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Interactive Roles for AMPK and Glycogen from Cellular Energy Sensing to Exercise Metabolism

The AMP-activated protein kinase (AMPK) is a heterotrimeric complex with central roles in cellular energy sensing and the regulation of metabolism and exercise adaptations. AMPK regulatory β subunits contain a conserved carbohydrate-binding module (CBM) that binds glycogen, the major tissue storage form of glucose. Research over the past two decades has revealed that the regulation of AMPK is impacted by glycogen availability, and glycogen storage dynamics are concurrently regulated by AMPK activity. This growing body of research has uncovered new evidence of physical and functional interactive roles for AMPK and glycogen ranging from cellular energy sensing to the regulation of whole-body metabolism and exercise-induced adaptations. In this review, we discuss recent advancements in the understanding of molecular, cellular, and physiological processes impacted by AMPK-glycogen interactions. In addition, we appraise how novel research technologies and experimental models will continue to expand the repertoire of biological processes known to be regulated by AMPK and glycogen. These multidisciplinary research advances will aid the discovery of novel pathways and regulatory mechanisms that are central to the AMPK signaling network, beneficial effects of exercise and maintenance of metabolic homeostasis in health and disease.

Introduction
The AMP-activated protein kinase (AMPK) is a heterotrimer composed of a catalytic α subunit and regulatory β and γ subunits, which becomes activated in response to a decrease in cellular energy status. Activation of AMPK results in metabolic adaptations such as increases in glucose uptake and glycolytic flux and fatty acid (FA) oxidation. AMPK activation simultaneously inhibits anabolic processes including protein and FA synthesis. AMPK can also translocate to the nucleus where it regulates transcription factors to increase energy production, meet cellular energy demands and inhibit cell growth and proliferation. Conversely, when energy levels are replete, AMPK activity returns to basal levels, allowing anabolic processes to resume. Given its central roles in cellular metabolic and growth signaling pathways, AMPK remains an appealing target for treating a range of pathologies associated with obesity and aging, including metabolic diseases such as obesity and type 2 diabetes (T2D).
In response to changes in energy supply and demand, glycogen, predominately stored in the liver and skeletal muscle, serves as an important source of energy to maintain metabolic homeostasis. Glycogen is synthesized by the linking of glucose monomers during periods of nutrient excess. In response to energy stress and decreased arterial glucose concentration, rising glucagon levels induce increased hepatic glucose output by promoting the breakdown of glycogen and the conversion of non-glucose substrates into glucose. The newly formed glucose is released into the bloodstream to help restore blood glucose levels. Skeletal muscle glycogen serves as an accessible source of glucose to form adenosine triphosphate (ATP) and to reduce equivalents via glycolytic and oxidative phosphorylation pathways during muscle contraction.
A significant body of evidence demonstrates that AMPK binds glycogen. This physical interaction is mediated by the carbohydrate-binding module (CBM) located within the AMPK β subunit and is thought to allow AMPK to function as a sensor of stored cellular energy. While glycogen is stored in multiple tissues throughout the body, this review will primarily focus on the physical interactions underlying the AMPK β subunit binding to glycogen and its potential functional links to glycogen storage dynamics in the liver and skeletal muscles, as these tissues are central to metabolic and exercise-regulated biological processes. In addition, as the majority of research on this topic has been undertaken in human and mouse model systems, studies in these species will be highlighted. Following a brief background on the regulation of AMPK and glycogen, this review will critically assess the recent advances and focus primarily on studies within the past two decades that have added to our understanding of the physical basis of AMPK-glycogen binding and its potential functional interactions in exercise and metabolism. Key remaining biological questions related to the interactive roles of AMPK and glycogen will be posed along with a discussion of research advancements that are feasible in the next decade with new technologies and experimental models to determine how AMPK-glycogen binding may be therapeutically targeted in health and disease.

AMPK Activation and Signaling
Structural biology-based studies over the past decade have provided new insights into the molecular mechanisms by which AMPK activation is regulated by nucleotides, including changes in AMP:ATP and ADP:ATP ratios (i.e., adenylate energy charge) that occur in response to cellular energy stress [1]. The binding of AMP and ADP to the cystathionine-β-synthase (CBS) domains of the γ subunit promotes AMPK activation through several complementary mechanisms. The binding of AMP promotes AMPK association with liver kinase B1 (LKB1) and the scaffolding protein axin which enhances the effect of T172 phosphorylation [2,3], the primary phosphorylation and activation site in the AMPK α subunit, while simultaneously preventing its dephosphorylation by protein phosphatases [4][5][6][7]. This activation is mediated by myristoylation of the G2 site on the N-terminus of the β subunit ( Figure 1), which promotes AMPK association with cellular membranes and LKB1 [2]. Furthermore, the binding of AMP, but not ADP, can cause the allosteric activation of AMPK without T172 phosphorylation [5,[7][8][9]. The combination of allosteric activation by nucleotides and increased T172 phosphorylation by LKB1 can increase AMPK activity 1000-fold [10]. Additionally, the phosphorylation of T172 can be regulated by changes in intracellular Ca 2+ concentrations via the upstream kinase calcium/calmodulin-dependent protein kinase kinase β (CAMKK2) in the absence of changes in adenylate energy charge [11][12][13].
Once activated, AMPK serves as a metabolic 'switch' to promote catabolic pathways and inhibit anabolic processes. For example, AMPK increases glucose uptake into skeletal muscle by phosphorylating and inhibiting Tre-2, BUB2, CDC16, 1 domain family, members 1 (TBC1D1) and 4 (TBC1D4), promoting glucose transporter 4 (GLUT4) vesicle translocation to the sarcolemmal membrane [14][15][16][17][18]. AMPK also functions in the regulation of lipids, acutely promoting lipid oxidation and inhibiting FA synthesis, primarily through phosphorylation and the inhibition of acetyl-CoA carboxylase (ACC) [19,20]. At the transcriptional level, AMPK phosphorylates and inhibits sterol regulatory element-binding protein 1, a transcription factor that regulates lipid synthesis [21]. Mitochondrial biogenesis is stimulated by AMPK activity through an increase in the peroxisome proliferator-activated receptor gamma coactivator 1-alpha (PGC-1α) transcription, thereby promoting oxidative metabolism [22]. AMPK can also inhibit anabolic pathways by phosphorylating the regulatory associated protein of the mechanistic target of rapamycin (mTOR) (Raptor) and the tuberous sclerosis complex 2 (TSC2), which in turn inactivates mTOR and prevents the phosphorylation of its substrates [23][24][25]. Figure 1. The AMPK is a heterotrimeric protein, consisting of a catalytic α subunit and regulatory β and γ subunits. The β subunit (β1 and β2 isoforms) possesses a glycogen-binding domain (CBM) that mediates AMPK's interaction with glycogen, an N-terminal myristoylation site (myr) and an αγ subunit binding sequence (αγ-SBS) involved in the heterotrimeric complex formation. Tissue expression of the β1 and β2 isoforms varies between humans and mice, as the β2 isoform is predominately expressed in both human liver and skeletal muscles, while mice predominately express the β1 isoform in the liver and the β2 isoform in the skeletal muscles.

The AMPK β Subunit and Carbohydrate-Binding Module
The AMPK β subunit exists in two isoforms (β1 and β2) and serves as a scaffolding subunit that binds to the AMPK catalytic α and regulatory γ subunits, playing an important role in the physical stability of the heterotrimer ( Figure 1) [9,26,27]. In human and mouse skeletal muscles, the β2 isoform is predominantly expressed (Table 1) [28]. In contrast, the liver β subunit isoform expression differs across the mammalian species: the β1 isoform is predominantly expressed in mice, while β2 is predominantly expressed in humans [29,30]. However, despite isoform differences between species, both the β subunit isoforms contain the CBM which mediates physical AMPK-glycogen interaction and binding. Furthermore, the CBM is highly conserved between species, suggesting that the region possesses evolutionary significance and plays similar roles across species [31,32]. The CBM spans residues 68-163 of the β1 subunit and residues 67-163 of the β2 subunit [32,33] and is nearly identical in structure and sequence in both isoforms, with the major difference being the insertion of a threonine at residue 101 in the β2 CBM [34,35]. This insertion is believed to have occurred early in evolutionary history and provides the β2 CBM with a higher affinity for glycogen [33,34]. However, the reason for this divergence in β subunit isoforms is unknown.

Glycogen Dynamics
A number of proteins are associated with glycogen particles and function as regulators of glycogen synthesis, breakdown, particle size, and degree of branching. Glycogenin initiates glycogen formation and functions as the central protein of the glycogen particle [37]. Glycogen synthase (GS) is the rate-limiting enzyme in glycogen synthesis responsible for attaching UDP-glucose donors together in α-1,4 linkages, the linear links of the glycogen particle. As glucose-6-phosphate (G6P) is a precursor to UDP-glucose, its accumulation is a potent activator of GS, capable of overriding the Figure 1. The AMPK is a heterotrimeric protein, consisting of a catalytic α subunit and regulatory β and γ subunits. The β subunit (β1 and β2 isoforms) possesses a glycogen-binding domain (CBM) that mediates AMPK's interaction with glycogen, an N-terminal myristoylation site (myr) and an αγ subunit binding sequence (αγ-SBS) involved in the heterotrimeric complex formation. Tissue expression of the β1 and β2 isoforms varies between humans and mice, as the β2 isoform is predominately expressed in both human liver and skeletal muscles, while mice predominately express the β1 isoform in the liver and the β2 isoform in the skeletal muscles.

