mice and humans. Increasing the circulating levels of irisin by overexpression of FNDC5 from adenoviral vectors in the liver led to an increase in "browning" of the white inguinal adipose tissue, i.e., the upregulation of mitochondrial gene expression, especially of *Ucp1*, and an increased glucose tolerance in mice. Both are among the major metabolic benefits of endurance exercise. By now, there are around 50 published papers that investigate the role of FNDC5 and/or irisin in exercise, including both rodent studies and clinical trials in humans. Induction of *Fndc5* mRNA in skeletal muscle by endurance exercise has been confirmed in several studies in both mice (Quinn et al. 2015; Tiano et al. 2015; Wrann et al. 2013) and humans (Albrecht et al. 2015; Lecker et al. 2012; Norheim et al. 2014) using qPCR or RNA sequencing. As with all clinical studies, there are a lot of variables to consider, such as retrospective studies vs. interventional trials, age and fitness level of the subjects and, most importantly, the type of exercise protocol used and time point of sampling. However, a consensus is building, supported by studies that reported positive associations between irisin plasma level and exercise, performed early sampling and high intensity training protocols levels (Daskalopoulou et al. 2014; Huh et al. 2014; Kraemer et al. 2014; Malcolm Eaton et al. 2017; Norheim et al. 2014). Plasma levels of irisin and BDNF have been shown to be positively correlated with cognitive function in endurance athletes (Belviranli et al. 2016).

The brief rise in circulating irisin levels after exercise is suggestive of an acute shedding event of irisin during exercise. There is little to no evidence thus far that FNDC5 or irisin is upregulated by resistance exercise in mouse or human This is not unexpected, since endurance exercise activates PGC-1α1, which has been shown to be the upstream regulator of Fndc5 gene expression, whereas resistance exercise activates a different isoform of PGC-1α1, PGC-1α4 (Ruas et al. 2012).

#### **FNDC5/Irisin in Neuronal Development**

*Fndc5* is highly expressed in the brain, including the Purkinje cells of the cerebellum (Dun et al. 2013; Ferrer-Martinez et al. 2002; Teufel et al. 2002). Irisin, the shed form of FNDC5, was identified in human cerebrospinal fluid by Western blot (Piya

et al. 2014). In addition, immunoreactivity against the extracellular domain of FNDC5/irisin was detected in human hypothalamic sections, especially paraventricular neurons (Piya et al. 2014). Other tissues with high FNDC5 levels include skeletal muscle and the heart. *Fndc5* gene expression increases during differentiation of rat pheochromocytoma-derived PC12 cells into neuron-like cells (Ostadsharif et al. 2011). FNDC5 levels are enhanced after the differentiation of human embryonic stem cell-derived neural cells into neurons (Ghahrizjani et al. 2015) as well as during the maturation of primary cortical neurons in culture and during brain development in vivo (Wrann et al. 2013).

Knockdown of FNDC5 in neuronal precursors impaired their development into mature neurons (and astrocytes), suggesting a developmental role for FNDC5 in neurons (Hashemi et al. 2013). Despite this finding, forced expression of FNDC5 during neuronal precursor formation from mouse embryonic stem cells increased mature neuronal markers (Map2, b-tubulinIII and Neurocan) and an astrocyte marker (GFAP) and BDNF. However, overexpression of FNDC5 in undifferentiated mouse embryonic stem cells did not have these effects, indicating that FNDC5 supports neural differentiation rather than lineage commitment (Forouzanfar et al. 2015). Pharmacological doses of recombinant irisin increased cell proliferation in the mouse H19-7 hippocampal cell line (Moon et al. 2013). Furthermore, forced expression of FNDC5 in primary cortical neurons increased cell survival in culture, whereas knockdown of FNDC5 had the opposite effect (Wrann et al. 2013).

