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20 Years of Secretagogin: Exocytosis and Beyond

Calcium is one of the most important signaling factors in mammalian cells. Specific temporal and spatial calcium signals underlie fundamental processes such as cell growth, development, circadian rhythms, neurotransmission, hormonal actions and apoptosis. In order to translate calcium signals into cellular processes a vast number of proteins bind this ion with affinities from the nanomolar to millimolar range. Using classical biochemical methods an impressing number of calcium binding proteins (CBPs) have been discovered since the late 1960s, some of which are expressed ubiquitously, others are more restricted to specific cell types. In the nervous system expression patterns of different CBPs have been used to discern different neuronal cell populations, especially before advanced methods like single-cell transcriptomics and activity recording were available to define neuronal identity. However, understanding CBPs and their interacting proteins is still of central interest. The post-genomic era has coined the term “calciomics,” to describe a whole new research field, that engages in the identification and characterization of CBPs and their interactome. Secretagogin is a CBP, that was discovered 20 years ago in the pancreas. Consecutively it was found also in other organs including the nervous system, with characteristic expression patterns mostly forming cell clusters. Its regional expression and subcellular location together with the identification of protein interaction partners implicated, that secretagogin has a central role in hormone secretion. Meanwhile, with the help of modern proteomics a large number of actual and putative interacting proteins has been identified, that allow to anticipate a much more complex role of secretagogin in developing and adult neuronal cells. Here, we review recent findings that appear like puzzle stones of a greater picture.

INTRODUCTION
Calcium ions (Ca 2+ ) are one of the major messengers used by cells to regulate their metabolism, drive gene expression and activate specific cellular functions like exo-and endocytosis or contraction. In order to fulfill these tasks resting cells actively expel calcium ions from the cytosol with the help of Ca 2+ transport ATPase (PMCA) and Na + /Ca 2+ exchanger (NCX) that are localized in the plasma membrane and transport Ca 2+ against a massive concentration gradient into the extracellular space (Rosado et al., 2006). Calcium is also stored intracellularly in the endoplasmatic reticulum (ER) or, as in specialized muscle cells, in the sarcoplasmic reticulum (SR) as well as in the mitochondria and is taken up into these compartments by the sarcoendoplasmic reticulum Ca 2+ -ATPase (SERCA) or the mitochondrial Ca 2+ uniporter. Activation of Ca 2+ -dependent signaling pathways can be initiated by membrane depolarization or extracellular signaling molecules that activate voltage-or ligand-activated calcium channels in the plasma membrane or via intracellular messengers that cause the release of Ca 2+ from intracellular stores, mainly via the 1, 4, 5-triphosphate receptor (IP3R) or the ryanodine receptor (RyR) from the ER or SR (Supnet and Bezprozvanny, 2010). Beside these transporters and channels, cells can draw from a huge number of calcium-dependent interactions and signaling components, that Berridge et al. (2000) call the ''Ca 2+ signaling toolkit.'' Parts of this universal toolkit are combined by individual cells to fulfill their special tasks. As Ca 2+ flows into the cells due to so-called ''ON mechanisms'' it switches on multiple signaling cascades. The amplitude and duration of the signal is shaped by cytosolic Ca 2+ buffers like parvalbumin, calbindin-D 28K and calretinin that transiently take up Ca 2+ and contribute to the spatial restriction of the signal. This is especially important in neurons where compartmentalization of signaling is mandatory for neuronal transmission (Sudhof, 2004). In contrast to high-affinity Ca 2+ buffers, lower affinity Ca 2+ sensors respond to significant elevations of intracellular Ca 2+ concentrations with conformational changes thereby facilitating interaction with downstream targets and initiating major cellular responses (Amici et al., 2009). Depending on the Ca 2+ binding motifs there are three classes of Ca 2+ binding proteins: the EF-hand proteins, the annexins and the C2 domain proteins (Bagur and Hajnoczky, 2017). The EF-hand is frequently found in buffers and sensors alike (structural vs. regulatory EF-hand) and contains Ca 2+ in a complex with seven ligands in a loop flanked by two perpendicular α-helices (da Silva et al., 1995). Prominent members of this class are calmodulin, parvalbumin, troponin C, calretinin, calcineurin and secretagogin.
While the other representatives have been known for a long time and their function is well characterized, secretagogin's precise role in specialized cell types has been beginning to emerge only recently. Here, we will review recent findings with regard to secretagogin's cellular localization, interactions and functions as a novel Ca 2+ sensor after a brief glance at the beginnings.

DISCOVERY OF SECRETAGOGIN
Secretagogin (SCGN) was discovered in an ''expression dictates function'' manner. Wagner et al. (1998) generated the murine monoclonal antibody D24 (mAb D24) against an unknown human insulinoma-specific antigen, that binds to all cells present in the pancreatic islets of Langerhans and to all tested insulinomas but does not stain the exocrine pancreatic tissue. Screening of a human pancreatic beta cell cDNA library with D24 mAb identified a unique mRNA sequence, which encoded a novel Ca 2+ -binding protein related to human and murine calbindin and calretinin. A bioinformatic sequence analysis suggested that this novel protein has six tandem repeats of an EF-hand and a molecular weight of 32kDa. The newly discovered protein was named ''Secretagogin'' likely because it was shown to facilitate insulin release. Initial studies in Rin-5F rat insulinoma cells identified secretagogin mainly in the cytosolic fraction with some scant presence in the nuclei. Overexpression of secretagogin resulted in a greater calcium flux in response to KCl along with increased insulin secretion rates compared to vector controls (Wagner et al., 2000). Interestingly, these SCGN overexpressing Rin-5F clones had significantly lower cell growth rates. This fact correlated with down-regulation of substance-P (a putative β-cell trophic factor), known to be largely restricted to proliferating fetal and neonatal islet cells and to pancreatic beta cell lines. Therefore, it is possible that SCGN-induced suppression of substance-P transcription is responsible for the observed reduction in cell growth rates (Maj, 2012).

CHARACTERISTICS OF SECRETAGOGIN AND INTERACTION NETWORK
Secretagogin is a hexa EF-hand protein that binds Ca 2+ with an overall half maximal affinity of approximately 25 µM, a relatively low affinity compared to calbindin [with a (Ca 2+ ) 0.5 of approx. 1 µM] or calretinin [with a (Ca 2+ ) 0.5 of approx. 1.5 µM; Rogstam et al., 2007]. The secretagogin gene and protein structure has been reviewed in detail previously (Alpár et al., 2012).
The tertiary structure of secretagogin changes significantly upon Ca 2+ binding, but not upon Mg 2+ binding, suggesting that SCGN belongs to the ''sensor'' family of Ca 2+ -binding proteins (Rogstam et al., 2007).
Expression of secretagogin can be influenced by insulin and glucose. In a cell culture study an insulin bolus was shown to increase secretagogin mRNA in hippocampal neurons after 24 h, while addition of glucose down-regulated SCGN mRNA levels already after 1 h . It has further been established, that SCGN mRNA expression in pancreatic islets negatively correlates with the incidence of diabetes and the levels of glycated hemoglobin in blood . As a mechanism to activate secretagogin expression Malenczyk et al. (2017) could pinpoint the family of Ca 2+ permeable TRP(V) channels, that, when activated, induce nuclear translocation of the Sp1 transcription factor to initiate secretagogin transcription. Sp1 has previously been shown to also mediate upregulation of the CBP calmodulin in response to an insulin stimulus (Solomon et al., 1997).
A Ca 2+ sensor responds to a strong Ca 2+ signal with changes in protein conformation that can only be translated in cellular processes via interaction with other proteins. Therefore identification of protein interaction partners is crucial to understand functional mechanisms. Rogstam et al. (2007) identified the first Ca 2+ dependent interaction partner of SCGN by affinity purification from mouse and cow brain and the rat insulinoma cell line RIN-5F, which was identified as 25kDa synaptosome-associated protein (SNAP-25) in LC-MS/MS. SNAP-25 is one of the components of the soluble N-ethylmaleimide-sensitive-factor attachment receptor (SNARE; soluble N-ethylmaleimide-sensitive fusion protein attachment protein receptor) complex, which is the main complex for membrane fusion events (Chen and Scheller, 2001). As SNAP-25 is a protein involved in Ca 2+ -induced exocytosis in neurons and neuroendocrine cells, it was anticipated that secretagogin might be involved in the exocytosis processes of neurotransmitters and insulin granules.
Three years later nine other proteins were identified from screening a human protein array for Ca 2+ -dependent secretagogin interactors: SNAP-23, DOC2alpha, ARFGAP2, rootletin, KIF5B, β-tubulin, DDAH-2, ATP-synthase and myeloid leukemia factor 2 (Bauer et al., 2011). All of these interaction partners are involved in secretion events and vesicle trafficking. For example, SNAP-23 is structurally and functionally similiar to SNAP-25, binds to the SNAREs syntaxin and synaptobrevin and is an important regulator of transport vesicle docking and fusion. DOC2a itself is a Ca 2+ sensor, that promotes among others the spontaneous release of neurotransmitters glutamate and GABA (Courtney et al., 2018). ARFGAP2 is involved in specific cargo sorting to the plasma membrane. Kinesin 5B in neurons has been shown to be essential as motor protein in the anterograde transport of active zone proteins (Cai et al., 2007). Rootletin functions as a linker of the centrosome, which is the central microtubule organization center. Dimethylarginine dimethylaminohydrolase (DDAH) 2 is an enzyme, that metabolizes the NO-synthesis antagonist ADMA (asymmetric dimethylarginine). ATP synthase is an enzyme, that produces the cell's main energy currency, ATP, which fires most cellular processes including intracellular transport. Tubulin as constituent of microtubuli is involved in major transport processes in the cell. Further, Ca 2+ -dependent interaction of secretagogin with the microtubule-associated protein (MAP) Tau was initially demonstrated by GST-pull downs from pancreatic cell lines and later confirmed from rat brains (Maj et al., 2010.
These SCGN interaction partners already implicate the possibility that secretagogin might link Ca 2+ signaling to exocytotic processes.
In the last 3 years the discovered secretagogin interactome has grown extensively due to huge proteomic efforts. In Supplementary Table S1, we summarize to the best of our knowledge all published confirmed and putative interactors of secretagogin. Beside the pioneering studies, there are results from large scale proteomics essentially from two major groups available (Huttlin et al., 2015(Huttlin et al., , 2017Romanov et al., 2015;Hanics et al., 2017;Malenczyk et al., 2017Malenczyk et al., , 2018. Without a special focus on secretagogin, Huttlin et al. (2015Huttlin et al. ( , 2017 found 16 putative interactions in their ''BioPlex Network,'' a high throughput combination of affinity purification with thousands of baits from a lentiviral library and mass spectrometry. Some of these hits were consistent with previously known proteins, indicating the validity of results. Malenczyk et al. (2017Malenczyk et al. ( , 2018 were specifically interested in SCGN interaction partners and used pull-down assays from pancreas and the INS-1E beta cell line combined with LC-MS/MS analysis. Hanics et al. (2017) focused on migrating SCGN + neurons in the rostral migratory stream (RMS) and used micropunches from that area in co-immunoprecipitations. Similarily, Romanov et al. (2015) identified Ca 2+ -dependent and independent interactors by co-immunoprecipitations from hypothalamus using MALDI-TOF analysis.
The resulting interactome is extensive and can be functionally grouped into proteins involved in vesicle-mediated transport and exocytosis, protein folding, ubiquitination and proteasomal degradation, cytoskeleton-related proteins and their organization, carbohydrate metabolism, lipid metabolism, mitochondrial organization and processes, gene expressionrelated processes from DNA-replication to protein translation, metabolism of nucleotides, kinases and phosphatases and some other proteins (Supplementary Table S1, Figure 1). Certainly, not all interactions have been characterized in detail yet, but in many cases it can be anticipated, that these interactions are important for the function of secretory cells.
In their study Malenczyk et al. (2018) focused on proteins, that might have protective roles in supporting protein folding, stabilization and in preventing ER stress. Proteins like ubiquitin carboxyl-terminal hydrolases USP9X and USP7, that de-ubiquitinate their substrates and thereby stabilize them, are kept in an active form by secretagogin, in order to promote β-cell proliferation. Another example for such an interaction partner is T-complex protein 1 (CCT), a chaperonin, or ADP-ribosylation factor (Arf)4, involved in vesicle trafficking and a marker of ER/Golgi stress (Reiling et al., 2013), that is inversly regulated to secretagogin levels (Malenczyk et al., 2018). The cytosolic chaperonin CCT has an important role in the biogenesis of the actin cytoskeleton (Grantham et al., 2002) and was previously found to bind to chromaffin granules possibly involved in exocytosis (Creutz et al., 1994).
In order to elucidate more of secretagogin's cellular functions, it is essential to note, that it is not only a pancreatic protein, but occurs in several other tissues, as it seems, frequently locally restricted to larger groups of cells. In this regard, secretagogin's distribution in the CNS is of major interest.

