CITED2 is a transcription factor implicated in the control of cell proliferation and cellular senescence. Aged human Achilles TDSCs were shown to display lower mRNA and protein expression of CITED2 as well as lower proliferation and higher cellular senescence compared to young TDSCs (Zhou et al., 2010; Hu et al., 2017). Moreover, TGF-b2 was reported to downregulate the expression of CITED2 gene and knockdown of CITED2 abolished the effect of TGF-b2–mediated TDSC senescence (Hu et al., 2017). The effects of TGF-b2 inhibitors and CITED2 activators in reversing the senescence of aged TDSCs and tendon aging await further studies.

### Accumulation of Damaged DNA and Proteins

Aging is one of the major risk factors for DNA damage, protein mis-folding and accumulation. Age-dependent deficits of the autophagy-lysosomal and ubiquitin-proteasome systems, which remove the damaged proteins, were reported in recent studies (Rubinsztein et al., 2011; Tomaru et al., 2012). However, other studies argued that it was the increase in damage caused by metabolic stress, not the decrease in autophagy potential and proteasome activity, which overwhelmed the protective capacity of proteolytic systems in aged cells (Warr et al., 2013). Inhibition of autophagy has been shown to inhibit self-renewal and differentiation of stem cells, including TDSCs (Cheng et al., 2015; Chen et al., 2016; Sbrana et al., 2016). Tendon aging is dependent at least in part on the actions of mechanistic target of rapamycin (mTOR). Long-term dietary administration of rapamycin, an inhibitor of mTOR, was reported to attenuate aged-associated increase in stiffness; decrease in elastiscity, cellularity, and progression of spontaneous tendon calcification; as well as enhance collagen turnover in aged mouse tendons (Wilkinson et al., 2012; Zaseck et al., 2016). Autophagy plays an important role in regulating the self-renewal capacity and stemness of human TDSCs through suppressing reactive oxygen species (ROS) production. Treatment of human TDSCs isolated from supraspinatus tendon of adult donors (26–34 years old) with starvation or rapamycin prevented H2O2-induced loss of selfrenewal capacity and stemness (Chen et al., 2016). The effects were mediated by increasing autophagy activity and suppressing ROS production in TDSCs (Chen et al., 2016). On the other hand, inhibition of autophagy reduced the protective effects of starvation and rapamycin on H2O2-treated cells (Chen et al., 2016).

## AGED NICHE

### Cell-Cell Communication

Stem cells interact with each other or niche cells via direct interaction of membrane proteins or secretion of soluble factors. The changes of membrane proteins or secretome of stem cells or niche cells during aging therefore could impact stem cell fate and functions. Ephrin are receptor tyrosine kinases for mediating shortdistance cell-cell communication. Specifically, ephrins were reported to regulate the attachment, spreading, migration, differentiation, and age-associated senescence of stem cells (Arthur et al., 2011; Goichberg et al., 2013; Li and Johnson, 2013). Dysregulated cell-cell communication and hence proliferation, motility, and actin turnover of aged TDSCs were reported to be mediated by down-regulation of ephrin receptors EphA4, EphB2, and EphB4 and ligands EFNB1 (Popov et al., 2016). Interestingly, activation of EphA4- or EphB2- dependent reverse signalingin aged TDSCs restored migratory ability and normalized actin turnover while activation of EphA4 reverse signaling increased proliferation of aged TDSCs (Popov et al., 2016).

### Extracellular Matrix

The ECM of tendon consists primarily of collagens (mainly collagen type I), proteoglycans, and glycoproteins. ECM functions to provide mechanical strength to tendon. In addition, it also functions to retain resident cells in place, localize and create gradients of instructive signals that guide stem cells in their processes of self-renewal and differentiation.

The alterations in the composition, topography and organization of ECM of aged tendons therefore can contribute to the loss of functions of tendon stem cells. While tendonderived decellularized matrix promoted tendinous phenotype and inhibited osteogenesis of TDSCs in the presence of osteogenic induction conditions, bone-derived decellularized matrix induced osteogenic differentiation of TDSCs (Yin et al., 2013). The accumulation of AGEs with age likely plays a significant role in stem cell dysfunction during tendon aging. AGEs are proteins or lipids that become nonenzymatically glycated and oxidized after exposure to carbohydrates. Proteins are usually glycated through the lysine resides. Collagen, which is rich in lysine, is frequently glycated. Non-enzymatic glycation of collagen type I diminished collagen-proteoglycan binding and weakened cell adhesion and migration (Reigle et al., 2008). The formation of AGEs also resulted in the denaturation and crosslinking of collagen and the AGEs were nearly irreversible once formed. The mechanical and biological functions of long-lived proteins like collagen are therefore affected in the aging process. AGEs also increased the activity of transglutaminase, a crosslinking enzyme of ECM proteins, in tenocytes in vitro, further promoting collagen cross-linking (Rosenthal et al., 2009). The collagen cross-links limited fiber sliding, reduced viscoelasticity of tendon, and hence increased its stiffness and brittleness (Reddy, 2004; Gautieri et al., 2017), consistent with the increase in tendon stiffness with age (Andreassen et al., 1981; Haut et al., 1992). Matrix stiffness is known to regulate proliferation and lineage commitment of MSCs (Engler et al., 2006; Nam et al., 2011; Lee et al., 2014; Navaro et al., 2015). Matrix with lower stiffness promoted chondrogenic differentiation, whereas matrix with high stiffness promoted osteogenic differentiation of stem cells (Nam et al., 2011; Navaro et al., 2015). The increase in matrix stiffness as a result of age-induced collagen cross-links might underlie higher nontenogenic differentiation potential of aged TDSCs. Bone morphogenetic protein-2 (BMP-2) stimulated non-tenogenic differentiation and promote proteoglycan deposition of TDSCs in vitro (Rui et al., 2013). A recent study showed that AGEs increased BMP-2 expression and accelerated progression of atherosclerotic calcification in diabetes (Wang et al., 2012). AGEs were also reported to increase the mRNA expression of BMP2, BMP4, and osteogenic markers of human yellow ligament cells isolated from cervical spine (Yokosuka et al., 2007). There has been no study about the effects of AGEs on TDSCs and is an area worthy of investigation.

The ECM composition can affect tendon stem cell fate. A deficiency in proteoglycans (biglycan and fibromodulin) resulted in altered differentiation of TDSCs towards the osteogenic linage via modulating the BMP signaling (Bi et al., 2007). Secreted protein acidic and rich in cysteine (Sparc), also known as osteonectin, is a collagen-binding matricellular glycoprotein involved in collagen fibril assembly and procollagen processing. It regulates cell-ECM interactions impacting upon cell signaling, adhesion, proliferation, migration, and survival. Sparc also regulates adipogenesis by modulating cell adhesiveness and cytoarchitecture. A recent study reported that the expression of Sparc decreased in aged mouse Achilles tendons (Gehwolf et al., 2016). The decrease in Sparc expression led to a more contracted phenotype, an altered actin cytoskeleton, and an elevated expression of the adipogenic marker genes Pparg and Cebpa with a concomitant increase in lipid deposits in both aged and Sparc -/-mouse tendons, a phenomenon that is commonly seen in elderly (Gehwolf et al., 2016).

