The protective effects of edible plants against AFB1-induced toxicity.
\r\n\tThis book shall focus on these antisense guided sequence specific silencing molecules with different mechanisms and potency for gene silencing, providing the reader with a comprehensive overview of the current state-of-the-art in ASO based therapeutics, featuring the more recent developments in terms of clinical translation and the use of nanomedicine for the effective delivery of therapeutic nucleic acids towards precision medicine.
",isbn:null,printIsbn:"979-953-307-X-X",pdfIsbn:null,doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,hash:"96f256f5bb2e750c7496b3c0b62cb95a",bookSignature:"Prof. Pedro Baptista and Prof. Alexandra R Fernandes",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/9571.jpg",keywords:"gene therapy, gene silencing, genome modulation, post-transcriptional modulation, modified oligonucleotides, PNAs, LNAs, siRNA, antisense nucleotides, vectorization of antisense nucleotides, nanotheranostics, clinical translation, nanoparticles for gene delivery",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"October 25th 2019",dateEndSecondStepPublish:"November 15th 2019",dateEndThirdStepPublish:"January 14th 2020",dateEndFourthStepPublish:"April 3rd 2020",dateEndFifthStepPublish:"June 2nd 2020",remainingDaysToSecondStep:"a year",secondStepPassed:!0,currentStepOfPublishingProcess:5,editedByType:null,kuFlag:!1,biosketch:null,coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"82671",title:"Prof.",name:"Pedro",middleName:null,surname:"Baptista",slug:"pedro-baptista",fullName:"Pedro Baptista",profilePictureURL:"https://mts.intechopen.com/storage/users/82671/images/system/82671.jpg",biography:"Pedro Viana Baptista (b.1972) holds a degree in Pharmaceutical Sciences (1996) from the Universidade de Lisboa. He obtained his PhD in Human Molecular Genetics from the School of Pharmacy, University of London in 2000. In 2001 moved to FCT-NOVA where he created the Nanomedicine Group, which he leads. Currently, he is Full Professor of Molecular Genetics & Nanomedicine at the Department of Life Sciences, FCT-NOVA and responsible for the NanoImunoTech Group – Nanomedicine in the Applied Biomolecular Sciences Research Unit. His work focuses on the biomedical applications of nanoparticle-based strategies towards light-induced cancer therapy and as gene silencing platforms (including siRNA, antisense and nanobeacons). 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Fernandes is an Assistant Professor at the Department of Life Sciences, FCT-NOVA where she leads the group of Cancer Therapeutics dedicated to assessing novel compounds against tumor cells and elucidate the underlying molecular mechanisms. 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Epidemiological studies among all continents found that Asia and Africa have higher incidence rate than western world [1]. Hepatocellular carcinoma (HCC), arise due to excessive growth of abnormal liver cells, is most commonly found among all liver cancer types [2]. Four main potential causes of HCC have been identified as viral infection (chronic hepatitis B and C), metabolic syndrome (diabetes and nonalcoholic fatty liver disease), immune-related disease (autoimmune hepatitis), and toxic substances (alcohol and aflatoxins) [3].
Aflatoxin B1 (AFB1) is a noxious carcinogen produced by certain fungi Aspergillus flavus and A. parasiticus which mostly contaminate in agricultural products such as rice, chili, and peanuts. AFB1 is a Class 1 carcinogen classified by the International Agency for Research on Cancer (IARC), suggesting sufficient evidence of carcinogenicity caused by AFB1 in both animals and human [4]. Consequently, it is considered as a serious contaminant in many foodstuffs.
Once the AFB1 is absorbed through human body, it is metabolized at the liver site by phase I metabolizing enzymes including hydroxylation, hydration, demethylation, and epoxidation. Nontoxic metabolites are resulted from hydroxylation, hydration, and demethylation while the reactive metabolite, AFB1-8,9-epoxide, is resulted from epoxidation [3, 4]. AFB1-8,9-epoxide is the genotoxic form and can react efficiently with DNA at the N7 site of guanine to form AFB1 adduct. This adduct can adversely affect DNA sequence and genetic materials. However, human defensive mechanisms are able to detoxify AFB1 toxicity through phase II metabolism enzymes. AFB1 can be converted into excretable forms after binding with glutathione and glucuronic acid generated by specific enzyme, glutathione S-transferase (GST) and UDP-glucuronosyltransferase (UGT), respectively [5, 6, 7]. Besides acute toxicity such as hepatic necrosis, bile drug proliferation, edema, and lethargy could also be observed after exposure to high dose of AFB1 [8].
Regarding the current situation, there are many ways to avoid the risk of AFB1-induced liver cancer as determined by two main periods, pre- and post-harvest period and exposure period [6]. During harvest time, several techniques are used for controlling and reducing the chance of harmful effects resulted from AFB1: cultivation of AFB1 tolerance plants, biocontrol using competitive fungi, irrigation, and insecticide. For exposure period, most researches aim to determine the effects of several foods or supplementary foods that are capable of decreasing AFB1-induced toxicity. For example, oltipraz, a synthetic derivative of natural compound originated from cruciferous vegetables, is reported on its capacity to reduce AFB1 toxicity. In addition, green tea polyphenol and chlorophyllin (a derivative of chlorophyll found in green leafy vegetables) are also stated. These natural compounds have a potential against AFB1-induced hepatocarcinogenicity by decreasing the absorption of AFB1, controlling metabolic pathway, and increasing AFB1 excretion [6, 9]. To update the involvement of edible plants as chemoprevention for AFB1, this review is aimed to emphasize the mechanistic alleviation of AFB1-induced liver toxicity by polyphenol-containing plants.
Cruciferous vegetables belong to Brassica genus, Brassicaceae family which are usually known as broccoli, Brussels sprouts, cabbage, cauliflower, kale, and radishes and commonly used for food consumption. They are not only rich sources of fibers, vitamins, and carotenoids as their important components, but also contain higher glucosinolate content than other vegetables [10]. Glucosinolates are secondary metabolites in cruciferous veggies and can be divided into three classes based on their structure: aliphatic glucosinolates, indole glucosinolates, and aromatic glucosinolates.
Nearly 200 types of glucosinolates have been reported in scientific literature, especially glucobrassicin and glucoraphanin. These two compounds can be transformed into hydrolysis products such as isothiocyanates, sulforaphane (SF), and indole-3-carbinol (I3C) by β-thioglucosidase (myrosinase) enzyme when plant cells are damaged. This mechanism could also be processed by bacteria in the gastrointestinal tract [11, 12].
The studies of anticancer effects of glucosinolates and their hydrolysis products revealed that numerous existing compounds also had anticancer mechanism against various types of cancers. For instance, the presence of sulforaphane could suppress carcinogen and prevent DNA adduct (a biomarker of AFB1 exposure) directly through an inhibition of phase I metabolism enzymes. At the same time, it induces phase II metabolism enzymes which play an important role in converting carcinogens to the inactive metabolites and excreting from the body. Their hydrolysis products exhibit an ability to scavenge the free radicals, inhibit inflammation and angiogenesis, and also induce an apoptosis of cancer cells [11].