CELL SPECIFIC DISTRIBUTION OF SECRETAGOGIN IN THE NERVOUS SYSTEM
Secretagogin has been studied in various organisms as it is expressed from zebrafish to human. Initial studies only roughly identified secretagogin positive areas in various species. Beside the pancreas, it was detected in the gastrointestinal tract, thyroid, adrenal medulla, adrenal gland and brain (Wagner et al., 2000). Especially prominent is SCGN in neuroendocrine cells such as the islet of Langerhans and subpopulations of developing or adult neurons Mulder et al., 2009Mulder et al., , 2010. However, secretagogin's expression pattern is not conserved from rodents to humans and also significant differences exist between mice and rats (Garas et al., 2016;Raju et al., 2018). At the mRNA and protein level secretagogin has the highest expression in human cerebellum , while in mouse and rat it is found predominantly in the olfactory bulb (Mulder et al., 2009;Maj et al., 2012). Interestingly, SCGN is also highly expressed in the hippocampus of both, men and rodents. Immunohistochemical analysis identified secretagogin positive neurons mostly as interneurons and pyramidal cells ; SCGN is highly expressed in the molecular layer of human cerebellum, while in rat olfactory bulb it is found mainly in cell bodies of the granular layer and neurites and some cell bodies of the glomerular and external plexiform layer .
As mentioned before, it is apparent, that expression of secretagogin frequently occurs in cell clusters, shown for rat brain by Maj et al. (2012), implicating that these neurons either share common functional characteristics or their developmental origin. For example, SCGN + clusters are seen in peripheral cell layers of the olfactory bulb, lining the surface above the optic nerve, in the supraoptic nuclei on both sides of the optic chiasm, in the suprachiasmatic nuclei of the hypothalamus, in the paraventricular nucleus (PVN), in the CA1-CA3 of the hippocampus, and outer cortex around the posterolateral cortical amygdaloid area. This specific expression of SCGN anticipates an important role in the mechanisms of olfaction, vision, activity of the hypothalamus, and memory formation in hippocampus.
Early immunocytochemical localization studies revealed that in addition to cytoplasmatic staining, SCGN seemed to be accumulated on specific subcellular structures in rat neurons. Specifically, SCGN + clusters were often found in direct contact with GM130 clusters, a marker of the cis-Golgi network. Adding to the fact, ultracentrifugation experiments using sucrose density centrifugation of rat hippocampal tissue extracts revealed that SCGN immunoreactivity was detectable in the membrane-fraction as well as in the cytosolic fraction . In semiquantitative electron microscopy analyses of the PVN of the hypothalamus, gold particlecoupled secretagogin antibodies bound to the plasma membrane and to endomembranes even outnumbered cytosolic particles (Romanov et al., 2015).
More detailed studies aimed to characterize SCGN-positive cell populations with regard to co-expression of other cell markers are required. Gyengesi et al. (2013) investigated the SCGN expression pattern in neurons of the basal forebrain, the major cholinergic output of the central nervous system. SCGN is expressed in the forebrain, where Ca 2+ -dependent signaling plays an important role in neuronal plasticity and synaptic function. Disturbance of Ca 2+ buffering capacity in the basal forebrain is observed to be increased in aged, cognitively impaired rats (Murchison et al., 2009). Hence, the study focused on characterizing cholinergic corticopedal neurons in the basal forebrain that play important roles in cortical activation, sensory processing, and attention. They demonstrated that SCGN is expressed in cell bodies of the medial and lateral septum, vertical and horizontal diagonal band nuclei and in the extension of the amygdala, but it is almost absent in the ventral pallidum. SCGN is co-localized with choline acetyltransferase (ChAT) in neurons of the bed nucleus of the stria terminalis, which is considered an important regulator site of the hypothalamicpituitary-adrenal (HPA) axis, the extension of the amygdala, and the interstitial nucleus of the posterior limb of the anterior commissure. SCGN is frequently co-localized with calretinin, but not with parvalbumin or neuropeptide Y (Gyengesi et al., 2013).
A detailed characterization of SCGN-positive cells in zebrafish retina has been provided by Dudczig et al. (2017). SCGN starts to be expressed at day-3 postfertilization coinciding with the occurrence of Ca 2+ -waves initiated by retinal bipolar cells at a time, when larvae show first visual responses. SCGN stains a population of interneurons (approximately 60% GABAergic) in the inner nuclear layer with neurites extending into the inner plexiform layer. The expression of SCGN shows significant overlap with calbindin and calretinin, but not parvalbumin expression.
Secretagogin has been demonstrated to label several subtypes of cone bipolar cell in the mouse, rat, and rabbit retina (Puthussery et al., 2010). This finding was supported by a second study showing that SCGN is strongly expressed in the bipolar cell type DB1 in the macaque retina (Puthussery et al., 2011). Utilizing SCGN as a DB1 cell-specific marker allowed to investigate the cone connectivity as well as characterization of neurotransmitter receptors. This showed that the DB1 cells make synaptic contact with both L/M as well as S-cone photoreceptors and only minimal contact with rod photoreceptors (Puthussery et al., 2011).
In the human hippocampus, secretagogin occurs exclusively in CA1-CA4 and subiculum pyramidal neurons (Attems et al., 2007). Interneurons targeting striatal projection neurons were regarded as a relatively homogenous cell population due to their common expression of parvalbumin. However, co-staining for secretagogin has defined two novel subpopulations that preferentially innervate different pathways in the striatum (Garas et al., 2016). A very distinct population of SCGN-positive neurons with regard to other CBPs like calbindin or calretinin occurs in the habenula . Here, large groups of SCGN + cells are flanked by similarily large groups of neurons expressing the other CBPs.
Secretagogin has also been found in a population of nociceptive dorsal root ganglia (DRG) neurons in the dorsal horn of mouse and human that coexpress calcitonin gene-related peptide (CGRP) and in rat DRGs not CGRP. SCGN-positive neurons were further found in the mouse, rat and human dorsal horn implicating a role of these neurons in processing of sensory information including pain (Shi et al., 2012).
An interesting fact is, that secretagogin is frequently expressed in young, not yet terminally differentiated neurons, like the migrating neuroblasts in the RMS and newly born dentate granule cells, possibly stimulating the release of factors necessary for the survival of these young neurons (Mulder et al., 2009). In the RMS of rodents, a large amount of newly born neuroblasts migrate towards the olfactory bulb to mediate sensory plasticity. Within the RMS a scaffold of wired secretagogin-positive neurons release on demand matrix-metalloprotease-2 (MMP-2) in order to loosen the extracellular matrix and to facilitate migration of the neuroblasts. MMP2-release is known to be mediated by annexin V, that has been identified as secretagogin interaction partner (see Supplementary Table S1).
Based on the unique SCGN expression and localization in the nervous system, this CBP broadens the repertoire of available cell-specific marker, which can be used in anatomical and neurophysiological studies, albeit with caution, as this population still might not be homogenous.
Meanwhile several studies have put effort in elucidating the mechanisms, how secretagogin actually functions in the cell. These have investigated its role in insulin release and have identified a neuronal correlate in hormone release.