The AMPK β Subunit and Carbohydrate-Binding Module
The AMPK β subunit exists in two isoforms (β1 and β2) and serves as a scaffolding subunit that binds to the AMPK catalytic α and regulatory γ subunits, playing an important role in the physical stability of the heterotrimer ( Figure 1) [9,26,27]. In human and mouse skeletal muscles, the β2 isoform is predominantly expressed (Table 1) [28]. In contrast, the liver β subunit isoform expression differs across the mammalian species: the β1 isoform is predominantly expressed in mice, while β2 is predominantly expressed in humans [29,30]. However, despite isoform differences between species, both the β subunit isoforms contain the CBM which mediates physical AMPK-glycogen interaction and binding. Furthermore, the CBM is highly conserved between species, suggesting that the region possesses evolutionary significance and plays similar roles across species [31,32]. The CBM spans residues 68-163 of the β1 subunit and residues 67-163 of the β2 subunit [32,33] and is nearly identical in structure and sequence in both isoforms, with the major difference being the insertion of a threonine at residue 101 in the β2 CBM [34,35]. This insertion is believed to have occurred early in evolutionary history and provides the β2 CBM with a higher affinity for glycogen [33,34]. However, the reason for this divergence in β subunit isoforms is unknown.

Glycogen Dynamics
A number of proteins are associated with glycogen particles and function as regulators of glycogen synthesis, breakdown, particle size, and degree of branching. Glycogenin initiates glycogen formation and functions as the central protein of the glycogen particle [37]. Glycogen synthase (GS) is the rate-limiting enzyme in glycogen synthesis responsible for attaching UDP-glucose donors together in α-1,4 linkages, the linear links of the glycogen particle. As glucose-6-phosphate (G6P) is a precursor to UDP-glucose, its accumulation is a potent activator of GS, capable of overriding the inhibitory effects of phosphorylation mediated by proteins such as AMPK, glycogen synthase kinase 3, and protein kinase A [38,39]. As its name implies, glycogen branching enzyme (GBE) is responsible for introducing α-1,6 branch points to the growing glycogen particle. The rate-limiting enzyme of glycogen breakdown is glycogen phosphorylase (GP), which is known to be activated by elevated intracellular Ca 2+ , epinephrine and cAMP concentrations [40,41]. When activated, GP degrades the α-1,4 links of glycogen particles and removes glycosyl units from the non-reducing ends of the glycogen particle [42]. The glycogen debranching enzyme (GDE) assists with the degradation of glycogen and is responsible for breaking the α-1,6 links to allow continued GP activity. Without GDE, GP can only degrade the outer tiers of glycogen particles and stops four glucose residues short of the α-1,6 branch point [39]. Further details regarding glycogen synthesis and breakdown are beyond the scope of this review, and readers are referred to other reviews covering this topic [39,43].

Glycogen Localization
Recently, there has been an increasing interest regarding the significance of glycogen's subcellular localization in skeletal muscles [44][45][46][47][48]. Glycogen can be concentrated beneath the sarcolemma (subsarcolemmal; SS), between the myofibers along the I band near the mitochondria and sarcoplasmic reticulum (intermyofibrillar; interMF), or within myofibers near the triad junction (intramyofibrillar; intraMF) [44,46]. Depletion of these different glycogen pools impacts muscle function and fatigue, such as impairing Ca 2+ release and reuptake. Therefore, it has been hypothesized that these different pools of glycogen play a significant role in muscle contraction and fatigue beyond their role as an energy substrate [45,47,48].
Human skeletal muscles contain large stores of glycogen, which can exceed 100 mmol glucosyl units/kg wet weight (~500 mmol/kg dry weight) in the vastus lateralis muscle [49,50] and are primarily concentrated in the interMF space [49]. Conversely, rodents tend to have higher stores of glycogen in the liver compared to the skeletal muscle. For example, mice store about 120 µmol/g wet weight in liver and 15-20 µmol glucose/g wet weight in the type IIA flexor digitorum brevis muscle, with the highest concentrations in the intraMF pool [51]. Interestingly, while the relative contributions of intraMF glycogen to total glycogen are different between humans and mice, the intraMF content as a percentage of the total fiber volume is very similar between species [49,51]. Additionally, substrate utilization is different between species during exercise, as humans rely predominately on intramuscular stores and rodents rely on blood-borne substrates [52][53][54][55]. These differences in glycogen storage and utilization between humans and rodents are important considerations in the study design across species when assessing glycogen depletion and/or repletion.

Molecular Evidence of AMPK-Glycogen Binding
In 2003, it was first demonstrated that recombinant AMPK β1 CBM bound glycogen using a cell-free assay system [31]. Structural prediction and mutagenesis experiments targeting conserved residues within the CBM thought to mediate glycogen binding demonstrated that W100G and K126Q mutations abolished glycogen binding to the isolated β1 CBM, while W133L, S108E, and G147R mutations partially disrupted glycogen binding. Additionally, the AMPK heterotrimeric complex was found to bind glycogen more tightly than the β1 subunit in isolation; however, the reasons for this differential binding affinity remain unclear [31]. In support of these findings, cell-free assays have also revealed that glycogen has an inhibitory effect on AMPK activity [56]. Furthermore, mutation of critical residues in the β1 CBM (W100G, W133A, K126A, L148A, and T148A) ablated glycogen's inhibition. In these cell-free assays, glycogen with higher branch points had a greater inhibitory effect on AMPK, indicating that glycogen particle size has the capacity to influence AMPK-glycogen interactions [56]. It was also observed that glycogen particles co-localized with the β subunit of AMPK in the cytoplasm of CCL13 cells [57]. A follow-up structural-based study determined the CBM crystal structure in the presence of β-cyclodextrin and confirmed that AMPK indeed interacts with glycogen [32]. Additional experimental approaches such as immunogold cytochemistry have also shown that the AMPK α and β subunits of rat liver tissue are associated with the surface of glycogen particles in situ, providing further molecular evidence supporting this concept of physical AMPK-glycogen interaction [58].
In addition to its role in binding glycogen, the CBM of the β subunit also physically interacts with the kinase domain of the catalytic α subunit, forming a pocket, referred to as the allosteric drug and metabolite (ADaM) site. Small molecule AMPK activators such as A-769662, a β1 subunit specific activator, bind to this site and directly activate AMPK [27,59]. However, to date, any connection of A-769662's subunit specificity in relation to glycogen has been highly speculative and further research is required to establish potential direct links. The ADaM site is stabilized by autophosphorylation of S108 on the CBM and is dissociated when T172 on the α subunit is dephosphorylated [9]. Mutation of S108 to a phosphomimetic glutamic acid (S108E) resulted in reduced glycogen binding [31] and increased AMPK activity in response to AMP and A-769662, even in the presence of a non-phosphorylatable T172A mutation [8]. Conversely, mutation of S108 to a neutral alanine (S108A) had no effect on glycogen binding [31], but reduced AMPK activity in response to AMP and A-769662 [8]. Collectively, these findings infer that glycogen binding may inhibit AMPK activity by disrupting the interaction between the CBM and the kinase domain of the α subunit [1,9,56]. The inhibitory role of the β subunit T148 autophosphorylation on AMPK-glycogen binding has also been a focus of recent research. The mutation of T148 to a phosphomimetic aspartate (T148D) on the β1 subunit inhibits AMPK-glycogen binding in cellular systems [60]. The results from subsequent experiments in isolated rat skeletal muscle suggest that T148 is constitutively phosphorylated both at rest and following electrical stimulation, therefore, preventing glycogen from associating with the AMPK β2 subunit [61]. Further research is necessary to further elucidate the role of T148 in the context of AMPK-glycogen interactions.
Recent research has provided further structural insights into the affinity of the AMPK β subunits for carbohydrates. Isolated β2 CBM has a stronger affinity for carbohydrates than the β1 CBM, binding strongly to both branched and unbranched carbohydrates, with a preference for single α-1,6 branched carbohydrates [34]. One possible explanation for this difference is that a pocket is formed in the CBM by the T101 residue, which is unique to the β2 subunit, therefore, allowing binding to branched carbohydrates [33,34]. In addition, the β1 CBM possesses a threonine at residue 134 which may form a hydrogen bond with the neighboring W133, restricting the ability of the β1 CBM to accommodate carbohydrates, while the β2 CBM possesses a valine which does not bond with W133 [34]. This difference may explain the increased affinity of the β2 subunit for branched carbohydrates even though the 134 residue does not directly contact carbohydrates [34]. These findings indicate that the glycogen structure and branching affect AMPK binding, specifically to β1 subunits, which may dictate the inhibitory effect of glycogen observed in previous studies [34,56]. While the role of AMPK β isoform glycogen binding in the contexts of glycogen structure and branching has been investigated in vitro, it remains to be determined how these characteristics alter the dynamics of AMPK-glycogen binding in vivo.