The cytoskeleton of a cell and cell-matrix interactions affect stem cell migration, proliferation, and differentiation. Cell migration is strongly dependent on fibronectin, actin cytoskeletal organization, and turnover rate of actin filament. Integrins bind stem cells to different ECM components and regulate downstream signaling which are important for stem cell self-renewal and proliferation (Piwko-Czuchra et al., 2009; Ellis and Tanentzapf, 2010). Aged/degenerated TDSCs showed lower actin turnover, gene expression of collagen type I, and collagen type I-binding integrins a1, a2, and a11 but higher gene expression of fibronectin, fibronectin-binding integrins av, b3, and b5 compared to young/healthy TDSCs (Kohler et al., 2013). The changes therefore might cause poor cell adhesion, migration, and actin dynamics of aged/degenerated TDSCs (Kohler et al., 2013). Integrin receptors and their ECM ligands function upstream of RhoA/ROCK signaling. Both the expression and activities of ROCK1 and ROCK2, which mediate RhoA-induced actin stress fiber formation via phosphorylation of myosin light chain (Schmitz et al., 2000), increased in aged/degenerated TDSCs (Kohler et al., 2013). Inhibition of ROCK1 and ROCK2 activities in aged/degenerated TDSCs with ROCK inhibitor Y-27632 restored the cell area, F-actin content and dynamics as well as migration ability, with results comparable to young/ healthy TDSCs (Kohler et al., 2013).

CD44 is a cell-surface glycoprotein involved in cell adhesion and migration. As the expression of CD44 is implicated in poor tendon healing (Favata et al., 2006; Ansorge et al.,2009), the increased expression of CD44 in aged rat TDSCs might contribute to reduced tendon repair capacity with age (Zhou et al., 2010). Overexpression and knockdown experiments of CD44 in aged TDSCs would shed light on the aging mechanisms.

Collagen fibers are poorly organized in aged tendons (Gehwolf et al., 2016). This is partly due to chronicinflammation and alteration in the production and activities of MMPs in aged tendons (Yu et al., 2013). The change of organization of collagen fibers from an intact, tightly-packed parallel orientation to a more random arrangement in aged tendons could affect the fate of resident tendon stem cells. Human fetal TDSCs seeded onto randomly-oriented fibrous scaffold underwent osteogenesis with lower levels of integrin a1, a5, and b1 subunits compared to cells seeded onto aligned fibers (Yin et al., 2010).

### Locally Produced Biological Signals

Aging causes changes in the local production of biological signals at the stem cell niche which can profoundly impact tissue stem cell functions. Growth factors such as FGF, insulin growth factor-1 (IGF-1), vascular endothelial growth factor (VEGF), TGF-b1, platelet-derived growth factor (PDGF) and BMPs, as well as proteins in the Wnt/b-catenin, notch, and angiopoietin-1 signaling pathways are important modulators of stem cell functions. The Wnt/b-catenin and BMP signaling pathways enhanced non-tenogenic differentiation of TDSCs in vitro (Lui et al., 2013; Rui et al., 2013). Dysregulation of Wnt/b-catenin signaling pathway was observed in different aged stem cell and animal models (Brack et al., 2007; Liu et al., 2007; Gehwolf et al., 2016). However, there was no differences in mRNA expression of connective tissue growth factor (CTGF), TGF-b1, or stromal cell-derived factor 1 in tendons of old and young rats (Kostrominova and Brooks, 2013). The mRNA and protein expression of TGF-b1 also did not change with age in rat Achilles tenocytes (Yu et al., 2013). Therewas disruption of circadian control in aged mouse tendon (Yeung et al., 2014). One of the circadian genes, Gremlin-2 (Grem2) oscillated antiphase to BMP signaling and inhibited BMP-2 induced osteogenic differentiation of human tenocytes (Yeung et al., 2014). The study reported reduced Grem2 expression, dysregulated BMP signaling, and spontaneous calcification in aged mouse tendon, suggesting that inhibition of Grem2 might be involved in the calcification of aged tendons (Yeung et al., 2014). The growth factor profile of aged tendons and their effects on functions of tendon stem cells should be examined. Modulation of these growth factor signaling pathways might shed light on strategies to prevent tendon aging and promote healing of aged tendons after injury.

## Inflamm-Aging

Aging is associated with reduced ability of the body to modulate inflammation, resulting in chronic low-grade inflammation termed "inflamm-aging" (Franceschi et al., 2000). Aged mice were reported to show higher serum levels of inflammatory cytokines such as interleukin-6 (IL-6) and tumor necrosis factor a (TNF-a) compared to young mice (Wahl et al., 2010). There were greater inflammatory changes with higher IL-1band IL-6 levels in flexor digitorum tendons of aged rats performing high repetition low force handle-pulling task (HRLF) compared to tendons in young rats performing HRLF and aged rats not performing HRLF (Kietrys et al., 2012). More macrophages and CTGF-immunoreactive fibroblasts were observed in the peritendon of supraspinatus tendons in aged rats after HRLF (Kietrys et al., 2012). The greater chronic inflammation in aged rat tendons after upper extremity overuse was associated with a decrease in forelimb agility and lack of improvement of success in reaching task (Kietrys et al., 2012). Dakin et al. (2012) measured the level of prostaglandin E2 (PGE2) in injured equine flexor tendons and found that the level of PGE2 increased while the level of formyl peptide receptor 2 (FRP2)/ ALX, a receptor responsible for suppressing inflammatory response, decreased during injury with increasing age of the horses. Uninjured tendon explants isolated from younger (<10 years) but not older horses (≥10 years) treated with IL-1b responded by increasing FRP2/ALX receptor (Dakin et al., 2012), suggesting that aged tendons have lower capacity to resolve inflammation. de Gonzalo-Calvo et al. (2010) measured the oxidative stress and inflammatory cytokines of three volunteer groups, consisting of normoxic middle-aged people, normoxic aged people older than 75 years, and hypoxic aged people older than 75 years. The results showed that the antioxidant defenses of older people with hypoxia was lower. Serum TNF-a was significantly increased in the aged population while IL-6 was highly elevated in the hypoxic elderly population (de Gonzalo-Calvo et al., 2010). Another recent clinical study on 831 subjects showed that circulating mitochondrial DNA (mtDNA) increased with age and significantly contributed to the maintenance of chronic, low-grade inflammation observed in the elderly subjects with an increase in serum TNF- a, IL-6, RANTES, and IL-1ra (Pinti et al., 2014). Moreover, mtDNA values of siblings were directly correlated, suggesting a role for familiar/genetic background in controlling the levels of circulating mtDNA (Pinti et al., 2014). In vitro stimulation of monocytes with mtDNA resulted in an increased production of TNF-a, supporting that mtDNA could modulate the production of proinflammatory cytokines (Pinti et al., 2014). These findings supported that aged individuals exhibited reduced capacity to resolve inflammation and aging might scramble tendon stem cell functions and promote the development of chronic tendinopathy through these pathways. Indeed, high concentration of PGE2 was reported to reduce the proliferation and enhance non-tenocyte differentiation of TDSCs (Zhang and Wang, 2014). IL-1b irreversibly inhibited tenogenic and adipogenic differentiation as well as reversibly inhibited chondrogenic and osteogenic differentiation of TDSCs isolated from injured mouse Achilles tendons (Zhang et al., 2015). IL-6 was reported to stimulate rat Achilles TDSCs' proliferation and entry into cell cycle but inhibited the expression of tendon-related markers (Chen et al., 2018). Recent studies showed that senescent cells exhibited a robust increase in mRNA expression and secreted numerous bioactive factors, including degradative enzymes, inflammatory cytokines, and growth factors, which might drive stem cell dysfunction in aged stem cells through paracrine signaling (Coppe et al., 2010; Campisi et al., 2011). Whether this mechanism also accounts for age-related degenerative disorders of tendons and the factors secreted by senescent cells during tendon aging needs further research. The damage of DNA of stem cells and niche cells were hypothesized to be major contributors to inflamm-aging and the effect was propagated and amplified by macrophages (Bonafe et al., 2012). More researches are needed to verify this hypothesis in aged tendon stem cells.

### SYSTEMIC CHANGES OF HORMONAL AND METABOLIC SIGNALS

Given the remarkable ability of stem cells to sense and respond to the external stimuli, it is not surprising that the age-related systemic changes t can also impact stem cells and their niches. Chronic conditions such as hormone deficiency, diabetes mellitus (DM) and hypertension are common in elderly. The systemic changes of hormonal and metabolic signals as well as drug used in the treatment of these chronic conditions can alter niche through circulation, causing stem cells to lose or deviate from their original functions.