SECRETAGOGIN'S ROLE IN INSULIN SECRETION
Early work already implicated that secretagogin influences insulin release (Wagner et al., 2000), while the detailed mechanism has only started to be understood recently. Glucose-stimulated insulin secretion (GSIS) is a multifactorial process that centrally involves an intracellular rise in Ca 2+ concentration after the opening of voltage-dependent Ca 2+ channels (Komatsu et al., 2013). The redox sensitive Ca 2+ sensor secretagogin has been postulated to dimerize by forming a disulfide bridge in response to the concomitantly elevated ROS, thereby strengthening its association with the actin cytoskeleton (Yang et al., 2016;Lee et al., 2017). Secretagogin dissociates from the syntaxin1A-inhibitor tomosyn after its de-SUMOylation by the protease SENP1 and involves in insulin granule trafficking and exocytosis, via its other identified interaction partners, the t-SNAREs (Ferdaoussi et al., 2017). Secretagogin then assumingly gets involved in a complex of syntaxin and the v-SNARE VAMP2 in order to recruit insulin-laden vesicles to the plasma membrane. Finally, insulin release from the dense-core vesicles is regulated by synaptotagmin, a N-terminal transmembrane C2-domain Ca 2+ sensor and v-SNARE, that also binds syntaxin and SNAP23/25 (MacDougall et al., 2018). However, secretagogin seems to have an additional function in restructuring the cytoskeleton during insulin release: it influences F-actin dynamics, in order to facilitate vesicle transport to the periphery, as well as focal adhesion remodeling (Yang et al., 2016). Proteomic studies have identified multiple SCGN-interacting proteins, that are either actin-binding proteins or have a regulatory function towards the actin cytoskeleton (see Supplementary  Table S1).

CONTROL OF STRESS HORMONE RELEASE
A major finding of the last years has been the involvement of SCGN in the control of stress hormone release (Romanov et al., 2015). It has previously been described, that secretagogin occurs in groups of neurons in the hypothalamus, especially the PVN (Mulder et al., 2009;Maj et al., 2012). The PVN essentially contains two sorts of neurosecretory neurons: the magnocellular neurons secreting oxytocin and vasopressin in the posterior pituitary gland, and the parvocellular neurons secreting corticotropin-releasing hormone (CRH), vasopressin and thyrotropin-releasing hormone (TRH). Additionally, the PVN contains many neurons expressing neuropeptides. Calbindin-D 28k and calretinin mainly occur in magnocellular neurosecretory cells (Sánchez et al., 1992;Arai et al., 1994), while secretagogin antibodies predominately stain a subpopulation of parvocellular neurons in the dorsolateral PVN. In their study secretagogin mRNA mainly occured together with transcripts for CRH, somatostatin, tachykinin 1, amphetamin-regulated transcript, neuromedin B, neuromedin U, neuropeptide Y and neuropeptide B, but not with vasopressin, oxytocin, thyrotropin-releasing hormone (TRH), galanin, cholecystokinin, neurotensin S, calcitonin, neuromedin S, natriuretic peptide C and adenylate cyclase activating polypetide 1. Axons of the parvocellular neurosecretory neurons project to the median eminence where they release their hormones to the hypophyseal portal system. On the ultrastructural level they found, that secretagogin not only occurs in the cytoplasm and close to the ER, but also in axonal terminals, frequently associated with the plasma membrane and the outer membrane of dense-core vesicles containing CRH implicating that it might be involved in CRH release. Indeed, knock-down of secretagogin reduced CRH release from PVN neurons, similiary like insulin release from pancreatic β-cells. in vivo knock-down of secretagogin by injection of siRNA into the lateral ventricle led to hypothalamic SCGN downregulation by 20-30% concomitant with CRH retention in the soma of parvocellular neurons. These results gain even more significance, as the authors were able to prove, that secretagogin plays a significant role in the overall stress response. CRH function is to stimulate the release of adenocorticotropic hormone (ACTH) from the anterior pituitary gland and ACTH then activates the adrenal glands to release steroid hormones like cortisol, the primary stress hormone. Romanov et al. (2015) could show in an in vivo stress model, that the acute stress led to the activation of the immediate early gene c-fos mostly in secretagogin-positive neurons of the PVN and the corresponding ACTH response including corticosterone levels in the blood could be blunted by secretagogin siRNA-pretreated animals.
Authors from the same group went on to find out, that CRH neurons not only mediate HPA-axis activation, but also influence cortical excitability in a secretagogin-dependent fashion. The prefrontal cortex (PFC) coordinates vigilance, alertness and behavioral responses to acute stress facilitated by noradrenergic afferents originating in the locus coeruleus. These norepinephrinergic (NE + ) cells on one hand are directly innervated by CRH + cells (Zhang et al., 2017), but on the other hand inter-neuronal communication is bypassed by volume transmission from ependymal cells via the cerebrospinal fluid in order to extend responses over longer periods. Specifically, CRH + cells from PVN also innervate ependymal cells of the 3rd ventricle inducing ciliary neurotrophic factor (CNTF) release into the cerebrospinal fluid. In the locus coeruleus, CNTF binds to its cognate Trk receptor on NE + neurons initiating a signaling cascade of ERK1 activation (a secretagogin-interactor) and subsequent tyrosine hydroxylase (TH) phosphorylation thereby stimulating its enzymatic activity. Alpár et al. (2018) proved a central role of secretagogin in mediating TH-phosphorylation in knock-down experiments and observed a blunted behavioral stress response in secretagogin knock-out animals. A detailed mechanism of secretagogin action in this context is still missing, however the necessity of secretagogin presence for proper signal transduction in the hypothalamus-locus coeruleus-prefrontal cortex circuit of stress response is obvious.
The data presented by Romanov et al. (2015) and Alpár et al. (2018) define a critical role of secretagogin in the molecular axis of stress responsiveness. Their results let the study of Gyengesi et al. (2013) appear in a new light: the bed nucleus of the stria terminalis functions as a regulator of the HPA-axis and many of the SCGN-positive neurons identified in this area are CRH-positive. Similarily, extrahypothalamic CRH also affects neuronal firing in the lateral habenula, a brain region that negatively regulates the dopamine-dependent reward circuit and mediates emotional responses to stress (Authement et al., 2018). Secretagogin is highly expressed in this area, as described above. However further investigations into SCGN function in this area of the brain have not been performed yet.
Dysfunction of the HPA-axis is associated with pathological developments as observed in Alzheimer's disease (AD), major depression, and serious metabolic consequences including hypoglycemia.

SECRETAGOGIN IN DISORDERS AND DISEASES OF THE CNS
The crucial roles of secretagogin in cellular function as defined by today already put it into an important position in the context of pathological developments.
Clinical data have suggested links between Type II diabetes (T2D) and AD, which some researchers have proposed to call a form of ''Type III diabetes.'' Animal models have shown that T2D promotes the formation of amyloid-β plaques, tau phosphorylation and neurofibrillary lesions, however in humans this evidence is scarce or ambigous. What has been shown, is that brain insulin resistance with an impaired signaling response to insulin is also a pathomechanism in AD (Arnold et al., 2018).
It is interesting to note that secretagogin's main expression in the brain occurs in regions with the highest density of insulin receptor, like the olfactory bulb, hypothalamus, hippocampus, cortex, striatum and cerebellum. De novo insulin production in the brain has been proven, but still needs further investigation. However, insulin-either secreted from neurons or entered into the brain via the blood-brain barrier-has enormous impact on neuronal function like trafficking of neurotransmitter receptors, synaptic plasticity, synapse formation and neuronal survival. It can only be speculated that secretagogin could have a role in neuronal insulin secretion.
In addition to its role in insulin secretion it has been discussed whether secretagogin expressing neurons could be protected from pathological manifestations and cell death in diseases like AD. It has been shown, that CBPs like parvalbumin, calbindin-D 28K and calretinin (i.e., Ca 2+ buffers) protect neurons from detrimental Ca 2+ overload as it occurs for instance in ischemic conditions (Turovsky et al., 2018). Although secretagogin is considered to be mainly a Ca 2+ sensor, it also has one high-affinity Ca 2+ binding site and could also have some buffering capacity (Rogstam et al., 2007). With this regard, Khandelwal et al. (2017) have shown, that the Ca 2+ affinity of secretagogin also depends on the redox-status of the protein. Under reducing conditions the overall affinity for Ca 2+ significantly increases to the nM range.
Dysregulated Ca 2+ signaling seems also to underlie neuronal dysfunction in the development of AD (Magi et al., 2016). Key manifestations of the disease are amyloid plaques and neurofibrillary tangles from hyperphosphorylated tau protein.
Post mortem studies found that colocalization of secretagogin and hyperphosphorylated tau was negligable in brains from patients with AD of Braak stage III or higher and that the number of secretagogin-positive cells was not significantly changed in brains with different tau burden (Attems et al., 2008). These findings supported the hypothesis that secretagogin-positive neurons are possibly resistant to neurodegeneration at least in this area of the brain. However, Attems et al. (2011) in a later study investigated secretagogin expression in the brains of P301L tau transgenic mice, a mouse model for tau pathology in AD. They found significant reduction of secretagogin expression in P301L homozygous mice.
A special situation obviously exists in the olfactory system of humans and primates. Here, Attems et al. (2012) identified a population of not terminally differentiated, yet synaptically integrated secretagogin-positive neurons in the olfactory tract of humans, that are selectively affected by neurofibrillary pathology in AD and seem to be lost in advanced stages of the disease. Perturbed olfaction is an early clinical sign in AD and this might involve the loss of these special shell cells. However, secretagogin-positive cells in the periglomerular layer seemed not to be affected.
A possible beneficial role of secretagogin was postulated in multiple sclerosis therapy. Patients with multiple sclerosis are recommended to take a dietary supplementation of vitamin D. However, the exact molecular mechanism behind the beneficial role of vitamin D are not well-known. Oveland et al., 2018 have recently shown that a high dose of the hormonally active 1, 25-dihydroxyvitamin-D3 (1, 25D) promotes myelin repair in the cuprizone model for de-and remyelination. They studied the brain proteome in the cuprizone model for de-and remyelination and found that 125 proteins were differentially regulated in brain tissue from 1, 25D-treated mice during remyelination, compared to placebo. Proteins upregulated in the early remyelination phase were involved in calcium binding, e.g., calretinin, S10A5 and secretagogin. Oveland et al., 2018 concluded that vitamin D may influence remyelination by mechanisms involving an increase in CBPs including secretagogin.
Although secretagogin is not co-released with glucosestimulated insulin, it is found in plasma and concentrations are elevated in T2D patients (Hansson et al., 2018). In their in vitro studies in human β-cells they found increased secretagogin release due to ER stress inducers or due to inflammatory cytokines, implicating higher plasma levels as a biomarker for islet dysfunction.
Secretagogin has also been proposed as a biomarker for tumors and in ischemic brain insults, i.e., stroke. However, its plasma concentrations were either found lower or higher than in controls, dependent on the time of blood sampling Montaner et al., 2011).
Another study demonstrated that SCGN plasma levels were significantly lower in autistic children as compared to the healthy controls (Alhowikan et al., 2017). Children with severe and mild to moderate autism had significantly lower SCGN levels than healthy controls, however, there was no significant difference between the severity of autism and SCGN levels. In schizophrenia, decreased levels of SCGN mRNA were measured in post-mortem pituitaries, as well as in serum from a cohort of living first-onset schizophrenia patients (Krishnamurthy et al., 2013).
These studies convey an insight in the complex roles of SCGN in the neuronal network, specifically in aging, neurodegeneration and neuro-psychiatric disorders. Future studies will have to pave the way for translation of novel diagnostics and to investigate, if SCGN can serve as a biomarker especially in neuro-psychiatric onsets.