Regulation of Cellular Energy Sensing by AMPK-Glycogen Binding
Several independent lines of evidence suggest that these physical AMPK and glycogen interactions also serve mechanistic functional roles in cellular energy sensing. A number of AMPK substrates are known to be directly involved in glycogen storage and breakdown, highlighting AMPK's role as an important regulator of glycogen metabolism. In vitro, AMPK regulates glycogen synthesis directly via the phosphorylation and inactivation of GS at site 2 [62]. In support of this finding, AMPK α2, but not α1, knockout (KO) mice display blunted phosphorylation of GS at site 2 and higher GS activity in response to stimulation by the AMPK activator 5-aminoimidazole-4-carboxamide ribonucleotide (AICAR) in skeletal muscle [63]. Paradoxically, chronic activation of AMPK also results in an accumulation of glycogen in skeletal and cardiac muscles [38]. While these divergent outcomes appear contradictory, it has been proposed that prolonged AMPK activation leads to glycogen accumulation by increasing glucose uptake and, subsequently, by increasing intracellular G6P, a known allosteric activator of GS. This hypothesis is further supported by recent independent findings using highly specific and potent pharmacological activators demonstrating that skeletal muscle AMPK activation results in increased skeletal muscle glucose uptake and glycogen synthesis in mice and non-human primates [64,65]. This accumulation of G6P overcomes the inhibition of GS by AMPK, thereby increasing GS activity [38]. Furthermore, AMPK activation also shifts fuel utilization towards FA oxidation post-exercise, allowing glucose to be utilized for glycogen resynthesis [66]. In addition to regulating GS activity, phosphorylation of GS at site 2 by AMPK causes GS to localize to the SS and interMF glycogen pools in humans [67]. These findings have been replicated in mouse models, as an R70Q mutation of the AMPK γ1 subunit results in the chronic activation of AMPK and glycogen accumulation in the skeletal muscle interMF region [68]. This has led to the suggestion that AMPK specifically senses and responds to interMF levels of glycogen [69]; however, further research is warranted to verify this hypothesis.
An increase in GP activity has also been observed to be associated with AMPK activation induced by AICAR treatment of isolated rat soleus muscles [70,71]. However, the ability to demonstrate a direct relationship between AMPK and GP activity has been limited by the identification of several AMPK-independent targets of AICAR, including phosphofructokinase, protein kinase C, and heat shock protein 90 [72]. Further research is therefore required to determine if this speculated relationship exists and elucidate the mechanism by which AMPK may regulate GP. In contrast, there is in vitro evidence that GDE binds to residues 68-123 of the AMPK β1 subunit [73]. Mutations in this region that disrupt glycogen binding (W100G and K128Q) do not affect the binding to GDE, indicating that GDE-AMPK binding is not likely mediated by glycogen [73]. AMPK's direct positive effect on glycogen accumulation, its known interaction with glycogen-associated proteins, and its ability to promote energy production through glucose uptake and fat oxidation when glycogen levels are low all support AMPK's role as a cellular energy sensor. Given the limited in vivo data currently available directly linking AMPK to glycogen-associated proteins, additional studies are necessary to further understand the potential direct binding partners and effects of AMPK on the glycogen-associated proteome.
It is important to consider additional factors that may impact physical and functional AMPK-glycogen interactions. In a proteomic screen utilizing purified glycogen from rat liver, AMPK was not included in the proteins detected to be associated with glycogen [74], and this has been replicated in a complementary study in adipocytes [75]. The authors suggested that this may be due to either AMPK protein below the level of detection being able to regulate glycogen or the predominance of the AMPK β1 subunit expression in the tissues studied, as this isoform has a lower affinity for glycogen compared to the β2 subunit [74,75]. In future studies interrogating AMPK and glycogen binding and functional interactions, considerations of the β subunit isoform expression and glycogen localization, as well as sample preparation and experimental variables that may limit the preservation and detection of AMPK-glycogen binding, are warranted in future studies to build upon this strong foundation of molecular and cellular evidence.

Regulation of Glycogen Storage by AMPK
In the fifteen years following the discovery of glycogen binding to the CBM on the β subunit, several studies utilizing AMPK isoform knockout (KO) mouse models have provided whole-body physiological evidence of AMPK's interactive functional roles with glycogen. Collectively, studies using AMPK α and β subunit KO mouse models have found that the ablation of AMPK alters liver and skeletal muscle glycogen content, supporting the role of AMPK in the regulation of tissue glycogen dynamics in vivo. Specifically, whole-body β2 KO mice have reduced basal glycogen levels in both liver and skeletal muscles associated with reduced muscle AMPK activity and attenuated maximal and submaximal running capacity compared to wild-type (WT) mice [76]. β2 KO mice also display reduced expression and activity of α1 and α2 subunits as well as compensatory upregulation of the β1 subunit in skeletal muscle [76]. Additional experiments utilizing this β2 KO model have demonstrated its negative impact on the whole-body and tissue metabolism and exercise capacity associated with attenuated AICAR-induced AMPK phosphorylation and glucose uptake in skeletal muscle [77]. As a result of these changes in the AMPK subunit expression and activity, it is difficult to elucidate the precise role of AMPK β2 in glycogen dynamics in this model. Similarly, muscle-specific AMPK β1/β2 KO mice display essentially no T172 phosphorylation in extensor digitorum longus (EDL) and the soleus muscle in response to electrical-stimulated contraction and have vastly reduced exercise capacity, carbohydrate utilization, and glucose uptake during treadmill running [55]. These defects were associated with reduced mitochondrial mRNA expression and reduced mitochondrial protein content [55]. Taken together, these findings suggest an important role of the β subunit in regulating AMPK activity and signaling, cellular glucose uptake and glycogen storage, mitochondrial function, and whole-body exercise capacity and metabolism.
In addition to mouse models targeting the AMPK β subunit(s), recent studies utilizing tissue-specific α1/α2 KO mice have provided support for the functional interactive roles of AMPK and glycogen. Liver-specific AMPK α1/α2 KO mice have an impaired ability to maintain euglycemia during exercise as a result of decreased hepatic glucose output due to decreased glycogenolysis [54]. Specifically, hepatic glycogen content was reduced in KO mice following both fasting and exercise. Phosphorylation of GS was unaffected in KO mice, but a decrease in UDP-glucose pyrophosphorylase 2 content was observed, suggesting reduced glycogen synthesis due to decreased glycogen precursors rather than an altered ability to synthesize glycogen [54]. In addition, when challenged with a long-term fast, these mice had reduced hepatic glycogenolysis and were unable to maintain liver ATP concentration without AMPK activity, providing further support of AMPK's role as an energy sensor [78]. Inducible muscle-specific α1/α2 KO mice have ablated skeletal muscle glycogen resynthesis and FA oxidation following exercise, even though glucose uptake was not affected, suggesting that AMPK functions as a switch to promote fat oxidation in order to preserve glucose for glycogen synthesis [79]. These findings indicate that AMPK can influence glycogen dynamics in physiological settings and that the ablation of AMPK activity reduces hepatic glucose output and is critical for skeletal muscle glycogen supercompensation following exercise. Collectively, studies using genetic models and pharmacological activators to date indicate that AMPK activation regulates glycogen synthesis in striated muscle (i.e., skeletal and cardiac muscles) secondary to increased glucose uptake and G6P accumulation, but not in the liver. Despite these important findings from AMPK transgenic mouse models, the precise role(s) of glycogen binding to the β subunits in the functional regulation of these physiological processes, as opposed to the ablation of the entire α subunits or β subunit(s) containing the CBM, remains to be elucidated.

Roles for Glycogen Availability in the Regulation of AMPK Activity
A series of physiological studies have demonstrated that low glycogen availability can amplify the AMPK signaling responses and adaptations to exercise. This was originally described in rat skeletal muscles in which AICAR treatment resulted in increased AMPK α2 activity and a markedly reduced glycogen synthase activity in a glycogen-depleted state compared to a glycogen-loaded state [80]. This observation was independent of adenine nucleotide concentrations and has subsequently been replicated in human skeletal muscle following exercise [81]. Additional studies of skeletal muscles have shown reductions in AMPK α1 and α2 association with glycogen, along with increased AMPK α2 activity and translocation to the nucleus following exercise in a glycogen-depleted state [82,83]. Furthermore, the consumption of a high-fat, low-carbohydrate diet followed by one day of a high-carbohydrate diet increases the resting skeletal muscle AMPK α activity in human skeletal muscle compared to a high-carbohydrate diet alone [84], supporting glycogen's inhibitory role on AMPK described in cell-free assays [56]. In a follow-up study, AMPK T172 phosphorylation was increased by exercise to a greater extent in the glycogen-depleted muscle than the normal glycogen repleted state [85]. Similarly, exercise in an overnight carbohydrate-fasted state resulted in increased AMPK T172 phosphorylation and the upregulation of signaling pathways involved in FA oxidation [86], while low glycogen stimulated peroxisome proliferator-activated receptor δ, a transcription factor that regulates fat utilization, in rat skeletal muscle following treadmill running [87]. Reduced glycogen availability is also associated with increases in the regulators of mitochondrial biogenesis, such as p53 and PGC-1α [88,89]. While none of these in vivo studies have directly assessed the functional role of AMPK-glycogen physical interaction, together they provide important physiological insights into how AMPK activity, subcellular localization, and signaling may be regulated by glycogen binding (Figure 2 functional role of AMPK-glycogen physical interaction, together they provide important physiological insights into how AMPK activity, subcellular localization, and signaling may be regulated by glycogen binding (Figure 2).

Figure 2.
There are several potential alterations in cellular metabolism and signaling as a consequence of dysregulated AMPK-glycogen physical and functional interactions that represent key knowledge gaps in our current understanding and warrant further investigation in future studies. These potential alterations include changes in AMPK localization, translocation, substrates, and signaling pathway crosstalk, and subsequently, alterations in gene expression, cellular metabolism and glycogen storage.

Metabolic and Glycogen Storage Diseases as Models to Investigate AMPK-Glycogen Binding
Metabolic diseases such as insulin resistance and T2D are associated with impairments in AMPK activity, signaling, and glycogen storage dynamics. Obese patients with T2D have reduced skeletal muscle AMPK, ACC, and TBC1D4 phosphorylation following an acute bout of exercise [90]. In support of these findings, insulin resistance has been associated with suppressed AMPK activity in humans and mice [90,91], although results have been equivocal [92]. The liver-specific AMPK α1/α2 KO mice display an inability to maintain hepatic glucose output during exercise, highlighting the role of AMPK in maintaining euglycemia [54]. Skeletal muscle GS activity has also been demonstrated to be affected by insulin resistance and T2D, as there is increased phosphorylation of GS at site 2, the site phosphorylated by AMPK, which is not seen in healthy controls, resulting in nearly complete GS inactivation and dysregulation of glycogen synthesis [93]. Continued research in metabolic disease populations and rodent models can provide more insight into the significance of dysregulated AMPK and glycogen dynamics.
In addition, glycogen storage diseases provide pathophysiological models that can help provide additional insights into the influence of glycogen dynamics on AMPK. McArdle's disease is characterized by the accumulation of skeletal muscle glycogen due to a deficiency of GP. Individuals with McArdle's disease display higher muscle glycogen both at rest and following exercise compared to healthy controls, and an increased AMPK α2 activity and reduced GS activity in response to exercise [94]. Patients with McArdle's disease also demonstrate increased glucose clearance and ACC phosphorylation, indicating that AMPK activity is increased in order to maintain ATP concentration by promoting glucose uptake and FA oxidation [94]. The inability to break down glycogen, when coupled with retained, albeit reduced, glycogen synthesis, likely results in glycogen accumulation and the failure to utilize this energy source during exercise in this setting of the disease. A mouse model of McArdle's disease containing a p.R50X mutation, a nonsense mutation of nucleotide 148 in exon 1 of the GP gene, showed increased basal AMPK phosphorylation in the tibialis anterior and quadriceps muscles, associated with an increased GLUT4 content and increased AMPK-mediated glucose uptake compared to WT [95]. Following exhaustive exercise, McArdle mice display increased AMPK phosphorylation in the tibialis anterior and EDL muscles, while WT mice display no There are several potential alterations in cellular metabolism and signaling as a consequence of dysregulated AMPK-glycogen physical and functional interactions that represent key knowledge gaps in our current understanding and warrant further investigation in future studies. These potential alterations include changes in AMPK localization, translocation, substrates, and signaling pathway crosstalk, and subsequently, alterations in gene expression, cellular metabolism and glycogen storage.

