Functional impact of microRNA regulation in models of extreme stress adaptation

Functional impact of microRNA regulation in models of extreme stress adaptation Abstract When confronted with severe environmental stress, some animals are able to undergo a substantial reorganization of their cellular environment that enables long-term survival. One molecular mechanism of adaptation that has received considerable attention in recent years has been the action of reversible transcriptome regulation by microRNA. The implementation of new computational and high-throughput experimental approaches has started to uncover the vital contributions of microRNA towards stress adaptation. Indeed, recent studies have suggested that microRNA may have a major regulatory influence over a number of cellular processes that are essential to prolonged environmental stress survival. To date, a number of studies have highlighted the role of microRNA in the regulation of a metabolically depressed state, documenting stress-responsive microRNA expression during mammalian hibernation, frog and insect freeze tolerance, and turtle and marine snail anoxia tolerance. These studies collectively indicate a conserved principle of microRNA stress response across phylogeny. As we are on the verge of dissecting the role of microRNA in environmental stress adaptation, this review summarizes recent research advances and the hallmark expression patterns that facilitate stress survival. hypometabolism, metabolism, temperature, metabolic rate depression, stress response Introduction A select few animals have developed an incredible ability to survive and overcome severe environmental challenges that include dehydration, oxygen deprivation (i.e. anoxia), as well as temperature changes and even freezing of body fluids (Storey and Storey, 2004). These fascinating feats and the study of how these resilient animals are able to survive such extreme stresses has captivated researchers for many years. In particular, the past few decades research in comparative stress biology has focused on elucidating the molecular survival mechanisms at play, those working to reorganize the cellular landscape for life in a new environmental extreme (Storey and Storey, 2004; Storey, 2015). These mechanisms work to reprioritize cellular processes, such that these animals can conserve vital energy stores, with some entering a state of dormancy that is characterized by the depression of metabolic rate to 10%–30% of basal levels (or even more), a state commonly referred to as hypometabolism (Guppy and Withers, 1999). Given the relatively rapid onset of many environmental stresses, molecular mechanisms that help to govern the transition into a hypometabolic state must be rapid, readily reversible, and capable of eliciting selective control over both essential and non-essential cellular processes. This review will discuss new advances in the roles of non-coding RNA in promoting animal survival in extreme environments, as well as the transition into (and survival within) the hypometabolic state. The function of small non-coding RNA, namely microRNA (miRNA), will be a primary focus given the extensive amount of miRNA research that has taken place in this field over the past 10 years (Morin et al., 2007; Biggar and Storey, 2015a, b; Frigault et al., 2017). Mechanisms that help to coordinate stress survival must be easily inducible and reversible so that they can be initiated once a stress is detected. The contribution of readily reversible protein modifications, such as Ser/Thr/Tyr phosphorylation, has been extensively studied as a dynamic mechanism to quickly modify protein function and achieve hypometabolism. These studies established that the basic principles involved in regulating stress-responsive signal transduction events included the coordinated reversible phosphorylation of many proteins, setting this modification as one of the hallmarks of the central mechanisms of metabolic reorganization for many years (Cowan and Storey, 2003). Indeed, reversible post-translational modifications have been shown to affect the function of numerous cellular proteins and to serve as a common response element in the survival of many environmental challenges and metabolic stresses (Brooks and Storey, 1995; Cowan et al., 2000; Biggar and Storey, 2012; Wu and Storey, 2013). Mechanisms of reversible control over protein translation Beginning in the early 1990s, it was established that an overall suppression of the energetically costly cellular process of protein translation was an essential part of hypometabolism (Brooks and Storey, 1993; Land et al., 1993; Frerichs et al., 1998; Fraser et al., 2001; Larade and Storey, 2002; Hittel and Storey, 2002). Indeed, a depression of translational rate necessarily contributes to energy rationing, thereby helping to reserve ATP turnover for essential survival-related cellular processes. For example, the rate of protein translation in oxygen-deprived turtles was shown to decrease below measurable levels following 3 h of anoxic exposure (at 23°C) as compared with normoxic values at the same temperature (Fraser et al., 2001). Similarly, in the brain of torpid thirteen-lined ground squirrels, the rate of protein translation was documented to be only 0.04% of euthermic rates (Frerichs et al., 1998) and, when corrected for differences in body temperature between euthermic and torpid state, rates in torpor were still approximately only one-third of the euthermic values. Together, these results demonstrated that temperature influences alone do not explain reductions in translational rates and that the depression of protein translation likely involves other molecular mechanisms. Indeed, the suppression of protein translation in the hypometabolic state is a multifaceted event, one that is likely achieved through a combination of different mechanisms that include reductions in gene transcription, inactivation of the translational machinery, and/or through mRNA-specific interference by miRNA. As a result of the high energetic costs of protein synthesis and mRNA transcription (accounting for greater than 25% of basal ATP turnover in many instances), it would not be surprising to see that a strong stress-responsive suppression of mRNA transcription would accompany the observed stress-responsive decrease in translational rate (Hochachka et al., 1996; Podrabsky and Hand, 2000). In this regard, previously studies have shown that reversible control over translational machinery may be a powerful driving force behind global translational silencing during hypometabolism. For example, research on protein translation in hibernating thirteen-lined ground squirrels has documented a dynamic stress-responsive increase in the relative phosphorylation of the translational initiation factor eIF2α (Hittel and Storey, 2002). The phosphorylation of eIF2α is a well-established marker of both translational arrest and the disruption of active polyribosomes (Yamasaki and Anderson, 2008). Indeed, the same study of hibernating ground squirrels also demonstrated a hibernation-responsive disaggregation of polyribosomes (Hittel and Storey, 2002). Both of these results indicate that a translationally silent state is imposed by a stress-responsive inactivation of the translational machinery. Furthermore, neither the total cellular polyadenylated RNA content nor the mRNA transcript levels of many genes were found to decrease in hibernation or anoxia models of hypometabolism (Epperson and Martin, 2002; Storey and Storey, 2004; Rouble et al., 2014). Therefore, it is reasonable to conclude that the suppression of global protein translation rates during hypometabolism may not be a direct result of altered cellular mRNA content, but rather achieved through post-translational modifications made to translational machinery. Despite a possible global shutdown of protein translation pathways, there still remains the question of how the proteins that are essential for survival continue to be created in the hypometabolic state. There must be mechanisms at play that allow for essential mRNA to be translated, while non-essential mRNA are blocked from translation. This requirement highlights the regulatory influence of miRNA in the translational control of specific mRNA. Perhaps such regulatory mechanisms would allow gene-specific control over protein translation during periods of cell stress. Indeed, this area of research has gained considerable interest in recent years within the field of comparative biochemistry. Reversible control of protein translation by microRNA MiRNA are short, non-coding RNA capable of rapidly and reversibly regulating the expression of targeted proteins within a cell and have been found in plants, animals, bacteria, as well as some viruses. It is well documented that these 18–24 nt transcripts are able to bind, with full or partial complementarity, to specific mRNA targets, resulting in either the inhibition of translation or degradation of the target mRNA (Bartel, 2004). As a reflection of their regulatory potential, miRNA have been predicted to be involved in all biological process in some aspect throughout the animal kingdom (Friedman et al., 2009). The biogenesis of a mature miRNA begins with the initial transcription of a primary miRNA (pri-miRNA). The pri-miRNA transcript is much larger than the final mature miRNA product, ranging from hundreds to thousands of nucleotides in length (Krol et al., 2010). Following transcription, the pri-miRNA is then cleaved to form a hairpin RNA structure called a precursor miRNA (pre-miRNA) and is exported from the nucleus (Yi et al., 2003; Zeng et al., 2005; Krol et al., 2010). Once in the cytoplasm, the pre-miRNA is further processed into a mature miRNA duplex by Dicer, an riboendonuclease protein. From the mature miRNA duplex, it is suggested that only one strand will function as a mature miRNA, leaving the passenger strand to be degraded (Krol et al., 2010). Although this is generally thought to be the case, the idea of passenger miRNA degradation has been challenged in light of recent research suggesting that the abundance, possible function, and physiological relevance of passenger miRNA has been underestimated (Cipolla, 2014). Following mature miRNA processing, the mature miRNA is then loaded into a protein complex referred to as the RNA-induced silencing complex (RISC). Interestingly, the miRNA–RISC and bound mRNA complex have also been found to aggregate within stress granules and p-bodies in a stress-responsive manner, such as in response to DNA damage or hypoxia stresses, and are