Therapeutic Potential of Dietary Phenolic Acids

Although modern lifestyle has eased the quality of human life, this lifestyle's related patterns have imparted negative effects on health to acquire multiple diseases. Many synthetic drugs are invented during the last millennium but most if not all of them possess several side effects and proved to be costly. Convincing evidences have established the premise that the phytotherapeutic potential of natural compounds and need of search for novel drugs from natural sources are of high priority. Phenolic acids (PAs) are a class of secondary metabolites spread throughout the plant kingdom and generally involved in plethora of cellular processes involved in plant growth and reproduction and also produced as defense mechanism to sustain various environmental stresses. Extensive research on PAs strongly suggests that consumption of these compounds hold promise to offer protection against various ailments in humans. This paper focuses on the naturally derived PAs and summarizes the action mechanisms of these compounds during disease conditions. Based on the available information in the literature, it is suggested that use of PAs as drugs is very promising; however more research and clinical trials are necessary before these bioactive molecules can be made for treatment. Finally this review provides greater awareness of the promise that natural PAs hold for use in the disease prevention and therapy.

Introduction
Since ancient times, plants and their products have been used as folk medicine in different parts of the world. It is believed that over 400,000 tropical flowering plant species have healing properties [1]. Several studies on medicinal plants determined the variety of compounds contributing to treatment of life-threatening diseases such as diabetes, cancer, and infections. Numbers of methods have been introduced for qualitative and quantitative analysis of plant secondary metabolites such as polyphenols, alkaloids, saponins, glycosides, resins, oleoresins, sesquiterpene, and lactones. These secondary compounds are involved in plant growth, development, reproduction, and disease resistance. Phenolic acids (PAs) are among the most common bioactive compounds throughout plant kingdom which have role in growth, reproduction, and defense against environmental stress and microorganisms [2,3]. The antioxidant activities of PAs are extensively implemented in food industry as preservatives since antiquity. They also have function in plethora of important biological activities such as antiageing, reducing the risk of life-threatening diseases such as HIV, diabetes, CVDs, and cancer. Bulk information available about potential health benefits regarding PAs has led to an increased interest in PAs and this review illustrates the role that PAs possess in important biological conditions (Figure 1). A comprehensive literature search was conducted in PubMed and Google Scholar by using the following mesh keywords: (1) phenolic acids, (2) antioxidants, (3) anticancer, (4) cardioprotective, (5) antiulcer, (6) anti-diabetic, (7) antimicrobial, and (8) hepatoprotective. Thus, the aim of the present paper is to systematically review and summarize the literature supporting the health benefits of PAs at a common platform. However, in case of plant metabolites, these refer to a distinct group of organic acids which contains two distinguishing carbon frameworks, hydroxybenzoic and hydroxycinnamic acid structures. PAs are usually subclassified into benzoic acids containing seven carbon atoms (C6-C1) and cinnamic acids with nine carbon atoms (C6-C3).
PAs are widely distributed in the plant kingdom and found in a wide variety of nuts and fruits, such as raspberries, grapes, strawberries, walnuts, cranberries, and black currants. These are secondary metabolites derived from phenylalanine and tyrosine via shikimate/chorismate pathway. However, these compounds exist predominantly as hydroxybenzoic acids which include gallic acid (GA), salicylic acid (SA), protocatechuic acid (PCA), ellagic acid (EA), and gentisic acid (GeA) and hydroxycinnamic acids which include p-coumaric acid (p-CA), caffeic acid (CA), ferulic acid (FA), chlorogenic acid (CGA), and sinapic acids (SA) or alternatively may occur as conjugated forms ( Figure 2).
The chemical properties of PAs in terms of availability of the phenolic hydroxyl groups predict their activity in vitro. The effective in vivo potential of PAs depends on multiple factors such as quantity of the compound ingested, absorbed, and/or metabolized, plasma and/or tissue concentrations, type and amount of single PA, and synergistic effects. However, the chemical structure of phenolic compounds differently affects all the factors described above. For instance, sulfate derivatives of FA and CA showed lower activities when compared with the antioxidant activities of the parental phenolic acids indicating the importance of the phenolic acids and their metabolites [4].

Antioxidant Properties
Biological system generates reactive oxygen species (ROS) as the byproduct of various metabolic activities and mitochondria are said to be major site responsible for ROS production. These free radicals are highly unstable or reactive and can cause direct damage to cell components such as nucleic acids, lipids, and proteins [5]. ROS is the major contributor to many diseases which include CVDs, cancer, age-related decline in the immune system, and degenerative diseases such as Parkinson's and Alzheimer's disease. Human body possesses several stress response enzymes including superoxide dismutase (SOD) and catalase (CAT) to counteract radical-induced damage. PAs counteract both ROS and RNS-induced cell damage by their direct free radical scavenging activity as well as the upregulation of the HO/BVR system, superoxide dismutase (SOD), and catalase (CAT) whose final aim is to detoxify ROS and RNS.
Oxidative stress has been implicated in the pathogenesis of multiple cardiovascular complications. Antioxidant role of FA in cardioprotection is well studied [6]. In hypersensitive rats, treatment with FA for 4-12 weeks increased SOD and CAT activities. FA treatment also increased SOD and CAT levels in myocardium and pancreatic tissue of streptozotocininduced diabetic rats in dose and time dependent manner [7].
Oral administration of vanillic acid (VA) at high doses (100 mg/kg) increased the antioxidant status by reducing lipid peroxidation and increased the SOD, CAT, and glutathione peroxidase (GPx) activities and reduced glutathione (GSH) levels in cisplatin induced nephrotoxicity in albino rats [9]. Similarly, GA consumption diminished this harmful effect via its antioxidant action by inhibiting the initiation and/or propagation of the lipid peroxidation [10]. In the later studies same group investigated the antioxidant properties of GA derived from Peltiphyllum peltatum against sodium fluoride (NaF-) induced hepatotoxicity in rats [11]. Pretreatment of animals with GA could mitigate the NaF-induced hepatotoxicity through suppression of lipid peroxidation expressed in reduced Thiobarbituric Acid Reactive Substances (TBARS) levels, restoration of GPx levels, CAT, and SOD corroborating its antioxidative role.
Antioxidant properties of PAs depend on not only their direct activity as a scavenger but also their capacity to strengthen the endogenous antioxidant defenses. Nuclear factor erythroid 2-(NFE2-) related factor 2 (Nrf2) is a key transcription factor which strictly regulates the antioxidant/ detoxifying enzyme genes by antioxidant response elements, 4 Advances in Pharmacological Sciences present in the promoter region of those genes [12]. PAs such as PCA have been demonstrated to induce the ARE-dependent antioxidant/detoxifying phase II enzymes such as GR and GPx by activating the transcription factor Nrf2 through JNKmediated phosphorylation [13]. Similarly, PCA induces antiapoptotic mechanism that likely related to the activation of the JNK-mediated survival signals that strengthen the cellular antioxidant defenses rather than antioxidant power of PCA. PAs induce phase II hepatic antioxidant enzyme and increase the antioxidant status of liver [14]. PAs including GEA, GA, p-CA, and FA selectively induce hepatic mRNA transcripts for CuZnSOD, GPx, and catalase likely through upregulation of gene transcription as well as the Nrf2 transcription factor indicating their potential antioxidant role in liver [15].

Antiulcer Activity
Gastric ulcer is a recurrent chronic illness caused by both endogenous and exogenous factors which include pepsin, stress, and noxious factors such as nonsteroidal anti-inflammatory drugs, Helicobacter pylori bacteria, smoking, and alcohol consumption [16]. Natural PAs have been investigated for their antiulcer activities which are apparent from wide ranges of studies in experimental animals. GA has been reported to be antiulcer in aspirin plus pylorus ligation model. GA exerts this effect by improving mucosal defensive with activation of antioxidant parameters and inhibition of some toxic oxidant parameters. Similarly, GA in combination with famotidine, an antiulcer drug, synergistically protected the experimentally induced peptic ulcer models [17]. Animals treated with low dose combinations like GA (50 mg/kg) + FM (10 mg/kg) along with high dose GA (100 mg/kg) + FM (10 mg/kg) showed significant ulcer protective effect induced by aspirin plus pylorus ligation.
Gastroprotective properties of another phenolic acid, EA, have also been reported. EA are well documented in acute and chronic ulcer rat models [18]. The gastroprotective properties of EA are partly due to increased endogenous production of nitric oxide which is an antioxidant effect by replenishing non-protein-sulfhydryls and attenuation of TNF-, whereas in indomethacin ulcer, gastroprotective properties are partly due to attenuation of elevated levels of TNF-, interferon-, and interleukins 4 and 6.