Metabolic and Glycogen Storage Diseases as Models to Investigate AMPK-Glycogen Binding
Metabolic diseases such as insulin resistance and T2D are associated with impairments in AMPK activity, signaling, and glycogen storage dynamics. Obese patients with T2D have reduced skeletal muscle AMPK, ACC, and TBC1D4 phosphorylation following an acute bout of exercise [90]. In support of these findings, insulin resistance has been associated with suppressed AMPK activity in humans and mice [90,91], although results have been equivocal [92]. The liver-specific AMPK α1/α2 KO mice display an inability to maintain hepatic glucose output during exercise, highlighting the role of AMPK in maintaining euglycemia [54]. Skeletal muscle GS activity has also been demonstrated to be affected by insulin resistance and T2D, as there is increased phosphorylation of GS at site 2, the site phosphorylated by AMPK, which is not seen in healthy controls, resulting in nearly complete GS inactivation and dysregulation of glycogen synthesis [93]. Continued research in metabolic disease populations and rodent models can provide more insight into the significance of dysregulated AMPK and glycogen dynamics.
In addition, glycogen storage diseases provide pathophysiological models that can help provide additional insights into the influence of glycogen dynamics on AMPK. McArdle's disease is characterized by the accumulation of skeletal muscle glycogen due to a deficiency of GP. Individuals with McArdle's disease display higher muscle glycogen both at rest and following exercise compared to healthy controls, and an increased AMPK α2 activity and reduced GS activity in response to exercise [94]. Patients with McArdle's disease also demonstrate increased glucose clearance and ACC phosphorylation, indicating that AMPK activity is increased in order to maintain ATP concentration by promoting glucose uptake and FA oxidation [94]. The inability to break down glycogen, when coupled with retained, albeit reduced, glycogen synthesis, likely results in glycogen accumulation and the failure to utilize this energy source during exercise in this setting of the disease. A mouse model of McArdle's disease containing a p.R50X mutation, a nonsense mutation of nucleotide 148 in exon 1 of the GP gene, showed increased basal AMPK phosphorylation in the tibialis anterior and quadriceps muscles, associated with an increased GLUT4 content and increased AMPK-mediated glucose uptake compared to WT [95]. Following exhaustive exercise, McArdle mice display increased AMPK phosphorylation in the tibialis anterior and EDL muscles, while WT mice display no significant increase in AMPK activity [96]. While increased AMPK activity in McArdle patients and rodent models seems contrary to previous findings, the authors hypothesized that since McArdle disease results in an inability to break down glycogen, there is a subsequent increase in the AMPK activity in order to maintain an energy balance via increased glucose uptake [95,96]. Other rodent models have directly targeted muscle GS, which is affected in patients with Glycogen Storage Disease 0 [97]. Muscle-specific glycogen synthase knock-out models display increased AMPK phosphorylation [98] and markedly reduced glycogen content in skeletal muscle in the basal state, likely due to the retained capacity to break down but an inability to resynthesize glycogen [99,100].

Multidisciplinary Techniques and Models to Interrogate Roles for AMPK-Glycogen Interactions
While much remains to be discovered with regard to the molecular and cellular roles and physiological relevance of AMPK-glycogen binding, recent multidisciplinary technical research advances can be used to help address remaining knowledge gaps. For example, global mass spectrometry-based phosphoproteomics have recently revealed a repertoire of new AMPK substrates, providing additional evidence regarding the complexity and interconnection of the AMPK signaling network. A recent phosphoproteomic analysis mapping the human skeletal muscle exercise signaling network before and immediately following a single bout of intense aerobic exercise, in combination with phosphoproteomic analysis of AICAR-stimulated signaling in rat L6 myotubes, identified several novel AMPK substrates [101]. Other recent efforts have predicted and identified novel AMPK substrate phosphorylation sites via chemical genetic screening combined with peptide capture in whole cells [102], as well as affinity proteomics approach to analyzing hepatocyte proteins containing the substrate recognition motif targeted by AMPK phosphorylation [103]. Together, these complementary large-scale approaches have expanded the range of biological functions known to be regulated by AMPK. While additional substrates residing in different subcellular locations and organelles are continuing to be uncovered, the mechanisms underlying AMPK subcellular localization and targeting to substrates residing in these different organelles remains unknown. Future global, unbiased studies such as phosphoproteomics can help identify novel glycogen-associated AMPK substrates, post-translational regulation of glycogen regulatory machinery, AMPK subunit-specific regulation, and subcellular substrate targeting. Furthermore, omics-based approaches will reveal how AMPK-glycogen binding may impact other levels of biological regulation, such as the transcriptome, proteome, metabolome, and lipidome, in the contexts of exercise, metabolism, and beyond [104].
Novel AMPK fluorescence resonance energy transfer (FRET)-based sensors have recently revealed heterogeneous activity and tissue-specific roles for AMPK. These AMPK FRET sensors have permitted the spatiotemporal and dynamic assessment of AMPK activity in single cells [105], 3D cell cultures [106], and transgenic mice [107]. These biosensors build upon traditional methods to interrogate AMPK activity such as kinase assays and immunoblotting, which are limited to targeted measures of mean cellular protein phosphorylation and do not allow the spatiotemporal and dynamic assessment of AMPK activity. Electron microscopy-based approaches have also been used to visualize AMPK-glycogen association in fixed rat liver samples [58]. While improved microscopy technologies and sensors have been used to assess AMPK or glycogen localization, few studies have directly assessed AMPK-glycogen interactions. Utilizing these recent technical advancements will allow for the interrogation of AMPK-glycogen interactions and dynamics across species and physiologically relevant settings (Figure 2).
Despite the large body of research using in vitro models and physiological evidence indicating the potential functional roles for AMPK-glycogen binding, to date, there are no models that have been developed to disrupt and/or examine this physical binding directly in vivo. AMPK subunit KO models, while providing important insights into the functions of AMPK, are limited by the potential compensatory upregulation of other subunit isoforms or the disrupted stability of the AMPK heterotrimer complex (e.g., Reference [76]). In addition, directly assessing the function of β subunit glycogen binding is challenging when additional functions are altered in the presence of subunit deletion, as AMPK activity is impaired when the scaffolding β subunit is removed. The design of novel in vivo models in the future will be informed by previous molecular and cellular findings to allow direct interrogation of the functional relevance of the β subunits and CBM. Generation of novel animal models to specifically target physical AMPK-glycogen binding will provide important advances regarding its physiological significance and capability to be therapeutically targeted in vivo to modulate metabolism and the health benefits of exercise.
Finally, previous studies have primarily utilized centrifugation-based assays to detect and quantify physical AMPK-glycogen association. Novel biotechnological platforms and proximity assays will aid this investigation of AMPK-glycogen binding and AMPK's proximity to glycogen with improved sensitivity and specificity across molecular, cellular, and physiological models. Furthermore, newly developed kinase activity reporters [108] and other non-radioactive activity assays [109] will help provide new measures of intracellular AMPK activity dynamics and complement traditional surrogate measures such as immunoblot analyses of AMPK and ACC phosphorylation. Together these technological advances expand the repertoire of available tools to monitor the range of biological processes regulated by AMPK and further our understanding of the mechanisms and physiological significance underlying AMPK-glycogen interactions.

Potential Therapeutic Relevance of Targeting AMPK-Glycogen Binding
Consistent with the therapeutic relevance of the CBM, several lines of evidence demonstrate that the CBM may play a direct functional role in AMPK conformation and activation. The CBM contains the critical S108 autophosphorylation site required for drug-induced AMPK activation in the absence of AMP [8]. Although located on opposite sides of the AMPK heterotrimer, the CBM is conformationally connected to the regulatory AMPK γ subunit and its stabilization is affected by adenine nucleotide binding (e.g., AMP) to the CBS motifs [26]. Despite physical AMPK-glycogen interaction being mediated by the β subunit, mutations in the γ subunit also result in alterations in AMPK activation and glycogen metabolism. The γ2 subunit is known to contain mutations that cause constitutive AMPK activation, resulting in glycogen storage diseases in humans. These mutations result in glycogen accumulation with coexisting deleterious effects on cardiac electrical properties that are characteristic of familial hypertrophic cardiomyopathy and Wolff-Parkinson-White syndrome [110]. In addition, gain of function mutations in the AMPK γ3 subunit predominantly expressed in skeletal muscle result in excess glycogen storage [111] as well as improvements in metabolism via increased mitochondrial biogenesis [112]. Constitutive AMPK activation associated with these γ subunit mutations promotes glycogen synthesis by increasing glucose uptake. As mentioned above, the CBM interacts with the α subunit, forming the ADaM site and stabilizing the kinase domain of the α subunit in its active formation [9,27]. However, when glycogen binds to the CBM, this interaction is destabilized, altering the ADaM site and inhibiting the AMPK activity [9]. For example, isoform-specific allosteric inhibition of AMPK has been shown to be dependent on the β2 subunit CBM in glycogen-containing pancreatic beta cells [113]. The CBM, therefore, functions as both a critical element of AMPK activation as well as a site for the allosteric inhibition by glycogen, highlighting the therapeutic potential of new drugs targeting the ADaM site.