eventually either degraded or directed into translation once the stress is lifted (Pothof et al., 2009; Wu et al., 2011). Indeed, the stress-responsive formation of reversible stress granules have been shown to occur in response to mammalian hibernation (Tessier et al., 2014). Take together, this reversible mRNA storage system represents a promising mechanism for reversible translational arrest in models of hypometabolism. MicroRNA discovery in non-model organisms In general, miRNA and their target recognition sites have been subject to exquisite conservation across phylogeny. The conservation of miRNA by >90% among vertebrates is reflective of the important regulatory function they impose in normal cell biology. This high level of sequence homology between species has made it possible to study miRNA expression patterns in many different species with relative ease. Indeed, multiple studies have been able to explore the dynamic expression of miRNA in response to a variety of environmental stresses in a diverse array of animals for which little or no genomic sequence information is available. To date, stress-responsive miRNA expression has been studied in models of hypometabolism that include ground squirrels, bats, frogs, turtles, and many invertebrate species (Table 1 with references). Indeed the dynamic change in stress-responsive miRNA expression may be the result of multiple regulatory steps, including transcriptional modulation, microprocessor or miRNA biogenesis co-factor regulation, changes in RNA/protein complex localization, and modifications of miRNA ends. Although the exact mechanisms controlling stress-induced miRNA expression are not yet known, some of these steps may play pivotal roles in cellular homeostasis in the context of the stress response. Table 1 Studies exploring the role of stress-responsive miRNA regulation in animal models of hypometabolism. Stress  Species  References  Hibernation  Ictidomys tridecemlineatus  Morin et al. (2007), Maistrovski et al. (2012), Lang-Ouellette and Morin (2014), Wu et al. (2014a), Luu et al. (2016), Wu et al. (2016), Frigault et al. (2016)    Spermophilus parryii  Liu et al. (2010)    Myotis lucifigus  Biggar and Storey (2014a, b), Kornfeld et al. (2012), Maistrovski et al. (2012)    Dromiciops gliroides  Hadj-Moussa et al. (2016)  Freezing  Rana sylvatica  Biggar et al. (2009), Bansal et al. (2016)    Littorina littorea  Biggar et al. (2012)    Eurosta solidaginis  Courteau et al. (2012), Lyons et al. (2013), Lyons et al. (2015a, b), Lyons et al. (2016)    Chrysemys picta  Shaffer et al. (2013), Biggar and Storey (2015a, b)  Freeze avoidance  Epiblema scudderiana  Lyons et al. (2015a, b)  Oxygen restriction  Littorina littorea  Biggar et al. (2012)    Trachemys scripta elegans  Biggar and Storey (2011), Zhang et al. (2013)    Ictidomys tridecemlineatus  Lee et al. (2012)  Aestivation  Xenopus laevis  Wu et al. (2013), Luu and Storey (2015)    Apostichopus japonicus  Chen et al. (2013), Chen and Storey (2014)  Stress  Species  References  Hibernation  Ictidomys tridecemlineatus  Morin et al. (2007), Maistrovski et al. (2012), Lang-Ouellette and Morin (2014), Wu et al. (2014a), Luu et al. (2016), Wu et al. (2016), Frigault et al. (2016)    Spermophilus parryii  Liu et al. (2010)    Myotis lucifigus  Biggar and Storey (2014a, b), Kornfeld et al. (2012), Maistrovski et al. (2012)    Dromiciops gliroides  Hadj-Moussa et al. (2016)  Freezing  Rana sylvatica  Biggar et al. (2009), Bansal et al. (2016)    Littorina littorea  Biggar et al. (2012)    Eurosta solidaginis  Courteau et al. (2012), Lyons et al. (2013), Lyons et al. (2015a, b), Lyons et al. (2016)    Chrysemys picta  Shaffer et al. (2013), Biggar and Storey (2015a, b)  Freeze avoidance  Epiblema scudderiana  Lyons et al. (2015a, b)  Oxygen restriction  Littorina littorea  Biggar et al. (2012)    Trachemys scripta elegans  Biggar and Storey (2011), Zhang et al. (2013)    Ictidomys tridecemlineatus  Lee et al. (2012)  Aestivation  Xenopus laevis  Wu et al. (2013), Luu and Storey (2015)    Apostichopus japonicus  Chen et al. (2013), Chen and Storey (2014)  Table 1 Studies exploring the role of stress-responsive miRNA regulation in animal models of hypometabolism. Stress  Species  References  Hibernation  Ictidomys tridecemlineatus  Morin et al. (2007), Maistrovski et al. (2012), Lang-Ouellette and Morin (2014), Wu et al. (2014a), Luu et al. (2016), Wu et al. (2016), Frigault et al. (2016)    Spermophilus parryii  Liu et al. (2010)    Myotis lucifigus  Biggar and Storey (2014a, b), Kornfeld et al. (2012), Maistrovski et al. (2012)    Dromiciops gliroides  Hadj-Moussa et al. (2016)  Freezing  Rana sylvatica  Biggar et al. (2009), Bansal et al. (2016)    Littorina littorea  Biggar et al. (2012)    Eurosta solidaginis  Courteau et al. (2012), Lyons et al. (2013), Lyons et al. (2015a, b), Lyons et al. (2016)    Chrysemys picta  Shaffer et al. (2013), Biggar and Storey (2015a, b)  Freeze avoidance  Epiblema scudderiana  Lyons et al. (2015a, b)  Oxygen restriction  Littorina littorea  Biggar et al. (2012)    Trachemys scripta elegans  Biggar and Storey (2011), Zhang et al. (2013)    Ictidomys tridecemlineatus  Lee et al. (2012)  Aestivation  Xenopus laevis  Wu et al. (2013), Luu and Storey (2015)    Apostichopus japonicus  Chen et al. (2013), Chen and Storey (2014)  Stress  Species  References  Hibernation  Ictidomys tridecemlineatus  Morin et al. (2007), Maistrovski et al. (2012), Lang-Ouellette and Morin (2014), Wu et al. (2014a), Luu et al. (2016), Wu et al. (2016), Frigault et al. (2016)    Spermophilus parryii  Liu et al. (2010)    Myotis lucifigus  Biggar and Storey (2014a, b), Kornfeld et al. (2012), Maistrovski et al. (2012)    Dromiciops gliroides  Hadj-Moussa et al. (2016)  Freezing  Rana sylvatica  Biggar et al. (2009), Bansal et al. (2016)    Littorina littorea  Biggar et al. (2012)    Eurosta solidaginis  Courteau et al. (2012), Lyons et al. (2013), Lyons et al. (2015a, b), Lyons et al. (2016)    Chrysemys picta  Shaffer et al. (2013), Biggar and Storey (2015a, b)  Freeze avoidance  Epiblema scudderiana  Lyons et al. (2015a, b)  Oxygen restriction  Littorina littorea  Biggar et al. (2012)    Trachemys scripta elegans  Biggar and Storey (2011), Zhang et al. (2013)    Ictidomys tridecemlineatus  Lee et al. (2012)  Aestivation  Xenopus laevis  Wu et al. (2013), Luu and Storey (2015)    Apostichopus japonicus  Chen et al. (2013), Chen and Storey (2014)  Much of the initial hypometabolism-focused miRNA research was carried out on vertebrate and invertebrate species with limited genomic sequence information available (Morin et al., 2007; Biggar et al., 2009,  2012; Courteau et al., 2012; Lyons et al., 2013). Given this restriction, these studies were limited to the analysis of highly conserved miRNA using methods that had been developed to aid in the amplification, sequencing, and validation of these conserved miRNA (Biggar et al., 2011, 2014). However, while vertebrate miRNA are highly conserved, low conservation between vertebrate and invertebrate miRNA slowed the initial progress of miRNA research in invertebrate species. Despite this challenge, several studies have explored the regulation of various relatively conserved invertebrate miRNA, particularly as part of winter survival by freeze-tolerant (Littorina littorea, Eurosta solidaginis) and freeze-avoidant (Epiblema scudderiana) species (Biggar et al., 2012; Courteau et al., 2012; Lyons et al., 2013, 2015a, b, 2016). Given the inherent difficulty of studying poorly conserved miRNA and an interest in discovering possible specifies-specific miRNA, computation-based methods have recently been developed specifically to identify (i) highly conserved, (ii) poorly conserved, and (iii) species-specific miRNA from organisms with newly sequenced genomes. The widely used miRDeep2 platform accomplishes this by using both an available genome sequence and small RNA sequencing information to map mature miRNA sequences back to a reference genomic location and to then evaluate the associated pre-miRNA structure and identify high-confidence miRNA (Friedlander et al., 2012). Given the importance miRNA sequence on its function, it is essential to validate all computationally predicted candidate miRNAs experimentally, including PCR-based methods, such that their expression and sequence of their mature forms can be verified. Recently, small RNA sequencing and miRDeep2 have been used to identify novel miRNA regulating gene expression during hibernation in thirteen-lined ground squirrels, Ictidomys tridecemlineatus (Luu et al., 2016). The study identified and experimentally validated 17 novel miRNA and characterized their relative expression in liver, skeletal muscle, and heart tissues over the torpor–arousal cycle. Interestingly, these squirrel-specific miRNA were predicted to target mRNA enriched in biological processes that are known to be dynamically regulated during hibernation, including lipid metabolism, ion-transport ATPases, and various cellular signaling cascades (Luu et al., 2016). This study provides an example of how computational identification of new miRNA and analysis of their cellular function is of growing interest in comparative research on environmental stress adaptation. Indeed, such knowledge may be crucial to developing a deeper understanding of the roles that miRNA may play in the hypometabolic stress response. The major restriction of this particular platform is the requirement for small RNA sequencing information. In the absence of this information, another platform, called SMIRP, has been developed recently to identify species-specific miRNA de novo directly from an available genome (Peace et al., 2015). The SMIRP platform identifies both known and novel species-specific miRNA by scanning the genome for pre-miRNA-like hairpins and evaluating select features of these hairpins (i.e. % nucleotide content, RNA folding measures, and other topological descriptors) against miRNA that have been previously identified from related species (Peace et al., 2015; Schaap et al., 2016; Adema et al., 2017). Recently, the SMIRP platform was used to identify species-specific miRNA from the schistosomiasis-transmitting freshwater Ramshorn snail (Biomphalara glabrata) (Adema et al., 2017). Identification of these miRNA provided information on miRNA evolution, conservation, and suggested influence on translational regulation that may lead to possible mechanisms for population control in this snail species. Researchers from this study performed an in silico prediction of miRNA using SMIRP within the