Previous studies investigated the effects of bioactive compounds such as I3C and 1-cyano-2 hydroxy-3 butene (Crambene), derivatives of glucosinolate group found in cruciferous veggies, on HCC occurrence. Glucosinolates did not only respond for abnormal liver cells, but they also enhance AFB1 detoxification in the rat model. Pre-exposure to the high-dose combination of I3C and Crambene (0.15 and 0.165%, respectively) protected the liver cells effectively more than low-dose combinations and single exposure [13]. Risk reduction of liver cancer could also be observed in rainbow trout when pre-exposed to I3C at the dose 2000 ppm prior to AFB1; however, the adverse effects and increase of liver cancer incidence were reported when the exposure sequence was reversed [14]. In addition, further studies revealed a dose-dependent relationship between I3C dose after exposure to AFB1 and the incidence of liver cancer and other cancer types [15]. Thus, it could be summarized that the incidence of liver cancer is induced by AFB1 relating to timing of I3C exposure. Pre-exposure to I3C prior to AFB1 reduced the liver cancer incidence, but post-exposure reversely raised the liver cancer incidence [15]. Accordingly, subsequent mechanistic studies indicated an induction of I3C on phase I and II metabolism enzyme activities [16]. Continuous exposure to I3C might enhance phase II enzyme activity, so the absorbed AFB1 would be excreted rapidly. In contrast, pre-exposure to AFB1 triggered the adverse effects such as DNA abnormality and increase of liver cancer risk. The explanation was that pre-exposure to AFB1 generates AFB1-8,9-epoxide and this reactive metabolite would be more activated when treated later with I3C. In addition, I3C could be able to induce both phase I and II metabolism enzyme activities, thus AFB1-8,9-epoxide was more generated as a result of activation of phase I metabolism. Although phase II enzyme was also stimulated, it was not enough to eliminate AFB1.
Not only I3C is frequently reported, but other glucosinolate derivatives like SF and H-1,2-dithiole-3-thione (D3T) are also stated. For example, while rats were pre-exposed to these derivatives, AFB1-DNA adduct in rat’s liver was reduced due to an increase of GST activity, a phase II detoxification enzyme for AFB1 [17]. Likewise, other previous studies reported that SF could competitively inhibit CYP1A2 in human liver cells [16], causing a decrease of AFB1-DNA adduct. Remarkably, upregulation of gene expression-related tissue repairing system and number of hepatocytes were observed after induction of SF [18].
The current epidemiological and clinical studies revealed that only lung, colorectal, breast, prostate, and pancreatic cancers were given the positive response to glucosinolates while animal model showed the effective inhibition of liver cancer and other cancer types through various mechanisms. Nevertheless, randomized clinical trial of glucosinolates on liver cancer showed different results [11, 19]; comparison between broccoli sprout extract treatments and control group was studied simultaneously. After treatment, AFB1-DNA adducts were clearly determined. The results indicated that no significant difference was observed among tested groups on AFB1-DNA adduct level (p = 0.68). On the contrary, an inverse linear correlation of dithiocarbamates, a metabolite of sulforaphane, and AFB1-DNA adduct excretion was noted (p = 0.002, R = 0.31). It can be implied that exposure to glucosinolates might decrease AFB1-induced toxicity [20]. Besides, various compounds of glucosinolates have the potential to increase excretion of many carcinogens through glutathione S-transferase stimulation. Once the GST was stimulated, carcinogenicity and risk of diseases in human were also decreased [21].
Green tea, Camellia sinensis, is a beverage that contains high contents of phenolic compounds at approximately 30% of dry weight. One of the major phenolic compounds in green tea is catechin, particularly epigallocatechin gallate (EGCG), epigallocatechin (EGC), epicatechin gallate (ECG), and epicatechin (EC) [22].
Recent studies have demonstrated the positive effects of green tea on many diseases and adverse human health conditions such as coronary artery disease, oral heath, bone integrity, thermoregulation balance, and kidney stones. Furthermore, an association between green tea consumption and the incidence of many types of cancer has also been reported such as oral and pharynx, esophageal, gastric, colorectal, bladder, prostate, breast, lung, skin, leukemia, pancreatic, and liver cancers [23, 24]. Various research methods including preclinical studies (in vitro and in vivo), epidemiology, and clinical trial were used to investigate the effect of crude green tea extract or single compound like EGCG on many types of cancer [25]. Overall, an anti-cancer mechanism of green tea extracts against cancer cells was evidently elucidated. Green tea extracts were able to induce apoptosis of cancer cells through inhibiting nuclear factor kappa light chain enhancer of activated B cells (NF-κB) activity and B-cell lymphoma extra-large (Bcl-xL) mRNA expression. Besides, the reduction of angiogenesis of cancer cells was also resulted by green tea extracts through inhibition of vascular endothelial growth factor (VEGF) expression [26].
Previous studies have been reported on several protective ways against AFB1-induced liver cancer from the exposure to catechin compounds and green tea extracts. For example, the reduction of chromosome aberration in rat bone marrow cells was observed after pre-exposure with green tea or EGCG for 24 hours prior to AFB1 [27]. Besides, hepatic nuclear AFB1-DNA binding and glutathione S-transferase placental form (GST-P) positive single hepatocyte, specific markers of hepatocarcinogenic potential in the rat model, were also reduced after pre-exposure with green tea extracts for 2–4 weeks prior to AFB1 [28]. Similarly, the levels of GST-P and γ-glutamyl transpeptidase positive hepatic foci induced by AFB1 and carbon tetrachloride were reduced during pre- or co-treatment with green tea extracts. Furthermore, the inhibition of hepatocarcinogenesis was also observed [29].
The studies of green tea against AFB1-induced human liver cancer are still currently limited, and most reports have been retrieved from China. As some Chinese commonly consume food contaminated with AFB1, the risk of HCC is higher than other regions. A clinical study demonstrated a protective effect of 500 and 1000 mg/day green tea polyphenol (GTP) on hepatocarcinogenesis in 124 HCC patients who presented with HBsAg and aflatoxin-albumin adducts. Results showed that 8-hydroxydeoxyguanosine (8-OHdG) level, an oxidative DNA damage biomarker originating in urine specimens, significantly decreased (p = 0.007) during co-exposure with GTP for 3 months [30]. Besides, AFB1-albumin adducts (AFB1-AA) and AFB1-mercapturic acid (AFB1-NAC) level in blood and urine specimens of volunteers were compared among 500 and 1000 mg GTP treatment group and control group. This result revealed a reduction of AFB1-AA level, an indicator of AFB1 exposure, for both 500 and 1000 mg GTP treatment groups within 3 months. This reduction was strongly related to dose and duration of GTP exposure (p = 0.049). Furthermore, AFB1-NAC, an indicator of AFB1 elimination activated by phase II metabolism enzymes, significantly increased (p < 0.001) in both treatment groups related to dose and duration of GTP exposure as well (p < 0.001). Therefore, it could be summarized that GTP effectively modulated AFB1 biotransformation by inhibition of phase I metabolism enzymes as can be seen from the reduction of AFB1-AA. GTP also has an induction effect to phase II metabolism enzymes which transform AFB1–8,9-epoxide to AFB1-NAC [31].
Furthermore, results from a meta-analysis investigating the effect of green tea extracts on HCC and other liver diseases also showed that regular green tea drinkers had a lower incidence of HCC than nonregular drinkers approximately 26% (R = 0.74, 95% CI = 0.56–0.97, p = 0.027). Although there were some inconsistent results in this study (I2 = 80.1%, p = 0.000), no publication bias was detected and no data from one study significantly influenced the final conclusion [25].