DM has been reported to increase the prevalence/incidence of tendinopathy (Ranger et al., 2016; Lui, 2017). A recent metaanalysis of clinical studies provide strong evidence that diabetes is associated with higher risk of tendinopathy (Ranger et al., 2016), despite that there were inconsistent findings regarding the effects of DM on structural and biomechanical properties of tendon in animal models (Lui, 2017). Potential mechanisms of DM in causing and exacerbating tendinopathy by modifying stem cell functions via AGE formation, hyperglycemia, insulin deficiency/resistance, and adipokine dysregulation have been discussed in another review and will not be repeated (Lui, 2017).

In women, estrogen can enhance tendon collagen synthesis. Estrogen deficiency was reported to negatively affect tendon metabolism and healing. Tenocytes isolated from ovariectomised rats were reported to show lower proliferation rate, collagen type I, aggrecan, elastin, fibronectin content in the supernatant, and migration rate but higher apoptosis, VEGF and MMP-13 compared to young tenocytes (Aydin et al., 2013; Torricelli et al., 2013). Collagen content was lower in Achilles tendons after ovariectomy compared to control rats (Ramos et al., 2012). The Achilles tendons from ovariectomised rabbits also showed lower gene expressions of proteoglycans aggrecan, biglycan, decorin, and versican compared to tendons from intact normal rabbits (Huisman et al., 2014). However, there was no changes in the biomechanical properties of patellar tendon of monkeys after ovariectomy (Wentorf et al., 2006). Ageassociated estrogen deficiency therefore can alter matrix composition of tendons and may influence the functions of tendon stem cells. Whether TDSCs are sensitive to estrogen is a first step to understand the influence of estrogen deficiency in age-associated loss or dysfunction of tendon stem cells.

## POTENTIAL FUTURE RESEARCH DIRECTIONS

### Research on Treatments to Reverse the Intrinsic Cellular Deficits and Aged Niche of Tendon Stem Cells

As a new cell type identified in tendon, more studies are needed to fill the gaps of the aging mechanisms of tendon stem cells. Interventions that can combat protein and DNA damages, cellular senescence, oxidative stress, chronic inflammation; and inhibit AGE formation or promote its removal may rejuvenate aged tendon stem cells. These are attractive anti-aging options to explore. The growth factor and cytokine profile of aged tendons and their effects on functions of tendon stem cells should be examined. Notably, medium of young TDSCs under hypoxic culture condition was reported to enhance the migration, proliferation and mRNA expression of tendon-related markers as well as reduced senescence of aged TDSCs (Jiang et al., 2014). Exposure of aged rat patellar tendon TDSCs to young decellularized tendon ECM enhanced their proliferation, tendon-related marker expression (Scx, Tnmd), and stem cell marker expression (Oct-4, SSEA-1) as well as reduced SA-b-gal activity (Jiang et al., 2018).

### Research on the Effects and Mechanisms of Common Aging Conditions on Tendon Stem Cells and Tendon

The effects of common aging conditions particularly diabetes, hypertension and hormonal deficiency and the drugs used on the functions of tendon stem cells and tendon should be examined. The sensitivity of TDSCs to estrogen has not been explored and should be examined. Similarly, studies on the effects of hyperglycemia, AGE, insulin deficiency/resistance, and adipokine dysregulation in diabetes on functions of tendon stem cells are needed.

### Research on the Effects and Mechanisms of Exercise on Tendon Stem Cells and Tendon

TDSCs are sensitive to mechanical load. High uniaxial cyclic tensile loading (8%) induced osteogenic differentiation of TDSCs while low uniaxial cyclic tensile loading (4%) promoted tenogenic differentiation of TDSCs (Zhang and Wang, 2010b; Rui et al., 2011; Wang et al., 2018). Modulating the mechanical load experienced by tendon stem cells therefore may counteract the effect of aging on tendon. In this regard, moderate mechanical stretching (4%) of aged TDSCs in vitro significantly increased the protein expression of nucleostemin and mRNA expression of Nanog. It also increased the expression of tenocyte-related genes (Col1 and Tnmd) without increasing the expression of non-tenocyte-related genes (Lpl, Sox9 and Runx2) of aged TDSCs (Zhang and Wang, 2015).

In an in vivo study, moderate treadmill running of aged mice increased the proliferation rate of aged TDSCs in culture, decreased lipid deposition, proteoglycan accumulation and calcification, and increased the expression of nucleostemin-expressing TDSCs in the patellar tendons (Zhang and Wang, 2015). Moderate exercises can also mitigate the deleterious effects of aging on the niche of tendon stem cells by improving tendon microcirculation, matrix composition, and organization as well as reducing AGE level and matrix stiffness (Couppe et al., 2014;Wood and Brooks, 2016).With training, Col1a1 and Mmp8 expression in aged mouse Achilles tendons were restored to levels similar to adult controls (Wood and Brooks, 2016). Moderate treadmill running prior to an injury augmented tendon wound healing in an aged rat model with reduced vascularity, lipid-like structures, and senescent cells as well as increased production of more collagen fibrils and better fibril organization (Zhang et al., 2016). The effect was likely mediated by the improvement of metabolism and functional fitness of aged tendon stem cells because aged TDSCs isolated from exercised rats showed higher stem cell marker expression, proliferation, colony-forming ability, and tenocyte-related gene expression as well as lower non-tenocyte-related gene expression compared to aged cells isolated from cage control rats (Zhang et al., 2016). The beneficial effect of exercise on aged tendons could also be due to systemic effects in addition to direct loading as tail tendons from aged and exercised rats assumed the elastic characteristics of their younger counterparts after exercise (Lacroix et al., 2013). Stem cells expressed different channels that could sense membrane tension (Soria et al., 2013). One study showed that the stretchmediated activation channel, cystic fibrosis transmembrane conductance regulator (CFTR), was up-regulated in TDSCs during tenogenic differentiation under mechanical stretching (Liu et al., 2017). Further studies to confirm the beneficial effects, optimal loading protocol, and mechanosensing mechanisms of excise on aged tendon stem cells and tendon should be done.

### CONCLUSION AND FUTURE PERSPECTIVES

In summary, the composition, structure, and mechanical properties of tendons deteriorate with aging and are driven at least in part by an age-associated decline in the metabolism and functional fitness of tendon stem cells. Unlike their young and healthy counterparts, aged TDSCs were shown to be larger and flatten. The number and stemness of TDSCs isolated from aged tendons were lower. They exhibited altered multi-lineage differentiation potential, deficiency in cell adhesion, and migration as well as early cellular senescence. The intrinsic cellular deficits, aged niche, and systemic hormonal and metabolic changes associated with aging can drive the aging process of stem cells, some of which have been demonstrated in TDSCs recently. There is no evidence about the factor(s)

FIGURE 1 | Diagram summarizing the key biological characteristics of aged tendon, and potential factors influencing the fate and functions of TDSCs during tendon aging. Current evidences on aging of other adult stem cells and limited data on tendon-derived stem cells (TDSCs) suggested that aging of tendon stem cell likely occurs via intrinsic cellular deficits, aging of stem cell niche, and age-associated systemic hormonal and metabolic changes. These factors interact with each other to affect stem cell function at multiple levels.

triggering the aging of tendon stem cells. The authors speculate that they may work together in inducing functional deficits of tendon stem cells as aging is a continued process. Figure 1 summarizes the key biological characteristics of aged tendon, and potential factors influencing the fate and functions of TDSCs during tendon aging. Uncovering the mechanisms underpinning the regenerative decline of metabolism and functions of aged tendon stem cells would have profound implications for the development of stem cell-based therapeutics to reset the aging clock of tendons.

### REFERENCES

### AUTHOR CONTRIBUTIONS

PL conceived the idea, wrote, and proof-read the manuscript. CW wrote and proof-read the manuscript.