B. glabrata genome scaffolds. Uniquely, SMIRP leveraged a novel approach to miRNA classifier construction in which models are built dynamically and are targeted toward B. glabrata. This differs from other methods (e.g. precursor miRNA prediction methods) that attempt to produce generalized models that are applicable to a large number of species. From this approach, 202 pre-miRNA (95 known and 107 novels) and associated mature miRNA from B. glabrata were identified (Adema et al., 2017). No homologous sequences to the identified novel B. glabrata precursor miRNA were found in either the sea slug (Aplysia californica) or the sea snail (Lottia gigantea) annotated miRNA, or present within their available genomes. Interestingly, this study also identified the possible biological context of novel B. glabrata miRNA, predicting mRNA targets from the 3′UTR of available B. glabrata transcripts (John et al., 2005). A significant proportion of the identified target genes of these novel miRNA included multi-miRNA gene regulation of proteins involved in cellular processes such as secretory mucal proteins (mucin-21-like) (Gabrial et al., 2011), matricellular proteins (thrombospondin-3b-like) (Marxen and Becker, 2011) and shell formation proteins (dentin sialophosphoprotein-like) (Volk et al., 2014) (Figure 1). These newly identified miRNA greatly enrich the repertoire of known mollusk miRNA, providing insights into mollusk miRNA function, as well as their evolution and biogenesis. Such species-specific miRNA can also provide possible biocontrol targets for B. glabrata population control, or may even play a role in the control of aestivation in this species (Britton et al., 2014). Figure 1 View largeDownload slide Prediction of species-specific miRNA function from the freshwater Ramshorn snail, Biomphalaria glabrata. Novel miRNA were identified from the B. glabrata genome (BgalB1; vectorbase.org) using SMIRP. Targets of novel snail miRNA were then identified using miRDeep2 from the available transcript assembly (BgalB1.5; vectorbase.org). Target genes of novel miRNA included multi-miRNA gene regulation of proteins involved in cellular processes such as secretory mucal proteins (mucin-21-like), matricellular proteins (thrombospondin-3b-like), and shell formation (dentin sialophosphoprotein-like). Figure 1 View largeDownload slide Prediction of species-specific miRNA function from the freshwater Ramshorn snail, Biomphalaria glabrata. Novel miRNA were identified from the B. glabrata genome (BgalB1; vectorbase.org) using SMIRP. Targets of novel snail miRNA were then identified using miRDeep2 from the available transcript assembly (BgalB1.5; vectorbase.org). Target genes of novel miRNA included multi-miRNA gene regulation of proteins involved in cellular processes such as secretory mucal proteins (mucin-21-like), matricellular proteins (thrombospondin-3b-like), and shell formation (dentin sialophosphoprotein-like). MicroRNA and adaptation to extreme environments A growing number of studies have begun to highlight the conserved response of miRNA in the regulation of the hypometabolic state. Beginning in 2007, the first study of this type explored the expression of several well-conserved miRNA in response to seasonal hibernation in the thirteen-lined ground squirrel (Morin et al., 2007). Although only exploring the expression of a handful of miRNA, this was a keystone paper in introducing the possible influence of miRNA as a mechanism to reversibly control mRNA translation in models of hypometabolism. More recently, several other studies have explored miRNA expression in hibernation of ground squirrels, documenting the torpor-responsive expression of 117 miRNA across four stages of the torpor–arousal cycle (Wu et al., 2016). A follow-up study identified 17 additional novel torpor-responsive miRNA, currently thought to be unique to the thirteen-lined ground squirrel (Luu et al., 2016). Another study utilized advances in parallel RNA sequencing to study the expression of >200 ground squirrel miRNA, including 18 novel miRNA, in the liver of the hibernating Arctic ground squirrel, Spermophilus parryii (Liu et al., 2010). To date, the regulatory influence of miRNA expression has been most extensively explored in the context of mammalian hibernation (Table 1). These studies, and those of a variety of other animals, have demonstrated the importance of studying these small regulatory molecules as a conserved dynamic response to environmental stress and adaptation across phylogeny. Given the growing implication of miRNA in the regulation of environmental stress tolerance, studies have now begun to dig deeper into miRNA function, asking specific research questions, such as what miRNA are stress responsive? what mRNA are stress-responsive miRNA targeting? what are the global functional implications of miRNA? MicroRNA-influenced control over cellular pathways and processes It is well established that a single miRNA sequence can exert regulatory effects on numerous different targets. Similarly, a single mRNA is likely the regulatory target of multiple miRNA, with different miRNA binding at varied locations within the 3′UTR. This complex regulatory system creates a model of enormous regulatory potential that cannot be ignored when studying miRNA function. However, given the limited genomic resources for many experimental models of hypometabolism, initial explorations into miRNA regulatory potential have been appropriately limited in scope. These studies primarily characterized miRNA and target expression patterns in a single miRNA, single mRNA target format (Morin et al., 2007; Biggar et al, 2009, 2012). One good example from early miRNA research on models of hypometabolism is the stress-responsive characterization of miR-21. This miRNA has been studied in multiple animals in the context of regulating anti-apoptotic genes in response to environmental stress (Morin et al., 2007; Biggar et al., 2009; Biggar and Storey, 2011, 2012; Wu et al., 2014b). Following the development of bioinformatics methods that allowed for the widespread study of multiple miRNA from a single set of RNA samples (Biggar et al., 2014), recent studies began to move away from single miRNA:target-based candidate characterization. The scope of current studies is growing larger as we begin to learn more about miRNA target selection and begin to apply new computational-based methods to explore the regulatory impact of a greater set of stress-responsive miRNA on the complete system of cellular processes (Luu et al., 2016; Wu et al., 2016). In 2016, an expansive study looking at the torpor-responsive expression of 117 conserved miRNA in hibernating thirteen-lined ground squirrels over four stages of the torpor–arousal cycle (euthermia, early torpor, late torpor, and interbout arousal) (Wu et al., 2016). Moving away from candidate miRNA expression analysis, this study found significant differential expression of a number of miRNA in both a tissue and torpor stage-specific manner, clearly demonstrating that miRNA likely play an active role in mammalian hibernation and dynamic metabolic regulation. Although miRNA expression profiles were largely tissue-specific for the three organs studied (liver, heart, skeletal muscle), gene ontology (GO) annotation analysis revealed that the putative targets of the upregulated miRNA were commonly enriched in cellular processes involved in the suppression of pro-growth. For example, in liver tissue, upregulated miRNA targeted genes enriched in cellular processes such as growth factor receptor signaling pathways, regulation of nuclear division, and glycolysis during the early torpor stage (Wu et al., 2016). To further expand upon this analysis, we worked to elucidate the organization of the miRNA-targeted cellular system using the targets of the significantly upregulated miRNA from the ET stage of liver tissue. By mapping the collective targets of torpor-responsive miRNA with their known protein interactions, we were able to obtain an intuitive representation of miRNA-regulated processes within a functional network. To accomplish this, we used the spatial analysis of functional enrichment (SAFE) tool within the Cytoscape software (v3.4.0) (Figure 2). For each miRNA target within the network, SAFE defined the local neighborhood of targets by identifying those that look to be clustered within the network. GO analysis was then carried to determine enriched miRNA-regulated cellular processes (Baryshnikova, 2016). Similar to the original study, we identified an enrichment in targeting cellular signaling pathways, including growth factor signaling and carboxylic acid catabolism. We also identified a significant enrichment in the regulation of lipid biosynthesis, hormone response signaling, and RNA processing (Figure 2). Lipid metabolism is known to be under tight regulatory control during hibernation as triglycerides are the primary energy source for the hibernating mammal and are known to influence the length of torpor bouts and metabolic rate (Florant, 1998; Dark, 2005; Wu et al., 2013). Since the entry into and arousal from torpor is a short process that requires rapid changes in many metabolic processes, this particular result is not surprising. Furthermore, with regards to the predicted enrichment of RNA processing in torpid thirteen-lined ground squirrels, a previous study has characterized the roles of three RNA binding proteins: T-cell intracellular antigen 1 (TIA-1), TIA-1 related (TIAR), and poly(A)-binding protein (PABP-1) (Tessier et al., 2014). TIA-1 was identified as a major component of sub-nuclear structures with up to a 7-fold increase in relative protein levels found in the nucleus during hibernation. Additionally, analysis of the formation of reversible aggregates that are associated with TIA-1 and TIAR function during stress suggested that enhanced protein aggregation was not present during torpor. Our identification of miRNA-targeted regulation of RNA processing agrees with this study as it identifies posttranscriptional regulatory mechanisms at play in reducing translational rates and/or mRNA processing. As demonstrated by our deeper network-based functional analysis into torpor-responsive miRNA function (Figure 2), a global analysis of miRNA targets can help to provide deeper, meaningful, insight into the role of miRNA in helping to coordinate the torpor–arousal cycle. Figure 2 View largeDownload slide Predicted functional interaction map of stress-responsive miRNA targets from the hibernating thirteen-lined ground squirrel. A protein interaction map of all predicted targets