#### **FNDC5/Irisin: Other Effects in the CNS**

The group of Dr. Mulholland (University of Michigan) had taken in interest in the central nervous effects of irisin. In a first study, they injected irisin either into third ventricle of rats or intravenously measured the effects on blood pressure and cardiac contractibility (Zhang et al. 2015a). Central administration of irisin activated neurons in the paraventricular nuclei of the hypothalamus, as indicated by increased c-fos immunoreactivity. Central irisin administration also increased both blood pressure and cardiac contractibility. In contrast, i.v. injection of irisin reduced blood pressure in both control and spontaneously hypertensive rats. In a second study, Zhang et al. showed that central treatment of rats with irisin-Fc led to an increase in physical activity as measured as total travel distance, ambulatory counts and time, and vertical counts and time compared to control animals receiving IgG Fc peptide (Zhang et al. 2015b). In addition, the centrally applied irisin also induced significant increases in oxygen consumption, carbon dioxide production and heat production, indicating an increase in metabolic activity- possibly through SNS activation. Similarly, intra-hypothalamic injection of irisin decreased food intake in rats possibly by stimulating anorexigenic peptides and inhibiting dopamine, norepinephrine and orexin-A (Ferrante et al. 2016).

A recent study investigated the neuroprotective potential of irisin treatments in cerebral ischemia. Irisin improved survival of cultured PC12 neuronal cells in

oxygen glucose deprivation. I.v. injection of recombinant irisin reduced brain infarct and edema volume and improved the neurological score in MCAO stroke model (Li et al. 2017). Systemic irisin administration has also been shown to ameliorate depressive-like behavior in a chronic unpredictable stress model in rats (Wang and Pan 2016).

#### **Exercise Induces Hippocampal BDNF Through a PGC-1α/ FNDC5 Pathway**

In a recent study, we have shown that FNDC5 is also elevated in the hippocampus of mice undergoing endurance exercise regimen of 30-days free-wheel running. Neuronal *Fndc5* gene expression is regulated by PGC-1α and Pgc1a−/− mice show reduced *Fndc5* expression in the brain. Forced expression of FNDC5 in primary cortical neurons increases *Bdnf* expression, whereas RNAi-mediated knockdown of FNDC5 reduces *Bdnf*. Importantly, peripheral delivery of FNDC5 to the liver via adenoviral vectors, resulting in elevated blood irisin, induces expression of *Bdnf* and other neuroprotective genes in the hippocampus. Taken together, our findings link endurance exercise and the important metabolic mediators, PGC-1α and FNDC5, with BDNF expression in the brain. Interestingly, a recent study investigating the effects of the flavonoid quercetin hypobaric hypoxia, reported that quercetin administration to hyperbaric hypoxic rats increased expression of PGC-1α, FNDC5, and BDNF in the hippocampus (Liu et al. 2015). While more research will be required to determine whether the FNDC5/irisin protein can improve cognitive function in animals, this study suggests that a hormone administered peripherally could induce some of the effects of endurance exercise on the brain (Fig. 2).

#### **Other Circulating Factors from the Muscle**

While FNDC5/irisin is a very interesting molecule that holds therapeutic promise, this is not to say that we think that FNDC5/irisin captures all the benefits of exercise on the brain or that is the only important secreted molecule from muscle in exercise. In fact, other such molecules have been described, including BDNF, IGF-1, and VEGF, kynurenic acid, cathepsin B, as well as a variety of cytokines and chemokines, to name a few (Agudelo et al. 2014; Moon et al. 2016; Phillips et al. 2014; Voss et al. 2013). We expect that in the future, additional molecules will be discovered and some may reach their full therapeutic potential.

**Fig. 2** Summary of FNDC5/irisin effects on the brain

**Acknowledgements** This work was supported by the NIH (NS087096). I also thank Dr. Mark Jedrychowski for support with the art work.

#### **References**

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Meet the editor

Prof. Dr. Maged Marghany is the author of "Advanced Remote Sensing Technology for Tsunami Modelling and Forecasting" which was published by Routledge Taylor and Francis Group, CRC. Prof. Dr. Maged Marghany is currently a professor of microwave remote sensing at Geomatika University College, Malaysia. He has led several projects related to the application of SAR to Malaysian coastal waters, funded by the Ministry of Sci-

ence and Technology, Malaysia (MOSTE) and also the Ministry of High Education in Malaysia (MOHE). His research is directed towards the use of synthetic aperture radar (SAR) data for modeling shoreline changes and developing a new approach for forecasting oil slick trajectory movements. He teaches at postgraduate levels on the topic of digital image processing and microwave remote sensing.

Prof. Maged Marghany established a new research method regarding four-dimensional forecasting, which was published in his book "Advanced Remote Sensing Technology for Tsunami Modelling and Forecasting" in addition to involving quantum theory for forecasting tsunamis.