### FUNDING

This study was supported by the one-line budget of The Hong Kong Polytechnic University (SF1920-CM).

changes in tendon structure and mechanical properties. Matrix Biol. 32 (1), 3– 13. doi: 10.1016/j.matbio.2012.11.005

modulation of TNF. J. Bone Miner. Res. 5 (1), 114–123. doi: 10.1359/ jbmr.090708

Conflict of Interest: 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 © 2020 Lui and Wong. 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(s) 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.

# Methods for Accurate Assessment of Myofiber Maturity During Skeletal Muscle Regeneration

Yuki Yoshimoto<sup>1</sup> , Madoka Ikemoto-Uezumi<sup>1</sup> , Keisuke Hitachi<sup>2</sup> , So-ichiro Fukada<sup>3</sup> and Akiyoshi Uezumi<sup>1</sup> \*

<sup>1</sup> Muscle Aging and Regenerative Medicine, Tokyo Metropolitan Institute of Gerontology, Tokyo, Japan, <sup>2</sup> Division for Therapies against Intractable Diseases, Institute for Comprehensive Medical Science, Fujita Health University, Toyoake, Japan, <sup>3</sup> Project for Muscle Stem Cell Biology, Graduate School of Pharmaceutical Sciences, Osaka University, Suita, Japan

#### Edited by:

Atsushi Asakura, University of Minnesota Twin Cities, United States

#### Reviewed by:

Masatoshi Suzuki, University of Wisconsin–Madison, United States Ashok Kumar, University of Houston, United States Takahiko Sato, Fujita Health University, Japan

> \*Correspondence: Akiyoshi Uezumi<EMAIL_ADDRESS>
#### Specialty section:

This article was submitted to Stem Cell Research, a section of the journal Frontiers in Cell and Developmental Biology

> Received: 19 January 2020 Accepted: 30 March 2020 Published: 22 April 2020

#### Citation:

Yoshimoto Y, Ikemoto-Uezumi M, Hitachi K, Fukada S and Uezumi A (2020) Methods for Accurate Assessment of Myofiber Maturity During Skeletal Muscle Regeneration. Front. Cell Dev. Biol. 8:267. doi: 10.3389/fcell.2020.00267 Adult skeletal muscle has a remarkable ability to regenerate. Regeneration of mature muscle fibers is dependent on muscle stem cells called satellite cells. Although they are normally in a quiescent state, satellite cells are rapidly activated after injury, and subsequently proliferate and differentiate to make new muscle fibers. Myogenesis is a highly orchestrated biological process and has been extensively studied, and therefore many parameters that can precisely evaluate regenerating events have been established. However, in some cases, it is necessary to evaluate the completion of regeneration rather than ongoing regeneration. In this study, we establish methods for assessing the myofiber maturation during muscle regeneration. By carefully comparing expression patterns of several muscle regeneration-related genes, we found that expression of Myozenin (Myoz1 and Myoz3), Troponin I (Tnni2), and Dystrophin (Dmd) is gradually increased as muscle regeneration proceeds. In contrast, commonly used regeneration markers such as Myh3 and Myh8 are transiently upregulated after muscle injury but their expression decreases as regeneration progresses. Intriguingly, upregulation of Myoz1, Myoz3 and Tnni2 cannot be achieved in cultured myotubes, indicating that these markers are excellent indicators to assess myofiber maturity. We also show that analyzing re-expression of Myoz1 and dystrophin in individual fiber during regeneration enables accurate assessment of myofiber maturity at the single-myofiber level. Together, our study provides valuable methods that are useful in evaluating muscle regeneration and the efficacy of therapeutic strategies for muscle diseases.

Keywords: skeletal muscle, muscle regeneration, muscle differentiation, satellite cells, muscle disease

# INTRODUCTION

Skeletal muscle consists mainly of myofibers, which are large cylindrical cells with many nuclei. Myofibers are terminally differentiated post-mitotic cells; however, skeletal muscles possess a high ability to regenerate. Regeneration of mature myofibers is dependent on satellite cells. Satellite cells are mononucleated cells located between the plasma membrane of the myofiber and basal lamina.

They normally remain in a quiescent state, but are activated upon muscle injury, and then they proliferate and differentiate to regenerate myofibers. Genetically engineered mice in which satellite cells are ablated show a complete lack of regenerative response (Lepper et al., 2011; Murphy et al., 2011; Sambasivan et al., 2011), indicating that satellite cells are absolutely required for muscle regeneration and cannot be compensated by other cell types. Furthermore, single-satellite cell transplantation revealed that these cells indeed possess self-renewal potential, in addition to the ability to differentiate into myofibers (Sacco et al., 2008). Thus, satellite cells are considered as definitive adult muscle stem cells.

Adult myogenesis is a highly ordered process in which satellite cells proliferate, differentiate, and generate new myofibers. Myogenic regulatory factors (MRFs) are important regulators of myogenesis and their expression is tightly regulated. Quiescent satellite cells do not express detectable levels of MyoD but they begin to express high levels of MyoD upon activation (Zammit et al., 2004). Expression of MyoD is maintained during the proliferation phase and continues until the early differentiation phase (Zammit et al., 2004). Myogenin is not expressed in quiescent satellite cells and proliferating undifferentiated myoblasts, but its expression is significantly upregulated when cells begin to differentiate (Bentzinger et al., 2012). Therefore, MyoD and Myogenin are commonly used as activation and differentiation markers of myogenesis, respectively. Expression levels of MRF4 are highest of the MRFs in adult mature muscle and are considered to reflect muscle fiber maturity (Bentzinger et al., 2012; Zammit, 2017). Adult muscle regeneration recapitulates many aspects of embryonic myogenesis, including expression of embryonic- or perinatal-type myosin heavy chain (MyHC) (Sartore et al., 1982). Thus, expression of these embryonic-type contractile proteins is a hallmark of muscle regeneration and is often used to detect activity of regeneration.

Expression of MRFs or embryonic or perinatal MyHC is useful to examine regenerating events. However, the most important goal in tissue regeneration is that the normal condition is restored. From this perspective, expression of the above described regeneration markers reflects conditions where muscle is still abnormal. In certain cases, therefore, assessment of "normality" becomes more important than evaluating regenerating events, especially in studies that examine the efficacy of therapeutic methods for degenerative muscle diseases. If diseased muscle is successfully treated and restored to its healthy state, expression of regeneration markers should be downregulated. Based on this notion, some studies examined downregulation of embryonic MyHC to assess therapeutic efficacy (Guiraud et al., 2019). However, little is known about indicators that directly reflect normality of muscle tissue.

Although experimental muscle regeneration is a highly ordered process, it is not completely synchronized, and thus there is a regional difference in the progression of regeneration within a single muscle. In a regeneration model of grafted muscle, it was reported that a radial gradient of regeneration is formed, with more mature muscle at the periphery and less mature muscle toward the center in the regenerating grafted muscle (Carlson and Gutmann, 1975). Likewise, other muscle regeneration models, including cardiotoxin injury models, do not show completely uniform regeneration, with some regions showing accelerated regeneration while other regions are in a delayed phase of regeneration. Therefore, it is important to develop a reliable method for evaluating muscle regeneration accurately and quantitatively, taking spatial non-uniformity of regeneration into account.

In this study, we carefully examined several regenerationrelated markers during muscle regeneration. These analyses revealed that expression of Myozenin (Myoz1 and Myoz3), Troponin I (Tnni2), and Dystrophin (Dmd) correlates very well with the progression of regeneration. Their expression highly reflects myofiber maturity because high expression of these genes can only be achieved in muscle tissue in vivo and not in cultured myotubes in vitro. We also developed a method that can distinguish advanced regenerating areas from delayed regenerating areas within single muscle, which enables accurate and quantitative evaluation of muscle regeneration. Our study provides useful information for the studies of muscle regeneration and therapy for muscle diseases.