Antidiabetic Activity
Impairment in glucose metabolism leads to physiological imbalance with the onset of the hyperglycemia and subsequently diabetes mellitus which can be either type 1 or type 2. It is well known that several physiological parameters of the body get altered in the diabetic conditions. Long term effects of diabetes may cause development of specific complications such as retinopathy, nephropathy, neuropathy, foot ulcers, sexual dysfunctions, and cardiovascular complications.
Insulin stimulates glucose transport by inducing the translocation of glucose transporter 4 (GLUT4) to the plasma membrane which is mediated by two major pathways, an insulin independent AMPK pathway and an insulin dependent PI3K pathway. The antidiabetic effects of natural PAs are well documented as evident from wide range of studies. CGA could enhance the glucose uptake in L6 myotubes via increasing expression of GLUT4 and PPAR-transcript [19]. Similarly, PCA also exerts its insulin-like activity in human omental adipocytes. PCA induces the glucose uptake associated with enhanced GLUT4 translocation and increased PPARg transcript [20]. GA derived from sea buckthorn leaf extract stimulated GLUT4 translocation and glucose transport in a concentration dependent manner with maximum stimulation at 10 M concentration [21]. Further, the role of atypical protein kinase Cf/k is revealed in GA mediated GLUT4 translocation and glucose uptake.
Similarly, CGA stimulates glucose transport in both skeletal muscle isolated from mice and L6 skeletal muscle cells [22]. Furthermore, in L6 myotubes, CGA increased glucose transport via GLUT4 transporter and in skeletal muscle via the activation of AMPK. CGA lowered AUC glucose, inhibited hepatic glucose-6-phosphatase (G-6-Pase) expression, and improved skeletal muscle glucose uptake and lipid profiles, in Lepr db/db mice. Mechanistically, CGA activated AMPK leading to suppression of hepatic glucose production and fatty acid biosynthesis. Inhibition and knockdown of AMPK abrogating these metabolic alterations suggested that CGA can improve glucose and lipid metabolism via the activation of AMPK. Another phenolic acid p-CA modulated glucose and lipid metabolism via AMPK activation in L6 skeletal muscle cells [23]. p-CA also promoted fatty acidoxidation, decreased oleic acid-induced triglyceride accumulation, and enhanced glucose uptake proving its beneficial effects in improving or treating metabolic disorders.

Cardioprotective Activity
Cardiac disease is the major cause of morbidity and mortality among the noncommunicable diseases. Implication of cardiovascular complications including cardiomyopathy in diabetes can be attributed to hyperlipidemia, hyperglycemia, and oxidative stress which promote atherosclerosis [24][25][26]. Cardiovascular complications are higher in diabetic patients than nondiabetics [27]. Cardioprotective role of the two phenolic acids caffeic acid (CA) and ellagic acid (EA) has been reported in STZ-induced diabetic mice. Treatment with CA or EA markedly elevated the insulin secretion, which might attenuate the dyslipidemia, improved glycemic control, and diminished cardiac oxidative stress [28].
Atherosclerosis is a chronic inflammatory disease characterized by accumulation of leukocytes in the vascular wall. Platelets play essential role in formation of atherosclerotic plaques and thrombosis by coaggregating with leukocytes via P-selectin glycoprotein ligand-1 (PSGL-1) and Pselectin interactions [29,30]. GA has been found to possess antiatherosclerotic activity by inhibiting platelet activation and its association with leukocytes, P-selectin expression stimulated by ADP which is likely to be through decreasing intracellular Ca 2+ mobilization via regulating the signals of PKC /p38MAPK and Akt/GSK3 [31]. Protocatechuic aldehyde (structurally similar to PCA) isolated from root of Salvia miltiorrhiza attenuated PDGF-induced proliferation and migration of VSMCs via (PI3K)/Akt and MAPK kinase pathways which regulate key enzymes associated with migration and proliferation of VSMCs [32]. It is reported that FA effectively reduced copper ion induced LDL oxidation and facilitated the uptake and degradation of cholesterol in the liver. p-CA is known to protect the LDL from ROS by scavenging • OH in vivo and thereby reduce lipid peroxidation and serum LDL cholesterol levels [33].
Cardioprotective activities of EA have also been investigated. EA effectively reduces oxidative stress, lowers the levels of plasma lipids, and inhibits lipid peroxidation. EA reduced the uptake of oxidized LDL in murine macrophages by downregulating membrane expression of SR-B1 [34]. SR-B1 is a membrane receptor on macrophages responsible for the internalization of oxidized LDL that promotes cellular accumulation of cholesterol [35]. Similarly, EA also promotes cholesterol efflux in lipid-loaded macrophages by inducing membrane receptor ABCA1 expression. ABCA1 is a membrane transporter abundant in macrophages and plays a crucial role in cholesterol homeostasis, thereby protecting against atherosclerosis [36]. Salicylic acid (SA) treatment has been reported to prevent complications of atherosclerotic cardiovascular disease such as myocardial infarction and occlusive stroke [37]. These effects of SA are attributed to its platelet-inhibitory function and stimulation of Paraoxonase 1, an enzyme that protects the serum lipids from oxidation and can reduce macrophage foam cell formation and attenuates atherosclerosis development [38].
Hypertension is the most common cardiovascular disorder mainly affected by lifestyle and dietary habits [39]. Nitric oxide (NO) plays a key role in the physiologic control of blood pressure and myocardial injury. Alterations in NO synthesis or bioavailability can cause vasoconstriction and might be involved in the pathogenesis of hypertension. VA has been proven to act against cardiovascular complications aroused due to hypertension [40]. VA normalized hypertension and left ventricular function in N -nitro-L-arginine methyl ester (L-NAME) induced hypertensive rat models. L-NAME is NO synthase inhibitor which causes reduction in its activity, leading to hypertension and arteriosclerosis [41][42][43]. Vanillic acid (VA) possessed cardioprotective effect, which is evident by lowered cardiac marker enzymes (CK, CK-MB, and LDH), left ventricular functions, improved tissue nitric oxide metabolite levels, and upregulated mRNA expression of eNOS in L-NAME induced hypertensive rat.