Conclusions
AMPK is a central regulator of cellular metabolism and, therefore, possesses significant therapeutic potential for the prevention and treatment of a range of metabolic diseases. A growing body of evidence demonstrates that AMPK physically binds glycogen and this interaction can alter the conformation of AMPK, and subsequently, its activity and downstream signaling. AMPK activity subsequently regulates glycogen metabolism. Recent research has described experimental and physiological settings that impact functional AMPK and glycogen interactions, including AMPK β isoform affinity, glycogen availability, and particle size. Despite our understanding of AMPK's relationship with glycogen, much remains to be elucidated. Further research using new technologies and experimental models can reveal additional mechanisms underlying AMPK and glycogen's interactive roles in cellular energy sensing, exercise, and metabolism. Together, these findings will help provide insights into the physiological and therapeutic relevance of targeting AMPK and glycogen binding in health and disease.

Acknowledgments:
The authors acknowledge that all publications related to AMPK and glycogen could not be discussed in this review due to word limitations and the primary focus on studies published within the past two decades.

Conflicts of Interest:
The authors declare no conflict of interest.


extfieldsofstudy:
Medicine
Computer Science

text:
FastQ Screen: A tool for multi-genome mapping and quality control

DNA sequencing analysis typically involves mapping reads to just one reference genome. Mapping against multiple genomes is necessary, however, when the genome of origin requires confirmation. Mapping against multiple genomes is also advisable for detecting contamination or for identifying sample swaps which, if left undetected, may lead to incorrect experimental conclusions. Consequently, we present FastQ Screen, a tool to validate the origin of DNA samples by quantifying the proportion of reads that map to a panel of reference genomes. FastQ Screen is intended to be used routinely as a quality control measure and for analysing samples in which the origin of the DNA is uncertain or has multiple sources.

Introduction
In general, reaching sound conclusions from sequencing experiments requires the origin of a sample to be identified correctly prior to mapping. To reduce the risk of contaminants leading to incorrect inferences, it is advisable to map sequencing results against not only the expected reference genome but also against reasonable sources of contamination. Common reasons for contamination include amplifying the wrong target molecule, unwanted DNA being present in reagents used in library generation, carry-over from samples previously loaded onto a sequencing machine or sample swaps.
The tool utilises either Bowtie 1 , Bowtie 2 2 or BWA 3 , as preferred by the user, to map reads against pre-specified genomes. FastQ Screen presents the mapping results in both text and graphical formats, thereby allowing the user to confirm the genomic origin of a sample or identify sources of DNA contamination. The tool summarises the proportion of reads that map to a single genome or to multiple genomes. In addition, it reports whether those alignments are to a unique position, or to more than one location, within the genome of interest ( Figure 1).
FastQ Screen functionality is generally independent of the laboratory protocol followed and so can be used to analyse genomic DNA, RNA-Seq 4 , ChIP-Seq or Hi-C experiments. In addition, FastQ Screen is compatible with Bismark 5 , and so can also be used to process bisulfite sequence data.
Other tools exist with similar functionality to FastQ Screen, most notably Multi Genome Alignment (MGA) 6 . FastQ Screen has a number of advantages over these tools, including directly reporting the proportion of multi-mapping reads, thereby helping identify DNA populations rich in low-complexity sequences. Another benefit of our program is the capability to create filtered FASTQ files. FastQ Screen is also the only quality control (QC) tool that aligns reads to multiple bisulfite reference genomes.

Implementation
The program utilises a short read sequence aligner to map FASTQ reads against pre-defined reference genomes. The tool records against which genome or genomes each read maps and summarises the results in graphical and text formats.

Operation
We coded FastQ Screen in Perl and made use of the CPAN module GD::Graph for the generation of summary bar plots. The software requires a functional version of Bowtie, Bowtie 2 or BWA, and should be run on a Linux-based operating system.

Amendments from Version 1
Corrected typographical errors in the Figure 1 caption text. Figure 1. Graphical output from FastQ Screen after mapping a publicly available RNA-Seq sample (SRR5100711) against several reference genomes. Reads either i) mapped uniquely to one genome only (light blue), ii) multi-mapped to one genome only (dark blue), iii) mapped uniquely to a given genome and mapped to at least one other genome (light red), or iv) multi-mapped to a given genome and mapped to at least one other genome (dark red). The reads represented by blue shading are significant since these are sequences that align only to one genome, and consequently, if are observed in an unexpected genome they suggest contamination.

REVISED
FASTQ Screen uses Plotly to enable visualisation of results in a web browser. The tool takes as input a text configuration file and FASTQ files, which are sub-sampled by default to 100,000 reads to reduce running times, and then mapped to a panel of pre-specified genomes.

Use cases
Preliminary sequencing QC: FastQ Screen provides preliminary evidence on whether a sequencing run has been successful, as demonstrated in Figure 1, which shows results using a publicly available RNA-Seq sample (SRR5100711) labelled as mouse. The software processed the deposited FASTQ file to generate summary results in text, HTML and PNG format. As expected, the dataset contained a substantial proportion of reads that mapped only to the mouse genome, and although a sizeable proportion of reads mapped to both the mouse and rat genomes, that may have also been expected considering the close evolutionary relationship between those two species. Of concern, however, was the discovery that 11.4% of the reads mapped solely to the human genome, suggesting the sample was contaminated. This may prove problematic if human-derived reads that also align to the mouse reference genome are not removed, since differences between mouse samples may then actually reflect the variation in the degree of contamination between the samples rather than genuine biological differences. Very few reads aligned to adapter sequences which was an encouraging observation.
Identifying sample origin from a range of alternatives: FastQ Screen was recently used by researchers to identify the origin of the clothes of the Tyrolean Iceman (popularly named Ötzi), a famous 5,300 year old natural mummy discovered in 1991 in the Italian Ötztal Alps. By screening sequences against probable sources of preserved leathers, the research team showed that the iceman's hat came from Brown Bear, his quiver from Roe deer and his loincloth came from sheep 7 . In a similar fashion, FastQ Screen has been used to determine the animal origin of vellum found in 13th century Bibles 8 .
Filtering results: FastQ Screen can also be used to filter reads mapping (or not mapping) to specified genomes. This has numerous applications, most typically to remove DNA contaminants, as exemplified by a recent clinical microbial metagenomics study in which nucleic acids were extracted from porcine faeces 9 . FastQ Screen was then used to filter-out host sequences, and the remaining reads were then mapped, leading to the identification of over 1,600 bacterial and Archaea species and strains of virus.
In contrast, in some experiments the source of contamination may be completely unpredictable and so we have incorporated a setting in which all unsuccessfully mapped reads are written to a FASTQ format output file. This may then be used by other resources, such as BLAST, to determine the origin of those sequences.

Summary
Since its release, FastQ Screen has been used to analyse a myriad of sequencing datasets. We initially envisioned the software as a QC tool to complement our related program FastQC, but we subsequently used the software to confirm the origin of samples and added functionality for filtering FASTQ reads. The program may be used in conjunction with several common aligners, including Bismark for processing bisulfite libraries. FastQ Screen has been incorporated by other groups into bioinformatics workflows, was reimplemented in the recently released QC tool Aozan 10 , and is compatible with MultiQC 11 , a tool to aid comparison of samples with respect to a large number of QC metrics.

Software availability
FastQ Screen is available from: https://www.bioinformatics. babraham.ac.uk/projects/fastq_screen The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. With this second version the authors answer all the concerns we raised about their FastQ Screen article. We will approve it for indexing.

Open Peer Review
No competing interests were disclosed.

Competing Interests:
We have read this submission. We believe that we have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

Is sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool? Yes
Are the conclusions about the tool and its performance adequately supported by the findings presented in the article? Yes No competing interests were disclosed. When dealing with multiple high throughput sequencing experiments, especially for core facilities, you need to pay great attention to quality controls. Contaminations from different species you are working with are one of the potential problems you can encounter. The FastQ Screen software as been designed by Steven Wingett and Simon Andrews in order to solve this drawback. Using different mapping softwares, FastQ Screen allows to identify from the reads present in your samples the different species they came from. The graphical and text outputs provided, detailed information on the potential level of contamination obtained.
The software objectives are clearly explained just as the way to use it and how to interpret its outputs. FastQ Screen source code is available through GitHub and a documentation is provided on the authors' website.
This software is publicly available since several years and is used today by many genomics laboratories. The tool is very stable, the command line help is easy to understand and we not found any issue when we launch it in all our tests. Finally, the output report is informative and very clear. The tool is very stable, the command line help is easy to understand and we not found any issue when we launch it in all our tests. Finally, the output report is informative and very clear.
FastQ Screen is a must have tool for everyone working with multiple species samples or who want to prevent unpredicted contamination of its samples.

Remarks
1. In order to more clearly explain the way FastQ Screen is working, more information should be provided on pre-defined reference genomes. How can it be chosen before running FastQ Screen. Is there a limitation among the number of genomes selected? Is there a significant impact on software running time according to the number or size of reference genomes selected? Is there a list of already available pre-defined reference genomes? Does it works using subset sequence database or with the whole genome?
2. It could also be interesting to get some information about the running time of several analyses. What are the specifications required for the computer needed to run FastQ Screen? I didn't findd this information through the documentation.
3. It seems that FastQ Screen when processing large dataset (FASTQ files more than 100 million reads) use a large amount of memory as it store the identifier of each read. It may be useful to advise users in documentation about this issue.
Minor remarks 1. For future evolution of FastQ Screen software it could be interesting to provide a clue for the "No Hits" reads obtained. In the paper the authors suggest to run BLAST analyses. Perhaps there is a way to use a subset of "NoHits" reads in order to guess where the possible contamination is coming from?

Is sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool? Yes
Are the conclusions about the tool and its performance adequately supported by the findings presented in the article? Yes No competing interests were disclosed.

Competing Interests:
No competing interests were disclosed.

Competing Interests:
We have read this submission. We believe that we have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however we have significant reservations, as outlined above.