of miRNA found to be significantly overexpressed during the early torpor (ET) stage of ground squirrel hibernation. Network was constructed using known human protein interactions from the STRING database (https://string-db.org/), contained 532 miRNA targets (i.e. nodes) and 1433 known interactions (i.e. edges), and was originally constructed using Cytoscape (v3.4.0). Spatial analysis of functional enrichment (SAFE)-based construction of a functional map of the network by combining all region-specific human GO terms into 12 functional domains based on the similarity of their enrichment landscapes (P values < 2 × 10−4, Fisher’s exact test). Different colors represent different functional domains. Each domain is labeled with a tag list, composed of the five words that occur most frequently within the names of the associated GO terms. Figure 2 View largeDownload slide Predicted functional interaction map of stress-responsive miRNA targets from the hibernating thirteen-lined ground squirrel. A protein interaction map of all predicted targets of miRNA found to be significantly overexpressed during the early torpor (ET) stage of ground squirrel hibernation. Network was constructed using known human protein interactions from the STRING database (https://string-db.org/), contained 532 miRNA targets (i.e. nodes) and 1433 known interactions (i.e. edges), and was originally constructed using Cytoscape (v3.4.0). Spatial analysis of functional enrichment (SAFE)-based construction of a functional map of the network by combining all region-specific human GO terms into 12 functional domains based on the similarity of their enrichment landscapes (P values < 2 × 10−4, Fisher’s exact test). Different colors represent different functional domains. Each domain is labeled with a tag list, composed of the five words that occur most frequently within the names of the associated GO terms. Temperature influence over microRNA–target interaction Given the state of miRNA research within the field of comparative biochemistry and the functional insight that it has provided to date, it is becoming increasingly clear that these small regulatory RNA are an important component of environmental stress survival and the hypometabolic response. Given this interest, there has also been an increasing attempt to explore the targets that these stress-responsive miRNA regulate (Figure 3). In recent years, there has also been a growing interest in the possibility of temperature influencing the regulatory function of miRNA (Figure 3). This has been previously discussed both in terms of miRNA base-content and its relationship to Tb (Figure 3A and B), as well as in the context of low-temperature influence over miRNA:mRNA binding thermodynamics (Figure 3C) (Carmel et al., 2012; Biggar and Storey, 2014a, b, 2015a, b, 2017). Figure 3 View largeDownload slide Temperature-associated regulation of miRNA function. (A) The average G/C content of complete miRNA sequences as well as their seed regions plotted against the physiological temperature of each organism. (B) For each of the organisms, miRNAs were divided into two subsets that contain (i) miRNAs that are specific to a taxonomic group and (ii) the rest miRNAs that are shared by other taxonomic groups. Each pair of bars shows the difference in G/C content between these two subsets for miRNAs (dark gray) and for seeds (light gray). Error bars show standard error of the difference between two means; (*) P < 0.05 and (**) P < 0.0005. (C) Depiction of proposed temperature influence in RNA binding thermodynamics. It is hypothesized that low temperature could act to stabilize miRNA:target interactions that were once unfavorable or unstable at higher temperatures. Data for A and B were derived from Carmel et al. (2012). Figure 3 View largeDownload slide Temperature-associated regulation of miRNA function. (A) The average G/C content of complete miRNA sequences as well as their seed regions plotted against the physiological temperature of each organism. (B) For each of the organisms, miRNAs were divided into two subsets that contain (i) miRNAs that are specific to a taxonomic group and (ii) the rest miRNAs that are shared by other taxonomic groups. Each pair of bars shows the difference in G/C content between these two subsets for miRNAs (dark gray) and for seeds (light gray). Error bars show standard error of the difference between two means; (*) P < 0.05 and (**) P < 0.0005. (C) Depiction of proposed temperature influence in RNA binding thermodynamics. It is hypothesized that low temperature could act to stabilize miRNA:target interactions that were once unfavorable or unstable at higher temperatures. Data for A and B were derived from Carmel et al. (2012). Given the relationship between miRNA seed binding potential (i.e. G/C content) and temperature, the question as to whether there are other mechanisms by which temperature can influence miRNA function should be asked. The targeting of miRNA to select mRNA sites relies primarily on seed region complementarity, with further binding from the 3′end of the miRNA only acting to stabilize and supplement the interaction. Critically, it has been previously reported that the thermodynamic threshold (mean free energy; mfe) used to predict whether a miRNA:mRNA target will occur is ~18 kcal/mol. This threshold, among other structural requirements of the miRNA:mRNA interaction, has been used in almost all target prediction programs that have been developed for the identification of human miRNA targets in mind (including miRanda, TargetScan, and Diana microT). These miRNA target identification programs typically overlook the possibility of non-human species existing at Tb values greater or lower than 37°C. Indeed, given the strong thermodynamic requirement for a successful miRNA:mRNA interaction, it is likely that a significant change in Tb (such as experienced by frozen frogs and turtles, hibernating mammals, and many other overwintering animals) will have a strong influence on the ability of miRNA target selection. In this way, a decrease in Tb would likely favorably stabilize miRNA–target interactions that were once unfavorable, allowing these interactions to become biologically relevant in a temperature-dependent manner. Indeed, one study using the FindTar3 miRNA target prediction software, which allows user control over temperature, showed a 16-fold increase in the number of potential mRNA targets when comparing those predicted at freezing temperatures (3°C) and those identified at 37°C (Biggar and Storey, 2015a, b). For example, in response to freezing (3°C) in the hatchling painted turtle (Chrysemys picta marginata), miR-21 expression levels were shown to increase by over 2-fold in heart tissue (Biggar and Storey, 2015a, b). When predicting the mRNA targets of miR-21, this study compared the targets predicted at both 37°C and 3°C, finding an increase from 47 to 756 targets at the lower temperature. Interestingly, these low-temperature targets were enriched in metabolic processes (Biggar and Storey, 2015a, b, 2017; Luu et al., 2016). Within the context of a global decrease in gene expression, it is likely that such an increase of miRNA targets would dramatically alter the miRNA targeting program. Overall, the increasing realization that Tb will likely have a dramatic impact on miRNA function, presents the possibility that distinct temperature-induced miRNA targeting programs may be at play and help to facilitate cellular function at various temperatures. In this way, there may exist distinct cold-influenced miRNA targeting programs that facilitate unique hypometabolic survival of extreme stresses. Given that the strict seed pairing requirement of miRNA would still be required, it is possible that the relatively lower G/C content within the seed region of organisms living at lower temperatures may also have a role in aiding this temperature-sensitive process (Figure 3). Thus, it is interesting to propose that miRNA could play a specific temperature-sensitive role in helping various species to cope with temperature-related stress. Conclusion Within the past decade, many studies have explored the role of miRNA as a mechanism to reversibly and rapidly regulate the cellular landscape and enable survival during periods of extreme stress. The overall impact of these studies has resulted in a common theme of stress-responsive miRNA expression across phylogeny. Building upon this initial research, and with the growing availability of genomic information and bioinformatic power, researchers have now begun to predict the overall cellular impact of stress-responsive miRNA expression in the context of global target regulation. As a result, it is clear that miRNA may be involved in the regulation of many cellular processes that enable efficient utilization of ATP turnover. As we learn more about miRNA function in different organisms and in response to various environmental conditions, the possibility of temperature having a role to play in influencing target selection may also open comparative miRNA research to an immense number of regulatory possibilities that will need to be explored. Acknowledgements Thanks go to J. Storey (Institute of Biochemistry, Carleton University, Canada) for editorial review of the manuscript. Funding This research was funded by a Discovery grant from the Natural Sciences and Engineering Research Council of Canada (NSERC; grant no. 6793) to K.B.S. Conflict of interest: none declared. References Adema, C.M., Hillier, L.W., Jones, C.S., et al.  . ( 2017). Whole genome analysis of a schistosomiasis-transmitting freshwater snail. Nat. Commun.  8, 15451. Google Scholar CrossRef Search ADS PubMed  Bansal, S., Luu, B.E., and Storey, K.B. ( 2016). MicroRNA regulation in heart and skeletal muscle over the freeze-thaw cycle in the freeze tolerant wood frog. J. Comp. Physiol. B  186, 229– 241. Google Scholar CrossRef Search ADS PubMed  Bartel, D. ( 2004). MicroRNAs: genomics, biogenesis, mechanism, and function. Cell  116, 281– 297. Google Scholar CrossRef Search ADS PubMed  Baryshnikova, A. ( 2016). Systematic functional annotation and visualization of biological networks. Cell Syst.  2, 412– 421. Google Scholar CrossRef Search ADS PubMed  Biggar, K.K., Dubuc, A., and Storey, K.B. 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Functional impact of microRNA regulation in models of extreme stress adaptation

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© The Author (2018). Published by Oxford University Press on behalf of Journal of Molecular Cell Biology, IBCB, SIBS, CAS. All rights reserved.