# MATERIALS AND METHODS

### Mice

C57BL/6 wild type mice were used to isolate satellite cells and to analyze muscle regeneration. DBA/2-mdx (D2-mdx) mice were provided from Central Institute for Experimental Animals in Japan. All animal experiments performed in this report were approved by the Animal Care and Use Committee of Tokyo Metropolitan Geriatric Hospital and Institute of Gerontology.

## Muscle Injury

Cardiotoxin (CTX, Sigma) was dissolved in sterile saline at a concentration of 10 µM. Tibialis anterior (TA) muscles of 2 to 3 month old mice were injected with 100 µl CTX. TA muscles were isolated at days 0, 3, 5, 7, and 14 of CTX injury, embedded in tragacanth gum, and frozen in liquid nitrogen-cooled isopentane.

# Satellite Cell Isolation

Isolation of mouse satellite cells was reported previously (Uezumi et al., 2016). Hind limb muscles were collected, minced and digested with 0.2% type II collagenase (Worthington) for 60 min at 37◦C using a magnetic stirrer. Digested muscles were passed through an 18-gauge needle several times and further digested for 30 min at 37◦C. Digested samples were filtered through a 100-µm cell strainer, and then through a 40-µm cell strainer. Cells were resuspended in washing buffer and labeled with APC-eFluor 780-conjugated rat antimouse CD45 (1:250) (Invitrogen), PE/Cy7-conjugated rat anti-mouse CD31 (1:250) (Biolegend), biotin-conjugated SM/C-2.6 (1:250) (Fukada et al., 2004), and PE-conjugated

goat anti-mouse PDGFRα (15 µl/test) (R&D systems), followed by secondary staining with streptavidin-brilliant violet 421 (Biolegend) (1:250). CD31−CD45−PDGFRα <sup>−</sup>SM/C-2.6<sup>+</sup> cells were sorted and collected as satellite cells with FACSAria II (BD Biosciences).

### Cell Culture

After cell sorting, satellite cells were seeded at a density of 1 × 10<sup>4</sup> cells/well on a 48-well cell culture plate coated with Matrigel (BD Biosciences) in growth medium (GM) consisting of DMEM supplemented with 20% FBS, 1% penicillin-streptomycin, and 2.5 ng/µl bFGF (Katayama Chemical), and cultured at 37◦C in 5% CO<sup>2</sup> and 3% O2. After 4 days of culture in GM, GM was changed to differentiation medium (DM) consisting of DMEM with 5% horse serum. Then cells were maintained at 37◦C in 5% CO<sup>2</sup> and 20% O<sup>2</sup> for 3 days to induce myogenic differentiation.

### RNA Extraction and Quantitative Reverse Transcription-PCR Analysis

Total RNA was extracted from cultured satellite cells and muscles using RNeasy Mini Kit (Qiagen) and miRNeasy Mini Kit (Qiagen), respectively. Pieces of muscle tissues collected from frozen TA muscles were crushed in QIAzol Lysis Reagent (Qiagen) using a Shakeman homogenizer (Bio Medical Science). Complementary DNA (cDNA) was synthesized using QuantiTect Transcription Kit (Qiagen). qRT-PCR was performed with SYBR Premix Ex Taq II (Takara) on a Takara Thermal Cycler Dice Real Time System (Takara) under the following cycling conditions: 94◦C for 30 s followed by 40 cycles of amplification (94◦C for 5 s, 60◦C for 20 s, 72◦C for 12 s) and dissociation curve analysis. For gene expression analysis in regenerating TA muscles and differentiating satellite cells, mRNA expression was normalized with Cmas. Relative mRNA expression was then calculated using the 2−<sup>11</sup> method. Specific primers used for qRT-PCR were listed in **Supplementary Table 1**. Primers for Actb were provided from QuantiTect Primer Assays Kit (Qiagen).

### Immunohistochemistry

Frozen transverse sections were cut at the thickness of 8 µm and fixed for 5 min in ice-cooled acetone. After blocking with M.O.M.TM mouse IgG blocking reagent (Vector Laboratories), sections were incubated overnight at 4◦C with primary antibodies diluted in M.O.M.TM diluent. After washing with PBS, sections were stained with secondary antibodies. Primary and secondary antibodies used were listed in **Supplementary Table 2**. Nuclei were counterstained with DAPI (Dojindo), and stained muscles were mounted with SlowFade Diamond anti-fade reagent (Invitrogen). Fluorescent signals were detected with confocal laser scanning microscope systems TCS-SP8 (Leica). The same sections were stained with hematoxylin and eosin (HE) after capturing fluorescent images. HE images were taken with microscope AXIO (Carl Zeiss) equipped with a digital camera, Axiocam ERc 5s (Carl Zeiss).

## Quantitative Analysis of Mature Myofibers

Cross-sections were made by cutting at the mid-belly of TA muscle (at the position about 3 mm from proximal end of TA muscle). After immunostaining, fluorescent images of entire cross-sections were captured with fluorescent microscope system BZ-X710 (Keyence). Image recognition and quantification were performed by using the Hybrid Cell Count Application (Keyence). First, entire cross-sectional areas of TA muscle were measured. For quantification of Myoz1-positive area, Myoz1-stained area was recognized based on the intensity of Myoz1 staining by adjusting threshold. For quantification of dystrophin-positive area, dystrophin-stained sarcolemma was first recognized based on the intensity of dystrophin staining by adjusting threshold, and then dystrophin-positive fiber area was recognized by using inversion function. After recognition of Myoz1- and dystrophin-positive areas, the misrecognized small areas were excluded by adjusting lower limit in histogram function. Finally, errors in recognition step were corrected manually, and then Myoz1- and dystrophin-positive areas were measured. Myoz1- or dystrophin-positive area was divided by entire cross-sectional area to calculate percentage of area positive for each marker. Two side unpaired t-test was used to compare two groups.

### Statistical Analysis

Statistical significance was evaluated using GraphPad Prism 8.0 (GraphPad Software). One-way analysis of variance (ANOVA) followed by Tukey's or Dunnett's test was used to compare more than two groups.

# RESULTS

### Optimum Internal Control Gene for Gene Expression Analysis During Muscle Regeneration

We first analyzed the expression of several internal control genes by qRT-PCR to determine the optimum control genes for the most accurate gene expression analysis during muscle regeneration. As shown in **Figure 1**, Gapdh and Actb (also called β-actin), commonly used control genes, were highly variable in their expression during muscle regeneration (**Figure 1**). Therefore, these genes are not suitable as internal control genes to normalize expression of target genes. One pioneering study on comprehensive gene expression analysis during muscle regeneration had previously pointed out this problem and identified two genes that are stably expressed across all time points during muscle regeneration (Zhao and Hoffman, 2004). Those two genes are Cmas (also called as CMP-Nacetylneuraminic acid synthase) and Eif3c (called as NIPIlike protein). We thus examined the expression of Cmas and found relatively stable expression of this gene during muscle regeneration (**Figure 1**). Therefore, we decided to use Cmas as an internal control gene for gene expression analysis during muscle regeneration.