Steven Wingett
Thank you for your detailed feedback. Both you and the reviewer Dr Hamilton pointed out that FastQ Screen would be better served if we made pre-made genome indices available. As described in our response to Dr Hamilton, FastQ Screen now has a --get_genomes option to obtain Bowtie2 genome indices. We have also provided more information in the documentation ( ), https://www.bioinformatics.babraham.ac.uk/projects/fastq_screen/_build/html/index.html explaining how a user may create the desired aligner index files.
We now also mention in the documentation that FastQ Screen has an upper limit to the number of genomes that may be used. This value, which is 32, is the result of how the script records read/genome mapping data as a 32-bit variable. This limit far exceeds every task we have performed with FastQ Screen. Should more genomes be required at a future date however, we would be able to modify the code to increase the maximum allowed value.
The time taken to process a dataset varies substantially depending on the input data and specified parameters. For example, larger files take longer to process than those with fewer reads. Similarly, screening against more genomes will increase processing times, as will the complexity and size of the genomes to which the aligner maps FASTQ reads. Using FastQ Screen in the default quality control (QC) mode, in which only a subset of the data is processed, is significantly quicker than using the tool to filter a dataset. Screening bisulfite libraries is also a more computationally intensive task and therefore takes significantly longer to complete. In contrast, times may be reduced by muti-threading submitted jobs. The hardware of a system will of course also substantially impact running times, as will the competition for system resources from the jobs being run concurrently with FastQ Screen.
There are similar points to consider when evaluating memory overheads. Most notably, running the program in QC mode will require substantially less memory than filtering a dataset. The software needs to hold in memory whether a read maps to any of the reference genomes. In QC mode, this will only be necessary for approximately 100,000 reads, but when filtering this will be required for every read, simultaneously, in the FASTQ file -and FASTQ files may comprise hundreds of millions of reads.
To help the user make sense of these considerations, we have now included in the documentation a report of the memory requirements and time taken to process different data files, using different parameters. Obviously, it is impossible to cover every scenario, but as a general rule using the tool to QC a dataset should take minutes whereas filtering a large dataset may take several hours.
We added extra information in the documentation pertaining to the system requirements necessary to run FastQ Screen. To summarise these, FastQ Screen should be run on an up-to-date Linux operating system that has Perl installed and has a working version of either Bowtie, Bowtie2 or BWA installed.
So far as we can tell, the following web link is functional: So far as we can tell, the following web link is functional: https://www.bioinformatics.babraham.ac.uk/projects/fastq_screen/_build/html/index.html#download . In case there is any confusion, this is an anchor to the "Download" section in the documentation and not a link to the software download page.
Thank you for bringing to our attention the need for follow-on support for reads that map to no genomes (extracted when using the --no_hits parameter). We intend to address this in future FastQ Screen releases by adding new functionality to the software.
No competing interests were disclosed. The tool is comprehensively documented and is available from the authors' web site and via Github.
I have personally used this tool since 2011 and it is incorporated in the quality control pipeline for our core facility on all sequencing runs. The tool has been used to detect contamination from a panel of commonly used genomes, and to estimate the contribution of sequence from mixed genome samples. It is also used to detect rRNA contamination in RNA-seq protocols. Recently the ability of FastQ Screen to filter individual reads has been incorporated into our single cell analysis pipeline.
The article is clear, well explained, and gives interesting use cases.

Is sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool? Yes
Are the conclusions about the tool and its performance adequately supported by the findings presented in the article? Yes I have been using the software routinely as part of my NGS pipelines for several years and find it to be an invaluable QC tool. Sample mix ups, if from different species, are easily detected. I have also found FastQ Screen useful as a proxy indicator of rRNA contamination in total RNA-Seq, due to rRNAs being similar between related species, they show up as multi-genome aligned reads, thus identifying samples for further processing.
I therefore have no reservations in recommending FastQ Screen for indexing.

Suggestions:
Each lab or facility will have their own unique requirements for genomes to screen against, but having a suggested "starter set" of genomes may ease the burden of installation / configuration for first time users. The bowtie website has some pre-made common, bowtie indexed, genomes for convenient download ( ). Or more http://bowtie-bio.sourceforge.net/bowtie2/index.shtml comprehensively, Illumina's iGenome project contains a wide range of bowtie/bowtie2/bwa 1.

2.
comprehensively, Illumina's iGenome project contains a wide range of bowtie/bowtie2/bwa indexed genomes ( ). http://support.illumina.com/sequencing/sequencing_software/igenome.ilmn One of the most useful features of FastQ Screen is its compatibility with MultiQC, where multiple samples are plotted together for assessment of entire sequencing runs or batches. Mentioning this in the documentation would alert users to this very useful feature. We agree with the excellent suggestion to create pre-built genomes for users. Indeed, the latest version of FastQ Screen (v0.13.0) now has a new option (--get_genomes) which instructs the script to download commonly used pre-built Bowtie2 reference genomes deposited on the Babraham Bioinformatics website. Along with the reference genomes, FastQ Screen also downloads a configuration file, which it subsequently edits to list the full path of the downloaded genomes as stored on the user's machine. This setup should be ready-to-use and the selection of genomes should suit most requirements.
We have updated the documentation to alert users that our tool is compatible with MultiQC.
No competing interests were disclosed. Competing Interests:


extfieldsofstudy: Biology Medicine

text:
Drosophila as a Model System to Study Cell Signaling in Organ Regeneration

Regeneration is a fascinating phenomenon that allows organisms to replace or repair damaged organs or tissues. This ability occurs to varying extents among metazoans. The rebuilding of the damaged structure depends on regenerative proliferation that must be accompanied by proper cell fate respecification and patterning. These cellular processes are regulated by the action of different signaling pathways that are activated in response to the damage. The imaginal discs of Drosophila melanogaster have the ability to regenerate and have been extensively used as a model system to study regeneration. Drosophila provides an opportunity to use powerful genetic tools to address fundamental problems about the genetic mechanisms involved in organ regeneration. Different studies in Drosophila have helped to elucidate the genes and signaling pathways that initiate regeneration, promote regenerative growth, and induce cell fate respecification. Here we review the signaling networks involved in regulating the variety of cellular responses that are required for discs regeneration.

Introduction
Regeneration is the ability that presents some organisms and allows them to partially or fully replace missing or damaged organs. This capacity is conserved among different phyla and it involves a wide range of processes, from wound healing to the induction of regenerative growth to reconstruct a whole new organ, as in urodele amphibians [1,2]. Studying regeneration should shed light on the mechanisms that regulate this process, paving the way for their potential therapeutic applications in regenerative medicine.
Drosophila melanogaster is a powerful model system to perform genetic analysis, and it has provided much of the information of our understanding about the genetic basis of organ morphogenesis. While the adult organs of Drosophila are incapable of regenerating, the primordia of these structures, known as imaginal discs, can undergo regenerative growth. Imaginal discs are epithelial sac-like structures that develop from the embryonic ectoderm and after a period of cell proliferation in the larval stages, they give rise to the adult cuticle.
A series of classic experiments from the mid-1940s to the 1970s laid the groundwork for our current understanding of regeneration in Drosophila imaginal discs [3][4][5]. The classic approach used to study regeneration in Drosophila was to grow the regenerating discs in vivo culture. The disc was extracted from the larva and after amputating a fragment of it, it was transplanted into the abdomen of an adult host, where the cells of the discs could proliferate and the disc would regenerate [3,6]. After regeneration the disc was then extracted from the adult host and examined [5,7].
One of the problems of these studies is that regeneration did not occur under physiological conditions, since the disc regenerates in an adult host. Moreover, the process of disc extraction and transplantation will cause some stress to the cells of the discs, provoking apoptosis and halting cell proliferation in the first few hours after transplantation [8]. An alternative approach based on the transient induction of cell death in specific regions of the discs has resolved some of these problems. This method implies the use of the Gal4/UAS binary system in combination with Gal80 ts to express a celldeath inducer, which makes it possible to genetically ablate a Figure 1: Drosophila wing imaginal discs regeneration process. After a cut or genetic ablation in the third instar wing imaginal discs various cellular processes occur. First, four hours after cut (AC), the wound heals restoring the epithelial continuity. Around 24 hours AC, some cells lose expression of markers of cell fate commitment. Finally, the pattern is restored and the discs give rise to a normal pattern and sized adult wings. However, there is a delay in pupariation to allow the tissue to regenerate. region of the disc in vivo for a predetermined period of time, after which the disc recovers [9,10]. Although this technique mimics some aspects of surgical amputation, there are important differences between these two approaches. For instance, unlike amputation the overexpression of a cell-death inducer may not be sufficient to eliminate all the cells in the region targeted, and thus dying cells will coexist with living cells during the period of recovery. A further method has been developed to study disc regeneration in its normal developmental context. This system consists of removing a section of the disc "in situ" inside the larvae without extracting the discs from the larvae [11,12]. The results obtained using these different approaches revealed that the initial stages of disc regeneration involve different processes. Thus, after wound healing a zone with a high rate of cell proliferation appears at the edges of the wound, similar to the blastema that originates during limb regeneration in amphibians and teleost fish. In addition, there is a temporary loss of markers of cell fate commitment and repatterning ( Figure 1) [5,7,10,12,13]. All these cellular processes are triggered and regulated by the action of different signaling pathways. In this article we will focus on the signaling networks that regulate the various cell responses required to control the early stages of imaginal disc regeneration.