Anticancer Activity
Cancer is one of the leading causes of the deaths worldwide and chemotherapy is mainly used to treat cancer. However, occurrence of severe side effects of the drugs led to search for alternatives. Phytochemicals exert their anticancer activities by protecting critical cellular components (DNA, proteins, and lipids) from oxidative insult and interfere with proliferative activity and induction of apoptosis of cancer cells. Epidemiological and experimental studies confirmed that consumption of dietary products such as fruits and vegetables may have significant impact on the development of different types of cancers. EA is proven to be capable of decreasing oxidative stress, a hallmark of developing cancer.
EA downregulates the expression and activity of PKC in lymphoma bearing mice via decreasing the oxidative stress as evident by reduced lipid peroxidation and protein carbonylation [44]. Similarly, EA downregulates the expression of oncogene Myc and upregulates tumor suppressor gene TGF-in lymphoma bearing mice. EA also exerts apoptosis through targeting PI3K/Akt kinases that in turn resulted in attenuation of its downstream Bcl-2 family proteins in 1,2dimethyl hydrazine-(DMH-) induced rat colon carcinogenesis [45]. EA could stimulate the apoptosis by decreasing NF-B activity, thus activating the mitochondrial death pathway associated with caspase 3 activation and cytochrome C release [46]. Additionally EA also lowers the expression of cyclins against inflammation mediated cell proliferation by downregulation of NF-B in EA-administered rats. Induction of apoptosis features were observed in prostate cancer using the transgenic rat for adenocarcinoma of prostate (TRAP) model and human prostate cancer cell line (LNCaP). These findings showed that EA inhibits the early stage of prostate carcinogenesis through induction of apoptosis via activation of caspase 3 [47]. Similarly inactivation of phosphatidyl inositol 3-kinase (PI3K)/Akt pathway, Bcl-2 downregulation and increased expression of Bax, caspase 3, and cytochrome c, increased annexin V apoptotic cells, and DNA fragmentation were observed in human adenocarcinoma cells [46]. Cytotoxic effects of EA have been reported in TSGH8301 [48]. Cells treated with EA resulted in arresting cell cycle at G0/G1, promoted ROS and Ca 2+ production, activities of caspases 9 and 3, and decreased the level of ΔΨ . EA and Embelin isolated from Ardisia japonica at low concentrations synergistically increased the apoptosis thereby decreasing the proliferation in vitro. Similarly, EA alone or in combination with Embelin alone decreased tumor size and tumor cellularity in a subcutaneous xenograft mouse model of pancreatic cancer.
Angiogenesis is a critical process in tumor development and metastasis. PAs have been showed to inhibit the angiogenesis in various cancer cell lines and in vivo. Caffeic acid phenethyl ester (CAPE), a major medicinal component of propolis, showed dose dependent VEGF secretion in MDA-231 cells and formation of capillary-like tubes by endothelial cells, implicate on antiangiogenesis [49]. Antiangiogenic actions of p-CA have been studied in the ECV304 human endothelial cell line. p-CA reduced the mRNA levels of two important angiogenic factors VEGF and bFGF that stimulate permeability, proliferation, and tube formation of endothelial cells [50].
Tumor metastasis is a complex cascade that is accompanied by metalloproteinase (MMP) upregulation and extracellular matrix degradation. Metastasis allows cancer cells to proliferate and invade blood or lymphatic system, further enhancing cancer cell invasion and worsening prognosis. Pomegranate juice (Punica granatum) and its three constituents luteolin, ellagic acid, and punicic acid (L + EA + P) have been shown to interfere with multiple biological processes such as suppression of cell growth, increased cell adhesion, inhibition of cell migration, and inhibition of chemotaxis towards proteins (SDF1 ) involved in breast cancer metastasis [51]. Similarly, L + EA + P exhibited beneficial effect on metastasis of prostate cancer in SCID mouse tumor 6 Advances in Pharmacological Sciences model and −/− , K-rasG 12D prostate tumors. Studies were conducted by injecting luciferase-expressing human PCa cells in the region of the prostate ectopically and monitored the tumor progression with bioluminescence imaging weekly. Results revealed that L + E + P inhibit PC-3M-luc primary tumor growth and suppress the CXCL12/CXCR4, implicated as therapeutic target for metastasis [52]. Further, L + E + P significantly inhibited the growth and metastasis in vivo. GA is known to inhibit metastasis and invasive growth of gastric cancer cells via increased expression of RhoB, downregulation of AKT/small GTPase signals, and inhibition of NF-B activity [53].

Anti-Inflammatory Activity
Inflammation is the complex biological process of vascular tissues to harmful stimuli, such as pathogens, chemical irritants, and damaged cells or tissue. It is a protective attempt by the organism in which immune system plays a major role. Inflammatory response includes (1) migration of immune cells from blood vessels and release of mediators at the site of damage followed by (2) recruitment of inflammatory cells, (3) release of ROS, RNS, and proinflammatory cytokines to eliminate foreign pathogens, and (4) repairing injured tissues. Though acute inflammation is rapid and self-limiting, prolonged inflammation causes various chronic disorders [54]. The link between chronic inflammation and various diseases such as cancer, metabolic disorder, type II diabetes, arthritis, autoimmune diseases, neurological diseases, pulmonary diseases, and cardiovascular complications is well known. Among other natural bioactive compounds, PAs are widely recognized for their anti-inflammatory properties in wide range disease conditions.
Platelets are key mediators of inflammation that may trigger an inflammatory response in vessel wall early in the development of atherosclerosis and contribute to the destabilization of advanced atherosclerotic lesions [55]. CGA possesses antiplatelet activity by reducing release of atherosclerotic-related inflammatory mediators (sP-selectin, IL-1 , sCD40L, and CCL5) and inhibits in vivo thrombus formation [56]. In addition, antiplatelet and antithrombotic effects shown by CGA are associated with A 2A receptor activation. Similarly, CGA induced an anti-inflammatory effect in lipopolysaccharide-(LPS-) inflamed murine RAW 264.7 macrophage cells through (1) suppression of proinflammatory cytokines such as IL-1 , TNF-, and IL-6, as well as the chemokine CXCL1 through downregulation of NF-B; (2) decreased NO production mediated by downregulation of iNOS; and (3) inhibition of an important adhesion molecule Ninjurin 1 (Ninj 1) [57]. CA attenuated LPS-induced NO production possibly by inhibiting phosphorylation of p38MAPK and JNK1/2, suggesting that CA selectively inhibit different LPS-induced proinflammatory signaling cascades in RAW 264.7 macrophages [58]. Further, CGA showed antiosteoarthritis (anti-OA) properties in IL-1 -stimulated chondrocytes [54]. CGA suppressed the production of NO and PGE2 via downregulating the expression of iNOS and COX-2 which are implicated in the pathogenesis of OA [59]. In another study, CGA leads to attenuation in TLR4mediated inflammatory responses such as NO and PGE2 production in LPS-treated RAW 264.7 cells and improves HCl/EtOH-induced acute gastritis symptoms [60].

Neuroprotective Activity
Neuroprotection means prevention of nerve cells from dying and usually involves an intervention of drug treatment. It is a mechanism used to protect neuronal injury or degeneration of CNS following acute disorders. The goal of neuroprotection is to limit neuronal dysfunction after injury and attempt to maintain the possible integrity of cellular interactions in the brain resulting in undisturbed neural function. There are wide ranges of natural compounds from plants and in particular PAs have been the focus of research. These compounds may act as free radical scavengers, antiexcitotoxic agents, apoptosis inhibitors, and so forth. Two hydroxycinnamic acids CGA and CA have been shown to be associated with anti-Alzheimer's properties [61]. Mechanistically, these properties could be attributed to inhibition of BChE and AChE activities; thus butyrylcholine breaks down and slows down acetylcholine in vitro. Furthermore, CGA and CA decrease the malondialdehyde (MDA) formed due to prooxidants such as FeSO 4 , sodium nitroprusside, and quinolinic acid. Recent studies demonstrated the novel evidence explaining the neurological effects of CGA. Voltage gated potassium channels (Kv) play crucial role in the electrophysiological processes of sensory neurons and have been identified as potential therapeutic targets for inflammation and neuropathic pain disorders. The effects of CGA on the two main subtypes of Kv in trigeminal ganglion (TG) neurons, namely, the IK,V and IK,A channels, are also highlighted [62]. Upon CGA treatment, activation and inactivation currents of both IK,V and IK,A were significantly shifted toward depolarization, which implies that Kv is triggered at a lower threshold with a prolonged duration thus enhancing Kv activities in both IK,A and IK,V channels. This would gradually decrease the excitability of neurons during trigeminal hyperalgesic conditions in neuropathic and inflammatory pain.
Functional restoration of injured spinal cord by selfassembled nanoparticles composed of FA modified glycol chitosan (FA-GC) has been investigated to a considerable extent [63]. These nanoparticles protected primary neurons from glutamate-induced excitotoxicity in vitro. In addition, significant recovery in locomotor function was observed in a spinal cord contusion injury rat model that was intravenously administered FA-GC nanoparticles at 2 h after injury. Further, histological analysis revealed that FA-GC treatment significantly preserved axons and myelin and also reduced cavity volume, inflammatory response, and astrogliosis at the lesion site.
The accumulation of A deposit owing to the metabolic disorders in the brain is key step in the pathogenesis of Alzheimer's disease (AD). A can stimulate chronic neuroinflammation response and worsen the neurological degradation. Two genes, namely, presenilins 1 and 2 (PS1 and PS2) genes, or the amyloid-protein precursor (APP) plays an important role in the processing of A . PCA derived from Radix Salviae Miltiorrhizae has been found to have a protective effect on improving cognitive deficits in STZ-induced AD rats [64]. The Morris water maze test revealed that PCA (100 mg/kg) significantly prolonged the mean latency time and the path length of A PP/PS1 mice. PCA reduced the number of A positive expressions in the hippocampus and cerebral cortex of A PP/PS1 mice by immunocytochemical assay with Congo red staining and decreased remarkably APP expression levels by western blot analysis. In addition, PCA improves the cognitive deficits of AD animal by decreasing the levels of inflammatory cytokines including TNF-, IL-1 , IL-6, and IL-8.