CONCLUSION AND OUTLOOK
Secretagogin's role as a Ca 2+ sensor is now well established from the available experimental data. Its location and interaction with proteins involved with vesicle trafficking and in the exocytotic machinery not only implicates involvement in the release of signaling molecules, but this function has already been proven for insulin release from pancreatic βcells and the stress response in the brain (CRH-release from parvocellular neurons of the PVN in the hypothalamus and activation of norepinephrinergic neurons in the locus coeruleus). Future research will prove, if secretagogin is engaged in the release of further hormones, or eventually even neuropeptides or other neurotransmitters. With this aim in mind, single-cell transcriptomics can be a suitable methodology for expression profiling of SCGN + cells. However secretagogin's protein interaction network, as known by today, is vast. Therefore it can be assumed, that in those selected cell types, where it is expressed, it might function as a kind of Ca 2+ -dependent switch or moderator for more cellular processes including cytoskeletal rearrangements and modulation of cellular stress. In order to delineate individual mechanisms, further functional investigations with known interactors will have to be undertaken.

AUTHOR CONTRIBUTIONS
All authors were involved in the conception of the article, MM and VT wrote the final version of the manuscript.

ACKNOWLEDGMENTS
We thank Marie-Louise Zach for professional graphical design of Figure 1.


Mesenchymal Stem Cells and Cancer: Clinical Challenges and Opportunities

Stem cell-based therapies exhibit profound therapeutic potential for treating various human diseases, including cancer. Among the cell types that can be used for this purpose, mesenchymal stem cells (MSCs) are considered as promising source of stem cells in personalized cell-based therapies. The inherent tumor-tropic property of MSCs can be used to target cancer cells. Although the impacts of MSCs on tumor progression remain elusive, they have been genetically modified or engineered as targeted anticancer agents which could inhibit tumor growth by blocking different processes of tumor. In addition, there are close interactions between MSCs and cancer stem cells (CSCs). MSCs can regulate the growth of CSCs through paracrine mechanisms. This review aims to focus on the current knowledge about MSCs-based tumor therapies, the opportunities and challenges, as well as the prospective of its further clinical implications.

Introduction
MSCs are nonhematopoietic cells that were first discovered from bone marrow and reported approximately 40 years ago by Friedenstein and his coworkers [1,2]. Studies have shown that MSCs exist in a variety of tissues. To date, MSCs have been successfully isolated from various organs including brain, liver, lung, kidney, muscle, thymus, pancreas, skin, bone marrow adipose tissue, fetal tissues, and umbilical cord [3]. Also, MSCs are known as multipotent cells which can differentiate into adipocytes, myocytes, osteocytes, and chondrocytes [4][5][6]. In 2006, the International Society for Cellular Therapy proposed three minimal criteria to define human MSCs. They must express CD105, CD90, and CD73 and lack expression of CD45, CD34, CD14 or CD11b, CD79 or CD19, and HLA-DR surface molecules. Additionally, they must adhere to plastic in culture and differentiate into osteocytes, chondrocytes, and adipocytes [7]. In addition, MSCs possess unique immunophenotypic capacity, tissue-repair capacity, and immunoregulatory capacity [8]. Therefore, owing to their relative immune evasiveness and general immune dampening activities, MSCs can be utilized in an allogenic setting and are promising seed cells for cell therapy and tissue engineering [9]. Moreover, various preclinical trials suggest that MSCs show great potential for cancer treatment, although obstacles and risks were described [10].
Studies have shown that MSCs are capable of migrating directionally to specific tissues, which is termed as homing. The tropism property of MSCs into sites of injury and tumor makes them ideal vehicles for targeted tumor therapy, 2 BioMed Research International although the exact mechanism of MSCs homing is not completely understood. Ongoing preclinical trials suggest that MSCs are suitable targets for cell therapy in a variety of cancers. However, the antitumor effects of MSCs are still controversial. In various types of cancer, some studies have shown proliferative effects, while others demonstrate inhibitory effects of MSCs on tumors [11]. For example, MSCs have tumoricidal effects on liver, lung cancer cell lines, and pancreatic tumors in vitro and in vivo [12][13][14]. In contrary, it has been shown that MSCs are capable of enhancing progression and metastasis of types of tumor, such as breast cancer and colon cancer [15][16][17][18]. In addition, MSCs may exert therapeutic function through an immune evasive mechanism, which will protect MSCs from immune detection and prolong their persistence in vivo [9]. Moreover, the survival of MSCs in the tumor and biodistribution of MSCs should take more attention when designing a trial, which may influence the results of study. For example, although human MSCs were found by staining in the tumors 1 day after IV injection in a mice model, the cells almost were cleared after 1 week [19]. However, even after 11 weeks MSCs were still observed in the tumor, although at very low numbers [19]. In an in vivo study of colon cancer, exogenous MSCs were still able to regulate immune response of the tumor microenvironment even 1 year after the last MSCs injection [20]. In this review, we summarize recent advances of MSCs in the treatment of cancer and insights into potential strategies for cancer therapy.  39], while other studies suggest that MSCs affect the pathways that can suppress both proliferation and apoptosis [13,40].

MSCs and Cancer
Researches have demonstrated that MSCs would be recruited into tumor sites, promoting tumor growth, and angiogenesis through differentiating into cancer-associated myofibroblasts and secretion of proangiogenic cytokines (e.g., interleukin (IL)-6, vascular endothelial growth factor (VEGF), and transforming growth factor-(TGF-) [21][22][23]. In the meanwhile, the recruited MSCs also enhanced tumor metastasis via increasing lysyl oxidase [24]. Another tumorpromoting effect of MSCs is attributed to their protection role for breast cancer cells from immune clearance through modulating regulatory T cells and inhibiting natural killer (NK) cells and cytotoxic T lymphocyte (CTL) functions [25]. Furthermore, MSCs have been found to form a cancer stem cell niche in which tumor cells can preserve the potential to proliferate and sustain the malignant process [41]. Also, increasing evidences suggest that MSCs promote tumor angiogenesis through their potential to differentiate into pericytes or endothelial-like cells as well as by their secretion of trophic factors and cytokines, proangiogenic factors, growth factors, and plasminogen activator [42,43]. Thus, MSCs promote tumor growth and metastasis through stimulation of angiogenesis, cancer stem cell niche maintenance, and immune protection. Moreover, it has also been showed that MSCs can affect tumor development and progression through miRNAs. In a xenograft tumor model, researchers demonstrated that human umbilical cord MSCs (hUCMSCs) powerfully promote the growth of lung adenocarcinoma (LUAD) cancer cells by transferring miR-410. The findings suggest that modification of hUCMSC-derived extracellular vesicles (hUCMSC-EVs) may be a promising therapeutic option for treatment of tumor [44]. In a mice model, study found that gastric cancer tissue-derived MSCs can significantly promote HGC-27 growth and migration via increasing the expression of miR-221, which may be as a novel biomarker in gastric cancer [45]. The studies supporting MSCs favor tumor growth are summarized in Table 1.
In contrast, it has been shown that the unmodified MSCs have antitumor effects both in vitro and in different animal models of cancer, which is attributed to the factors secreted by MSCs that can suppress the proliferation of glioma, melanoma, hepatoma, and breast cancer cells [46][47][48]. Studies have indicated that MSCs exhibit antiglioma effect through inhibiting vascular growth in glioma cells, which is mediated by the downregulation of platelet-derived growth factor (PDGF)/PDGFR axis [28]. Also, human umbilical cord-derived MSCs (hUC-MSCs) have been shown to inhibit progression of breast cancer by inducing tumor cells death and suppressing angiogenesis [29]. Another study reported that human bone marrow-derived MSCs exhibit the potential to suppress the growth of breast cancer and inhibit lung metastasis by reducing their proliferative ability [30]. Furthermore, MSCs have been shown to have antiangiogenic effect both in vitro and in vivo [49]. MSCs also can inhibit tumor growth in a highly inflammatory and angiogenic Kaposi's sarcoma model [50]. Both in vitro and in vivo studies have shown that MSCs derived from fetal skin can inhibit the growth of human hepatocellular carcinoma (HCC) cells and can reduce cell proliferation, colony formation, and expression of oncogenes [48]. To conclude, MSCs play critical roles in processes of tumor angiogenesis, tumor immune response, and metastasis. The studies reporting MSCs inhibit tumor growth are summarized in Table 2.