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Abstract

Abstract When confronted with severe environmental stress, some animals are able to undergo a substantial reorganization of their cellular environment that enables long-term survival. One molecular mechanism of adaptation that has received considerable attention in recent years has been the action of reversible transcriptome regulation by microRNA. The implementation of new computational and high-throughput experimental approaches has started to uncover the vital contributions of microRNA towards stress adaptation. Indeed, recent studies have suggested that microRNA may have a major regulatory influence over a number of cellular processes that are essential to prolonged environmental stress survival. To date, a number of studies have highlighted the role of microRNA in the regulation of a metabolically depressed state, documenting stress-responsive microRNA expression during mammalian hibernation, frog and insect freeze tolerance, and turtle and marine snail anoxia tolerance. These studies collectively indicate a conserved principle of microRNA stress response across phylogeny. As we are on the verge of dissecting the role of microRNA in environmental stress adaptation, this review summarizes recent research advances and the hallmark expression patterns that facilitate stress survival. hypometabolism, metabolism, temperature, metabolic rate depression, stress response Introduction A select few animals have developed an incredible ability to survive and overcome severe environmental challenges that include dehydration, oxygen deprivation (i.e. anoxia), as well as temperature changes and even freezing of body fluids (Storey and Storey, 2004). These fascinating feats and the study of how these resilient animals are able to survive such extreme stresses has captivated researchers for many years. In particular, the past few decades research in comparative stress biology has focused on elucidating the molecular survival mechanisms at play, those working to reorganize the cellular landscape for life in a new environmental extreme (Storey and Storey, 2004; Storey, 2015). These mechanisms work to reprioritize cellular processes, such that these animals can conserve vital energy stores, with some entering a state of dormancy that is characterized by the depression of metabolic rate to 10%–30% of basal levels (or even more), a state commonly referred to as hypometabolism (Guppy and Withers, 1999). Given the relatively rapid onset of many environmental stresses, molecular mechanisms that help to govern the transition into a hypometabolic state must be rapid, readily reversible, and capable of eliciting selective control over both essential and non-essential cellular processes. This review will discuss new advances in the roles of non-coding RNA in promoting animal survival in extreme environments, as well as the transition into (and survival within) the hypometabolic state. The function of small non-coding RNA, namely microRNA (miRNA), will be a primary focus given the extensive amount of miRNA research that has taken place in this field over the past 10 years (Morin et al., 2007; Biggar and Storey, 2015a, b; Frigault et al., 2017). Mechanisms that help to coordinate stress survival must be easily inducible and reversible so that they can be initiated once a stress is detected. The contribution of readily reversible protein modifications, such as Ser/Thr/Tyr phosphorylation, has been extensively studied as a dynamic mechanism to quickly modify protein function and achieve hypometabolism. These studies established that the basic principles involved in regulating stress-responsive signal transduction events included the coordinated reversible phosphorylation of many proteins, setting this modification as one of the hallmarks of the central mechanisms of metabolic reorganization for many years (Cowan and Storey, 2003). Indeed, reversible post-translational modifications have been shown to affect the function of numerous cellular proteins and to serve as a common response element in the survival of many environmental challenges and metabolic stresses (Brooks and Storey, 1995; Cowan et al., 2000; Biggar and Storey, 2012; Wu and Storey, 2013). Mechanisms of reversible control over protein translation Beginning in the early 1990s, it was established that an overall suppression of the energetically costly cellular process of protein translation was an essential part of hypometabolism (Brooks and Storey, 1993; Land et al., 1993; Frerichs et al., 1998; Fraser et al., 2001; Larade and Storey, 2002; Hittel and Storey, 2002). Indeed, a depression of translational rate necessarily contributes to energy rationing, thereby helping to reserve ATP turnover for essential survival-related cellular processes. For example, the rate of protein translation in oxygen-deprived turtles was shown to decrease below measurable levels following 3 h of anoxic exposure (at 23°C) as compared with normoxic values at the same temperature (Fraser et al., 2001). Similarly, in the brain of torpid thirteen-lined ground squirrels, the rate of protein translation was documented to be only 0.04% of euthermic rates (Frerichs et al., 1998) and, when corrected for differences in body temperature between euthermic and torpid state, rates in torpor were still approximately only one-third of the euthermic values. Together, these results demonstrated that temperature influences alone do not explain reductions in translational rates and that the depression of protein translation likely involves other molecular mechanisms. Indeed, the suppression of protein translation in the hypometabolic state is a multifaceted event, one that is likely achieved through a combination of different mechanisms that include reductions in gene transcription, inactivation of the translational machinery, and/or through mRNA-specific interference by miRNA. As a result of the high energetic costs of protein synthesis and mRNA transcription (accounting for greater than 25% of basal ATP turnover in many instances), it would not be surprising to see that a strong stress-responsive suppression of mRNA transcription would accompany the observed stress-responsive decrease in translational rate (Hochachka et al., 1996; Podrabsky and Hand, 2000). In this regard, previously studies have shown that reversible control over translational machinery may be a powerful driving force behind global translational silencing during hypometabolism. For example, research on protein translation in hibernating thirteen-lined ground squirrels has documented a dynamic stress-responsive increase in the relative phosphorylation of the translational initiation factor eIF2α (Hittel and Storey, 2002). The phosphorylation of eIF2α is a well-established marker of both translational arrest and the disruption of active polyribosomes (Yamasaki and Anderson, 2008). Indeed, the same study of hibernating ground squirrels also demonstrated a hibernation-responsive disaggregation of polyribosomes (Hittel and Storey, 2002). Both of these results indicate that a translationally silent state is imposed by a stress-responsive inactivation of the translational machinery. Furthermore, neither the total cellular polyadenylated RNA content nor the mRNA transcript levels of many genes were found to decrease in hibernation or anoxia models of hypometabolism (Epperson and Martin, 2002; Storey and Storey, 2004; Rouble et al., 2014). Therefore, it is reasonable to conclude that the suppression of global protein translation rates during hypometabolism may not be a direct result of altered cellular mRNA content, but rather achieved through post-translational modifications made to translational machinery. Despite a possible global shutdown of protein translation pathways, there still remains the question of how the proteins that are essential for survival continue to be created in the hypometabolic state. There must be mechanisms at play that allow for essential mRNA to be translated, while non-essential mRNA are blocked from translation. This requirement highlights the regulatory influence of miRNA in the translational control of specific mRNA. Perhaps such regulatory mechanisms would allow gene-specific control over protein translation during periods of cell stress. Indeed, this area of research has gained considerable interest in recent years within the field of comparative biochemistry. Reversible control of protein translation by microRNA MiRNA are short, non-coding RNA capable of rapidly and reversibly regulating the expression of targeted proteins within a cell and have been found in plants, animals, bacteria, as well as some viruses. It is well documented that these 18–24 nt transcripts are able to bind, with full or partial complementarity, to specific mRNA targets, resulting in either the inhibition of translation or degradation of the target mRNA (Bartel, 2004). As a reflection of their regulatory potential, miRNA have been predicted to be involved in all biological process in some aspect throughout the animal kingdom (Friedman et al., 2009). The biogenesis of a mature miRNA begins with the initial transcription of a primary miRNA (pri-miRNA). The pri-miRNA transcript is much larger than the final mature miRNA product, ranging from hundreds to thousands of nucleotides in length (Krol et al., 2010). Following transcription, the pri-miRNA is then cleaved to form a hairpin RNA structure called a precursor miRNA (pre-miRNA) and is exported from the nucleus (Yi et al., 2003; Zeng et al., 2005; Krol et al., 2010). Once in the cytoplasm, the pre-miRNA is further processed into a mature miRNA duplex by Dicer, an riboendonuclease protein. From the mature miRNA duplex, it is suggested that only one strand will function as a mature miRNA, leaving the passenger strand to be degraded (Krol et al., 2010). Although this is generally thought to be the case, the idea of passenger miRNA degradation has been challenged in light of recent research suggesting that the abundance, possible function, and physiological relevance of passenger miRNA has been underestimated (Cipolla, 2014). Following mature miRNA processing, the mature miRNA is then loaded into a protein complex referred to as the RNA-induced silencing complex (RISC). Interestingly, the miRNA–RISC and bound mRNA complex have also been found to aggregate within stress granules and p-bodies in a stress-responsive manner, such as in response to DNA damage or hypoxia stresses, and are eventually either degraded or directed into translation once the stress is lifted (Pothof et al., 2009; Wu et al., 2011). Indeed, the stress-responsive formation of reversible stress granules have been shown to occur in response to mammalian hibernation (Tessier et al., 2014). Take together, this reversible mRNA storage system represents a promising mechanism for reversible translational arrest in models of hypometabolism. MicroRNA discovery in non-model organisms In general, miRNA and their target recognition sites have been subject to exquisite conservation across phylogeny. The conservation of miRNA by >90% among vertebrates is reflective of the important regulatory function they impose in normal cell biology. This high level of sequence homology between species has made it possible to study miRNA expression patterns in many different species with relative ease. Indeed, multiple studies have been able to explore the dynamic expression of miRNA in response to a variety of environmental stresses in a diverse array of animals for which little or no genomic sequence information is available. To date, stress-responsive miRNA expression has been studied in models of hypometabolism that include ground squirrels, bats, frogs, turtles, and many invertebrate species (Table 1 with references). Indeed the dynamic change in stress-responsive miRNA expression may be the result of multiple regulatory steps, including transcriptional modulation, microprocessor or miRNA biogenesis co-factor regulation, changes in RNA/protein complex localization, and modifications of miRNA ends. Although the exact mechanisms controlling stress-induced miRNA expression are not yet known, some of these steps may play pivotal roles in cellular homeostasis in the context of the stress response. Table 1 Studies exploring the role of stress-responsive miRNA regulation in animal models of hypometabolism. Stress  Species  References  Hibernation  Ictidomys tridecemlineatus  Morin et al. (2007), Maistrovski et al. (2012), Lang-Ouellette and Morin (2014), Wu et al. (2014a), Luu et al. (2016), Wu et al. (2016), Frigault et al. (2016)    Spermophilus parryii  Liu et al. (2010)    Myotis lucifigus  Biggar and Storey (2014a, b), Kornfeld et al. (2012), Maistrovski et al. (2012)    Dromiciops gliroides  Hadj-Moussa et al. (2016)  Freezing  Rana sylvatica  Biggar et al. (2009), Bansal et al. (2016)    Littorina littorea  Biggar et al. (2012)    Eurosta solidaginis  Courteau et al. (2012), Lyons et al. (2013), Lyons et al. (2015a, b), Lyons et al. (2016)    Chrysemys picta  Shaffer et al. (2013), Biggar and Storey (2015a, b)  Freeze avoidance  Epiblema scudderiana  Lyons et al. (2015a, b)  Oxygen restriction  Littorina littorea  Biggar et al. (2012)    Trachemys scripta elegans  Biggar and Storey (2011), Zhang et al. (2013)    Ictidomys tridecemlineatus  Lee et al. (2012)  Aestivation  Xenopus laevis  Wu et al. (2013), Luu and Storey (2015)    Apostichopus japonicus  Chen et al. (2013), Chen and Storey (2014)  Stress  Species  References  Hibernation  Ictidomys tridecemlineatus  Morin et al. (2007), Maistrovski et al. (2012), Lang-Ouellette and Morin (2014), Wu et al. (2014a), Luu et al. (2016), Wu et al. (2016), Frigault et al. (2016)    Spermophilus parryii  Liu et al. (2010)    Myotis lucifigus  Biggar and Storey (2014a, b), Kornfeld et al. (2012), Maistrovski et al. (2012)    Dromiciops gliroides  Hadj-Moussa et al. (2016)  Freezing  Rana sylvatica  Biggar et al. (2009), Bansal et al. (2016)    Littorina littorea  Biggar et al. (2012)    Eurosta solidaginis  Courteau et al. (2012), Lyons et al. (2013), Lyons et al. (2015a, b), Lyons et al. (2016)    Chrysemys picta  Shaffer et al. (2013), Biggar and Storey (2015a, b)  Freeze avoidance  Epiblema scudderiana  Lyons et al. (2015a, b)  Oxygen restriction  Littorina littorea  Biggar et al. (2012)    Trachemys scripta elegans  Biggar and Storey (2011), Zhang et al. (2013)    Ictidomys tridecemlineatus  Lee et al. (2012)  Aestivation  Xenopus laevis  Wu et al. (2013), Luu and Storey (2015)    Apostichopus japonicus  Chen et al. (2013), Chen and Storey (2014)  Table 1 Studies exploring the role of stress-responsive miRNA regulation in animal models of hypometabolism. Stress  Species  References  Hibernation  Ictidomys tridecemlineatus  Morin et al. (2007), Maistrovski et al. (2012), Lang-Ouellette and Morin (2014), Wu et al. (2014a), Luu et al. (2016), Wu et al. (2016), Frigault et al. (2016)    Spermophilus parryii  Liu et al. (2010)    Myotis lucifigus  Biggar and Storey (2014a, b), Kornfeld et al. (2012), Maistrovski et al. (2012)    Dromiciops gliroides  Hadj-Moussa et al. (2016)  Freezing  Rana sylvatica  Biggar et al. (2009), Bansal et al. (2016)    Littorina littorea  Biggar et al. (2012)    Eurosta solidaginis  Courteau et al. (2012), Lyons et al. (2013), Lyons et al. (2015a, b), Lyons et al. (2016)    Chrysemys picta  Shaffer et al. (2013), Biggar and Storey (2015a, b)  Freeze avoidance  Epiblema scudderiana  Lyons et al. (2015a, b)  Oxygen restriction  Littorina littorea  Biggar et al. (2012)    Trachemys scripta elegans  Biggar and Storey (2011), Zhang et al. (2013)    Ictidomys tridecemlineatus  Lee et al. (2012)  Aestivation  Xenopus laevis  Wu et al. (2013), Luu and Storey (2015)    Apostichopus japonicus  Chen et al. (2013), Chen and Storey (2014)  Stress  Species  References  Hibernation  Ictidomys tridecemlineatus  Morin et al. (2007), Maistrovski et al. (2012), Lang-Ouellette and Morin (2014), Wu et al. (2014a), Luu et al. (2016), Wu et al. (2016), Frigault et al. (2016)    Spermophilus parryii  Liu et al. (2010)    Myotis lucifigus  Biggar and Storey (2014a, b), Kornfeld et al. (2012), Maistrovski et al. (2012)    Dromiciops gliroides  Hadj-Moussa et al. (2016)  Freezing  Rana sylvatica  Biggar et al. (2009), Bansal et al. (2016)    Littorina littorea  Biggar et al. (2012)    Eurosta solidaginis  Courteau et al. (2012), Lyons et al. (2013), Lyons et al. (2015a, b), Lyons et al. (2016)    Chrysemys picta  Shaffer et al. (2013), Biggar and Storey (2015a, b)  Freeze avoidance  Epiblema scudderiana  Lyons et al. (2015a, b)  Oxygen restriction  Littorina littorea  Biggar et al. (2012)    Trachemys scripta elegans  Biggar and Storey (2011), Zhang et al. (2013)    Ictidomys tridecemlineatus  Lee et al. (2012)  Aestivation  Xenopus laevis  Wu et al. (2013), Luu and Storey (2015)    Apostichopus japonicus  Chen et al. (2013), Chen and Storey (2014)  Much of the initial hypometabolism-focused miRNA research was carried out on vertebrate and invertebrate species with limited genomic sequence information available (Morin et al., 2007; Biggar et al., 2009,  2012; Courteau et al., 2012; Lyons et al., 2013). Given this restriction, these studies were limited to the analysis of highly conserved miRNA using methods that had been developed to aid in the amplification, sequencing, and validation of these conserved miRNA (Biggar et al., 2011, 2014). However, while vertebrate miRNA are highly conserved, low conservation between vertebrate and invertebrate miRNA slowed the initial progress of miRNA research in invertebrate species. Despite this challenge, several studies have explored the regulation of various relatively conserved invertebrate miRNA, particularly as part of winter survival by freeze-tolerant (Littorina littorea, Eurosta solidaginis) and freeze-avoidant (Epiblema scudderiana) species (Biggar et al., 2012; Courteau et al., 2012; Lyons et al., 2013, 2015a, b, 2016). Given the inherent difficulty of studying poorly conserved miRNA and an interest in discovering possible specifies-specific miRNA, computation-based methods have recently been developed specifically to identify (i) highly conserved, (ii) poorly conserved, and (iii) species-specific miRNA from organisms with newly sequenced genomes. The widely used miRDeep2 platform accomplishes this by using both an available genome sequence and small RNA sequencing information to map mature miRNA sequences back to a reference genomic location and to then evaluate the associated pre-miRNA structure and identify high-confidence miRNA (Friedlander et al., 2012). Given the importance miRNA sequence on its function, it is essential to validate all computationally predicted candidate miRNAs experimentally, including PCR-based methods, such that their expression and sequence of their mature forms can be verified. Recently, small RNA sequencing and miRDeep2 have been used to identify novel miRNA regulating gene expression during hibernation in thirteen-lined ground squirrels, Ictidomys tridecemlineatus (Luu et al., 2016). The study identified and experimentally validated 17 novel miRNA and characterized their relative expression in liver, skeletal muscle, and heart tissues over the torpor–arousal cycle. Interestingly, these squirrel-specific miRNA were predicted to target mRNA enriched in biological processes that are known to be dynamically regulated during hibernation, including lipid metabolism, ion-transport ATPases, and various cellular signaling cascades (Luu et al., 2016). This study provides an example of how computational identification of new miRNA and analysis of their cellular function is of growing interest in comparative research on environmental stress adaptation. Indeed, such knowledge may be crucial to developing a deeper understanding of the roles that miRNA may play in the hypometabolic stress response. The major restriction of this particular platform is the requirement for small RNA sequencing information. In the absence of this information, another platform, called SMIRP, has been developed recently to identify species-specific miRNA de novo directly from an available genome (Peace et al., 2015). The SMIRP platform identifies both known and novel species-specific miRNA by scanning the genome for pre-miRNA-like hairpins and evaluating select features of these hairpins (i.e. % nucleotide content, RNA folding measures, and other topological descriptors) against miRNA that have been previously identified from related species (Peace et al., 