Hepatoprotective Activity
Liver diseases are a major health problem which causes high mortality and morbidity affecting the human of all ages. Liver diseases are classified as autoimmune hepatitis, alcoholic liver disease, and viral hepatitis. Despite advances in modern medicine, still there is lack of effective drug that completely cures liver complications. Therefore, there is urgent need for search of effective drugs to replace current drugs. Natural PAs are undoubtedly valuable as a source of new medicinal agents. Hepatoprotective effect of EA in concanavalin A-(Con A-) induced hepatitis mice model has been evaluated [65]. Pretreatment with EA significantly decreased the expression levels of the toll-like receptor 4 (TLR4) and TLR2 implicated in hepatitis possibly by inflammation. Further, EA decreased the expression of inflammatory cytokines and in turn inhibit the phosphorylation of MAPK and nuclear translocation of NF-B. This may account for a reduction in TNF-, IL-6, and IL-1 expression by inhibition of NF-B-mediated transcriptional activation and I B-degradation and prevention of JNK, p38, and ERK MAPK phosphorylation.
Resistin is a hormone implicated in enhanced hepatic steatosis via downregulation of content and activities of mitochondria [38]. EA reduced serum resistin levels and improved hepatic steatosis and serum lipid profile in high-fat fed obese diabetic KK-Ay mice [66]. EA supplementation also improved hepatic steatosis by reducing triglycerides, which play key role in liver steatosis and ameliorated serum HDLC and non-HDLC levels which might be caused by reduced serum resistin levels. Moreover, EA upregulated mRNA levels of PPARa and its target genes including cpt1a suggesting that EA acts as transcriptional activator of PPARa in the liver.

Antiaging Activity
Aging is the accumulation of changes over time associated with increasing susceptibility to different diseases and ultimately leading to death. Several hypotheses have been proposed to explain how aging occurs. However, many evidences led to the general acceptance of the oxidative stress theory that the accumulation of molecular damage caused by reactive oxygen species is a major factor in aging. PCA derived from dried fruits of Alpinia oxyphylla Miq. showed strong ability to attenuate ageing alterations of antioxidative defense systems in spleen and liver male Sprague-Dawley rats [67]. Intraperitoneal injection of PCA showed effects on splenic weight, antioxidant enzyme activities, and MDA levels in spleen and liver of rats. PCA isolated from Veronica peregrine exhibited significant antiaging effects on Caenorhabditis elegans model system [68]. In the presence of PCA, lifespan of the wild-type worms increased in a dose dependent manner. PCA also improved tolerance of worms against heat shock and osmotic and oxidative stress which might result in extension of lifespan. Pharyngeal pumping rate and progeny production are the two factors evidenced for prolonged lifespan and improved longevity. Interestingly, both factors were significantly reduced after PCA exposure. In addition, PCA-treated aged worms showed improved body movement compared to untreated controls suggesting PCA could enhance health span as well as lifespan.

Antimicrobial Activity
Illnesses resulting from pathogens and the emergence of resistance to conventional antibiotics are vital concern in public health and demand new antimicrobial compounds [69,70]. Plants do not possess immune system like animals but instead synthesize phenolic compounds in response to the presence of pathogens, herbivores, and insects [71]. Research on natural products has demonstrated significant progress in the discovery of novel compounds with antimicrobial activity [72][73][74].
The antibacterial activities of PAs have been demonstrated in various studies on different pathogens. Veronica montana L. water extract and its main phenolic constituent, PCA, showed being highly effective against the growth of Listeria monocytogenes which causes listeriosis, which occurs mostly in elderly people, immune-compromised patients, and pregnant women [75,76]. The mechanism of action of PCA towards L. monocytogenes appears to be alteration of permeability of bacterial cytoplasmic membrane. Antibacterial activity and mode of action of two phenolic acids have been investigated on E. coli, P. aeruginosa, S. aureus, and L. monocytogenes. It was found that FA and GA had antimicrobial activity against the bacteria tested with MIC of 500 mg/mL for P. aeruginosa; 1500 mg/mL for E. coli; 1750 mg/mL for S. aureus; and 2000 mg/mL for L. monocytogenes with GA. Similarly, FA showed the antimicrobial activity with MIC of 100 mg/mL for E. coli and P. aeruginosa and 1100 mg/mL and 1250 mg/mL for S. aureus and L. monocytogenes, respectively. CGA has been shown to have potent antibacterial and antibiofilm properties against emerging nosocomial pathogen S. maltophilia. The minimum inhibitory concentration was shown to be 8 to 16 g mL −1 and dose-dependently reduced the adhesion of S. maltophilia to polystyrene plate. Influence of subinhibitory concentrations of CGA on virulence associated factors of enterotoxigenic S. aureus has been investigated [77]. At subinhibitory concentrations (1.25 mg/mL), CGA significantly inhibited the hemolysis and coagulase titter. Reduced binding to fibrinogen and decreased production of SEA (an enterotoxin) were observed at concentrations ranging from 1/16MIC to 1/2MIC. Finally, CA markedly inhibited the expression of sea, hla, and agr genes in S. aureus [78].

Conclusion
The new lifestyles adopted by people have considerably enhanced the risk of acquiring various diseases. Extensive research on natural PAs provides new insight into the comprehension of the cellular and molecular mechanisms responsible for the immense potential therapeutic activity of these compounds against a number of human diseases. However, evidence of such properties has been collected from cellular and animal models, while clinical studies are still lacking. It is worth mentioning that further studies and clinical trials are needed to fully establish the preventive and therapeutic effectiveness of PAs and also to prove their safety for human consumption. Moreover, natural PAs also have the potential to drive the flux of funds to the corporate sector for investment in drug development which eventually may influence the quality of life for many patients. Certainly more intricate molecular mechanisms need to be investigated to further enhance their widespread usage and pharmaceutical potential.


extfieldsofstudy: Biology Medicine

text:
An Individual-Based Diploid Model Predicts Limited Conditions Under Which Stochastic Gene Expression Becomes Advantageous

Recent studies suggest the existence of a stochasticity in gene expression (SGE) in many organisms, and its non-negligible effect on their phenotype and fitness. To date, however, how SGE affects the key parameters of population genetics are not well understood. SGE can increase the phenotypic variation and act as a load for individuals, if they are at the adaptive optimum in a stable environment. On the other hand, part of the phenotypic variation caused by SGE might become advantageous if individuals at the adaptive optimum become genetically less-adaptive, for example due to an environmental change. Furthermore, SGE of unimportant genes might have little or no fitness consequences. Thus, SGE can be advantageous, disadvantageous, or selectively neutral depending on its context. In addition, there might be a genetic basis that regulates magnitude of SGE, which is often referred to as “modifier genes,” but little is known about the conditions under which such an SGE-modifier gene evolves. In the present study, we conducted individual-based computer simulations to examine these conditions in a diploid model. In the simulations, we considered a single locus that determines organismal fitness for simplicity, and that SGE on the locus creates fitness variation in a stochastic manner. We also considered another locus that modifies the magnitude of SGE. Our results suggested that SGE was always deleterious in stable environments and increased the fixation probability of deleterious mutations in this model. Even under frequently changing environmental conditions, only very strong natural selection made SGE adaptive. These results suggest that the evolution of SGE-modifier genes requires strict balance among the strength of natural selection, magnitude of SGE, and frequency of environmental changes. However, the degree of dominance affected the condition under which SGE becomes advantageous, indicating a better opportunity for the evolution of SGE in different genetic models.