### Gradual Upregulation of Myoz1, Myoz3, Tnni2, and Dmd Reflects the Myofiber Maturity During Regeneration

We next examined expression of several regeneration-related genes. As expected, expression of MyoD and Myogenin were highly induced upon muscle injury, and gradually downregulated thereafter (**Figure 2A**). We also observed similar dynamics in the expression of embryonic-type contractile genes. As shown in **Figure 2B**, expression of Myh3 and Myh8 was detected at day 3 of muscle injury, reached its peak at day 5, and then decreased to levels comparable to intact muscle. Thus, expression of abovedescribed genes is transient during muscle regeneration and therefore does not reflect completion of regeneration accurately. Zhao et al. (2002) performed temporal gene expression profiling of muscle regeneration and showed that expression of muscle structural component genes is downregulated at early stages and then upregulated at late stages of muscle regeneration. Those include Myozenin, which encodes a Z-disk associated protein myozenin, and Tnni2, which encodes a fast skeletal type troponin I, a protein responsible for the calcium-dependent regulation of muscle contraction. Therefore, we examined expression of these muscle structural component genes. Expression of Myoz1, Myoz3, and Tnni2 was sharply downregulated at day 3 of muscle injury, and then gradually upregulated as regeneration proceeded (**Figure 2C**), indicating that expression of these genes well reflects the extent of muscle regeneration. We also analyzed expression of Dmd, which encodes a dystrophin protein, and Myh4, which encodes a MyHC-IIb, a predominant type of MyHC expressed in TA muscle (Kammoun et al., 2014). Similar to Myoz1, Myoz3 and Tnni2, expression of Dmd correlated well with the progression of regeneration (**Figure 2C**). Although Myh4 showed a similar expression pattern, it reflected the extent of regeneration less accurately because there was no statistically significant difference in its expression levels between day 3 and day5, or day 5 and day 7 (**Figure 2C**). In contrast to these genes, expression of Myoz2 and Myf6 did not reflect muscle maturity (**Figure 2D**). These results clearly show that Myoz1, Myoz3, Tnni2, and Dmd are excellent markers for the assessment of myofiber maturity during muscle regeneration.

### High Level Expression of Myoz1, Myoz3, and Tnni2 Cannot Be Achieved in Cultured Myotubes

Results described above strongly suggest that expression of Myoz1, Myoz3, Tnni2, and Dmd correlates with myofiber maturity. It is well-known that cultured myotubes cannot mature into myofibers. To further confirm the relationship between expression of Myoz1, Myoz3, Tnni2, and Dmd and myofiber maturity, we examined the expression of these genes during myogenesis of cultured satellite cells. Satellite cells were FACSsorted from hind limb muscles and cultured in vitro to obtain myotubes (**Figure 3A**). During the first 4 days of the growth

period, satellite cells became activated, and they proliferated extensively (**Figure 3B**). Upon induction of differentiation, they rapidly formed myotubes at day 5 of culture, and generated numerous myotubes by day 7 as they further differentiated (**Figures 3C,D**). In CTX muscle regeneration model, satellite cells proliferate extensively within 2 to 3 days of injury and begin to form regenerated myofibers approximately 5 days after injury, and regenerated myofibers mature afterward (Hawke and Garry, 2001). Thus, proliferation period and timing of differentiation of satellite cells are similar between in vitro myogenesis and in vivo regeneration model. As expected, undifferentiated myoblasts expressed very low levels of Myoz1, Myoz3, Tnni2, and Dmd similarly to CTX-injected muscle at day 3 (**Figures 2C**, **3E**). In later time points, expression levels of Myoz1, Myoz3 and Tnni2 remained quite low compared

to levels in intact and regenerating (CTX day 5 and day 7) TA muscles in vivo (**Figure 3E**). Although expression of Dmd remained low levels until day 5 of culture, its expression in myotubes increased to the levels comparable to in vivo regenerating muscle at day 7 (**Figure 3E**). These data further reinforce the view that expression of Myoz1, Myoz3, and Tnni2 well-reflects myofiber maturity that cannot be achieved in cultured myotubes.

### Centrally Nucleated Fibers With Myoz1 and Dystrophin Expression Identify Areas of Advanced Regeneration

Evaluating Myoz1, Myoz3, Tnni2, and Dmd expression would be very useful for assessing the degree of muscle

Magnified images of boxed areas in (A). Upper panels show area with advanced regeneration and lower panels show area with delayed regeneration. Scale bars: 100 µm (A) and 20 µm (C).

regeneration at the whole-tissue level. However, in some cases, it is necessary to assess muscle regeneration at the single-myofiber level because muscle regeneration is not a uniform process, with some regions showing advanced regeneration while other regions showing a delayed phase of regeneration. To overcome this problem, we developed a method that can accurately assess this spatial non-uniformity of regeneration. Centrally located nuclei are commonly used as an index of regenerated myofibers. However, central nuclei already exist in nascent myotubes, precluding its use as a reference index when assessing myofiber maturity. Among the markers whose expression correlate well with the progression of regeneration (Myoz1, Myoz3, Tnni2, and Dmd), we could obtain clear staining results for Myoz1 and dystrophin proteins. Because Myoz1 is a Z-disk associated protein, Myoz1-stained muscle showed sarcomere pattern, suggesting the specificity of the antibody used in this study (**Supplementary Figure 1**). We found that Myoz1 expression disappears upon muscle injury but gradually reappears as muscle regeneration proceeds (**Figures 4A,B**). Intriguingly, although well-differentiated large centrally nucleated fibers were strongly positive for Myoz1, small basophilic nascent myotubes were scantly positive or negative for Myoz1 even in contiguous areas of the same muscle

(**Figure 4C**). Dystrophin staining resulted in similar expression pattern, although dystrophin re-expression tended to be restricted to more mature myofibers (**Figure 5**). These results indicate that centrally nucleated myofiber with recovered Myoz1 or dystrophin expression is useful for assessing the spatial non-uniformity of muscle regeneration at single-myofiber level.

### Re-expression of Myoz1 and Dystrophin Is a Good Indicator of Myofiber Maturity at the Single-Myofiber Level

To further understand the relationship between Myoz1 or dystrophin expression and myofiber maturity, we examined embryonic MyHC (eMyHC) expression, which is transiently upregulated in immature myofibers but downregulated as myofibers mature (d'Albis et al., 1988). Approximately 40 and 90% of eMyHC-positive immature myofibers were negative for Myoz1 and dystrophin, respectively (**Figure 6A**), indicating that re-expression of these markers occurs in myofibers with advanced maturation stage.

We also examined usefulness of these markers in evaluating disease progression of D2-mdx mice, a severe mouse model of Duchenne muscular dystrophy (DMD) (Fukada et al., 2010). Muscle of mdx background appears normal until approximately 3–4 weeks of age, but myofibers undergo massive degeneration afterward (DiMario et al., 1991). Because D2-mdx mice lack dystrophin expression, we examined expression of Myoz1 before and after disease onset. At 3 weeks of age, all myofibers of D2-mdx mice appeared normal and uniformly expressed Myoz1 (**Figure 6B**). After onset of symptoms, however, small immature myofibers located in degenerated area were scantly positive or negative for Myoz1, and total Myoz1-positive area was significantly decreased in ratio compared with pre-symptomatic stage (**Figure 6B**). These results indicate that expression of Myoz1 and dystrophin is a good indicator of myofiber maturity and useful for evaluating "normality" of myofiber in pathological conditions.

## DISCUSSION

In this study, we describe methods for the assessment of myofiber maturity during skeletal muscle regeneration. Expression of Myoz1, Myoz3, Tnni2, and Dmd significantly correlates with progression of muscle regeneration, and therefore, these genes are quite useful to quantify and evaluate the extent of muscle regeneration at the whole-muscle tissue level. Meanwhile, reexpression of Myoz1 and dystrophin is an excellent indicator for the assessment of myofiber maturity at the singlefiber level.

Myozenin is specifically expressed in striated muscle and localized at Z-disks (Faulkner et al., 2000; Takada et al., 2001). Myozenin is reported to interact with other Z-disk proteins including a-actinin, filamin-C, telethonin and myotilin, and thought to be involved in the connection between the contractile apparatus and the sarcolemma (Faulkner et al., 2000; Frey et al., 2000; Takada et al., 2001; Frey and Olson, 2002; Gontier et al., 2005). In addition to its role as a structural protein, Myoz1 was shown to modulate calcineurin/NFAT activity (Frey et al., 2008), raising the possibility that Myozenin functions as a signaling molecule. In this study, we showed that expression of Myoz1 and Myoz3 is gradually upregulated as myofibers mature during muscle regeneration, and cultured myotubes do not express these genes at high levels. Therefore, it would be reasonable to assume that Myozenin plays some functional role in myofiber maturation.