2015; Schaap et al., 2016; Adema et al., 2017). Recently, the SMIRP platform was used to identify species-specific miRNA from the schistosomiasis-transmitting freshwater Ramshorn snail (Biomphalara glabrata) (Adema et al., 2017). Identification of these miRNA provided information on miRNA evolution, conservation, and suggested influence on translational regulation that may lead to possible mechanisms for population control in this snail species. Researchers from this study performed an in silico prediction of miRNA using SMIRP within the B. glabrata genome scaffolds. Uniquely, SMIRP leveraged a novel approach to miRNA classifier construction in which models are built dynamically and are targeted toward B. glabrata. This differs from other methods (e.g. precursor miRNA prediction methods) that attempt to produce generalized models that are applicable to a large number of species. From this approach, 202 pre-miRNA (95 known and 107 novels) and associated mature miRNA from B. glabrata were identified (Adema et al., 2017). No homologous sequences to the identified novel B. glabrata precursor miRNA were found in either the sea slug (Aplysia californica) or the sea snail (Lottia gigantea) annotated miRNA, or present within their available genomes. Interestingly, this study also identified the possible biological context of novel B. glabrata miRNA, predicting mRNA targets from the 3′UTR of available B. glabrata transcripts (John et al., 2005). A significant proportion of the identified target genes of these novel miRNA included multi-miRNA gene regulation of proteins involved in cellular processes such as secretory mucal proteins (mucin-21-like) (Gabrial et al., 2011), matricellular proteins (thrombospondin-3b-like) (Marxen and Becker, 2011) and shell formation proteins (dentin sialophosphoprotein-like) (Volk et al., 2014) (Figure 1). These newly identified miRNA greatly enrich the repertoire of known mollusk miRNA, providing insights into mollusk miRNA function, as well as their evolution and biogenesis. Such species-specific miRNA can also provide possible biocontrol targets for B. glabrata population control, or may even play a role in the control of aestivation in this species (Britton et al., 2014). Figure 1 View largeDownload slide Prediction of species-specific miRNA function from the freshwater Ramshorn snail, Biomphalaria glabrata. Novel miRNA were identified from the B. glabrata genome (BgalB1; vectorbase.org) using SMIRP. Targets of novel snail miRNA were then identified using miRDeep2 from the available transcript assembly (BgalB1.5; vectorbase.org). Target genes of novel miRNA included multi-miRNA gene regulation of proteins involved in cellular processes such as secretory mucal proteins (mucin-21-like), matricellular proteins (thrombospondin-3b-like), and shell formation (dentin sialophosphoprotein-like). Figure 1 View largeDownload slide Prediction of species-specific miRNA function from the freshwater Ramshorn snail, Biomphalaria glabrata. Novel miRNA were identified from the B. glabrata genome (BgalB1; vectorbase.org) using SMIRP. Targets of novel snail miRNA were then identified using miRDeep2 from the available transcript assembly (BgalB1.5; vectorbase.org). Target genes of novel miRNA included multi-miRNA gene regulation of proteins involved in cellular processes such as secretory mucal proteins (mucin-21-like), matricellular proteins (thrombospondin-3b-like), and shell formation (dentin sialophosphoprotein-like). MicroRNA and adaptation to extreme environments A growing number of studies have begun to highlight the conserved response of miRNA in the regulation of the hypometabolic state. Beginning in 2007, the first study of this type explored the expression of several well-conserved miRNA in response to seasonal hibernation in the thirteen-lined ground squirrel (Morin et al., 2007). Although only exploring the expression of a handful of miRNA, this was a keystone paper in introducing the possible influence of miRNA as a mechanism to reversibly control mRNA translation in models of hypometabolism. More recently, several other studies have explored miRNA expression in hibernation of ground squirrels, documenting the torpor-responsive expression of 117 miRNA across four stages of the torpor–arousal cycle (Wu et al., 2016). A follow-up study identified 17 additional novel torpor-responsive miRNA, currently thought to be unique to the thirteen-lined ground squirrel (Luu et al., 2016). Another study utilized advances in parallel RNA sequencing to study the expression of >200 ground squirrel miRNA, including 18 novel miRNA, in the liver of the hibernating Arctic ground squirrel, Spermophilus parryii (Liu et al., 2010). To date, the regulatory influence of miRNA expression has been most extensively explored in the context of mammalian hibernation (Table 1). These studies, and those of a variety of other animals, have demonstrated the importance of studying these small regulatory molecules as a conserved dynamic response to environmental stress and adaptation across phylogeny. Given the growing implication of miRNA in the regulation of environmental stress tolerance, studies have now begun to dig deeper into miRNA function, asking specific research questions, such as what miRNA are stress responsive? what mRNA are stress-responsive miRNA targeting? what are the global functional implications of miRNA? MicroRNA-influenced control over cellular pathways and processes It is well established that a single miRNA sequence can exert regulatory effects on numerous different targets. Similarly, a single mRNA is likely the regulatory target of multiple miRNA, with different miRNA binding at varied locations within the 3′UTR. This complex regulatory system creates a model of enormous regulatory potential that cannot be ignored when studying miRNA function. However, given the limited genomic resources for many experimental models of hypometabolism, initial explorations into miRNA regulatory potential have been appropriately limited in scope. These studies primarily characterized miRNA and target expression patterns in a single miRNA, single mRNA target format (Morin et al., 2007; Biggar et al, 2009, 2012). One good example from early miRNA research on models of hypometabolism is the stress-responsive characterization of miR-21. This miRNA has been studied in multiple animals in the context of regulating anti-apoptotic genes in response to environmental stress (Morin et al., 2007; Biggar et al., 2009; Biggar and Storey, 2011, 2012; Wu et al., 2014b). Following the development of bioinformatics methods that allowed for the widespread study of multiple miRNA from a single set of RNA samples (Biggar et al., 2014), recent studies began to move away from single miRNA:target-based candidate characterization. The scope of current studies is growing larger as we begin to learn more about miRNA target selection and begin to apply new computational-based methods to explore the regulatory impact of a greater set of stress-responsive miRNA on the complete system of cellular processes (Luu et al., 2016; Wu et al., 2016). In 2016, an expansive study looking at the torpor-responsive expression of 117 conserved miRNA in hibernating thirteen-lined ground squirrels over four stages of the torpor–arousal cycle (euthermia, early torpor, late torpor, and interbout arousal) (Wu et al., 2016). Moving away from candidate miRNA expression analysis, this study found significant differential expression of a number of miRNA in both a tissue and torpor stage-specific manner, clearly demonstrating that miRNA likely play an active role in mammalian hibernation and dynamic metabolic regulation. Although miRNA expression profiles were largely tissue-specific for the three organs studied (liver, heart, skeletal muscle), gene ontology (GO) annotation analysis revealed that the putative targets of the upregulated miRNA were commonly enriched in cellular processes involved in the suppression of pro-growth. For example, in liver tissue, upregulated miRNA targeted genes enriched in cellular processes such as growth factor receptor signaling pathways, regulation of nuclear division, and glycolysis during the early torpor stage (Wu et al., 2016). To further expand upon this analysis, we worked to elucidate the organization of the miRNA-targeted cellular system using the targets of the significantly upregulated miRNA from the ET stage of liver tissue. By mapping the collective targets of torpor-responsive miRNA with their known protein interactions, we were able to obtain an intuitive representation of miRNA-regulated processes within a functional network. To accomplish this, we used the spatial analysis of functional enrichment (SAFE) tool within the Cytoscape software (v3.4.0) (Figure 2). For each miRNA target within the network, SAFE defined the local neighborhood of targets by identifying those that look to be clustered within the network. GO analysis was then carried to determine enriched miRNA-regulated cellular processes (Baryshnikova, 2016). Similar to the original study, we identified an enrichment in targeting cellular signaling pathways, including growth factor signaling and carboxylic acid catabolism. We also identified a significant enrichment in the regulation of lipid biosynthesis, hormone response signaling, and RNA processing (Figure 2). Lipid metabolism is known to be under tight regulatory control during hibernation as triglycerides are the primary energy source for the hibernating mammal and are known to influence the length of torpor bouts and metabolic rate (Florant, 1998; Dark, 2005; Wu et al., 2013). Since the entry into and arousal from torpor is a short process that requires rapid changes in many metabolic processes, this particular result is not surprising. Furthermore, with regards to the predicted enrichment of RNA processing in torpid thirteen-lined ground squirrels, a previous study has characterized the roles of three RNA binding proteins: T-cell intracellular antigen 1 (TIA-1), TIA-1 related (TIAR), and poly(A)-binding protein (PABP-1) (Tessier et al., 2014). TIA-1 was identified as a major component of sub-nuclear structures with up to a 7-fold increase in relative protein levels found in the nucleus during hibernation. Additionally, analysis