. . MSCs, Cancer Stem Cells and Cancer Microenvironment.
Cancer remains as one of the most challenging diseases despite extensive studies have been performed and novel systemic treatment advances during recent decades. In particular, when cancer is diagnosed to have metastasized, treatments are much less successful; while it can often be treated successfully by surgery or local irradiation before it has spread [51]. Therefore, it is necessary and imperative to better understand the biological processes behind the progression of tumor cells towards metastasis.
Cancer cells in primary tumors reside in a complex microenvironment comprising numerous cell types, including endothelial cells of blood vessels, lymphatic circulation, fibroblasts, and various bone marrow-derived cells, such as macrophages and MSCs. It has been well documented   [52,53]. Thus, the process of tumor progression has been regarded as a result of an evolving crosstalk between different cell types within the tumor and its surrounding host tissue and organ or tumor stroma [54]. Cancer stem cells (CSCs), which possess chemotherapy resistance, have been considered as the root of cancers and can resist chemotherapy, explaining cancer recurrence even many years after therapy is ended. The evidence that CSCs selectively resist therapy is provided by a multitude of observations in cell culture, animal models, and cancer patients. For example, direct analysis of apoptosis during cell culture showed that differentiated colon cancer cells are induced to die after chemotherapy, while CSCs from the same culture survive after toxic damage. Moreover, these surviving CSCs are able to reestablish the culture, indicating that they are responsible for therapy failure [55]. Chemotherapyresistant CD133+ CSCs were also observed in lung and liver cancer [56,57]. Similarly, the phenomenon of CSCs escape from therapy was also observed in xenograft studies. Chemotherapy treatment of xenotransplanted CSCs leads to an increase in CD133+ CSCs in the tumor [58]. This showed that CD133+ CSCs are more resistant to chemotherapy drugs in vivo compared to differentiated CD133+ cells. And breast CSCs and GBM CSCs isolated from patient specimens have showed selective resistance to a variety of chemotherapies [59,60].
Furthermore, various studies have shown that the tumor stroma plays important roles in the survival, growth, and metastatic progression of cancer. In the hypoxic environment, the tumor stroma can increase its secretion of signaling proteins such as tumor necrosis factor-(TNF-), TGF-, PDGF, and hepatocyte growth factor (HGF) [61]. In the meanwhile, tumor oxygenation status is tightly associated with its aggressive behavior. Experimental solid tumors contain a significant fraction of microregions that are chronically or transiently hypoxic. Hypoxia plays critical roles in tumor progression including tumor angiogenesis, mutation rate, metastasis and resistance to radiation and chemotherapy [62]. Many molecular pathways have been demonstrated to mediate these hypoxia-induced responses in tumors. Among them, hypoxia-inducible factor-1 (HIF-1) is a key signaling pathway regulating tumor responses to hypoxia [63]. Transiently hypoxic microenvironment in solid tumor may represent the stem cell niche to some extent, in which HIF-1 stabilization and activation of stromal-cell derived factor-1 (SDF1), VEGF, and Chemokine (C-X-C motif) Receptor 4 (CXCR4) occur, attracting MSCs homing and recruitment consequently [64,65]. Furthermore, the state of tumorinduced hypoxia, which often perpetuates the inflammatory state, induces the secretion of numerous growth factors (e.g., endothelial growth factor-A, and fibroblast growth factor), thereby inducing MSCs recruitment and tumor growth through stimulation of tumor angiogenesis [23,66]. The cancer microenvironment, MSCs, and CSCs are illustrated in Figure 1.

MSCs and Antitumor Therapy
. . MSCs-Derived Exosomes as Vehicles for Antitumor erapy. Exosomes are nano-sized (<100 nm) and lipid-bilayerenclosed extracellular vesicles that are released by many types of cells. They are found to play critical roles in intercellular communication via the transfer of genetic molecules such as mRNA and microRNAs, as well as proteins [67]. A common characteristic of human cancers is the aberrant expressions of either oncogenes, oncomiRs, or tumor suppressors. The MSCs-derived exosomes which contain a variety of miRNAs can be taken up by cancer cells and function in them. For example, miR-100 has been found to be downregulated in all subtypes of breast cancer, including the luminal A, luminal B, basal-like, and human epidermal growth factor receptor 2 (HER2) subtypes [34]. It is enriched in MSCs-derived exosomes and could suppress in vitro angiogenesis through modulating the mTOR/HIF-1 /VEGF signaling axis in breast cancer cells [35].
However, to date, studies with controversial outcomes on MSC-derived exosomes in tumor progression have been reported, including promoted effects [36][37][38] and suppressive effects [68,69], as summarized in Table 3. The controversy effects of MSCs-derived exosomes may result from different tissue-derived MSCs used and different component of exosomes applied, different protocols applied for exosome collection, as well as different cancer model and stages of cancer studied. In addition, there is also another issue that the exosomes secreted by MSCs are not created equal. Thus, comprehensive studies are required to advance our knowledge and concerns over cancer research and treatment using MSCs-derived exosomes. One possible approach for clinical application of MSCs-derived exosomes for cancer treatment is that MSCs should be genetically engineered for stable expression of some cancer killer genes before the isolation of exosomes from MSCs, just as Sueon Kim et al. reported for generating antigen-specific CD8+ T cells for adoptive cell therapies against viral infection and tumors [70].
. . MSCs as Vehicles for Antitumor erapy. MSCs have the characteristics of tumor tendency and avoidance of immune clearance; thus, it is promising that MSCs are utilized as vehicles to deliver anticancer treatments [71]. It has been demonstrated in a number of preclinical in vitro migration experiments and in various tumor models, such as hepatoma [72], leukemia cells [73], breast cancer [52], and osteosarcoma [74]. It may be an appropriate strategy that MSCs carrying anticancer drugs targeted treatment of tumors. For example, Bonomi et al. observed that MSCs-Paclitaxel (PTX) inhibit the proliferation of human myeloma cells in vitro 3D dynamic culture system [75]. The anticancer effect of MSCs-PTX has also been shown on pancreatic carcinoma cells in vitro [76]. MSCs are also promising tool for cisplatin (CDDP) delivery towards the tumor [71]. In addition, researches have shown that MSCs with suicide genes or apoptotic genes targeting for tumor is a promising approach. In vivo and in vitro studies have shown that the expression of interferon-in MSCs transfected by adenovirus can effectively kill glioma cells [77]. It is worth noting that in a model of lung metastasis of prostate cancer, MSCs expressing IFN-could prolong the survival period, and its possible mechanism is that IFNcould promote tumor cell apoptosis, inhibit angiogenesis, and increase the activity of natural killer cells [78]. Similarly, adenovirus-transfected MSCs expressing interferoninhibit proliferation of leukemia cells and induce apoptosis of leukemia cells in vitro [79]. In models of lung metastatic carcinoma, a study has found that MSCs carrying TNF-related apoptosis-inducing ligand (TRAIL) reduce tumor growth and recurrence and inhibit the growth of lung metastatic foci in most mice [80]. Study has reported that in glioma mice, tumor tropism of umbilical cord MSCs carrying TRAIL was enhanced after irradiation and its proapoptotic effect on tumor cells was enhanced by MSCs-TRAIL [81]. In addition, the previous and our studies also have demonstrated that MSCs could be genetically modified with herpes simplex virus thymidine kinase (HSV-TK), and the cancer cells could be killed by HSV-TK/GCV suicide gene therapy [82][83][84]. A recent study showed that histone deacetylase inhibitors (HDACis) induced apoptosis of chemoresistant cells effectively, like CD123/CD47-positive cells, which were found as maybe serving as a key role for chemoresistance in tumor microenvironment. Furthermore, HDACis efficiently targeted and removed chemoresistant leukemia blasts in a xenograft AML mouse model [85].
The immune system plays an important role in monitoring the growth of malignant cells. Therefore, stimulating the body's own immune system for antitumor treatment is a highly promising strategy. Interleukins (ILs) are cytokines that regulate inflammation and immune response and have been shown to exhibit antitumor effects through direct tumor-killing or active regulation of the endogenous immune system [86]. MSCs have been utilized to deliver interleukins that can improve the anticancer immune surveillance by activating NK cells and cytotoxic lymphocytes [86]. For example, the IL-18 secreted MSCs were correlative with enhanced T cell infiltration and antitumor immunity in mice bearing invasive and noninvasive gliomas [87]. Similarly, MSCs engineered to express IL-12 prevented metastasis into the lymph nodes and other internal organs as well as increased tumor cell apoptosis in mice bearing preestablished metastases of melanoma, breast, and hepatoma tumors [88]. Also, MSCs engineered to express IL-12 was tested in different mouse tumor models of melanoma and glioma [46]. Other immune-stimulatory molecules, like CX3C chemokine fractalkine (CX3CL), have also been engineered in MSCs. CX3CL1 is known as a strong T cell chemoattractant. Recent studies reveal that CX3CL1 is a driver of T cell migration to the omentum in esophagogastric adenocarcinoma (EAC). Previous research has shown that injection of an adenoviral vector expressing CX3CL1 can induce strong antitumor immune responses by activating both NK cells and T cells [89]. Similarly, intravenous or intratracheal delivery of MSCs-CX3CL1, activating T cells and NK cells, was observed to strongly inhibit process of lung metastasis and increase survival of mice carrying lung metastases cells [90,91]. Taken together, the tumortrophic homing capacity makes MSCs ideal cellular delivery vehicles for personalized cell-based targeted-cancer gene therapy. And the strategies of targeted-cancer therapy were summarized in Figure 2.
. . Inhibition of Migration for Antitumor erapy. Tumor has been seen as a "wound that never heals" which enrolls MSCs in its microenvironment through production of paracrine and endocrine signals. So theoretically, inhibition of MSCs homing will prevent the growth of tumor. For instance, PDGF receptor (PDGFR ) has been reported to play an important role in recruitment of MSCs towards tumor sites [92]. And Simona Camorani et al. have demonstrated that interfere with the PDGFR -mediated crosstalk between BM-MSCs and tumor cells using a nuclease-resistant RNA aptamer could inhibit the migration of MSCs towards tumor cells and hampering tumor aggressiveness [93]. The classic signaling governing MSCs homing is SDF1-CXCR4 axis. SDF1 is highly expressed in active multiple myeloma, as well as in bone marrow sites of tumor metastasis, neutralizing SDF1 with a highaffinity L-RNA Spiegelmer to SDF-1 has been demonstrated to diminish the disease progression [94]. In tumor biology, a number of studies observed the requirement of Akt and Wnt signaling for the migration, invasion, and survival of MSCs expressing anti-cancer proteins (e.g., interleukin-2 or interferon-) Figure 2: Strategies of targeted-cancer therapy. MSCs have been utilized as vehicles to deliver anticancer treatments due to their tumor-tropic property. Genetically modified MSCs expressing suicide genes (e.g., TK gene) have been used to treat cancer effectively in vitro and in vivo through inducing cell death. Additionally, MSCs can be induced to express anticancer proteins (e.g., IL-2 or IFN-), to generate prodrug activating enzymes or to deliver oncolytic viruses and the active drug for tumor-targeting. Simultaneously, MSCs-derived exosomes also suppressed tumor growth.
tumor cells [95][96][97]. Recent studies have shown that MSCs are involved in mediating these signaling pathways to influence migration of tumors. In glioma cells and in the nude mice tumors, upregulation of PTEN by hUCBSC downregulated Akt and (phosphoinositide 3-kinase) PI3K signaling pathway results in the inhibition of migration [98]. Similarly, results of a study demonstrated that overexpression of HNF4 suppresses HCC progression by reducing hepatoma cell growth and metastasis through downregulating the Wnt/ -catenin signaling pathway [99].