We demonstrated that dystrophin begins to be reexpressed when myofibers mature in the late phase of muscle regeneration. Therefore, dystrophin re-expression closely reflects myofiber maturity. Although this method will provide a very powerful means of assessing muscle regeneration, there is a certain limitation. Because this evaluation method depends on dystrophin expression, it cannot be used in dystrophin-deficient conditions such as DMD. Evaluating Myoz1 expression offers an alternative method in such a situation as we showed in this study. However, definitive therapy for DMD is restoration of dystrophin expression. Therefore, dystrophin expression is commonly studied in research when evaluating the effects of DMD therapy. If the therapeutic strategy is based on endogenous gene expression machinery (such as exon skipping or gene editing therapy) and not on forced expression, restored dystrophin expression reflects not only proof of principle but also maturity of treated myofibers. Thus, our study provides additional rationale for examining dystrophin expression in DMD therapy research.

Methods described here are based on gene expression and histological analyses, but one of the most important functions of skeletal muscle is to contract for force generation. Therefore, measurement of contractile ability is one of the best ways to evaluate skeletal muscle property. However, evaluating contractile properties is accompanied by some technical difficulties. Our methods presented here are relatively stable and easy to perform. In addition, markers used in our methods seem to be functionally important because myozenin and troponin I are associated with contractile apparatus, and dystrophin is a causative gene for DMD. Thus, we believe that our methods provide convenient opportunity to assess myofiber maturity.

In conclusion, our study provides meaningful information that can be applied for accurate and quantitative assessment of muscle regeneration and effectiveness of therapy for muscle diseases.

## DATA AVAILABILITY STATEMENT

All datasets generated for this study are included in the article/**Supplementary Material**.

## ETHICS STATEMENT

fcell-08-00267 April 20, 2020 Time: 17:28 # 11

The animal study was reviewed and approved by The Animal Care and Use Committee of Tokyo Metropolitan Geriatric Hospital and Institute of Gerontology.

### AUTHOR CONTRIBUTIONS

YY performed qPCR, immunostaining and cell culture experiments, and analyzed the data. MI-U and KH induced muscle regeneration and retrieved samples. MI-U performed FACS experiments. AU and SF interpreted results and coordinated the project. AU conceived the whole project and wrote the manuscript.

### FUNDING

YY was funded by Grant-in-Aid for JSPS Research Fellows for FY2019 and The Japanese Society for Bone and

### REFERENCES

Mineral Research Frontier Scientist Grant for FY 2019. MI-U was funded by JSPS KAKENHI Grant Number JP19K09614. AU was funded by JSPS KAKENHI Grant Number JP19H04063, The General Insurance Association of Japan, and Research Fund of Mitsukoshi Health and Welfare Foundation.

### ACKNOWLEDGMENTS

We would like to thank Editage (www.editage.com) for English language editing.

### SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fcell.2020.00267/ full#supplementary-material

muscle-specific filamins. J. Cell Sci. 118(Pt 16), 3739–3749. doi: 10.1242/jcs. 02484

**Conflict of Interest:** 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.

The reviewer TS declared a shared affiliation, with no collaboration, with the author KH, to the handling Editor, at the time of review.

Copyright © 2020 Yoshimoto, Ikemoto-Uezumi, Hitachi, Fukada and Uezumi. 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(s) 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.


**10.2 Gender difference in duration of care**

*Suggestions for Addressing Clinical and Non-Clinical Issues in Palliative Care*

**108**

**G** F

10–20

22

Married

79

Primary (Basic)

22 (8.5)

Employed

89

3–8

16 (6.2)

<1

67 (25.8)

(34.2)

(30.)

(8.5)

21–30

31

Single

93

Secondary (high)

31

Un-employed

31

9–12

145

1–2

90 (34.6)

(55.8)

(11.9)

(11.9)

(35.8)

(11.9)

31–40

63

Divorced

10

Tertiary

77

Business/

79

13–24

38

2–3

32 (12.3)

(14.6)

(30.4)

(29.6)

farming

(3.8)

(24.2)

41–50

46

Widowed

8 (3.1)

Never been to school

20 (7.7)

Employed

89

—

—

>3

10 (3.8)

(34.2)

(17.7)

>51

37

Living together/

9 (3.5)

Left school for

49

Un-employed

31

—

—

—

—

(11.9)

**199 (76.5)**

**199 (76.5)**

**199 (76.5)**

(18.8)

caregiving

**199 (76.5)**

domestic partner

**199 (76.5)**

(14.2)

T M

10–20

3

Married

34

Primary (Basic)

4 (1.5)

Employed

11 (4.2)

3–8

36

<1

34 (13.1)

(13.8)

(13.1)

(1.2)

21–30

8

Single

15

Secondary (high)

16 (6.2)

Un-employed

29

9–12

17 (6.5)

1–2

19 (7.3)

(11.2)

(5.8)

(3.1)

31–40

22

Divorced

4 (1.5)

Tertiary

28

Business/

21 (8.1)

13–24

8 (3.1)

2–3

6 (2.3)

(10.8)

farming

(8.5)

41–50

15

Widowed

5 (1.9)

Never been to school

2 (0.8)

Employed

—

—

—

>3

2 (0.8)

(5.8)

>51

13

Living together/

3 (1.2)

Left school for

11 (4.2)

Un-employed

—

—

—

—

—

caregiving

**61 (23.5)**

**61 (23.5)**

**61 (23.5)**

**61 (23.5)**

domestic partner

**61 (23.5)**

(5.0)

T

**61 (23.5)** *Note: G, gender; F, female; M, male; T, total.*

**Table 1.** *The socio-demographic characteristics of caregiver respondents.*

**199 (76.5)**

**Age (years)**

**Marital status**

**Educational qualification**

**Occupation**

**Duration of care**

**Years of care**

**Range n (%)**

**(hr/day)**

**Range n (%)**

**Type n (%)**

**Type n(%)**

**Type n (%)**

**Range n (%)**

Gender difference existed in the duration of care provided for PLWHA. The least duration 3–8 hours was spent by 36 (13.8) male caregivers while only 16 (6.2) female caregivers spent the same. 9–12 hours majority were spent by 145 (55.8) female caregivers. 13–24 hours of care was provided mostly by 38 (14.6) female caregivers. With reference to the years spent in caregiving, the majority 90 (34.6) female caregivers spent between 1 and 2 years, while the least 10 (3.8) female and 2 (0.8) male caregivers provided care to PLWHA for more than three years (**Table 1**).

**Table 2** describes the household income as well the number of people in the household. The majority 130 (50%) family caregivers of PLWHA lived below a household income of US\$6.00/day while only 15 (5.8%) family caregivers spent US\$14.00 and above. The majority 153 (58.9%) lived in a household with about three to six members and only about 19 (7.3%) lived in small households with one to three persons (**Table 2**).

**Table 3** describes gender difference in the level of burden among family caregivers of PLWHA ranging between 14 and 71 on ZBI Scale (**Table 3**); among the 49 (18.8%) caregivers that experienced no burden, the majority were females 39 (15.0) while 10 (3.9%) males. The 46 (17.7) female caregivers experienced mild to moderate while providing care. Ninety-three (35.8) caregivers experienced moderate to severe burden and the majority 69 (26.6) were females. Fifty-one (19.6%) family caregivers experienced severe level of burden in providing care to PLWHA and majority 45 (17.3) caregivers were females.

#### **Table 2.**

*Household income and the number of people in the household.*

#### **Table 3.**

*The distribution of burden on Zarit burden interview (ZBI) scale.*

#### **Table 4.**

*The relationship between gender and caregivers burden.*

Significant statistical relationship existed between gender and caregivers burden assessed with the students *t* test. Calculated *t* test value of 1.27 at *p* < 0.05, indicated a significant relationship between the levels of burden experienced by gender (**Table 4**).