Reactive Oxygen Species (Ros) Are Induced in Response to Disc Damage
One of the first reactions of the imaginal disc in response to damage is the production of reactive oxygen species (Ros). Ros have been increasingly implicated in the physiological regulation of many developmental processes, including the emergence of stem cells, inflammatory cell recruitment, or the differentiation of embryonic cardiomyocytes. Ros act at many distinct levels in biological processes, affecting gene expression, protein translation, and protein-protein interactions [14][15][16][17]. Ros are the by-products of aerobic metabolism and they include superoxide (O 2 − ) and hydrogen peroxide (H 2 O 2 ). There are numerous potential sources of Ros within the cells and various organelles within the cell can produce Ros. One important generator of oxidant is a family of NADPH-dependent oxidases (Nox/Duox). These transmembrane proteins regulate the generation of Ros and an increase in their activity is produced in response to different stimuli after damage, such as the liberation of Ca 2+ . Indeed, wounding in Drosophila embryos provokes the binding of Ca 2+ to Duox and its activation [18].
The specific effects of Ros are largely modulated by the reversible oxidation and reduction of reactive Cys residues, which in turn provokes the reversible modification of enzymatic activity of redox-sensitive targets, such as Tyrosine phosphatases or kinases [16,17].
There is increasing evidence indicating that, during regeneration in vertebrates, the response to damage involves oxidative stress and, consequently, the stimulation of stressactivated protein kinases [19][20][21]. Ros have been also proposed to play a key role in disc regeneration [22][23][24][25][26][27]. Physical injury or genetic ablation of part of the imaginal disc provokes Ros production in cells at the wound site [22][23][24][25]. The initial burst of Ros acts as a chemoattractant for macrophages and it is necessary for the activation of different signaling pathways [22,25,27] (reviewed in [26]) ( Figure 2). It is not clear how the first burst of Ros is generated, although different mechanisms have been identified that contribute to the increase in Ros levels in response to damage (see below). Shortly after trauma, Ros is also detected in cells adjacent to the injured region, although at a lower concentration [22].  Figure 2: Signaling network promoting apoptosis and Ros production after discs damage. Damage causes the activation of different signals that lead to apoptosis and high levels of Ros accumulation in dying cells. The function of Duox leads to extracellular Ros production that is responsible for hemocyte recruitment and promotes the activation of different signaling pathways in surrounding cells. Hemocytes secrete Eiger, which activates JNK pathway in the adjacent cells. Apoptotic cells produce also different signals that influence surrounding cells.

Activation of JNK Signaling Promotes Ros Production and Triggers Multiple Responses
JNK signaling is initially triggered at the wound site in response to Ros, and probably by other cellular stress signals [28]. Ros has been proposed to regulate the activity of MAP3K (MEKK1) and apoptosis signal-regulating kinase 1 (ASK1 or MAP3K5), kinases that reside upstream of JNK [16,17,[29][30][31]. In addition, Ros can also block the function of the MAP kinase phosphatases that inhibit JNK signaling [30,32]. JNK signaling promotes the activation of proapoptotic genes like reaper (rpr), head involution defective (hid), and grim. The proteins encoded by these genes bind to and inhibit the activity of Drosophila IAP-1 (Diap1), which in turn blocks the initiator caspase-9 orthologue Dronc (Drosophila NEDD2-like caspase). Dronc activates the effector caspases Dcp1 and Drice (Drosophila interleukin converting enzyme), inducing apoptosis. It has been shown that rpr and hid alter cytochrome Cytochrome C in the mitochondria, leading to mitochondrial disruption [33][34][35][36]. While the origin of Ros after damage remains unclear, the action of rpr and hid on mitochondria could favor Ros production by apoptotic cells (Figure 2). The tumor suppressor Dp53 is another factor activated by JNK signaling and that is necessary to trigger apoptosis, playing a fundamental role in the elimination of cells that cannot complete DNA repair [37]. Both dp53 and JNK can activate each other in a Dronc-independent manner, and they establish a feedback loop that amplifies the initial apoptotic signals [38]. This loop is very important to promote cell death in response to the activation of the apoptotic pathway ( Figure 2) [38]. Dp53 is required to induce compensatory proliferation and to reestablish the patterning of the damaged discs. Interestingly, it has been proposed that these functions are not dependent on apoptosis [39,40].
JNK signaling also increases the levels of Ros by transcriptionally activating the gene moladietz (mol) [23]. This gene encodes the Duox-maturation factor NIP that is required for the production of Ros. Therefore, the activation of mol favors the production of Ros and it promotes a positive feedback signal that ensures the prolonged JNK activation necessary for regenerative growth ( Figure 2) [23,25]. Ros are also involved in the activation of Cap-n-collar (cnc). The transcriptional targets of cnc constrain Ros levels within a range in which regeneration is most efficient [24]. The cells with the highest levels of JNK signaling die and produce high levels of Ros. Indeed, at the wound edge there are apoptotic cells that have high levels of Ros in conjunction with high levels of JNK [22]. Ros propagate from dying or dead cells to the nearby surviving cells, and they activate multiple factors and signaling pathways. Indeed, Ros can propagate from cell to cell through aquaporins [41] or gap junctions [42].
Like other signals generated by apoptotic cells, Ros can affect surrounding cells. Various studies have shown that apoptotic cells can influence the proliferation and survival of nearby surviving cells. Indeed, apoptotic cells have been proposed to send signals that induce surrounding cells to divide or to die; events are known as apoptosis-induced proliferation (AiP) or apoptosis-induced apoptosis (AiA) [43]. It has been suggested that apoptotic cells can release mitogenic factors, such as the Drosophila Wnt1 homologue Wingless (Wg), the bone morphogenetic protein (BMP) Decapentaplegic (Dpp), and the leptin-like (IL-6 family) cytokine ligands Unpaired proteins (Upd, Upd2, Upd3) ( Figure 2) (see below). Therefore, apoptosis at the wound site might fulfill a fundamental role in regulating regenerative proliferation. However it has been shown that the partial inhibition of apoptosis does not have a major effect on disc regeneration [8,44]. A possible explanation for this paradox might be that apoptosis was not fully suppressed in any of these analysis. Thus, the signals emitted by the few apoptotic cells that remain might be sufficient to induce proliferation of the surrounding cells. More studies will be necessary to clarify the true role of apoptotic cells during regeneration.

P38 and JNK Signaling Promotes JAK/STAT Activation
Ros generated during apoptosis promotes tolerable levels of JNK in nearby surviving cells. Thus, in addition to apoptotic cells at the wound site, nonapoptotic cells also appear that have nondeleterious levels of JNK and low levels of Ros [22]. Hemocytes stimulated by Ros are also involved in activating JNK in surviving cells adjacent to the wound, since hemocytes release the TNF ligand Eiger that can induce JNK signaling [25]. JNK signaling plays a key role in regulating many biological processes involved in regeneration, including wound healing, compensatory proliferation, apoptosis, and cytoskeletal rearrangement. Inhibition of JNK during disc regeneration impairs wound healing and reduces regenerative proliferation [8,28,45,46]. These regenerative responses depend on the activation of several downstream pathways by JNK.
In addition to the activation of JNK signaling, Ros can regulate the P38 stress-activated MAP kinases in surviving cells. The activation of P38 signaling is independent of the JNK pathway [22] and it has been proposed that Ros may promote the P38 pathway through the oxidative modification of intracellular kinases, such as the redox-sensitive activating protein-1 ASK1 [31]. The nondeleterious activation of JNK and P38 MAP kinases by Ros may have multiple effects, among them the induction of cytokine expression [11,22,29,47,48]. Drosophila has three leptin-like (IL-6 family) cytokine ligands known as the Unpaired proteins (Upd, Upd2, and Upd3). After physical injury or cell death, the three upd genes are upregulated in the wound's edges in a manner dependent on JNK signaling [22,47,48]. It is unclear whether these factors are expressed exclusively in dead cells (as we mentioned before), or they are also expressed in surviving cells surrounding the damage region. In this review we have considered that both dead and surviving cells can express these ligands. The Upd ligands bind to the IL-6R type receptor Domeless (dome), which activates the Janus kinase Hopscotch (Hop), and this phosphorylation cascade promotes the translocation of a Stat3-like transcription factor (Stat92E) to the nucleus (Figure 3). All these factors constitute the JAK/STAT signaling pathway, which has important roles in disc development, for instance appendage patterning [49][50][51] and the control of cell proliferation [49,[52][53][54]. The elimination of JAK/STAT components during leg disc regeneration impairs local cell proliferation [48]. Similarly, during wing disc regeneration, reduced JAK/STAT activity also partially disrupts adult wing recovery, leading to the generation of much smaller adults wings [22,47]. As such, it was proposed that JAK/STAT signaling functions downstream of JNK/P38 signaling and that it is necessary to induce compensatory cell proliferation and to form the blastema (Figure 4) [48]. However, instead of promoting compensatory cell proliferation, it has been proposed that JAK/STAT might be necessary to restrain the excessive tissue damage caused by the activation of the JNK pathway, which would facilitate the initiation of compensatory responses [47]. JAK/STAT could act as a suppressor of JNK signaling and this repression could either be mediated by direct transcriptional effects on JNK components or indirectly, by suppressing apoptosis. This mechanism could restrain the nonautonomous activation of JNK and excessive apoptosis [47]. This function of JAK/STAT would be mediated by Zfh1 and Zfh2 (Zinc-finger homeobox) proteins. These ZEB proteins that act as transcriptional repressors [47] have been previously identified to be downstream effectors of JAK/STAT [47,49,55]. In the promoter region of hid as well as in the promoter of the gene key, which encodes for the AP-1 component dFos, appears multiple, highly clustered mammalian ZEB1-binding motifs [47]. Therefore, it has been proposed that Zfh1 and Zfh2 might be restraining JNK activation by repressing kay. In addition, ZEB proteins might be also competing with AP-1 for transcriptional repression of hid, thereby limiting the apoptosis induced by JNK signaling through hid (Figure 3) [47].
In addition to the possible role that JAK/STAT might have in controlling cell proliferation, this pathway also induces a physiological response by activating Drosophila insulin-like peptide (dilp8) [47,48]. This paracrine factor is activated after damage and it regulates the timing of pupariation [56,57]. It has been reported that Dilp8 regulates both developmental delay and growth coordination between regenerating and undamaged tissue. Dilp8 inhibits the production of the neuropeptide prothoracicotropic hormone (PTTH), causing developmental delay [56,57]. Moreover Dilp8 activates Nitric oxide synthase (NOS) in the prothoracic gland. NOS limits the growth of undamaged tissues by reducing ecdysone biosynthesis [58,59]. The function of Dilp8 is mediated by the Orphan leucine-rich G-protein coupled receptor Lgr3. Lgr3 activity is necessary in the Central nervous system (CNS), as well as in the prothoracic gland, for NOS activation following damage [60][61][62]. As we mentioned the activation of dilp8 after damage depends on the JAK/STAT pathway [47,48]; therefore, JAK/STAT signaling might favor regeneration by delaying development [47,48,56,57].