Anthocyanins, members of flavonoid groups, are mostly found in blue, purple, orange, and red vegetables. Anthocyanins in plants play a vital role in attraction of bugs for pollination and insect resistance [32]. Pharmacologically, purple corn extracts have been known for its anti-diabetic and antiadipogenic effects, anti-prostate carcinogenesis, and others [33, 34, 35] while blue butterfly pea flower has a definite potential anti-inflammatory effect [36]. Furthermore, anthocyanin-rich plants were shown to protect neurodegenerative and also cardiovascular disease [37].
Purple rice bran (Oryza sativa L. var. indica) contained flavonoids and anthocyanins approximately 53 and 2 mg/g, respectively. Both compounds were reported to reduce AFB1-induced toxicity, and they were capable of inhibiting mutagenicity in Salmonella typhimurium strains TA98 and TA100 [38]. In animal model, rats were pre-treated with purple rice bran extracts for a month before exposure to AFB1. Then, the expression of CYP450 including CYP1A2 and CYP3A was investigated; both of them have an identical role in transforming AFB1 to AFB1-epoxide. The results showed that the extracts could not only inhibit the expression of CYP1A2 and CYP3A, but also increase the expression of GST and UGT which encouraged AFB1 excretion. Further in in vivo studies, the genotoxicity was evaluated by micronucleus assay, and the result showed lower micronucleus formation in extract-pretreated group than AFB1 treated alone, confirming the capability of purple rice bran extract on the prevention of AFB1-induced genotoxicity [39].
Apart from purple rice bran extract, other anthocyanin-rich plants are also studied for their effects on AFB1-induced cytotoxicity. For instance, Lannea microcarpa, a tropical African plant, has been studied for its activities against hepatotoxicity, DNA fragmentation, and oxidative stress induced by AFB1. Before exposure to AFB1, animals were pre-exposed with Lannea microcarpa extracts for 6 months. Results showed that hepatotoxicity, DNA fragmentation, and oxidative stress was lower in extract-pretreated group when compared to AFB1-treated group [40].
Turmeric is a flowering plant widely used as a food ingredient in South Asia for a long period of time. It has been also applied in pharmacognosy field as a powerful anti-inflammatory resulting from rheumatoid arthritis, bruise, epilepsy, abdominal pain or discomfort, and asthma [41]. An in vivo study of turmeric clearly showed the anticancer properties of turmeric on liver, skin, and colorectal cancers. It has a strong potential to inhibit cancer cell growth through stimulating apoptosis and inhibiting phase I metabolism enzymes. It can also stimulate phase II metabolism enzyme activities which play an important role in converting reactive metabolites to excretable forms. Also, turmeric exhibits the antioxidant capacity which can effectively detoxify oxidative stress [42].
Curcumin is a major active component of turmeric. It belongs to curcuminoid group and commonly found in 2–8%. Previous in vivo studies investigated the effects of turmeric and curcumin on AFB1-induced toxicity, and results showed that turmeric and curcumin decreased AFB1-adduct formation, biomolecule damage, and hepatotoxicity [43, 44, 45, 46], and it also inhibited acute toxicity through disturbing the lysis of erythrocytes [47]. During AFB1 metabolism, free radicals generated by AFB1 could be readily inhibited by turmeric and curcumin via decreasing lipid peroxidation and enhancing glutathione content. Likewise, they could activate several antioxidant enzymes such as glutathione peroxidase (GPx), superoxide dismutase (SOD), catalase (CAT), GST, and UGT which play a fundamental role in converting AFB1 to excretable forms [43, 44, 45, 46].
Turmeric is found to be capable of reducing both AFB1-induced toxicity and HCC. Besides, it could also stimulate apoptosis of liver cancer cells through a mitochondria-dependent pathway and accumulation of calcium ions within the cells [48]. Turmeric showed the protective effect against AFB1-induced liver cancer in animal model by inhibition of metastasis and growth factor expression related to the progression of angiogenesis [49].
Chlorophyll (chla), a main component of green vegetables, consists of a porphyrin ring structure where magnesium is the central atom of the ring. Chla is important for plants’ photosynthesis pathway and used as food additives. One of the characteristics of chla is almost insoluble in water while chlorophyllin (CHL), a derivative of chla, is completely soluble. CHL can be transformed into water-soluble form by saponification, a reaction that magnesium central atom is replaced with copper. In vivo and clinical studies in pharmacological researches of both chla and CHL revealed that they provided the therapeutic uses such as wound healing, anti-inflammation, anti-oxidation, anti-mutagenesis, and anti-carcinogenesis [50, 51].
Previous studies on the protective effects of chla and CHL on AFB1 toxicity indicated that both compounds could reduce absorption of AFB1 from apical to basolateral sides in Caco-2 cell line [52]. Accordingly, a crossover clinical trial demonstrated that chla and CHL exposure could reduce maximum concentration (Cmax) and area under the curves (AUC) of AFB1 compared to untreated group [53]. These findings suggest that chla and CHL have a strong potential to decrease AFB1 absorption. The effects of chla and CHL co-exposure with AFB1 have also been studied in animal model by emphasizing on antioxidant activities. Both bioactive compounds are capable of reducing AFB1 toxicity through enhancing the expression of glutathione level and several antioxidant enzyme activities such as GPx, SOD, and CAT [54].
A recent study investigated the effects of CHL on AFB1-induced hepatotoxicity and incidence of carcinogenesis in animal model. Exposure with CHL reduced hepatotoxicity and incidence of liver cancer [54, 55]. In a clinical study, a randomized controlled trial reported that daily exposure with CHL for 4 months decreased AFB1-N7-guanine level in urine compared to placebo group [56].
Several studies were in agreement that chla and CHL reduce AFB1-induced liver cancer through decreasing AFB1 absorption in digestive tract contributing to the decrease of AFB1 bioavailability. Besides, chla and CHL are the powerful antioxidants which effectively lower AFB1-induced oxidative stress. These two compounds not only reduce hepatotoxicity, but also incidence of liver cancer. Thus, the consumption of green vegetables is one of the alternatives to reduce toxicity caused by consuming AFB1-contaminated foods.
Ginger (Zingiber officinale Roscoe) contained high content of phenolic compounds in which 6-gingeerol and 6-shogaol are main constitutions [57]. Ginger plays a critical role as hepatoprotective effects through antioxidant mechanism; for example, liver injury by administration of country-made liquor (CML) and iron-induced nonalcoholic fatty liver disease (NAFLD) [58] and liver cirrhosis induced by carbon tetrachloride [59]. It was also reported to show the protective effects against AFB1-induced toxicity.
In in vitro model of AFB1-treated HepG2 cells, ginger extract-pretreated cells exhibited higher percent cell viability and lower intracellular ROS production and DNA strand break when compared to AFB1 treatment alone. In Wistar rats, pretreatment with ginger extract also increased the activities of antioxidant enzymes: GPx, GST, CAT, and SOD, decreased malondialdehyde (MDA) level, and increased reduced glutathione (GSH) content. Co-incubation with ginger extract along with AFB1 also showed a hepatoprotective effect as seen by the lower level of serum enzymes: alanine aminotransferase (ALT), aspartate transaminase (AST), alkaline phosphatase (ALP), and lactate dehydrogenase (LDH). Moreover, fat droplets and hepatocyte infiltration with macro-vesicles in liver induced by AFB1 were normalized when pre-treated with ginger extract, clearly showing the effectiveness of ginger on AFB1-induced hepatotoxicity [57].