INTRODUCTION
The level of gene expression is one of the important factors which affects an individual's fitness. Many studies have shown that organisms have changed the expression level of certain genes and adapted to new habitats (Maury et al., 2008;Stern and Orgogozo, 2008;Fraser, 2013). Intuitively, natural selection has shaped the levels and patterns of gene expression, and strict control of the gene expression levels by genotype must be highly advantageous. However, recent studies suggested that for many organisms, a stochasticity in gene expression (SGE) varies phenotypes even across individuals in isogenic populations (Shahrezaei and Swain, 2008a). For example, Acar et al. (2008) engineered a yeast strain that expresses multiple phenotypes due to SGE. Similary, Tsuru et al. (2011) engineered E. coli strain and found that a SGE made a phenotype which can survive in histidine disturbed condition. Even in multicellular organisms, SGE has been reported to be beneficial in cell-fate determination (Colman-Lerner et al., 2005;Kaern et al., 2005;Losick and Desplan, 2008;Gomes et al., 2011) and cell growth (Fraser et al., 2004;Newman et al., 2006;Batada and Hurst, 2007;Lehner, 2008).
Throughout this study, we refer SGE as stochasticity in gene expression that causes stochastic variation of a hypothetical phenotype which is defined for each individual and subject to natural selection (See Mineta et al., 2015 for more details). Note that the phenotypic variation of interest in this study is among individuals, and that we do not specifically study variations of gene expression along the time (such as circadian rhythm-or developmental stage-dependent gene expression) or among organs. The major consequence of SGE is a provision of stochastic phenotypic variation and differences in fitness among individuals even in an isogenic population. The importance of SGE has been predicted in several theoretical studies as a "bet-hedging" strategy, for instance in microbes in fluctuating environments (Kussell and Leibler, 2005). They compared the growth rates of two populations, with and without stochastic switching in phenotype, and found that the stochastic switching can be favored under infrequent environmental fluctuation. Acar et al. (2008) also theoretically compared the growth rates of populations assuming two phenotypes with the stochastic phenotypic switch. They suggested that fast phenotypic switching becomes advantageous in quickly fluctuating environments. In addition, Salathe et al. (2009) incorporated asymmetric fitness landscape, in which the cost of being maladaptive is different in different environments. They found that under the asymmetric fitness landscape, the evolution of a stochastic phenotypic switching became difficult. These theoretical studies were excellent examples of how SGE can potentially be associated with evolution in the biological context, although they used rather complex and sometimes very specific models of SGE. Thus, the general importance of SGE in molecular evolution is still largely an open question.
In the present study, we performed individual-based computer simulations to quantitatively evaluate the condition in which enhanced SGE can be advantageous in fluctuating environments. Previous studies suggested that SGE can be caused by both extrinsic factors like transcription factor activity, and intrinsic factors like mRNA decay ratio (Elowitz et al., 2002;Raser and O'Shea, 2005). Therefore, SGE itself is non-genetic, but regulatory systems that enhance or reduce the magnitude of SGE could be controlled by genetic mechanisms (Mineta et al., 2015). If the effect of SGE on fitness is environment-dependent, and if the magnitude of SGE is regulated by a genetic component ("SGE modifier gene" in Mineta et al., 2015), such a genetic component might evolve and enhance the magnitude of SGE under certain conditions. The production and degradation rates of protein and mRNA critically are known to affect the magnitude of SGE (Shahrezaei and Swain, 2008b;Li and Xie, 2011;Grima et al., 2012), implying that the genes regulating the production-degradation pathway might be SGE modifier gene candidates.
In the current study, we considered two loci: first we constructed a simple population genetic model assuming that individual phenotype was basically determined by one locus. The allele set of this locus provides the basal genotype, which primarily determines the individual phenotype, which in turn is subject to stochastic deviation due to SGE given by a Gaussian function (Figure 1). Second, we considered the other locus that regulates the magnitude of SGE (i.e., SGE modifier gene). The fitness of organisms was then determined by their basal genotypes and stochastic variation, whereas the level of fitness variation was determined by the genotype at the modifier locus in our model. Recently developed models on SGE tend to incorporate relationships among several processes, which take into consideration noises in transcription, translation, mRNA and protein decay (Draghi and Whitlock, 2015;Wolf et al., 2015). Our model was constructed to be rather simple because we wanted to address the questions whether and when SGE and its modifier gene can evolve more in general, although we acknowledge the fact that part of our assumptions might be too simple to explain specific empirical data.
Conducting the individual-based simulations above, we evaluated the conditions in which the frequency of SGEenhancing alleles can increase in the population. Although we intended to evaluate the predictions made by Mineta et al. (2015) above, there is an important difference between the assumptions behind this study and those made by Mineta et al. (2015). In Mineta et al. (2015), the symmetric fluctuation of fitness by SGE was assumed. Therefore, the SGE modifier gene was basically assumed to evolve selectively neutrally under stable environmental conditions in the previous study. However, if an individual has a highly advantageous basal genotype in the environment, SGE which fluctuates the phenotype around the optimal may cause more disadvantageous effects than advantageous effects. Similarly, SGE may cause more advantageous effects when an individual's basal genotype is highly disadvantageous. To address this issue, we assumed viability selection with 0-1 fitness boundary in the present study. If the phenotype is affected by strong viability selection rather than fecundity selection, a certain level of SGE may decrease the viability of individuals to 0. In such cases, viability = 0 acts as the absorbing boundary so that even if the effect of SGE on the phenotype is symmetric, the average effect of SGE on viability can be advantageous. Similarly, SGE occurring in the optimal FIGURE 1 | The outline of the individual-based model in this study. The viability of each individual is primarily determined by the genotype of locus A, and the magnitude of SGE, which causes fitness deviation, is regulated by locus B. In our model of viability selection, viability 0 and 1 are assumed to be absorbing boundaries.
basal genotype can be disadvantageous because viability = 1 is also the absorbing boundary (for another means of making the evolutionary effect of SGE asymmetric, see Zhang et al., 2009). Previously, many studies have reported that there are examples showing that the expression level of a single or a few major genes which relate to stress or disease tolerance can dramatically change individuals' viability (Hou et al., 2009;Morran et al., 2011;Shi et al., 2012;Yang et al., 2012;Ravaux et al., 2013). Therefore, SGE acting on these genes would potentially have a large effect on viability. In such cases, the evolution of the SGE modifier gene might be influenced by the asymmetric SGE around the viability boundary.

Individuals
Individuals are assumed to be diploid and monoecious, and two loci (locus A and B) were considered in our model (Figure 1). Locus A regulates the trait directly affected by natural selection, and locus B regulates the magnitude of SGE. For simplicity, we assumed that each locus has two alleles (allele A and a for locus A, allele B and b for locus B), and the degree of dominance was manipulated. The fitness of each individual was determined by the genotype and the effect of SGE (see Viability Selection section). In this model, we assumed free recombination between loci, and we did not consider new mutation. The linkage disequilibrium might alter the fate of SGE evolution, but it is beyond the scope of the current study. The code for our simulations can be provided by T.M. upon request.

Environmental Change
We consider the fluctuating environment assuming that two environments, environment 1 and environment 2, switch every T generation. In the different environment, each individual is affected by different viability selection.

Viability Selection
In environment 1, the viability of individuals with three genotypes at locus A, AA, Aa, and aa, is defined as 1, 1 − h 1 S, and 1 − S, respectively, where h 1 is the degree of dominance (0 ≤ h 1 ≤ 1) and S is the selection coefficient. In contrast, the viability of individuals with the genotypes AA, Aa, and aa becomes 1 − S, 1 − h 2 S, and 1, respectively (0 ≤ h 2 ≤ 1), in environment 2. Therefore, allele A becomes adaptive in environment 1, and allele a becomes adaptive in environment 2. In addition, the viability is expected to vary among individuals due to SGE (Wang and Zhang, 2011). A normally distributed random variable q with mean 0 and standard deviation σ is considered, which represents the effect of SGE on viability of individuals in addition to the effect of locus A. Later, we consider the effect of locus B, which influences the value of σ. Namely, each genotype at locus B (BB, Bb, and bb) defines a different level of σ (σ BB , σ Bb , and σ bb ), and we assumed σ BB ≥ σ Bb = h b σ BB ≥ σ bb = 0 (0 ≤ h b ≤ 1). Thus, in environment 1, the viability of individuals with genotypes AA, Aa, and aa can be described as 1 + q, 1 − hS + q, and 1 − S + q, respectively, and locus B affects the range of q ( Table 1). Note that in this study, we assume that viability 0 and 1 are the absorbing boundary, and viability lower than 0 and higher than 1 are considered as viability 0 and 1, respectively. Because locus B regulates the magnitude of SGE, we call this locus SGE modifier gene in the following sections. σ bb = 0 (no SGE due to the genotype bb) would be an unrealistic assumption in the real world, but it was set as such in order to illuminate the effect of SGE modifier gene on fitness. 1 | Viability of three genotypes AA, Aa, and aa in two environments.