of the formation of reversible aggregates that are associated with TIA-1 and TIAR function during stress suggested that enhanced protein aggregation was not present during torpor. Our identification of miRNA-targeted regulation of RNA processing agrees with this study as it identifies posttranscriptional regulatory mechanisms at play in reducing translational rates and/or mRNA processing. As demonstrated by our deeper network-based functional analysis into torpor-responsive miRNA function (Figure 2), a global analysis of miRNA targets can help to provide deeper, meaningful, insight into the role of miRNA in helping to coordinate the torpor–arousal cycle. Figure 2 View largeDownload slide Predicted functional interaction map of stress-responsive miRNA targets from the hibernating thirteen-lined ground squirrel. A protein interaction map of all predicted targets of miRNA found to be significantly overexpressed during the early torpor (ET) stage of ground squirrel hibernation. Network was constructed using known human protein interactions from the STRING database (https://string-db.org/), contained 532 miRNA targets (i.e. nodes) and 1433 known interactions (i.e. edges), and was originally constructed using Cytoscape (v3.4.0). Spatial analysis of functional enrichment (SAFE)-based construction of a functional map of the network by combining all region-specific human GO terms into 12 functional domains based on the similarity of their enrichment landscapes (P values < 2 × 10−4, Fisher’s exact test). Different colors represent different functional domains. Each domain is labeled with a tag list, composed of the five words that occur most frequently within the names of the associated GO terms. Figure 2 View largeDownload slide Predicted functional interaction map of stress-responsive miRNA targets from the hibernating thirteen-lined ground squirrel. A protein interaction map of all predicted targets of miRNA found to be significantly overexpressed during the early torpor (ET) stage of ground squirrel hibernation. Network was constructed using known human protein interactions from the STRING database (https://string-db.org/), contained 532 miRNA targets (i.e. nodes) and 1433 known interactions (i.e. edges), and was originally constructed using Cytoscape (v3.4.0). Spatial analysis of functional enrichment (SAFE)-based construction of a functional map of the network by combining all region-specific human GO terms into 12 functional domains based on the similarity of their enrichment landscapes (P values < 2 × 10−4, Fisher’s exact test). Different colors represent different functional domains. Each domain is labeled with a tag list, composed of the five words that occur most frequently within the names of the associated GO terms. Temperature influence over microRNA–target interaction Given the state of miRNA research within the field of comparative biochemistry and the functional insight that it has provided to date, it is becoming increasingly clear that these small regulatory RNA are an important component of environmental stress survival and the hypometabolic response. Given this interest, there has also been an increasing attempt to explore the targets that these stress-responsive miRNA regulate (Figure 3). In recent years, there has also been a growing interest in the possibility of temperature influencing the regulatory function of miRNA (Figure 3). This has been previously discussed both in terms of miRNA base-content and its relationship to Tb (Figure 3A and B), as well as in the context of low-temperature influence over miRNA:mRNA binding thermodynamics (Figure 3C) (Carmel et al., 2012; Biggar and Storey, 2014a, b, 2015a, b, 2017). Figure 3 View largeDownload slide Temperature-associated regulation of miRNA function. (A) The average G/C content of complete miRNA sequences as well as their seed regions plotted against the physiological temperature of each organism. (B) For each of the organisms, miRNAs were divided into two subsets that contain (i) miRNAs that are specific to a taxonomic group and (ii) the rest miRNAs that are shared by other taxonomic groups. Each pair of bars shows the difference in G/C content between these two subsets for miRNAs (dark gray) and for seeds (light gray). Error bars show standard error of the difference between two means; (*) P < 0.05 and (**) P < 0.0005. (C) Depiction of proposed temperature influence in RNA binding thermodynamics. It is hypothesized that low temperature could act to stabilize miRNA:target interactions that were once unfavorable or unstable at higher temperatures. Data for A and B were derived from Carmel et al. (2012). Figure 3 View largeDownload slide Temperature-associated regulation of miRNA function. (A) The average G/C content of complete miRNA sequences as well as their seed regions plotted against the physiological temperature of each organism. (B) For each of the organisms, miRNAs were divided into two subsets that contain (i) miRNAs that are specific to a taxonomic group and (ii) the rest miRNAs that are shared by other taxonomic groups. Each pair of bars shows the difference in G/C content between these two subsets for miRNAs (dark gray) and for seeds (light gray). Error bars show standard error of the difference between two means; (*) P < 0.05 and (**) P < 0.0005. (C) Depiction of proposed temperature influence in RNA binding thermodynamics. It is hypothesized that low temperature could act to stabilize miRNA:target interactions that were once unfavorable or unstable at higher temperatures. Data for A and B were derived from Carmel et al. (2012). Given the relationship between miRNA seed binding potential (i.e. G/C content) and temperature, the question as to whether there are other mechanisms by which temperature can influence miRNA function should be asked. The targeting of miRNA to select mRNA sites relies primarily on seed region complementarity, with further binding from the 3′end of the miRNA only acting to stabilize and supplement the interaction. Critically, it has been previously reported that the thermodynamic threshold (mean free energy; mfe) used to predict whether a miRNA:mRNA target will occur is ~18 kcal/mol. This threshold, among other structural requirements of the miRNA:mRNA interaction, has been used in almost all target prediction programs that have been developed for the identification of human miRNA targets in mind (including miRanda, TargetScan, and Diana microT). These miRNA target identification programs typically overlook the possibility of non-human species existing at Tb values greater or lower than 37°C. Indeed, given the strong thermodynamic requirement for a successful miRNA:mRNA interaction, it is likely that a significant change in Tb (such as experienced by frozen frogs and turtles, hibernating mammals, and many other overwintering animals) will have a strong influence on the ability of miRNA target selection. In this way, a decrease in Tb would likely favorably stabilize miRNA–target interactions that were once unfavorable, allowing these interactions to become biologically relevant in a temperature-dependent manner. Indeed, one study using the FindTar3 miRNA target prediction software, which allows user control over temperature, showed a 16-fold increase in the number of potential mRNA targets when comparing those predicted at freezing temperatures (3°C) and those identified at 37°C (Biggar and Storey, 2015a, b). For example, in response to freezing (3°C) in the hatchling painted turtle (Chrysemys picta marginata), miR-21 expression levels were shown to increase by over 2-fold in heart tissue (Biggar and Storey, 2015a, b). When predicting the mRNA targets of miR-21, this study compared the targets predicted at both 37°C and 3°C, finding an increase from 47 to 756 targets at the lower temperature. Interestingly, these low-temperature targets were enriched in metabolic processes (Biggar and Storey, 2015a, b, 2017; Luu et al., 2016). Within the context of a global decrease in gene expression, it is likely that such an increase of miRNA targets would dramatically alter the miRNA targeting program. Overall, the increasing realization that Tb will likely have a dramatic impact on miRNA function, presents the possibility that distinct temperature-induced miRNA targeting programs may be at play and help to facilitate cellular function at various temperatures. In this way, there may exist distinct cold-influenced miRNA targeting programs that facilitate unique hypometabolic survival of extreme stresses. Given that the strict seed pairing requirement of miRNA would still be required, it is possible that the relatively lower G/C content within the seed region of organisms living at lower temperatures may also have a role in aiding this temperature-sensitive process (Figure 3). Thus, it is interesting to propose that miRNA could play a specific temperature-sensitive role in helping various species to cope with temperature-related stress. Conclusion Within the past decade, many studies have explored the role of miRNA as a mechanism to reversibly and rapidly regulate the cellular landscape and enable survival during periods of extreme stress. The overall impact of these studies has resulted in a common theme of stress-responsive miRNA expression across phylogeny. Building upon this initial research, and with the growing availability of genomic information and bioinformatic power, researchers have now begun to predict the overall cellular impact of stress-responsive miRNA expression in the context of global target regulation. As a result, it is clear that miRNA may be involved in the regulation of many cellular processes that enable efficient utilization of ATP turnover. As we learn more about miRNA function in different organisms and in response to various environmental conditions, the possibility of temperature having a role to play in influencing target selection may also open comparative miRNA research to an immense number of regulatory possibilities that will need to be explored. Acknowledgements Thanks go to J. Storey (Institute of Biochemistry, Carleton University, Canada) for editorial review of the manuscript. 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Published: Jan 9, 2018

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