Mechanism of ginger extract to reduce AFB1-induced hepatotoxicity was demonstrated by in vivo study. The expression of nuclear factor-E2-related factor 2 (Nrf2), a redox-responsive transcription factor, was increased when pre-treated with ginger extract. Nrf2 was translocated into the nucleus to regulate the antioxidant response element (ARE) which is the promotor of detoxification and antioxidant genes. Moreover, administration of ginger extract induced the expression of heme oxygenase 1 (HO-1) which is associated with the normalization of redox status [57]. Therefore, ginger extract could reduce AFB1-induced hepatotoxicity in both in vitro and in vivo through antioxidant activities controlled by the function of Nrf2 and HO-1.
Dialium guineense is a fruit-bearing tree known as the velvet tamarind. Their bark, leaves, seeds, and fruit showed biological properties such as antimicrobial activities, anti-infectious diseases, and wound-healing [60, 61]. Extract from Dialium guineense showed ROS scavenging activities and could normalize the levels of enzyme biomarkers of hepatotoxicity: ALP, AST, and AST induced by AFB1. Furthermore, treatment of velvet tamarind extract before AFB1 exposure increased the antioxidant activities of various enzymes including SOD, GPx, GR, CAT, GSH, and oxidized glutathione (GSSH), decreased lipid peroxidation, protein carbonyl and DNA fragmentation. In vitro and in vivo experiments have also confirmed the protective effects of velvet tamarind extract against hepatotoxicity induced by AFB1 via antioxidant properties [62].
Parkia biglobosa, known as the African locust bean tree (ALBT), is a perennial tree legume growing in West Africa. Several parts of ALBT (bark, leaves, pods, stem, and fruit pulp) showed medicinal properties such as antimicrobial activities, antihypertensive effects, antidiabetic activity, antidiarrheal activity, and others [63]. Pulp extract of ALBT exhibited abilities against antioxidant imbalance induced by AFB1. When pretreated in animal model, pulp extract of ALBT were capable of inducing SOD, CAT, GPx, GR, and glucose-6-phosphate dehydrogenase (G6PD) activities and increasing GSH and GSSG content. In addition, pretreatment with pulp extract of ALBT reduced lipid peroxidation products, protein carbonyl, and DNA fragmentation induced by AFB1. AFB1 treatment also resulted in decrease of hepatocellular enzyme activities: ALP, ALT, and AST compared to control while the pretreatment with pulp extract of ALBT increased these enzyme activities in a dose-dependent manner. Accordingly, antioxidant imbalance and hepatotoxicity induced by AFB1 were able to be alleviated by pretreatment with pulp extract of ALBT [64].
Carotenoids, natural plant pigments giving the color of fruits and vegetables, are responsible for the red, orange, and yellow colors in mangoes, corns, carrots, pumpkins, tomatoes, etc. More than 700 different carotenoids have long been characterized and classified as two main groups regarding their basic functional group [65]. Xanthophylls, yellow or orange-yellow pigments, are found widely in nature and the majority of their structure consists of oxygen as the core element such as lutein and zeaxanthin. Carotenes, one of another division of carotenoids, are hydrocarbon compounds without other functional groups including α-carotene, β-carotene, and lycopene [66]. Both xanthophylls and carotenes are almost known as fat-soluble compounds dissolved well in petroleum, ether, chloroform, and hexane but carotenes seem to be more soluble in these nonpolar aliphatic solvents compared to xanthophylls; some are water-soluble [67]. Carotenoids have a potential role as a provitamin A compound which can be converted within the body to vitamin A, and they are broadly accepted as free radical antioxidants inhibiting several types of cancers [68, 69].
Several carotenoids like β-carotene, canthaxanthin, lycopene, and cryptoxanthin were studied on the mitigation of AFB1-induced mutagenesis in bacterial mutation assay. Mutagenesis was inhibited by the addition of all carotenoids, except lycopene, and cryptoxanthin was shown to be the most potent inhibitor among all tested carotenoids [70]. The comparison of both ionone rings, α and β type of carotenoids, was observed through suspended disc culture. The α-ionone ring carotenoids, α-carotene, lutein, or α-ionone, showed more inhibition of AF biosynthesis than β-ionone ring, and the existence of hydroxyl groups on the rings seemed to lessen the inhibition capacity [71].
Previous study demonstrated the effects of antioxidants β-carotene and lycopene on AFB1-induced hepatotoxicity. The result showed the presence of lycopene followed by the addition of AFB1 increased cell viability at approximately 14%, while pretreatment with β-carotene had the highest increase in cell survival up to 54%. Both carotenoids recovered mitochondrial dehydrogenase (MD) activity up to 85%, upregulated p53 gene expression in AFB1-exposed cells, and decrease in AFB1-N7-guanine adducts. These results clearly showed that both β-carotene and lycopene could prevent AFB1-induced toxicity in HepG2 cells [69].
Lycopene, a strong free radical scavenger having the greatest ability to cope with the singlet oxygen compared to the other carotenoids, can alleviate AFB1-induced oxidative stress through the conjugation of the p-electron system with several reactive oxygen species. It can protect DNA, proteins, and lipid damages against the carcinogenesis onset contributed to its numerous conjugated double bonds, high lipophilicity, and acyclic structure [72]. Regarding several scientific publications, lycopene has been confirmed as the carotenoid that exhibited robust positive effects on AFB1 toxicities via several pathways.
Allium plants like garlic and onion are well-known in Asian countries as food ingredients and remedial foods. They have been documented as medicinal foods worldwide due to their pharmacological properties. Allium fistulosum (A. fistulosum), a perennial herb in Allium genus, has been commonly utilized as appetite inducer and medication against cold symptoms [73]. Also, it has ability to activate the immune response and antihypertensive effect as well as antioxidant defense system. The consumption of A. fistulosum extract increased estrogen level, mediated the conversion of testosterone to estrogen, and conducted hormone balance in female rats resulting in the enhancement of ovarian function [74]. The extract is able to downregulate the accumulation of lipid in HepG2 cells without cytotoxic effect and fatty acid gene synthesis. Similarly, mice fed high-fat, high-sucrose diet displayed an increase in body weight, hepatic weight, and fat accumulation in hepatocytes, but these adverse effects were attenuated by extract supplementation [75].
The effects of Allii Fistulosi Bulbus (VEAF) extract on cytotoxicity and oxidative stress caused by AFB1 exposure were observed in HepG2 cells. Preincubation with VEAF followed by the addition of AFB1 obviously enhanced cell viability. It inhibited oxidative stress through declining ROS level and TBAR content induced by AFB1 and promoting GSH level. The determination of 8-OHdG, an indicator of oxidative damage on DNA, was then investigated. The result showed the inhibitory effect in VEAF treatment group up to 59.1% suppression compared to AFB1-treated group. This evidence proved the alleviating potential of VEAF on AFB1-induced oxidative stress resulting in cytoprotection against AFB1 toxicity [76].