AA
Aa aa

Mating
After the viability selection, surviving individuals mate to create the offspring population. In this model, we assume random mating with constant population size N and discrete generations. Therefore, mating pairs are randomly chosen to allow a replication from the surviving individuals and make the offspring until the population size reaches N. These offspring become the parental population in the next generation. We assume that each mating pair can make only one offspring per mating event. Mineta et al. (2015) predicted that SGE can reduce effective population size and thus, fixation probability of slightly deleterious mutations might be high under SGE. First, we tested this prediction in our model, assuming fixation of allele B at locus B (i.e., σ = σ BB ). We set N = 1000, h 1 = 0.5 (no dominance) and T = infinite (stable environment 1), and considered the initial condition that the frequency of advantageous allele A is (2N − 1)/2N and deleterious allele a is 1/2N at locus A. Starting from this condition, we simulated how many times the deleterious allele could fix in the population under variable S and σ. We considered four S-values representing different levels of natural selection; S = 0.00025, 0.0005, 0.00125, 0.0025, and 0.005 (2NS = 0.5, 1.0, 2.5, 5.0, and 10.0, respectively). We also examined five σ BB values; σ BB = 0 (without SGE), 0.001, 0.01, 0.1, 1.0. The simulation was replicated 50,000 times in each parameter set.

Increased Fixation Probability of Deleterious Mutations Due to SGE
In Figure 2, fixation probabilities, relative to the expected fixation probability under strict neutrality (=1/2N), were measured with/without SGE and with different strengths of natural selection. A relative increase in the fixation probability was observed with SGE, as opposed to without, particularly when σ and 2NS were large, suggesting that the positive effect of SGE on the fixation probability of deleterious mutation was larger when the allele exhibited a more deleterious effect. However, even under very strong effects of SGE (σ BB = 1.0), the fixation probability of the deleterious alleles never became higher than in the case of strict neutrality (i.e., relative fixation probability of 1.0 in Figure 2). As shown in Mineta et al. (2015), SGE gradually decreases the effective population size as the number of generations increase. Thus, although SGE greatly increased the fixation probability of the deleterious mutation compared to the cases without SGE, SGE is most likely subject to purifying selection in these conditions. FIGURE 2 | Average fixation probability of deleterious allele (a) relative to neutral expectation (1/2N). Population size (N) was fixed to be 1000 and different natural selection strengths (2NS) were examined by changing selection coefficient (S) under different magnitudes of SGE (given by the standard deviation of SGE fitness effect, σ). Five different selection coefficients, S = 0.00025, 0.0005, 0.00125, 0.0025, and 0.005 (2NS = 0.5, 1.0, 2.5, 5.0, and 10.0, respectively), and five different magnitudes of SGE, σ = 0 (without SGE), 0.001, 0.01, 0.1, 1.0 were considered. Viability of each genotype was given as AA = 1 + q, Aa = 1 − 0.5S + q, and aa = 1 − S + q, where q represents SGE fitness effect by choosing a random value from normal distribution with mean = 0 and standard deviation = σ. Initial frequency of a was 1/2N. Fixation probability was calculated among 50,000 replicates in each parameter set assuming constant population size, random mating, and no mutation, and this process was replicated 100 times to calculate the average and standard deviation of the fixation probability.

Evolutionary Fate of SGE Modifier Gene under Stable and Fluctuating Environment
We subsequently investigated the evolutionary fate of the genetic regulation on SGE. Initially, we examined which of the enhanced or suppressed SGE was advantageous in a stable environment (environment 1). We considered Hardy-Weinberg equilibrium for both loci (A and B) with allele frequencies of 0.5 as the initial condition. Three population sizes, N = 100, 1000, and 10,000, were examined to evaluate the effect of the population size and selection coefficient separately. For other parameters, we set h 1 = h b = 0.5 and T = infinite. By changing the selection coefficient of locus A (S) and the magnitude of SGE (σ BB ), we examined whether the frequency of allele B, which enhances the magnitude of SGE, could increase in the population or not. The simulation was replicated 1000 times in each parameter set; if the average frequency of allele B was larger than 0.5 after 1000 generations, we considered that the enhanced SGE was advantageous. Although 1000 generations is too short to get the equilibrium state in large population size, our strategy to observe the change of the average frequency of allele B from the initial frequency 0.5 allows us to evaluate whether the enhanced SGE is advantageous or not, even within a short generation. As the result, in the stable environment, the allele frequency of B was substantially lower than 0.5 in all conditions, indicating that enhanced SGE was disadvantageous compared with suppressed SGE and therefore, decreased its frequency after long generations (Figure 3). In N = 100, the effect of genetic drift became large, which caused the higher frequency of B than that in N = 1000 and 10,000 ( Figure 3A). In Supplementary Figures 1, 2, we show the average frequency of allele A and B in each generation with N = 1000 and S = 0, 0.5, and 0.9 as the representatives. If S was larger than 0, allele A became advantageous in environment 1 and increased in frequency and finally fixed (Supplementary Figure 1, if S = 0, both of allele A and a had the same fitness and thus, the average frequency of A was about 0.5). Allele B frequency never increased in the population (Supplementary Figure 2), suggesting that SGE decreased the average fitness in the population by causing the deviation from the optimal phenotype, and acted as a load as predicted by Mineta et al. (2015). The disadvantageous effect of allele B increased as S and σ BB became large.
Next, we examined the evolutionary fate of the SGE modifier gene in fluctuating environments. Similarly to the above scenarios, we considered Hardy-Weinberg equilibrium for all loci A and B as the initial condition and three population sizes (N = 100, N = 1000, and N = 10,000). We also set h 1 = h 2 = h b = 0.5. The switch between two different environments 1 and 2 was caused every T generations (initial environment was 1). As explained in the Model section, individuals are affected by different selection in different environments (Table 1). Thus, as shown in Supplementary Figure 3, polymorphism at locus A was maintained during the simulation and the population could adapt to the current environment using this polymorphism. However, just after the environmental change, most individuals would have low fitness and SGE could be adaptive. As shown in Figure 4, the enhanced SGE could be advantageous only in very restricted conditions; when the natural selection in each environment was strong, environment switched frequently and the magnitude of SGE was large (e.g., fixation of the allele B in the population in N = 1000, S = 0.9, T = 1 and large σ BB ; Figure 4B). If the magnitude of SGE was small, allele B could be maintained in wide parameter range, but the frequency was smaller than 0.5 in N = 1000 and 10,000, suggesting that enhanced SGE was not advantageous overall (Figures 4B,C). We confirmed that the allele B was always lost from the population after 10,000 generations in these cases (data not shown). Exceptionally, in N = 100, the combination of T = 10, large S and σ BB caused the increase of allele B frequency ( Figure 4A). This is presumably because polymorphism at locus A was lost in these cases. Without polymorphism at locus A, FIGURE 3 | The average frequency of SGE enhancing allele B under stable environment 1. Viability of each genotype in this environment was 1 + q, 1 − h 1 S + q, and 1 − S + q for AA, Aa, and aa, respectively. The magnitude of SGE was regulated by locus B as σ BB ≥ σ Bb = h b σ BB ≥ σ bb = 0 (0 ≤ h b ≤ 1). As the initial condition, Hardy-Weinberg equilibrium was assumed for locus A and B. Parameters h 1 and h b were set to 0.5, and T was set to infinite. Different panels show the result in different population size: (A) N = 100, (B) N = 1000, and (C) N = 10, 000. The simulation was replicated 1000 times for each parameter set and gray scale shows the average frequency of allele B after 1000 generations. In the simulation, constant population size, random mating, free recombination, and no mutation were assumed. adaptation of the locus directly affected by natural selection is impossible, and the advantageous effect of SGE might increase. Fixation of the allele at locus A would occur more frequently in T = 100. In this case, however, long intervals between environmental switches also caused the loss of allele B before the first environmental switch. This is presumably why allele B frequency did not increase in the population (Figure 4A). Only when population size was large (N = 1000, 10,000) and the magnitude of SGE was small (σ BB = 0.1), allele B could be maintained until the first environmental switch. In such cases, allele B frequency could increase in the population (Figures 4B,C). Thus, high frequencies of allele B in T = 10 and 100 was a consequence of the loss of polymorphism at locus A. As shown in the next paragraph, without polymorphism at locus A, allele B frequency increased even with large T, which is consistent with the result shown in Figure 4A. With N = 100 and σ BB = 0.01, the frequency of allele B was around 0.5 suggesting that the evolution of allele B may occur almost neutrally in these cases. In Supplementary Figure 4, we show the frequency change of allele B in each generation when N = 1000, S = 0.8 or 0.9 and T = 1 or 10. As consistent with the result in Figure 4B, the allele B could be advantageous and increase its frequency only under extremely strong selection (S = 0.9) and frequent environment switch (T = 1; Supplementary Figure 4D).
Above results suggest that the polymorphism at locus A made the adaptation of the locus directly affected by natural selection possible and decreased the advantageous effect of SGE. Thus, if the population did not have the polymorphism at locus A, the importance of SGE might increase in a fluctuating environment. To examine this hypothesis, we conducted the simulation FIGURE 4 | The average frequency of SGE enhancing allele B under fluctuating environments. Viability of each genotype 1 + q, 1 − h 1 S + q, and 1 − S + q in environment 1, and 1 − S + q, 1 − h 2 S + q, and 1 + q in environment 2 for AA, Aa, and aa, respectively. Two environments switched every T generation. The magnitude of SGE was regulated by locus B as σ BB ≥ σ Bb = h b σ BB ≥ σ bb = 0 (0 ≤ h b ≤ 1). As the initial condition, Hardy-Weinberg equilibrium was assumed for locus A and B. Parameters N was set to 1000 and h 1 , h 2, and h b were set to 0.5. In this simulation, four σ BB values, σ BB = 1.0, σ BB = 0.5, σ BB = 0.1, and σ BB = 0.01 were considered. Different panels show the result in different population size: (A) N = 100, (B) N = 1000, and (C) N = 10,000. The simulation was replicated 1000 times for each parameter set, and gray scale shows the average frequency of allele B after 1000 generations. In the simulation, constant population size, random mating, free recombination, and no mutation were assumed. assuming fixation of allele A at locus A. We considered the same parameters as we considered above, except for locus A. As we expected, the enhanced SGE could be more advantageous in a wider parameter range under the allele A fixation scenario than under the polymorphic scenario ( Figure 5) even in T = 10. However, the strong selection was still required and the enhanced SGE could not be advantageous in S ≤ 0.7. Also in T = 100, enhanced SGE could not be advantageous except in σ BB = 0.1. As explained above, this is because allele B was lost before the first environmental switch in most of the cases. If we assumed initial fixation of allele a at locus A, enhanced SGE became advantageous in the initial environment, and allele B frequency could increase in the population in a wider parameter range. In Supplementary Figure 5, we show the frequency change of allele B in each generation when N = 1000, S = 0.8 or 0.9 and T = 10.