Prof. Dr. Maged Marghany (1967) was awarded the ESA Post-doctoral Fellowship by the International Institute of Aerospace and Earth Observation (ITC) in Enschede, the Netherlands, funded by the European Space Agency (ESA) for one year from March 2000 to March 2001. Prof. Dr. Maged has published over 270 papers in international conferences and indexed journals. He is also the editor of three books published by the rapidly growing IntechOpen publisher.

**Preface III**

Synthetic Aperture Radar Image Processing and Applications **1**

**Chapter 1 3**

**Chapter 2 15** On Feature-Based SAR Image Registration: Appropriate Feature and Retrieval

**Chapter 3 47** L-Band SAR Disaster Monitoring for Harbor Facilities Using Interferometric

Advanced Image Data Processing **65**

**Chapter 4 67** Utilization of Deep Convolutional Neural Networks for Remote Sensing Scenes

**Chapter 5 85** Sub-Pixel Technique for Time Series Analysis of Shoreline Changes Based on

Reconstruction **105**

**Chapter 6 107**

Infrastructure Investigations, Tsunami Disaster Mapping and Novel 3-D

Utilization of Dynamic and Static Sensors for Monitoring Infrastructures

*by Chang Luo, Hanqiao Huang, Yong Wang and Shiqiang Wang*

Introductory Chapter: Advanced Ocean Current Simulation from TanDEM

**Section 1**

Contents

Satellite Data *by Maged Marghany*

Algorithm

Analysis *by Ryo Natsuaki*

**Section 2**

Classification

**Section 3**

Multispectral Satellite Imagery *by Qingxiang Liu and John C. Trinder*

*by Chung C. Fu, Yifan Zhu and Kuang-Yuan Hou*

*by Dong Li, Yunhua Zhang and Xiaojin Shi*

## Contents

Preface

The advance in space machineries has created a novel technology for observing and monitoring the Earth from space. Most earth observation remote sensing considerations focus on using conventional image processing algorithms or classic edge detection tools. Nevertheless, these techniques do not implement modern physics, applied mathematics, signal communication, remote sensing data, and innovative space technologies. This book provides readers with methods to comprehend how to monitor coastal environments, disaster areas, and infrastructure from space with advanced talent remote sensing technology to bridge the gaps between modern space technology, image processing algorithms, mathematical models and the critical issue of the coastal and infrastructure investigations. In other words, advanced remote sensing technology, which covers sensor developments, and image processing algorithm modifications, which are based on modern physics, artificial intelligence, and machine learning. In these regards, their applications cover a wide range of coastal observations, for instances, high risk of a tsunami depends on the depth of water, the coastal geomorphology, the direction of the tsunami wave, and the existence of rivers or other water canals. In these circumstances, coastal zones are required for new urban planning and specific infrastructure designing to reduce

In spite of numerous of synthetic aperture radar (SAR) space technology, the developing country researchers and scientists are still focusing on optical remote sensing technology. In fact, microwave remote sensing require use of mathematics and physics behind the SAR technology. The first chapter introduces a new technology for measuring sea surface current using along-track interferometry of TanDEM-X satellite data. This chapter delivers a novel algorithm to retrieve sea surface current using the multichannel MAP height estimator algorithm, which is considered the first study of ocean current in the coastal waters of Peninsular

The available SAR data increases dramatically with the recent operation of many spaceborne and airborne SAR systems. This makes the joint processing of multiple images for accurate understanding and perception of a scene and target possible. For SAR image pairs acquired from different imaging geometries or by different sensors, there is always a geometrical warp between them, which should be compensated first before any deep application. Image registration is aimed to retrieve the warp function to align the same pixel position in each SAR image to the same target position in the global system. A lot of SAR image registration techniques have been developed hitherto. In the second chapter, the algorithms that conduct registration based on image features, such as contour, region, line, and point are accurately addressed. Contour, region, and line, as well as their combinations, are often used for registration of multi-modality images. For SAR images with geometrical distortion and speckle, point feature is generally much clearer and easier extracted. Tie points, corner, and key points are the commonly-used features in SAR image registration. Tie points usually refer to the features extracted from tie

the impact of such a disaster.

patches in SAR image registration.

Malaysia.