Quercetin, flavonol, is one of the major bioactive compounds in Allium plants. It shows the potential to scavenge free radical and improve health effects, that is, aging, allergy, angioprotective properties, anti-inflammatory, anti-cancer, anti-obesity, arthritis, asthma, diabetes, etc. [77]. For AFB1 biosynthesis in Aspergillus flavus, quercetin notably decreased AFB1 production (51%) in corn flour supplemented with quercetin at 48-hour incubation. Quercetin has an ability to inhibit the expression of necessary enzymes for AFB1 biosynthesis such as acetyl CoA synthetase, esterase, and O-methyl transferase A and involves in the MAPK pathway which is the major pathway to form AFB1. Quercetin, therefore, has the ability to be an anti-aflatoxigenic agent [78]. Quercetin also inhibited proliferation of Aspergillus flavus and its AFB1-biosynthesis through regulating the expression of development-related genes and aflatoxin production-related genes [79].
In HepG2 cells, quercetin decreased AFB1-induced cytotoxicity and ROS production and increased GSH content while in vivo study showed enhanced antioxidant activities and reduced lipid peroxidation [80]. After AFB1 consumption, quercetin depicted the prevention of genotoxicity caused by AFB1 in rat liver microsomes. Co-incubation with quercetin significantly decreased micronuclei formation compared to treated with AFB1 alone (p < 0.05) [81]. Corresponding to another study, serum cytokines, procollagen III, and nitric oxide were significantly reduced during co-administration with quercetin and AFB1 (p < 0.05). Quercetin also upregulated the antioxidant enzymes that may affect the decrease of DNA fragmentation and apoptosis [82]. Likewise, the administration between AFB1-contaminated diet in rat resulted in a decrease of total proteins and RNA content and fatty acid synthase (Fas) and tumor necrosis factor (TNF) gene expression in the liver tissue caused by AFB1 while co-administration with quercetin normalized these parameters [83].
Even though numerous studies revealed the hepatoprotective effects of quercetin against xenobiotic-induced cellular toxicity, low bioavailability of quercetin absorbed into circulation is the remarkable barrier [84]. One of the supreme strategies widely used is nanoformulation. Quercetin nanoparticles not only demonstrated a noteworthy reduction of AFB1-induced cell death, but it also suppressed the liver toxicity caused by AFB1 including ROS formation, lipid peroxidation, mitochondrial membrane potential collapse, and GSH depletion. In addition, both quercetin and quercetin nanoparticles significantly enhanced the function of hepatic enzymes (AST, ALT, and ALP) and hepatic antioxidant enzymes (SOD, CAT, and GPx) (p < 0.05). Interestingly, quercetin nanoparticles showed higher effects than quercetin [84]. These result reflexes an inhibiting ability of AFB1 toxicity by administration of quercetin AFB1.
AFB1 also caused increase of cytotoxicity in a bovine mammary epithelial cell line. The pre-incubation with quercetin affected to increase cell viability, AFM1 biosynthesis (low toxic metabolite of AFB1), GSH content, and mRNA level of glutathione S-transferase alpha 1 (GSTA1) which are important for AFB1 detoxification [85].
Rosemary plant (Rosmarinus officinalis L.), naturally found in the western Mediterranean region, has been widely used as a food additive. As it contains high polyphenolic contents, it shows many pharmacological properties such as antioxidant activity and antimicrobial and antimycotic properties, etc. [86]. Previous study proved that the growth of Aspergillus flavus and A. parasiticus were significantly inhibited by 4% commercial rosemary essential oil from 28.2 to 59.5% and 41.5 to 52.4%, respectively [87]. Apart from antimycotic properties, dose-dependent exposure of carnosic acid—major polyphenolic compound in rosemary plants—clearly decreased cell death caused by 10 μM AFB1. Pre-treatment to carnosic acid also reduced the production of ROS and the concentration of 8-OH-deoxyguanine, clearly confirming an involvement of carnosic acid in the protection of cytotoxicity induced by AFB1 [88]. Furthermore, both rosemary extract and its active components (carnosol and carnosic acid) exhibited a potent inhibition of DNA adduct formation. They not only inhibit phase I metabolizing enzymes but also induce phase II metabolizing enzymes such as GST that promote the cellular defensive mechanism against AFB1 [89].
Consumption of AFB1-contaminated food is the current major cause of HCC in many countries. Many studies aim to lower AFB1-induced toxicity particularly the utilization of edible plants as protective foods. This review proposed the edible plants which could alleviate AFB1-induced toxicity and concluded the possible mitigation of AFB1 toxicities through several related pathways (Table 1 and Figure 1). Although the detoxification mechanism of AFB1 activated by various plants has been investigated in a pre-clinical study for a decade, clinical trial is still rarely clarified. Further investigation on a risk reduction of AFB1 still needs to be carried out especially in the clinical study.
Plants | Reference | Protective effects | |||||||
---|---|---|---|---|---|---|---|---|---|
Inhibit AFB1 biosynthesis | Inhibit AFB1 absorption | Anti-oxidant | Anti-genotoxicity | Reduce cytotoxicity | Modulate metabolism enzymes | Inhibit hepatotoxicity | Decrease liver cancer | ||
Cruciferous vegetables | [10] | / | / | / | |||||
[14] | / | / | |||||||
[15] | / | ||||||||
[16] | / | / | |||||||
[17] | / | / | |||||||
[20] | / | ||||||||
[21] | / | ||||||||
Green tea | [25] | / | |||||||
[27] | / | ||||||||
[28] | / | ||||||||
[29] | / | ||||||||
[30] | / | ||||||||
[31]* | / | ||||||||
Purple rice | [38] | / | |||||||
[39] | / | / | |||||||
[40] | / | / | / | ||||||
Turmeric | [43] | / | / | ||||||
[44] | / | / | / | / | |||||
[45] | / | / | |||||||
[46] | / | / | |||||||
[47] | / | ||||||||
[48] | / | / | |||||||
[49] | / | / | / | ||||||
Green vegetables | [52] | / | |||||||
[53] | / | ||||||||
[54] | / | / | / | ||||||
[55] | / | / | / | ||||||
[56] | / | ||||||||
Ginger | [57] | / | / | / | / | ||||
Dialium guineense | [62] | / | / | / | |||||
Parkia biglobosa | [64] | / | / | ||||||
Carotenoid-rich fruits and vegetables | [69] | / | / | / | |||||
[70] | / | ||||||||
[71] | / | ||||||||
[72] | / | / | |||||||
Allii Fistulosi Bulbus | [76] | / | / | / | |||||
[78] | / | ||||||||
[79] | / | ||||||||
[80] | / | / | |||||||
[81] | / | / | |||||||
[82]* | / | / | |||||||
[83]** | / | / | / | ||||||
[84] | / | / | / | ||||||
[85] | / | / | / | ||||||
Rosemary | [87] | / | |||||||
[88] | / | / | |||||||
[89] | / | / |
The protective effects of edible plants against AFB1-induced toxicity.
Alleviate serum cytokine and procollagen III, NO.
Alleviate content of nucleic acid of liver tissue.
Protective effects of edible plants against AFB1-induced toxicity.
This work was funded and supported by Faculty of Pharmaceutical Sciences, Khon Kaen University, Khon Kaen, Thailand.
Authors declare no conflict of interest.