**ANTICOAGULATION THERAPY**

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#### **Anticoagulation Therapy**

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**4. Results**

In this section, we evaluate the proposed contextual mining of sedentary behaviour model and present the results of the performed experiments. Our approach belongs to the family of instance-based learning (i.e., *k*-nearest neighbour). Such approaches do not require optimizing the classifier parameters. It stores the training instances and classifies the new data by calculating the similarities of the stored instances. In order to get these training instances, we asked the participants to annotate the daily routines by miming short duration trials and keeping a note of the start and end time of each context. The training dataset is labelled over the time intervals' information. To assess the performance of our approach, we split the dataset into a ratio of 60:10:30 (i.e., training:validation:test) of the annotated dataset. Our dataset is balanced by considering the equal instances of each considered context. In this experimental setting, the simple performance metric "accuracy" is able to provide correct information about the ability of the model. Initially, we get an overall accuracy of 93% over the collected dataset. We analysed the dataset and performed the data pre-processing. In this step, we discard the first and last few instances of the recorded context. Our analysis showed that start and end instances do not present the true representation of the class. Such a setting enhanced the quality of the training instances in terms of better context representation. In the same experimental setting with the same dataset, we get an accuracy of 98%. The real test setup consists of six volunteer graduate students. The participants installed our developed application on their smartphone for two weeks in order to have enough time to analyse the significance and to perform a comparative analysis of sedentary behaviour. Examples of the scenes of context mining are shown in Figure 6.

**Figure 6.** Example photos for "short break", "active", "working on a PC", "watching TV" and "sedentary-context unknown".

**Figure 7.** Current progress of the user in the last hour.

In Figure 7, we can observe the progress of the last hour in real time by identifying the context, either active or still. We can see in the "progress graph" (i.e., Figure 7) that the *x*-axis presents the recognized context, the while *y*-axis provides the time stamp in minutes. Furthermore, each point presents each minute of the human behaviour and reports the information about the last 52 min. The annotation of the recognized context shows that the participant was waling from the dormitory to the campus. A user can also visualize the hourly status of the sedentary behaviour of the day. We presented the hourly status of the behaviour, which is the "today context", as shown in Figure 8.

In Figure 8, each bubble presents the number of minutes, and the size of each bubble increases or decreases with the recognized context. For example, at 11:00, the person is in sedentary activity for the whole 60 min. We can also observe that 0.00 means that person is not active even for a single minute.

**Figure 8.** Hourly sedentary behaviour recognition.

In order to provide rich contextual information, we facilitate the user awareness about the micro-contexts of sedentary behaviour, which explains how much time the user spent watching TV, working on a PC or sedentary-context unknown. As we discussed earlier, our micro-context recognition list is very limited due to limited processing of environmental sound. Figure 9 shows the details of micro-contexts that our model identifies by processing the environmental sound.

**Figure 9.** Micro-context recognition.

In Figure 9, the *x*-axis represents the time in hours, while *y*-axis presents the recognized micro-context. All the sedentary contexts other than watching TV and working on a PC are considered as sedentary-context unknown. In the unknown context, a user can be located on a public bus, in a library, in a cafeteria, sleeping or any other situation. We also present the entire week of behaviour in terms of recognized context and visualize it through our developed application. The user can query any specific context from the recognized context to get the information about the time spent. Figure 10 shows the total active hours of the user each day throughout the week.

**Figure 10.** Total time spent during a week while being "active".

It is obvious in Figure 10 that very limited activity is observed during Friday and Wednesday against the context "active", while the user is very active on Tuesday and Sunday. Figure 11 presents the total duration spent in short breaks for each day. Our model recognized the short breaks between the sedentary hours of a user. Along the *x*-axis, we placed the time in hours, and the *y*-axis presents the days.

**Figure 11.** Total time spent during a week for "short breaks".

In Figure 11, the user took a small number of short breaks during Saturday, while a large number of short breaks can be seen on Monday. This information about the number of short breaks may help the subject to avoid longer sedentary activity, as well as provide an abstraction to compare different

days. In Figure 12, we present the recognized context information while the user is working on a PC. During the wee, the user spent a maximum of 7 h on a PC, while zero hours were recognized on Wednesday.

**Figure 12.** Total time spent during a week for "working on a PC".

In Figure 13, we can observe the total time spent while watching TV during leisure time. In the presented "weekly context", the *x*-axis presents the context of watching TV, while the *y*-axis presents the time spent in hours. Furthermore, zero means that the user did not watch the television on Monday, Tuesday, Wednesday and Thursday.

**Figure 13.** Total time spent during a week for "watching TV".

In Figure 14, we present the sedentary behaviour when the context is unknown. In unknown context sleeping time is also included and sedentary behaviour that is other than watching TV or working on a PC. The *y*-axis presents the context for the whole week, and each bar presents the number of hours spent during the sedentary activity. We found that the SitCoach [27] application is aligned in the same direction of our research. The SitCoach application monitors office workers' prolonged sitting routines and generates alerts. The alerts may help to reduce sedentary behaviour, and their intervention successfully helps office workers. Their application is restricted in terms of visualization of the user behaviour, as well as mining the micro-contexts. In our proposed research, we are providing rich information to the user about the recognized contexts. We found that self-awareness helps to reduce the sedentary behaviour and motivate the user to avoid prolonging sitting. The following section provides more details about this.

**Figure 14.** Total time spent during a week for "sedentary-context unknown".


**Rizk Melissa 1,2,3,\*,**†**, Mattar Lama 4,**†**, Kern Laurence 5, Berthoz Sylvie 3,6, Duclos Jeanne 7,8, Viltart Odile 9,10 and Godart Nathalie 1,3**

Received: 27 November 2019; Accepted: 2 January 2020; Published: 9 January 2020

**Abstract:** Abnormally high levels of physical activity have been documented throughout the literature in patients with eating disorders (ED), especially those diagnosed with anorexia nervosa (AN). Yet no clear definition, conceptualization, or treatment of the problematic use of physical activity (PPA) in ED patients exists. The aim of this review is to propose a new classification of PPA, report the prevalence, triggers, predictors, maintainers and other related factors of PPA in ED patients, in addition to proposing a comprehensive model of the development of PPA in AN. A total of 47 articles, retrieved from Medline and Web of Science, met the inclusion criteria and were included in the analysis. As a result, the new approach of PPA was divided into two groups (group 1 and group 2) according to the dimension (quantitative vs qualitative approach) of physical activity that was evaluated. The prevalence of PPA in ED was reported in 20 out of 47 studies, the comparison of PPA between ED versus controls in 21 articles, and the links between PPA and psychological factors in ED in 26 articles, including depression (16/26), anxiety (13/26), obsessive–compulsiveness (9/26), self-esteem (4/26), addictiveness (1/26), regulation and verbal expression of emotions (1/26) and anhedonia (1/26). The links between PPA and ED symptomatology, PPA and weight, body mass index (BMI) and body composition in ED, PPA and age, onset, illness duration and lifetime activity status in ED, PPA and ED treatment outcome were reported in 18, 15, 7, 5 articles, respectively. All of the factors have been systematically clustered into group 1 and group 2. Results focused more on AN rather than BN due to the limited studies on the latter. Additionally, a model for the development of PPA in AN patients was proposed, encompassing five periods evolving into three clinical stages. Thus, two very opposite components of PPA in AN were suggested: voluntarily PPA increased in AN was viewed as a conscious strategy to maximize weight loss, while involuntarily PPA increased proportionally with weight-loss, indicating that exercise might be under the control of a subconscious biological drive and involuntary cognition. **Keywords:** review; eating disorders; physical activity; problematic use of physical activity


**Acknowledgements**

This work was supported by the National Natural Science Foundation of China (Grant no. 51371135, 51771141). The authors of this chapter thank G.J. Li, Y.Y. Song, and X.L. Zhang for their contributions.