The Effect of the Degree of Dominance
In the simulations above, we assumed additive effect for both locus A and B. Next, we examined the effect of the degree of dominance for the evolutionary fate of the SGE modifier gene. We considered three values for h 1 , h 2 , and h b (0, 0.5, and 1) and simulated the frequency of allele B assuming initial Hardy-Weinberg equilibrium at both loci with fluctuating environments. We used N = 1000 to examine the effect of the degree of dominance because the above results suggested that the parameter range for allele B to be advantageous was not affected by population size except in circumstances where the polymorphism at locus A was no longer present at N = 100 ( Figure 4A). When h 1 or h 2 = 0, the enhanced SGE could not become advantageous even in extremely strong selection (S = 0.9) and frequent environment switch (T = 1; Supplementary  Figures 6A,B). In these cases, genotype Aa can have high fitness FIGURE 5 | The average frequency of SGE enhancing allele B under fluctuating environments assuming fixed allele A. Viability of genotype AA was 1+ q in environment 1 and 1 − S + q in environment 2. Two environments switched every T generation. The magnitude of SGE was regulated by locus B as σ BB ≥ σ Bb = h b * σ BB ≥ σ bb = 0 (0 ≤ h b ≤ 1). In this simulation, four σ BB values, σ BB = 1.0, σ BB = 0.5, σ BB = 0.1, and σ BB = 0.01 were considered. As the initial condition, Hardy-Weinberg equilibrium was assumed for locus B and allele A was assumed to be fixed at locus A. Parameter h b was set to 0.5. Different panels show the result in different population size: (A) N = 100, (B) N = 1000, and (C) N = 10, 000. The simulation was replicated 1000 times for each parameter set and gray scale shows the average frequency of allele B after 1000 generations. In the simulation, constant population size, random mating, free recombination, and no mutation were assumed. on average, and polymorphism at locus A can be maintained for relatively longer generations even when the natural selection is strong (small q in Charlesworth and Charlesworth, 2009, p. 55). Therefore, the importance of SGE just after the environmental switch decreased, and it became difficult for the enhanced SGE to be advantageous. On the other hand, high h 1 or h 2 decreased the fitness of heterozygotes and thus, increased the advantage of SGE just after the environmental switch. If we set h 1 or h 2 = 1, the enhanced SGE could be advantageous in a wider parameter range than in h 1 = h 2 = h b = 0.5 (Supplementary Figures 6C,D). In these cases, allele B almost always fixed in S = 0.8, T = 10, but did not in S = 0.7 and T = 100.
In contrast to h 1 and h 2 , h b did not have a large effect on the parameter range in which the enhanced SGE could be advantageous (average frequency of allele B > 0.5, Supplementary Figure 7). However, small h b decreased the disadvantageous effect of SGE in heterozygotes and allowed the maintenance of allele B in the population (Supplementary Figure 7A). Especially in T = 100, fixation at locus A would occur frequently. Thus, if allele B was maintained until the first environmental switch, this allele could have a large chance to fix in the population. As a result, average frequency of allele B became higher than in shorter T cases (Supplementary Figure 7A).

Evolutionary Fate of SGE Modifier Gene under Symmetric Effect of SGE on Fitness
Thus far, we considered viability selection with the 0-1 boundary. This boundary can cause an asymmetric effect of SGE on fitness, which might influence the evolutionary fate of the SGE modifier gene. To evaluate the impact of this boundary on the results, we examined the evolutionary fate of a SGE modifier gene under the condition that the SGE effect on fitness was always symmetric. We considered the same selection scheme shown in Table 1 and removed the upper boundary 1 to allow individuals to take fitness higher than 1. The fitness of each individual was defined as the mating probability calculated by the relative value to the highest fitness in the population (fecundity selection or sexual selection). To make sure that fitness did not take a negative value, we considered S = 0.5 and σ BB = 0.1, in which nearly 100% of individuals with disadvantageous genotypes could have absolute fitness >0. Thus, in this model and parameter set, the effect of SGE on fitness became symmetric and never changed the average fitness. For other parameters, we set h 1 = h 2 = h b = 0.5 and considered three population size (N = 100, 1000, and 10,000). In Figure 6, we show the average frequency of allele B after 1000 generations. In contrast to the results under the viability selection, the SGE modifier gene evolved neutrally and took allele frequencies around 0.5 in changing environments when we assumed the symmetric effects of SGE on fitness. This result supports our suggestion that if there is a mechanism to make asymmetric effects of SGE on fitness and the average fitness is increased by SGE, enhanced SGE can be advantageous and evolve in the population as mentioned in Zhang et al. (2009).