AFB1 | Aflatoxin B1 |
AFB1-AA | AFB1-albumin adducts |
AFB1-NAC | AFB1-mercapturic acid |
ALBT | African locust bean tree |
ALP | Alkaline phosphatase |
ALT | Alanine aminotransferase |
ARE | Antioxidant response element |
AST | Aspartate transaminase |
AUC | Area under the curves |
Bcl-xL | B-cell lymphoma-extra large |
Cmax | Maximum concentration |
CAT | Catalase |
CHL | Chlorophyllin |
chla | Chlorophyll |
CML | Country-made liquor |
D3T | H-1,2-dithiole-3-thione |
EC | Epicatechin |
ECG | Epicatechin gallate |
EGC | Epigallocatechin |
EGCG | Epigallocatechin gallate |
Fas | Fatty acid synthase |
G6PD | Glucose-6-phosphate dehydrogenase |
GPx | Glutathione peroxidase |
GSH | Reduced glutathione |
GSSH | Oxidized glutathione |
GST | Glutathione S-transferase |
GSTA1 | Glutathione S-transferase alpha 1 |
GST-P | Glutathione S-transferase placental form |
GTP | Green tea polyphenol |
HCC | Hepatocellular carcinoma |
HO-1 | Heme oxygenase 1 |
I3C | Indole-3-carbinol |
IARC | International Agency for Research on Cancer |
LDH | Lactate dehydrogenase |
MD | Mitochondrial dehydrogenase |
MDA | Malondialdehyde |
NAFLD | Nonalcoholic fatty liver disease |
NF-κB | Nuclear factor kappa light chain enhancer of activated B cells |
Nrf2 | Nuclear factor-E2-related factor 2 |
8-OHdG | 8-hydroxydeoxyguanosine |
ROS | Reactive oxygen species |
SF | Sulforaphane |
SOD | Superoxide dismutase |
TNF | Tumor necrosis factor |
UGT | UDP-glucuronosyltransferase |
VEAF | Allii Fistulosi Bulbus |
VEGF | Vascular endothelial growth factor |
In recent years, agent-based applications have been developed inspired by natural systems. The natural systems have a dynamic structure defined by a complex, distributed, open, heterogeneous, and large-scale systems. Therefore, it is too hard to model these systems in the artificial world. Agent-based modeling and simulation (ABMS) technique has advantage in explanation of the dynamics of the behavior in the complex systems including biological, physical, and social systems. ABMS which is used in the solution or modeling of a problem in the literature seems to be inspired by living systems. Living systems offer an organization and operation at different levels ranging from the genetic to the social experience. The most common applications that can be shown in living systems are biological systems including human physiology which examine major systems such as cardiovascular system, immune system, nervous system, endocrine system, etc., and predator-prey relationship in the ecosystem, birds and fish flocks, organisms that live in colonies such as foraging ants, bees, wasps, and termites, and etc. [1].
\nABMS allows the researchers an experimental experience to create, analyze, and explicate the relationship between the artificial and the real world. In comparison with other modeling approach based on mathematical and numerical analysis, control theory, biomechanical techniques, etc., ABMS is referred to as “individual-based model” [2]. Individual is called agent which has a set of attributes and autonomous behavior. Agents are situated in some set of spaces and time. Agents interact with other agents in the simulation environment. The simulation environment includes agents that perform their actions and achieve their goals.
\nIn this chapter, we will focus on the use of computer simulation for building the agent-based models in biological systems. This chapter intends to provide brief descriptions of the agent-based models that illustrate how to build and implement case studies, which reflect the relationship in the real world.
\nThis chapter is organized as follows: Section 2 gives a brief overview of ABMS; Section 3 presents the description of Repast Simphony toolkit which has ability to display and schedule in real time; Section 4 provides implementation of case studies involving different scenarios to better understand ABMS phenomena; and Section 5 concludes with a brief summary of this chapter.
\nAgent-based modeling and simulation (ABMS) can be defined in very diverse disciplines like artificial intelligence, complexity science, game theory, etc. [3, 4]. ABMS provides a suitable simulation modeling technique for the analysis of complex systems and emergent phenomena in biological systems, social sciences, economy, management systems, etc. [5, 6]. ABMS is a computational model implemented as computer simulation in which there are individual entities and their behaviors and interactions. It focuses on rules and interaction among the individuals or components of the real system. In the ABMS, the systems are characterized by the autonomous and independent entities known as agents performing some kind of behaviors (action and interactions) in the simulation environment [7]. In the literature, it is possible to see many examples of agent-based modeling in the different fields including traffic control, biomedical research, ecology, energy analysis, etc. [4].
\nABMS has advantage of creating a model compared to traditional approaches. No any set of formulas or mathematical equations are needed to build an agent-based model. ABMS focuses on the rules that will determine the behaviors of agents [8]. In order to develop an agent-based model, firstly, it must be understood how to design and implement the model. In other words, the scenario of a real system must determine the limitations of the model. Some questions must be answered to initialize the model design, like what the agents should be in the model, what the agents’ environment is, how to interact with each other and environment, how to define the rules determined the behaviors of agents, what are roles of the agents in the model, etc. [9].
\nThere are some simulation software toolkits to perform ABMS [10]. Toolkits can facilitate to manage the simulation process. One of the most popular toolkits in the literature is Repast Simphony supported by libraries of predefined methods and functions [11, 12].
\nRepast (Recursive Porous Agent Simulation Toolkit) Simphony is an agent-based modeling and simulation framework based on the object-oriented programming using Java language. It is free and open source so that it offers the users the widespread use of the agent development environments. Repast Simphony uses Eclipse-integrated development environment (IDE) for developing computer code [13]. Repast Simphony tool offers researchers a flexible way to write models including graphical user interface, toolbar to control the simulation processes (start, step, pause, stop, exit, etc.), displaying agents and their environment, monitoring the output data (time chart, histogram bar), scheduling of simulations, parameter management, data sets, data loaders, etc. Repast Simphony is the most suitable simulation framework for agent-based model development. Classes of agents and their interactions are displayed in Repast Simphony. The output data are graphically presented in time charts and/or histogram bar. Repast Simphony allows the users to record inbuilt data to txt files and displays as movies or images. Also, the users obtain the snapshots of graphics and/or display. Repast Simphony has advantage to display, schedule, analyze, update, or manipulate a running simulation in real time.
\nAfter downloading the latest version on Repast Simphony from its web page, creating a new Repast Simphony project is very easy. The first step is to run Eclipse IDE. After the new Repast Simphony Project, which includes a source directory, and default package is created, the scenario directory structure is prepared by creating agent classes.
\nTo build an agent-based model, it is necessary to create classes. More agent classes can be created according to the scenario of the model. The classes include any number of methods to describe the attributes and roles of agents. Setup or step methods are called for each iteration of the simulation. In the each iteration of the simulation, the simulation runtime is described with time steps or tick counts. During the simulation runtime, the agents perform their actions. The get and set methods, which describe agents’ attributes, may update the value returned or stored in each tick count. The agents may continue or update their actions according to the results of the previous action they performed.
\nAgents are situated in continuous space and/or grid in the simulation environment which provides a context for interaction and communication of agents. Agents may be distributed to the environment randomly or with some rules. They may have the energy to make them survive. If the agent’s energy is exhausted, the agent may die. If the agent’s energy reaches the reproduction threshold, it may reproduce. In the simulation environment, there are heterogeneous agents which have different types. For example, an agent may represent the animal, while the other may represent the human. A style class in two-dimensional (2D) or three-dimensional (3D) simulation environment can be created in a way that defines the physical properties of agents such as size, color, and shape. Global parameters associated with agent classes, including initial values of project given by users, may be defined in an xml file.