The Role of Wg and Dpp Signaling in Disc Regeneration
The Wingless family of proteins (Wnt class) are involved in regeneration in different organisms with this capacity [2,63,64]. Intriguingly, different responses to wg expression have been observed during disc regeneration depending upon the proapoptotic gene employed or the methods of inducing the wound. Thus, wg (the Drosophila Wnt1 homologue) is ectopically expressed near the lesion edges before blastema formation in amputated leg and eye imaginal discs [48,[65][66][67]. In addition, wg is upregulated in the wing cells that form the blastema after genetic ablation by expressing Eiger or the proapoptotic gene reaper [9]. During these processes wg is activated by JNK signaling [48]. Using these experimental approaches regenerative proliferation was impaired when wg was reduced [9,48]. Accordingly, it was proposed that wg is required for regenerative proliferation (Figure 4). This effect is at least in part due to the down regulation of Notch, which leads to Myc upregulation [9]. It has also been proposed that JAK/STAT signaling cooperates with Wg signaling to induce regenerative cell proliferation [48]. Paradoxically, when apoptosis was induced by overexpressing the proapoptotic gene head involution defective (hid) or when a portion of a disc is eliminated in situ [12], wg expression was not altered during disc regeneration [68]. Moreover, knocking down wg did not block discs regeneration after in situ amputation or hid expression [12,68]. The basis for these differences in wg expression and its requirements are not yet clear. They might in part reflect differences in the efficiency of genetic ablation, or that different methods of inducing a wound elicit different responses in terms of gene expression. Alternatively, wg function might be redundant with the activity of other genes of Wnt family present in Drosophila, such as wnt6. In fact, wnt6 and wg share the same regenerative enhacer (see below). Therefore, more work is needed to define the role of wg signaling in the regenerative response.
The bone morphogenetic protein (BMP) Decapentaplegic (Dpp) activates a signaling pathway that plays an important role in inducing growth and patterning during imaginal disc development [69][70][71][72]. Therefore, it was suggested that the Dpp pathway might be redeployed to control regenerative growth. However, the contribution of the Dpp signal to this process remains unclear. Thus, although dpp is transcriptionally activated in response to genetic ablation in wing discs [9], this is not the case in amputated wing discs [46]. Moreover, while Dpp is required for the hyperplastic growth caused by "undead" cells, when apoptotic cells are protected with P35 [73,74], this factor is dispensable for compensatory cell proliferation when P35 is not ectopically expressed in apoptotic cells, even though Dpp is expressed in apoptotic cells [74,75]. As yet, the basis for these differences remains unclear.
It has been proposed that both wg and dpp are activated in apoptotic cells and diffuse to surrounding cell to promote proliferation [73][74][75]. However, as JNK signaling pathway is active, although at low levels, in surviving regenerating cells [22], and wg and Dpp are targets of JNK signal, we do not exclude the possibility that these factors might be also expressed in some regenerating cells (Figure 3).

The Hippo Pathway Is Necessary for Regenerative Growth
Hippo signaling is a conserved pathway that regulates growth during development and regeneration, and its deregulation is associated with oncogenesis (reviewed in [76,77]). This signaling pathway is constituted by a kinase cascade that can be activated by different stimuli. Hippo signaling is mediated by a transcriptional coactivator protein, Yorkie (Yki in Drosophila, YAP in vertebrates: reviewed in [77,78]) ( Figure 2). Yki remains inactive when the signaling pathway is active and it is retained in the cytoplasm due to its phosphorylation by the kinase Warts (Wts) [79]. When Wts is inactive, unphosphorylated Yki accumulates in the nucleus [77,78] and in conjunction with different DNA-binding proteins, it promotes the transcription of downstream genes necessary to promote cell proliferation, such as Cyclin E and cMyc [77,78] (Figures 3 and 4).
The Hippo pathway plays a key role in inducing regenerative growth after disc damage [80][81][82]. This pathway can be activated by multiple upstream inputs, including Fat-Dachsous signaling, sense tissue damage, and JNK signaling [82,83]. JNK signaling can directly promote the activation of Yki by phosphorylating Ajuba family LIM proteins and enhancing their binding to Wts, thereby preventing their activation by Hippo [84]. Interestingly, the ability of JNK to activate YAP is conserved in mammalian cells [83,84]. Thus, JNK increases Yki activity after wounding, a process essential to induce compensatory cell proliferation and regeneration.

The Control of Cell Plasticity during Imaginal Disc Regeneration by the Polycomb Group (PcG)
One of the processes associated with organ regeneration is the repatterning of the regenerating tissue, which implies genetic reprogramming of cells in order to switch their fates (Figure 4). After damage, newly formed tissue is derived from surviving cells that lie nearby and some of these cells must change their state of determination to contribute to the lost region. During disc regeneration several observations indicate that cell fates are respecified and that there is a process of cell reprograming. For example, there is a temporary loss of markers of cell fate commitment after genetic ablation or disc amputation [9,12,85]. It has also been reported that after genetic ablation in the wing pouch, the cells of the hinge generate cells that become part of the pouch [68,86]. Moreover, cell fate changes between compartments have been reported after surgical excision [66] or genetic ablation [87]. Indeed, cells near the anterior/posterior or dorsal/ventral boundary can change their identities and contribute to the compartment on the other side of the boundary [87]. Finally, BioMed Research International 7 during regeneration the cells of one disc occasionally acquire the identities of different imaginal discs, switching cell fate and generating disc-inappropriate structures, a process known as transdetermination [88].
The preservation of a specific cell fate or determination state depends on a particular genetic program, which is largely maintained through epigenetic modifications that are established during development. The polycomb group (PcG) proteins function as epigenetic modifiers and they are required to maintain cell fates by controlling the expression of developmental regulators [89]. This group of proteins forms two different types of complexes, Polycomb repressive complex 1 (PRC1) and Polycomb repressive complex 2 (PRC2). PcG can silence large numbers of genes by establishing repressive marks like histone H3 lysine 27 trimethylation (H3K27me3). There is evidence that JNK signaling downregulates PcG genes during regeneration, thereby allowing the transcription of otherwise silenced genes [90]. This process is important for cell reprogramming during regeneration. Inappropriate or excessive downregulation of the PcG by JNK during regeneration may activate genes that induce a genetic program corresponding to a different disc, provoking transdetermination. Indeed, the frequency of transdetermination is enhanced in PcG mutant discs [90]. Interestingly, ectopic activation of wg can induce transdetermination, possibly because wg might be a direct target of the PcG [90].
The preservation of the anterior/posterior compartment identity during regeneration is mediated by taranis (tara), that is, the homologue in Drosophila of the vertebrate TRIP-Br (Transcriptional Regulators Interacting with plant homeodomain (PHD) zinc fingers and/or Bromodomains) family of proteins. In mutant conditions for tara, regenerating wing disc undergoes posterior-to-anterior transformations late in regeneration. These changes are consequence of the misregulation of posterior selector gene engrailed (en). The deregulation of en leads to the autoregulatory silencing of the engrailed locus, which requires the PRC1. The misregulation and subsequent silencing of en are induced by JNK signaling. It has been proposed that Tara stabilizes engrailed expression downstream of JNK signaling to maintain the posterior cell fate identity during regeneration [91].
Recently, a defined regulatory element was identified that is responsible for the activation of wg expression after damage [92]. Interestingly, this regenerative enhancer (BRV118) regulates the expression of wg and wnt6. This observation suggests that the function of different members of Wnt family might be involved during regeneration in Drosophila. It has been described that within this enhancer there is a damageresponsive module that remains active throughout the third instar stage and an adjacent silencing element that nucleates increasing levels of epigenetic silencing during development. This latter element can restrict the activity of this enhancer [92]. Therefore, the loss of the regenerative capacity of the discs as development proceeds might be explained by a blockade of the damage-responsive enhancers through the activity of the silencing elements. This mechanism might prevent gene expression in the mature organism without compromising the gene activity regulated by developmental signals [92]. Interestingly, PcG-mediated epigenetic silencing is required to regulate the activity of this enhancer. Hence, the inability of the cells in adult tissue to reactivate programs necessary to promote regenerative growth or cell fate respecification could limit regeneration in adult stages.

Perspectives
The urodele amphibians have been used extensively as a model system to study regeneration as they present a remarkable regenerative capacity and they can fully regenerate amputated appendages [93]. While the studies carried out on these organisms allowed multiple cellular processes involved in limb regeneration to be identified [93], much less is known about the genetic mechanisms that control them, as amphibians are not the best model organisms for genetic analyses. Moreover, most studies into regenerative biology aimed at developing biomedical applications have been carried out on stem cells cultivated in vitro. To better understand the processes that occur during regeneration, these phenomena must be studied in vivo, in the context of the complex genetic and cellular interactions that take place. Drosophila is a complex model organism in which the mechanistic details of genetic and cellular processes can be defined. In addition, Drosophila has been extensively used as a model system to carry out unbiased genetic screens to identify genes involved in different cellular processes. These features make Drosophila an excellent model to identify and characterize genes involved in all aspects of regeneration. In fact, different genetic screens and studies of the changes in gene expression during disc regeneration have identified multiple signaling pathways and genes required for different processes associated with regeneration [23,24,94].
The conservation between flies and vertebrates of basic signaling pathways and their regulatory elements justifies using Drosophila as a model organism to establish mechanisms and genetic processes that can be translated to vertebrates. Different studies have confirmed that most of the signaling pathways required for disc regeneration are also involved in regeneration in vertebrates; for example, the Hippo pathway appears to play a fundamental role in vertebrate limb regeneration and in skin wound healing [95,96]. JNK is very important in mammalian liver regeneration and one of its targets, the AP-1 transcription factor subunit c-Jun, is activated during liver regeneration [97][98][99], the cytokines TNF-and IL-6 also being required during this process [99]. Finally, and as in Drosophila, PcGs are downregulated during murine skin repair, which provokes the derepression of dmyc [100]. Moreover, it has been suggested that that loss of polycomb-mediated silencing might contribute to the induction of repair genes in mammals.
In summary, basic regenerative research carried out in Drosophila can provide insights into the genetic and cellular responses involved in mammalian regeneration. This knowledge might serve to develop new therapies in regenerative medicine.

Conflicts of Interest
The authors declare that there are no conflicts of interest regarding the publication of this paper.