Clinical Trials of MSCs in Cancer Therapy
A small number of registered clinical trials for the treatment of solid tumors with MSCs are underway. These trials have been inspired by successful preclinical trials, although some results have not been published yet. The first clinical trial of gastrointestinal tumors worldwide utilizing genetically engineered MSCs in humans has been reported (TREATME1) [100]; this trial uses MSCs-delivery of HSV-TK under the control of the CCL5 promoter. This is a successful phase I/II clinical trial. Another two registered clinical trials with MSCs have primarily focused on ovarian cancer. One of them is a phase I clinical trial sponsored by M.D. Anderson Cancer Center, in which human MSCs transfected with interferon beta (MSCs-IFN ). The purposes of this clinical trial are to test the safety of MSCs-IFN and to find the highest tolerable dose of human MSCs-IFN that can be given to patients with ovarian cancer. Similarly, Mayo Clinic initiated a phase I/II trial to find the side effects and best dose of MSCs infected with oncolytic measles virus encoding NIS (MV-NIS) and to observe its effect on patients with ovarian cancer. In a clinical approach for the treatment of lung cancer, allogeneic MSCs expressing a full length version of TRAIL have been used. MSCs as gene-therapeutic vehicle aim to deliver the TRAIL. In addition, one clinical trial for treating liver cancer with MSCs is on the registry and is recruiting subjects. The purpose of this trial was to study whether MSCs may influence the outcome of graft versus host response in liver transplantation of liver cancer patients. In a phase I clinical test, allogeneic bone marrow-derived MSCs were infused in men with localized prostate cancer [101]. The primary objective was to assess safety and cancer-homing ability of MSCs. However, in this study, MSCs did not home primary tumors in sufficient levels to kill cancer cells or inhibit tumor growth. Although the treatment results have not been published or have not achieved the expected objectives, more attention and patience are needed to promote the clinical transformation of MSCs in tumor treatment. In summary, MSCs and their secreted exosomes have great potential for tumor therapy. Meanwhile, to accelerate the transformation from preclinical research to clinical application, more efficacy and safety of these therapeutic approaches need to be provided by preclinical studies.

Conclusions and Prospective
This article primarily discusses the recent progress of the complex roles of multipotent MSCs in tumor microenvironment, progression, and potential clinical applications. The function of CSCs in tumor microenvironment should be paid more attention to, which is critical for development of cancer cells. The roles of miRNAs and signaling pathways in tumor microenvironment need to be intensively studied, which may provide us with new means to accurately treat cancer. We should also pay more attention to the molecular mechanism of antitumorogenic activity of MSCs, which may improve the precision of targeted therapy. Importantly, reducing the growth stimulation and malignant transformation of MSCs in tumor targeted therapy will accelerate clinical transformation.

Conflicts of Interest
The authors declare no financial conflicts of interest.

Authors' Contributions
Weiping Lin and Linfeng Huang contributed equally as the first authors.


extfieldsofstudy: Biology Medicine

text:
Minireview: demystifying microbial reaction energetics

Summary The biology literature is rife with misleading information on how to quantify catabolic reaction energetics. The principal misconception is that the sign and value of the standard Gibbs energy (ΔGr0) define the direction and energy yield of a reaction; they do not. ΔGr0 is one part of the actual Gibbs energy of a reaction (ΔGr), with a second part accounting for deviations from the standard composition. It is also frequently assumed that ΔGr0 applies only to 25 °C and 1 bar; it does not. ΔGr0 is a function of temperature and pressure. Here, we review how to determine ΔGr as a function of temperature, pressure and chemical composition for microbial catabolic reactions, including a discussion of the effects of ionic strength on ΔGr and highlighting the large effects when multi‐valent ions are part of the reaction. We also calculate ΔGr for five example catabolisms at specific environmental conditions: aerobic respiration of glucose in freshwater, anaerobic respiration of acetate in marine sediment, hydrogenotrophic methanogenesis in a laboratory batch reactor, anaerobic ammonia oxidation in a wastewater reactor and aerobic pyrite oxidation in acid mine drainage. These examples serve as templates to determine the energy yields of other catabolic reactions at environmentally relevant conditions.

The biology literature is rife with misleading information on how to quantify catabolic reaction energetics. The principal misconception is that the sign and value of the standard Gibbs energy (ΔG 0 r ) define the direction and energy yield of a reaction; they do not. ΔG 0 r is one part of the actual Gibbs energy of a reaction (ΔG r ), with a second part accounting for deviations from the standard composition. It is also frequently assumed that ΔG 0 r applies only to 25 C and 1 bar; it does not. ΔG 0 r is a function of temperature and pressure. Here, we review how to determine ΔG r as a function of temperature, pressure and chemical composition for microbial catabolic reactions, including a discussion of the effects of ionic strength on ΔG r and highlighting the large effects when multi-valent ions are part of the reaction. We also calculate ΔG r for five example catabolisms at specific environmental conditions: aerobic respiration of glucose in freshwater, anaerobic respiration of acetate in marine sediment, hydrogenotrophic methanogenesis in a laboratory batch reactor, anaerobic ammonia oxidation in a wastewater reactor and aerobic pyrite oxidation in acid mine drainage. These examples serve as templates to determine the energy yields of other catabolic reactions at environmentally relevant conditions.

Introduction
Microbial catabolic reactions, in fact all chemical reactions, can only proceed if there is an energetic drive. In phototrophy, this drive is supplied by solar radiation. In chemotrophy, however, it stems from thermodynamic disequilibrium for the redox reaction of interest, and is commonly quantified with an expression of the change in the Gibbs energy of reaction (ΔG r ). Unfortunately, misleading information on how to determine ΔG r has been perpetuated in the environmental microbiology community through incomplete and, sometimes, incorrect explanations in the literature. The fundamental cause for this confusion resides in the difference between ΔG r and ΔG 0 r or, stated differently, the difference between the actual Gibbs energy of a reaction and a hypothetical reference frame (or standard state). The same issues arise when expressing catabolic reaction energetics in terms of actual and standard redox (or electrode) potentials denoted as E and E 0 respectively (Amend and Teske, 2005). The purpose of this communication is not to identify the origin of this confusion, but to remedy it. The first step is to recognize its pervasiveness in the literature, including in many microbiology textbooks (e.g., Madsen, 2015;Madigan et al., 2018;Willey, 2020).
Another point of confusion relates to the definition of ΔG 0 r . Contrary to countless proclamations in the literature (we again point to many widely read textbooks as the launchpad for this confusion), ΔG 0 r does not represent the Gibbs energy of reaction at 298.15 K (25 C) and 1 bar (10 5 Pa), with all reactants and products at concentrations of 1 molar (M) or 1 molal (m). ΔG 0 r is, in fact, a function of temperature and pressure, and environmentally relevant temperatures and pressures can have substantial effects on the value of ΔG 0 r that must be taken into account. Furthermore, the standard states of reactants and products are expressed as activities, not concentrations; although activity and concentration are related, they are not the same thing. In this review, we hope to clear up some critical and common misconceptions about microbial reaction energetics, and then provide something akin to a 'how-to manual' for determining values of ΔG r (or E) for many relevant redox processes at physicochemical conditions that are commonly encountered in natural systems, impacted environments, or laboratory experiments.
The value of ΔG 0 r is misleading The value of ΔG 0 r is only one part of the total Gibbs energy yield of a reaction, ΔG r . The other part accounts for the chemical composition of the environment of interest (the Q-term): where R and T stand for the gas constant and temperature (in K), respectively, and Q r represents the activity product as defined below. Values of ΔG 0 r are typically calculated from those of ΔG 0 i , which represent the standard Gibbs energies of formation from the elements for every reactant and product species (i) in the reaction (r). For detailed discussions, see Amend and Shock (2001) or LaRowe and Amend (2019b).
Values of ΔG 0 i (and therefore values of ΔG 0 r ) are functions of temperature and pressure. Using thermodynamic properties determined at 25 C and 1 bar to describe microbial processes in, for example, the cold deep ocean or a hot spring system leads to unnecessary errors and sometimes flawed conclusions. Values of ΔG 0 i for a wide range of compounds can be found in countless thermodynamic data tables; most of these, however, are restricted to 25 C and 1 bar. For more than four decades, efforts by several research groups have determined the necessary parameters to calculate the values of ΔG 0 i as a function of temperature and pressure for now more than 3000 compounds. For a recent compilation and detailed discussion of the approach, see Dick (2019) and references therein. To ease the calculation of microbial reaction energetics, Amend and Shock (2001) tabulated values of ΔG 0 i at 0-200 C for >300 minerals, aqueous solutes and gases. All thermodynamic values provided in this review were calculated using SUPCRT92 (Johnson et al., 1992). The computer program CHNOSZ, available in the R environment, can also be used to carry out the thermodynamic calculations summarized below (Dick, 2019).
In addition to noting the critical difference between ΔG r and ΔG 0 r , we also remind the reader that it is essential to identify the phase (e.g., gas, aqueous, specific mineral) of each reactant and product in a reaction. To elucidate the importance of this point, let us consider gas (g) and aqueous (aq) versions of the same net catabolic processhydrogenotrophic methanogenesis-where H 2 O refers to liquid water: and CO 2 aq ð Þ+ 4H 2 aq ð Þ= CH 4 aq At 25 C and 1 bar, ΔG 0 2 = −130.4 kJ/mol and ΔG 0 3 = −193.7 kJ/mol, a difference of 63.3 kJ/mol. Let us now also consider the effect of temperature on these reactions. At 85 C and 1 bar, ΔG 0 2 = −106.0 kJ/mol, a difference of 24.4 kJ/mol compared with its value at 25 C.
The 85 C value for ΔG 0 3 = −184.1 kJ/mol, a difference of 9.6 kJ/mol compared with its 25 C value.
Some investigators prefer to write the hydrogenotrophic methanogenesis reaction with bicarbonate (HCO 3 − ) in place of CO 2 At 25 C and 1 bar, ΔG 0 4 = −229.9 kJ/mol, a difference of 99.5 kJ/mol compared with the version written with gaseous CO 2 (Reaction (2)) and a difference of 36.2 kJ/mol to the version with aqueous CO 2 (Reaction (3)). If CO 2 (g), CO 2 (aq) and HCO 3 − are in chemical equilibrium in a system, then ΔG 2 =ΔG 3 =ΔG 4 , even though, as just shown, ΔG 0 2 6 ¼ΔG 0 3 6 ¼ΔG 0 4 , where values differ by almost 100 kJ/mol. This is a clear example of why the value of ΔG 0 r is important (since it is a component of ΔG r ), but is misleading on its own. To show that the sign of ΔG 0 r can also be misleading, we must first discuss the chemical composition of the system of interest.