\nRepast Simphony provides the users a graphical user interface (GUI). GUI allows the users to manage the simulation processes and to control the parameters. GUI has a user panel that includes run options, parameters, and scenario tree. To form the scenario tree of a project, context builder Java file is defined in data loaders to display agents and the environment on which agents are located. It is possible to observe agents’ behavior outputs on the plots and charts. Data sets are created to graphically illustrate time charts defining variables over time. The data set source is determined by pointing out the relevant methods. Histogram bar chart illustrates the distribution of variables.
\nAgent-based models utilizing Repast Simphony have been developed for a diverse range of scenario including biological systems. In this chapter, three different case studies are presented to better understand ABMS phenomena. These case studies described in subsections are highlighted local behaviors of a real system.
\nThis case study [14] presents the predator prey relationship model in the ecology. In this model, three types of agents are defined as sunn pest, wheat, and parasitoid. The sunn pest called bug agent in the model is both the predator and the prey roles. Wheat called habitat in the model is a cereal plant widely cultivated for food. The sunn pest is fed with wheat grain. The parasitoid is the predator which parasitizes the sunn pest’s eggs. We have a grid where the sunn pests are randomly distributed illustrated in Figure 1.
\nDistribution of the sunn pests on the 10 × 10 grid size.
The grid includes sunn pest, wheat, and parasitoid. In Figure 2, the green color shades indicate the growth of wheat, the red color cells indicate the sunn pest, and the white color cells indicate sunn pests’ nymphs. About 7000 sunn pest agents and 1000 parasitoid agents are randomly distributed in the 28,000 grid cells.
\nThe graphical user interface during the running of the simulation [14].
In modeling of sunn pest-wheat scenario, agent classes and methods are built according to the definitions in Tables 1–3.
\nRoles | \nPredator, prey | \n
---|---|
Attributes | \nSize, energy, gender, survival probability, state, generation | \n
Actions | \nMove, grow, mortality, reproduce, die | \n
Rules | \nFemale and male ratio is 50%. Randomly goes to one of the neighbour cells around him and feeds and grows from that cell. When the adults come to the field, the simulation starts. If the sunn pest is female and its size is more than 12 mm (i.e. the biological state is mature), lay eggs 5 times, leaves a total of 80 to 150 eggs and dies. If the sunn pest is a male, it dies in condition that the probability of survival (95%) being smaller than a random number determined. Grows 0.3 mm per step in the embryo phase and except this; it grows as much as the amount that it eats. If the size of sunn pest is in the range of 0 - 0.8, its biological stage is an “Embryo”. If the size of sunn pest is in the range of 0.8 - 2.0, its biological stage is a “First nymph”. If the size of sunn pest is in the range of 2.0 - 3.5, its biological stage is an “Second nymph”. If the size of sunn pest is in the range of 3.5 - 5.0, its biological stage is a “Third nymph”. If the size of sunn pest is in the range of 5.0 - 6.0, its biological stage is a “Fourth nymph”. If the size of sunn pest is in the range of 6.0 - 6.0, its biological stage is a “Fifth nymph”. If the size of sunn pest is greater than 10.0 mm, its biological stage is in the “Adult”. | \n
Local knowledge of sunn pest (bug agent).
Roles | \nFood value layer | \n
---|---|
Attributes | \nproduction rate, availability value | \n
Actions | \ngrow | \n
Rules | \nDefined at certain ratio within each cell in the grid. Grows certain ratio in each step, and it becomes availability value. The sunn pest consumes food as much as its growth rate in the cell where it is located. Its color scale changes according to its production rate. | \n
Local knowledge of wheat (habitat cell).
Roles | \nParasitoid | \n
---|---|
Attributes | \n— | \n
Actions | \nmove, hunt, kill | \n
Rules | \nRandomly distributed in the grid. Parasitizes sunn pest’s embriyo at the neighbouring cells around and locates in its cell. If there is no sunn pest’s embriyo in the neighbouring cells, it changes its position and randomly moves to another cell. Remains in the grid until the end of the simulation. | \n
Local knowledge of parasitoid.
The simulation runs during 90th tick counts which is represented in sunn pests’ lifecycle (biological stages) and cultivation cycle of wheat. The aim of this case study is to simulate the chemical and/or biological struggles against sunn pest and obtaining maximum gain to produce the wheat. The parasitoids are used only in biological struggles against sunn pest. In the initial time, all of agents distribute randomly on the grid. If the biological struggle is to be done, the parasitoid agents are activated. Until the 15th tick count, sunn pest agents act on the grid and fed from the habitat cells. At the 15th tick count, female sunn pests lay eggs (embryos) and die. In Figure 2, white color cells on the grid indicate the embryos, and the histogram bar shows the sunn pests’ total numbers for each biological stage. Through 90th tick counts, the sunn pests complete their lifecycle against the parasitoid. At the end of the simulation, the sum of food availability on the habitat cells has been observed illustrated in Figure 3.
\nThe graphical user interface at the end of simulation [14].
In the result of this case study, the relationship between sunn pest and parasitoid is simulated with the agent-based modeling approach. This case study represents the behavior of a real biological system, even if it is not identified with all the details. In the computer simulation studies, some assumptions can be done, such as in this study, the climate conditions are not included in the simulation. The boundaries of the study must be specified, otherwise undesirable results can be obtained and the system drifts the chaos.
\nThis work [15] presents bacterial population and resistance to antibiotics. Bacterial population known as bacterial flora are nonharmful microorganisms in the human body that live in the human skin, in the mouth, in the digestive system, etc. There are immune cells that suppress the bacterial flora. Immune cells and bacterial flora should always be balanced in the body. In this model, two types of agents are defined as bacteria and immune system cell. About 4000 bacterial agents and 100 immune system cell agents are randomly distributed on the 100 × 100 grid cells. The grid represents a human tissue or organ.
\nBacteria agents are grouped within themselves depending on the range of disease called virulence factor. The virulence factor is assigned between 1 and 4. In Figure 4, the white cells on the grid indicate the bacterial agents which have the virulence factor of 1, the yellow cells on the grid indicate the bacterial agents which have the virulence factor of 2, the red cells on the grid indicate the bacterial agents which have the virulence factor of 3, the purple cells on the grid indicate the bacterial agents which have the virulence factor of 4, and the blue cells on the grid indicate the immune system cell agents which have the virulence factor of 4.
\nBacterial agents and immune system cell agents on the grid at the initial time [15].
The simulation has three parts: the first one is bacterial competition on flora, the second is antibiotic usage, and the third is antibiotic resistance. According to these parts, the local knowledge of bacterial agents and immune system cell agents is defined in Table 4.