DISCUSSION
In the present study, we tested a two-locus model in which one locus is under viability selection and the other one regulates the magnitude of SGE. Our results suggest that SGE acting on the gene under viability selection causes deleterious effects under the stable environment in our model. Our results also suggest that although enhanced SGE occasionally became advantageous in fluctuating environments, the enhanced SGE FIGURE 6 | The average frequency of SGE enhancing allele B under symmetric effect of SGE on fitness. Viability of each genotype 1 + q, 1 − h 1 S + q, and 1 − S + q in environment 1, and 1 − S + q, 1 − h 2 S + q, and 1 + q in environment 2 for AA, Aa, and aa, respectively. The magnitude of SGE was regulated by locus B as σ BB ≥ σ Bb = h b * σ BB ≥ σ bb = 0 (0 ≤ h b ≤ 1). In this simulation, individuals were allowed to take fitness higher than 1, and the relative fitness to the highest fitness in the population was used as the mating success. Parameters S and σ BB were set to 0.5, 0.1 respectively, and h 1 , h 2 , and h b were set to 0.5 and the magnitude of SGE was regulated by locus B as σ BB ≥ σ Bb = h b * σ BB ≥ σ bb = 0 (0 ≤ h b ≤ 1). In panel (A), Hardy-Weinberg equilibrium was assumed for locus A and B, and in panel (B), allele A was assumed to be fixed at locus A. In each case, three population size N = 100, 1000, and 10,000 was considered. The simulation was replicated 1000 times for each parameter set and gray scale shows the average frequency of allele B after 1000 generations. In the simulation, constant population size, random mating, free recombination, and no mutation were assumed. was disadvantageous in the majority of the parameter range. The major cause for the limited parameter range in which the enhanced SGE became advantageous in our model is our assumption of viability selection with a 0-1 boundary. Although it is beyond the scope of this study, the enhanced SGE might be advantageous in a wider parameter range when the viability of the adaptive basal genotype is lower than 1 and SGE can increase the fitness of individuals with the genotype. However, given that we assumed rather strong viability selection here, it would be inevitable for wild types to reach their adaptive optimal within a short evolutionary time. In other words, our results suggest that it is difficult for enhanced SGE to be advantageous in the population after fine-tuning adaptive evolution.
Compared to recently developed complex models (Draghi and Whitlock, 2015;Wolf et al., 2015), our model was designed much simpler in order to provide a foundation for future studies to evaluate more complicated case scenarios. Nevertheless, we believe that the main conclusions of this study are largely applicable to various real case scenarios, because many examples indicate that the expression level of a single or a few major genes can dramatically change the viability of individuals (Hou et al., 2009;Morran et al., 2011;Shi et al., 2012;Yang et al., 2012;Ravaux et al., 2013). Our results showed that in a stable environment, SGE acts as a noise, reduces the effective population size, and increases the fixation probability of slightly deleterious alleles, as predicted by Mineta et al. (2015) (Figure 2). In this case, SGE was always disadvantageous and the allele that enhances the magnitude of SGE could not increase its frequency in the population (Figure 3 and Supplementary Figure  2). Despite the disadvantageousness, SGE itself can be nongenetic and inevitably caused by both extrinsic and intrinsic molecular mechanisms (Elowitz et al., 2002;Raser and O'Shea, 2005). Therefore, our result suggests that non-genetic SGE can significantly change the fixation probabiliy of slightly deleterious alleles and have an impact on the evolutionary processes of populations (See also Mineta et al., 2015).
In fluctuating environments, on the other hand, the gene which enhances the magnitude of SGE could be advantageous, especially if one allele was fixed at the locus directly contributing to the phenotypic trait (locus A; Figure 5). This result suggested that the enhanced SGE can be advantageous in fluctuating environments and was consistent with previous studies supporting the selective advantage of stochastic switching in fluctuating environments (Kussell and Leibler, 2005;Acar et al., 2008;Tsuru et al., 2011). However, with polymorphism at locus A, the conditions under which the enhanced SGE became advantageous were more tightly limited (Figure 4), which was most likely due to interactions between adaptive responses at loci A and B. Namely, extremely strong natural selection and frequent environment switch were necessary for the enhanced SGE to be advantageous. In this model, locus A can be adapted to the environment during T generations, which would have decreased the fitness of individuals with SGE after T generations. In other words, in a fluctuating environment, SGE can be advantageous just after the environmental change because at that time, most individual would have the genotype which is adaptive to the previous environment, and these individuals are disadvantageous without SGE. Still, polymorphism at locus A makes the adaptation to the new environment possible. Therefore, the advantage of SGE decreases as the frequency of the genotype adaptive to the new environment increases, and eventually SGE becomes disadvantageous. This is presumably why the enhanced SGE could not be advantageous when the interval between environmental switches (T) was long.
The advantageous effect of stochastic variation in phenotype has been suggested in previous studies (Bull, 1987;Gavrilets and Hastings, 1994;Kussell and Leibler, 2005;Acar et al., 2008;Salathe et al., 2009), and our results are consistent with these studies. However, our model incorporated the non-inherited stochastic fluctuation in phenotype caused by SGE, genetic control of SGE, and a 0-1 boundary of viability. As mentioned in the Introduction, SGE itself is non-genetic (Elowitz et al., 2002;Raser and O'Shea, 2005), but the magnitude of SGE can be genetically regulated. Considering previous theoretical studies suggesting the importance of the production and degradation rate of protein and mRNA on the magnitude of SGE (Shahrezaei and Swain, 2008b;Li and Xie, 2011;Grima et al., 2012), genes that encode proteins functioning in these processes (e.g., RNA polymerase, nuclease or protein act as chaperon) might be SGE modifier gene candidates. Although Bull (1987) and Gavrilets and Hastings (1994) considered the genetic control of the fluctuation of phenotype and its non-inheritance, they assumed that the same locus regulates the basal phenotype and its fluctuation. Salathe et al. (2009) also considered the genetic control of the fluctuation of phenotype, but they assumed that the fluctuating phenotype is inherited to the next generations. Given that stochastic fluctuation in phenotype would not be inherited unless it occurred in the germ cell, our assumption of non-inherited SGE would be realistic. In addition, there are several reports showing examples that the expression level of a single or a few major genes, which relate to stress or disease tolerance, can dramatically change individuals' viability (Hou et al., 2009;Morran et al., 2011;Shi et al., 2012;Yang et al., 2012;Ravaux et al., 2013). Our results can be applicable to these examples in principle although a more complex model is required to explain the detailed empirical data in future studies.
Our result also suggested the importance of the degree of dominance at the locus affected by natural selection for the evolutionary fate of the SGE modifier gene (Supplementary Figure 6). The degree of dominance at these loci regulates the fitness of heterozygotes and therefore, affects the advantage of SGE as mentioned above. The maintenance of the polymorphism in heterozygotes is a feature of diploid organisms and was not considered in the previous studies (Kussell and Leibler, 2005;Acar et al., 2008). If the deleterious allele in one environment is dominant, natural selection can efficiently decrease the frequency of this allele and therefore, the advantage of the enhanced SGE just after the environmental change becomes large. On the other hand, if the deleterious allele is recessive, a population can maintain this allele with relatively high frequency and decrease the advantage of SGE. In contrast to the locus under natural selection (locus A), the degree of dominance at the SGE modifier gene (locus B) showed only a small effect on the parameter range in which enhanced SGE becomes advantageous (Supplementary Figure 7). This may be because of the initial condition of Hardy-Weinberg equilibrium at locus B, under which individuals can easily make homozygote of the advantageous allele at this locus. If the initial frequency of the advantageous allele is low and almost all of advantageous alleles are heterozygous, degree of dominance would become important. Although we did not consider other initial condition, if the initial frequency of allele B is very low, the effect of h b might become larger. In addition, we found that small h b prevents the enhanced SGE from being removed from the population in wide parameter ranges. Even if the enhanced SGE was not advantageous, the maintenance of allele B would increase the probability of evolution of enhanced SGE by genetic drift in this case.
Finally, we will briefly discuss one interesting feature of SGE we found. As discussed above, in most cases, SGE acts as a noise which decreases individual's fitness. However, SGE was found to be capable of increasing heterozygosity at the selectively neutral locus when there was strong selection under a fluctuating environment even if the enhanced SGE itself was disadvantageous (Supplementary Figure 8). This means that SGE can contribute to the maintenance of genetic diversity within the population, supporting that SGE was indeed not just a noise in the models of the fluctuating environment. Given the fact that environments and their effects on living organisms do fluctuate in many ways in nature (Rees et al., 2004), the potential contribution of SGE to the level of genetic diversity and effective population size may also be of evolutionary importance.

CONCLUSIONS
Although it is difficult for enhanced SGE to be advantageous, SGE itself affects the genetic structure of the population; increasing fixation probability of slightly deleterious mutations in stable environments and increasing heterozygosity in fluctuating environments. The enhanced SGE could be advantageous only in very tight, and maybe unrealistic conditions for diploid organisms. However, like the effect of the degree of dominance, other genetic models might affect the evolutionary fate of the SGE modifier gene. Note here that our simulations largely neglected genomic heterogeneity and complex genetic architecture with which SGE in one gene can be indirectly associated with phenotypic variance. It is also noteworthy that linkage disequilibrium between a SGE modifier gene and its target gene might alter the fate of SGE evolution. As discussed in Mineta et al. (2015), there are several sources of SGE. Some papers mentioned that the competition between transcription factors and nucleosomes cause SGE (Tirosh and Barkai, 2008;Choi and Kim, 2009;Macneil and Walhout, 2011). Others mentioned that cluster of essential genes around open chromatin leads to more robust expression levels (Batada and Hurst, 2007;Field et al., 2008). These cases including more realistic number of loci relating to the phenotype should be examined in the future studies both theoretically and experimentally.

FUNDING
This work was supported by the Swiss National Science Foundation Grant to HA (No. 31003A_125213).