The chemical composition of the environment matters
The Q-term in Equation (1) accounts for the chemical composition of the system of interest and, therefore, how different the environment is from the standard state. Its contribution to ΔG r can be positive or negative and, depending on the environment and catabolic reaction, can exceed several hundred kJ/mol. In other words, ignoring the composition of the system by assuming concentrations of 1 M or 1 m for all reactants and products (as is often done) will lead to substantial-and entirely unnecessaryerrors in energy calculations. [In bioenergetics, molality (m) is preferred, because a kg of water-as opposed to a litre of solution-is not affected by changes in ionic strength or density.] Q r , the activity product can be evaluated with the expression where a i represents the activity of species i, and ν i stands for its stoichiometric reaction coefficient. If we again consider methanogenesis (Reaction (3)) as an example, Activities are related to, but certainly not equal to, concentrations. The activity of any species i can be determined with the relation where C i stands for the concentration (usually in molal units), C 0 i represents the standard state concentration (usually 1 m), and γ i denotes the corresponding activity coefficient (which is unitless). In Table 1, we provide values of γ i for uncharged aqueous species, cations (+1, +2, +3) and anions (−1, −2, −3) at temperatures from 0 to 100 C and in solutions with ionic strengths (I) of 0.001-0.7 m. The numbers given in Table 1, calculated with the CHNOSZ program (Dick, 2019), may be slightly different from those of the relatively few individual aqueous species for which activity coefficients have been experimentally determined. They do, however, serve as very close estimates for all neutral and charged species of interest in catabolic reactions. For context, the ionic strength of most rivers and lakes is 0.001-0.005 m, and that of seawater is~0.7 m. Note in Table 1 that, for uncharged species, γ i is~1, regardless of temperature or ionic strength, and thus a i ≈ C i (but unitless). Consequently, for Reaction (3), where [i] represents the concentration of i in the aqueous solution. Note that since the standard state for pure liquids (including water) is an activity of 1 and, in dilute aqueous solutions, the activity of water is very close to 1, [H 2 O] does not appear here. For ions, values of γ i can be far from unity (see Table 1). This is especially true at elevated ionic strengths and for multivalent ions (e.g., SO 4 2− , Fe 2+ , Mn 2+ , PO 4 3− ) even at Table 1. Individual ion and neutral species activity coefficients (unitless) as a function of ionic strength (I), species charge and temperature.  low ionic strengths. For catabolic processes in seawater, a wastewater reactor, intracellular fluid or other elevatedsalinity solution using concentrations in place of activities will result in substantial, and again, unnecessary error. Consider, for example, sulphate in a marine system: the total average concentration of all aqueous sulphate-bearing species is 2.8 × 10 −2 m, but the corresponding activity of the SO 4 2− ion is almost an order of magnitude lower (~2.9 × 10 −3 , see below). Calculating ΔG r at 25 C for sulphate reduction using total sulphate concentration in the place of the activity of SO 4 2− leads to an error of~6 kJ/mol.
Of course, ignoring activity coefficients for the other species in the reaction will further compound the error. When considering gases in catabolic reactions, activities (a i ) are replaced with fugacities (f i ). Fugacity of the ith gas is related to its partial pressure (P i ) via a unitless fugacity coefficient (λ i ), using [Note that f i is formally in units of pressure, but for the Q-term, it is rendered unitless by normalizing it with a ref- erence value (f 0 i ) of 1 bar.] In the abyssal ocean, the deep subsurface or any system with in situ pressures above~50-100 bar, values of λ i for most gases are <0.5 and can be <0.2. In those ecosystems, partial pressures must be converted into fugacities to obtain accurate values of ΔG r . However, in lower pressure environments, regardless of the temperature or ionic strength, values of λ i are 0.99-1.00, and thus, very little error is introduced by equating f i = P i . That is not to say, however, that the Q-term can be ignored. On the contrary, and as shown below, it can contribute tens of kJ/mol to ΔG r even in low-pressure systems.
The 'biological standard state' gets it only partially right It has long been recognized that assuming all concentrations to be 1 m (or activities = 1) is problematic. To remedy this situation, the Interunion Commission on Biothermodynamics proposed the biological standard state, where pH is set to 7 (i.e., a H + = 10 −7 ), because the cytoplasm of most cells is circumneutral (Wadsö et al., 1976). This biological standard state is expressed as ΔG 00 r for the Gibbs energy and as E 0 0 for the electrode potential of redox half-reactions. In bioenergetic calculations, there are at least four potential pitfalls with the biological standard state. First, we now know many acidophiles and alkaliphiles with intracellular pHs far from 7. For example, in the hyperacidophilic thermophile Picrophilus oshimae that grows optimally at pH < 1, the intracellular pH is as low as 4.6 (van de Vossenberg et al., 1998). Second, pH 7 only corresponds to solution neutrality at 25 C and 1 bar. At 2 C, the temperature of much of the abyssal ocean, neutral pH is 7.4, and at 100 C, where numerous hyperthermophilic archaea and bacteria live and thrive, neutral pH is 6.1. Third, and arguably the most important, the concentration (or activity) of every other reactant and product in the reaction of interest is still kept at 1.0 m in the biological standard state which, depending on the chemical species and the environment, can be off by many orders of magnitude. Fourth, the biological standard state typically specifies a temperature (usually 25 C, but sometimes 37 C), thereby perpetuating the myth that values of ΔG 0 r and ΔG 00 r are not functions of temperature. In other words, the proton, whether as a reactant or product species, cannot be omitted from the Q-term; it should be treated like any other chemical species in this regard. In fact, the energetics of most reactions can be very pH sensitive, because proton activities can easily vary over more than ten orders of magnitude in microbial environments of interest.