\n\n | Bacterial agents | \nImmune system cell agents | \n
---|---|---|
Roles | \nmicroorganism | \nmicroorganism | \n
Attributes | \nvirulance factor, survivalProbability, mutationProbability, size | \nId | \n
Actions | \nmove, grow, reproduce | \nmove, send signal, kill, disappear | \n
Rules | \nRandomly distributed in the grid. Divided up into empty cells during the simulation runtime. With a low virulence factor reproduce very rapidly. With the virulence factor of 1 is resistant to antibiotic which has a concentration value that can kill bacteria in each cell. | \nObserves the neighbour 48 cells. If there is a bacteria agent in the neighbour cells, it kills and locates on that cell. If it kills two bacteria, it sends signals to another immune system cell agent and dies. If there are more than 40 bacteria agents in the neighbour cells, it sends signals to other immune system cell agents. If there are between 2 and 15 bacteria agents in the neighbour cells, it does not see any danger state and disappear. | \n
The local knowledge of bacterial agents and immune system cell agents.
In the first part of the simulation, the aim is to balance the population of bacterial agents and immune system cell agents on the grid. Also, bacterial agents compete with their neighbors for space and food resources. A food layer is defined in the simulation environment to live, grow, and reproduce. In the second part of the simulation, antibiotic usage is defined against the bacterial agents. An antibiotic layer is included in the simulation environment. The aim is to help the immune system cell agents and kill the bacterial agents.
\nFigure 5 shows how the antibiotic usage suppresses the bacterial agent population when the immune system cell agents are insufficient. The third part of the simulation presents the relationship between antibiotic-resistant bacterial agents and immune system cell agents. Most of the bacterial agents with virulence factor between 2 and 4 are killed by the antibiotic, whereas rest is killed by immune system cell agents. However, bacterial agents with virulence factor of 1 survive because they are antibiotic-resistant. Bacterial agents with low virulence factor are divided very rapidly so that the number of immune system cell agents is increased. Figure 6 shows the struggle between immune system cell agents and bacterial agents on the grid. The grid, which indicates green color in Figure 6, represents tissue/organ.
\nRelationship between bacterial agents and immune system cell agents in the antibiotic usage [15].
Struggle between immune system cell agents and bacterial agents [15].
Figure 7 shows, graphically, the populations of immune system cell agents and bacterial agents during the simulation runtime. At the initial time, there are 4000 bacterial agents and 100 immune system cell agents on the grid. When simulation starts, immune system cell agents kill some of the bacterial agents. Surviving bacterial agents grow and reproduce. When the population of bacterial agents reaches the maximum value, the population of immune system cell agents starts to increase. Antibiotic usage helps to reduce the population of bacterial agents because the population of bacterial agents is very large.
\nRelationship between bacterial agents and immune system cell agents in the antibiotic usage [15].
This case study provides an introduction to understand the dynamics of microbiological systems that take place in the process of bacterial evolution. During the simulation runtime, it is observed that how the system dynamics can be adaptive to external influences and effect interactions of them.
\nHomeostasis is a steady state that regulates the keeping of state variables at a constant or stable condition. Homeostasis is defined as a closed-loop control system that balances changes of target values. Biological systems like human body struggle to control its internal environment against internal and external influences. If homeostasis is unsuccessful in the body, vital functions cannot continue to work and the system drifts into chaos. Almost all homeostatic control mechanisms involve negative feedback loop which provides long-term control to maintain a steady state. Negative feedback has a self-regulating mechanism for maintaining homeostasis. Negative feedback mechanism involves some important factors. The first one is sensor or receptor which senses changes in the system variables that need to be regulated. The second is a control center which has a set point or threshold value that keeps the optimal value of the system variable. The other is an effector which produces a response that eliminates or reduces the changes of the system variables. Negative feedback loop runs until the system variables are adjusted at optimum values.
\nThere are many negative feedback control mechanisms in the biological systems. The human physiology is one of the best examples of the biological systems in which the negative feedback mechanisms are observed. Some negative feedback control mechanisms that occur in the human body include regulation of blood pressure, keeping the pH constant, regulation of oxygen and carbon dioxide concentration in the blood, hormonal regulation of blood glucose levels, thyroid regulation, the control of body temperature, etc.
\nIn this chapter, an example of negative feedback control mechanism that occurs in the human body is presented with ABMS approach.
\nIn this case study [16], an agent-based homeostatic control model that regulates the body temperature during fever is presented. Fever is defined by an increase in body temperature above the normal range. Three types of agents, receptor agent, controller agent, and effector agents, are defined. Receptor agent is represented by thermoreceptor agent which senses changes in the body temperature. Controller agent has a set point which keeps the optimal value of body temperature. Effector agents are a set of dynamic autonomous agents which represent blood vessel that is a component of cardiovascular system [17]. Effector agents have been developed with ABMS approach [8]. The blood vessel is divided into segments. Each segment represents an agent. All of the agents in the negative feedback control mechanism are illustrated in Figure 8.
\nNegative feedback control mechanism of thermoregulation [16].
During the simulation runtime, each agent defined in the negative feedback control mechanism interacts with other agents. All of the agents interact with each other by using Java message service. Java message service supports “publish/subscribe” message delivery model. Receptor agent monitors the change of the body temperature and publishes it to the controller agent who has subscribed to the value. Controller agent receives message that includes the body temperature value. Controller agent compares the body temperature value to its set point value. Controller agent sends a message to the effector agents which start or stop the negative feedback mechanism. Effector agents produce a response based on the message that they receive, and they publish to the receptor agent to correct the deviation with negative feedback. Negative feedback control mechanism achieves a balance between heat production and heat loss. The output of the negative feedback mechanism is illustrated in Figure 9.
\nRegulation of the body temperature [16].
The simulation has a scenario of fever disease. This scenario achieves a balance with the agent-based negative feedback control mechanism as follows:
At the initial time, the core body temperature fluctuates between 36.7 and 37.2°C which is an acceptable normal range inside the human body. The set point of the body temperature is set to 37°C.
An infection that causes a fever disease is assumed that it starts with increasing the body temperature. The set point of the body temperature is set to 40°C at which the maximum value is assumed by homeostasis. The body temperature is less than the new set point of the body temperature.
Increased body temperature triggers shiver which is the reaction of the body. Shiver tries to gain the body heat which causes the constriction of the blood vessel called vasoconstriction. The controller agent publishes message “VASOCONSTRICTION” to the effector agents. The effector agents decrease their radius values to produce a response. Local blood flow parameter depending on the radius helps the heat gain.
The body temperature reaches the new set point of the body temperature. The infection is assumed to be cleared inside the body. The set point of the body temperature is set to 37°C. The body temperature is more than the new set point of the body temperature.
Condition at fourth step triggers sweat. Sweat tries to reduce the body heat which causes the dilation of the blood vessel called vasodilation. The controller agent publishes message “VASODILATION” to the effector agents. The effector agents increase their radius values to produce a response. Local blood flow parameter depending on the radius helps the heat loss. Thus, the body temperature returns to the optimal value.
In the result of this case study, it is observed graphically how the body temperature is regulated during fever. Agent-based negative feedback control mechanism can be called adaptation loop [18]. This is because the negative feedback control mechanism is run by a set of dynamic autonomous agents. In this mechanism, it is possible to observe their local behaviors.
\nThis chapter has introduced the reader to ABMS, and it described implementations of different case studies utilizing the Repast Simphony toolkit. ABMS offers an extensible way to model biological systems consisting of autonomous and interacting agents which perform their actions and adapt their behaviors. Computer simulation helps the researcher to explore the behavior of a dynamic system. This chapter is concluded by observing interactions of real systems’ components in the abstraction level.
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