Chemical speciation is often ignored; it should not be
All chemical reactions, including catabolic reactions, must be written in terms of chemical species. In determining reaction energetics, this is often-perhaps unknowingly-overlooked. Clearly, to calculate the values of ΔG 0 r , we need to know the values of ΔG 0 i for all the species in the reaction as written. For the Q-term, we need to know the corresponding activities (a i ), which are generated from the concentrations (C i ) and activity coefficients (γ i ) as in Equation (7). However, most analytical methods used for aqueous solutions typically determine 'total' and not 'species-specific' concentrations. For example, an analysis by ion chromatography may yield a total sulphate concentration ([SO 4 2− ] Total ), which is the sum of concentra- nearly an order of magnitude less than its total concentration. It follows that SO 4 2− is not the dominant sulphatebearing species in seawater; that label belongs to MgSO 4 0 , followed by NaSO 4 − (see Fig. 1B).
To accurately 'speciate' an aqueous solution can be rather involved and computationally intensive. It is accomplished by minimizing the Gibbs energy of the system for a given number of elements at a particular temperature, pressure and chemical composition. One also needs to know the values of ΔG 0 i for every species of interest (including complexes) at the in situ temperature and pressure. In all but the simplest cases, computer codes are typically used to determine rapid and accurate solutions to the Gibbs energy minimization problem. The most commonly used codes for such equilibrium speciation calculations include WATEQ (Truesdell and Jones, 1974;Ball et al., 1987), MINEQL (Westall et al., 1976;Schecher and McAvoy, 1998), EQ3 (Wolery, 1992), PHREEQC (Parkhurst and Appelo, 1999), and The Geochemists Workbench (Bethke, 2007). To be clear, the Gibbs energy minimization should not affect the redox state in the chemical speciation calculation. In other words, redox disequilibrium must be maintained to accurately assess the potential catabolic energy landscape. For a review of these and other software packages and the imbedded minimization techniques, see Leal et al. (2014).
It should be noted, however, that unless a system is of high ionic strength or the investigation centres on minor components (e.g., Cu-, As-, and Se-redox processes), the most important speciation is often the pH-dependence. Here, we'll use acetic acid as an example, and consider the two species CH 3 COOH(aq) and CH 3 COO − , related by the relation At pH < 4, [CH 3 COOH] >> [CH 3 COO − ], and therefore, the concentration of 'total' acetate is essentially that of acetic acid. Conversely, at pH > 6, [CH 3 COO − ] >> [CH 3 COOH], and 'total' acetate represents the concentration of CH 3 COO − . At 4 < pH < 6, the 'total' acetate must be speciated to avoid considerable and unnecessary error. Note that since chemical speciation is an equilibrium assessment, there is a temperature and pressure dependence that must be taken into account. For Reaction (10), however, this is relatively minor (Shock, 1995).
The sign of ΔG 0 r can be misleading If ΔG r < 0, the reaction is exergonic. Whether ΔG 0 r < 0 is meaningless in this regard. Here, we provide two example reactions where the signs of ΔG r and ΔG 0 r are, in fact, opposite. Consider sulphur disproportionation, which can be represented by where S 0 denotes elemental sulphur with an activity of 1.0 (as for all pure minerals). At 25 C and 1 bar, the standard Gibbs energy of this reactions (ΔG 0 11 ) is 120.5 kJ/mol. Clearly, if the sign (and value) of ΔG 0 r was meaningful, this reaction, in the direction as written, would be impossible. However, this is a documented catabolism carried out by members of the Deltaproteobacteria, Thermodesulfobacteria and other bacterial phyla (Bak and Pfennig, 1987;Finster et al., 1998;Slobodkin et al., 2012;Kojima et al., 2016;Slobodkina et al., 2016). In environments with low levels of sulphide and sulphate and circumneutral to alkaline pH, the Q-term for Reaction (11) has a large negative value that can counter the positive value of ΔG 0 11 , resulting in a net negative value of ΔG 11 . For example, at activities of 10 −6 for H 2 S(aq) and SO 4 2− , and a pH of 7, Reaction (11) is strongly exergonic (ΔG 11 = −96.4 kJ/mol). Similarly, values of ΔG 0 r are positive for most of the major fermentation reactions occurring in marine sediments (Canfield et al., 2005), but these reactions can be exergonic, because a negative Q-term renders the overall Gibbs energy negative (LaRowe and Amend, 2019a).
Conversely, the sign of ΔG 0 r can be negative, but owing to unfavourable environmental conditions, that of ΔG r can be positive-and therefore, the reaction would be endergonic. Here, we consider acetogenesis At 25 C and 1 bar, ΔG 0 12 = −169.8 kJ/mol. Again, if the sign and value of ΔG 0 r were meaningful, this reaction should be energy-yielding. However, in field or laboratory systems with relatively low levels of H 2 and CO 2 , that is not the case. For example, at activities of H 2 and CO 2 equal to 10 −6 and that of acetic acid equal to 10 −3 , this reaction is endergonic (energy-consuming) with ΔG 12 = 18.6 kJ/mol. values of ΔG 0 2 , ΔG 0 3 and ΔG 0 4 at 25 C are −129.2, −195.9 and −231.2 kJ/mol, respectively, differences that are only slightly more pronounced at 1.5, 2.2 and 1.3 kJ/mol compared to the 1 bar values. Pressure can, however, significantly affect the Q-term (and hence ΔG r ), especially if free or dissolved gases (e.g., O 2 , CO 2 , CH 4 , H 2 , H 2 S) are part of the target catabolism. Gas solubility can increase demonstrably with increasing pressure, resulting in higher concentration (and thus activity) of the corresponding aqueous solute. For example, if aqueous and gaseous H 2 are in equilibrium at constant temperature (e.g., 25 C), then the activity of H 2 (a H2 ) can increase by several orders of magnitude from 7.85 × 10 −4 at 1 bar to 1.54 × 10 −2 at 20 bar to 1.93 × 10 −1 at 350 bar.
The Gibbs energy function that matterscalculating ΔG r In the sections above, we discussed the wide-ranging effects of temperature (and pressure) on ΔG 0 r , as well as the wide-ranging effects of chemical composition, including speciation, on the Q-term. In Box 1, we work through five specific examples (A-E) on how to calculate values of ΔG r (from values of ΔG 0 r and the Q-term) for microbial catabolisms at defined environmental conditions. In light of the limited effects of pressure on ΔG 0 r and to permit comparisons, the energetics in these examples were determined at 1 bar. It should also be explicitly stated that the calculated values of ΔG r apply only to the specified chemical composition. In open systems, it is assumed that these conditions are maintained. In closed systems (e.g., sealed bioreactors), the value of ΔG r changes as the concentrations (and hence, activities) of reactants and products change with time. The chosen examples consider oxic and anoxic environments; freshwater and seawater; aqueous solutes, gases and minerals; organic and inorganic electron donors; natural, impacted, engineered and laboratory systems; and a range of temperatures. These examples are not intended to represent all, or even a majority, of environmental microbial catabolisms. However, each example can serve as a template to determine ΔG r for similar processes under similar environmental conditions. The steps are: 1. Write a mass-and charge-balanced chemical reaction, and identify the phases. 2. Determine the temperature and chemical composition of the system. 3. Speciate the aqueous solution as necessary. 4. Estimate activity and/or fugacity coefficients as necessary.

Obtain values of ΔG 0
r and the Q-term to calculate ΔG r .
The first example (A) represents mesophilic aerobic respiration of glucose (C 6 H 12 O 6 ) in a freshwater (low ionic strength) ecosystem. Six-carbon sugars and their polymer parent materials are important electron donors for many heterotrophic microorganisms. To better understand biogeochemical processes in lakes, rivers, wetlands, soils, laboratory experiments and countless other natural and engineered systems, it may be necessary to determine the energetics of the oxidation of carbohydrates, proteinaceous materials and an array of simple to complex organic compounds with O 2 as the terminal electron acceptor.
The second example (B) represents psychrophilic anaerobic respiration of acetate in marine sediments. The upper-most layer of marine sediments is typically oxic, where aerobic respiration dominates. Below the oxic zone, sulphate becomes the most important terminal electron acceptor, responsible for much of the oxidation of organic carbon. For the vast majority of the global ocean, the seawatersediment interface is cold (~2-4 C), but with increasing depth and the accompanying geothermal gradient, the sediment temperature increases. Other oxidants to be considered in these anoxic environments include NO 3 − , Fe III -minerals (e.g., haematite, goethite, ferrihydrite) and Mn IV -minerals (e.g., pyrolusite).
The third example (C) represents thermophilic methanogenesis in a 2-phase laboratory experiment. Because laboratory experiments allow for careful control, constant monitoring and wide-ranging chemical analyses, determining energetics is relatively easy. Note also that it is common to slightly overpressure sealed culturing vessels if a gas phase is present. If, in the example described here, the total pressure was 3 bar (P CO2 = 0.6 bar, P H2 = 2.4 bar), then ΔG 15 would equal −135.3 kJ/mol. As alluded to in the section above, this difference of 16.4 kJ/mol compared with the 1 bar value is due almost entirely to the change in the Q-term; the difference in ΔG 0 15 between 1 and 3 bar is trivial at <0.1 kJ/mol.
The fourth example (D) represents mesophilic anaerobic ammonia oxidation (i.e., anammox) in a wastewater reactor. The chemical compositions in engineered and impacted systems can depart significantly from those in natural environments, making speciation calculations particularly important. In addition, fluctuating temperatures and pHs, constant aeration, settling out of solid phases, changing water activities and other physicochemical factors must be taken into account to calculate ΔG r .
The fifth example (E) represents mesophilic aerobic pyrite oxidation in acid mine drainage. In many catabolisms, minerals can serve as electron acceptor or electron donor, in addition to providing a physical template. It is worth noting that different minerals with the same chemical formula have different values of ΔG 0 i . For example, at 25 C and 1 bar, values of ΔG 0 i for anhydrous iron oxyhydroxides such as goethite, lepidocrocite and 2-line ferrihydrite (all FeOOH) are −491.6, −479.9 and − 465.3 kJ/mol respectively. As noted above, activities of pure minerals (and pure liquids) are typically set to 1.0, but those of amorphous solids (e.g., Mn IV -oxyhydroxides, opaline silica, coal, kerogen), solid-solutions (e.g., olivine) or complex liquids (petroleum, bitumen) are not.

Conclusion
As environmental microbiologists, we are typically confronted with communities of catabolically diverse organisms in complex chemical systems. Some of these organisms may be well characterized, with isolates in culture collections and genomes fully sequenced, but many may be largely unknown, including and perhaps dominated by clades that have no cultured representatives (microbial dark matter) (Lloyd et al., 2018). The systems of interest may be natural and pristine, contaminated or engineered. To answer important research questions, investigations may be carried out in situ, or environmental samples may be used in controlled laboratory studies. To obtain robust and quantitative results, we can make use of field measurements, biomolecular data, laboratory experiments, analytical chemistry and numerical modelling. An often desirable but also often daunting task in environmental microbiology research is to explain findings in an energetics context.
In this review, we attempted to demystify microbial reaction energetics by clearing up some of the most common confusions and misconceptions. Topping that list are the ideas that: 1) a negative ΔG 0 r means the reaction is exergonic-it does not; 2) the value of ΔG 0 r represents the energy yield-it does not; and 3) ΔG 0 r applies to 25 C, 1 bar, and concentrations of 1 M-it does not. ΔG 0 r is a temperature-and pressure-dependent function that, together with the Q-term that accounts for the chemical composition of the system, enables the calculation of ΔG r . The sign of ΔG r informs on the direction in which the reaction is exergonic (or if it is in equilibrium, where ΔG r = 0), and the value of ΔG r quantifies the accompanying energy yield. Similarly, when considering redox half-reactions, the same rules apply to values of E 0 and E. Finally, it is worth reiterating that ΔG r < 0 does not mean that the corresponding reaction will occur, only that it may occur. A quantitative assessment of thermodynamically favourable redox reactions in an environment only provides a framework within which to better understand microbial metabolism.