WSN 49(2) (2016) 162-191 - World Scientific News

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WSN 49(2) (2016) 162-191

EISSN 2392-2192

Computational predictive mutagenicity of similar chemicals for anthraquinone, β-sitosterol and quercetin found in Alternanthera tenella by using QSAR modeling software Mahmudul Haque1,*, Ram Kumar Bhakat1, Aloke Bhattacharjee2, Soumendra Nath Talapatra3 1

Department of Botany and Forestry, Vidyasagar University, Midnapore – 721102, West Bengal, India 2

Centre for Advanced Study, Department of Botany, Burdwan University, Burdwan – 713104, West Bengal, India 3

Career Advancement Solutions, Maheshtala, Kolkata – 700142, India

E-mail address: [email protected]

ABSTRACT The present study aims to evaluate the mutagenic potential of secondary metabolites viz. anthraquinone, β-sitosterol and quercetin present in Alternanthera tenella and their related analogus compounds similar in their molecular structure. Nine similar putative allelochemicals analogus to each of anthraquninone, β-sitosterol and quercetin respectively were selected, a total of twenty seven similar chemicals were studied for mutagenicity prediction. Ames mutagenicity prediction was carried out by using T.E.S.T. (Toxicity Estimation Software Tool) of USEPA. All experimental metadata were obtained from Toxicity Benchmark and T.E.S.T. The results clearly indicated that the allelochemicals, anthraquinone and its related eight compounds were mutagenic positive except benzanthrone (mutagenic negative) but all experimental data were found mutagenic positive. βsitosterol showed mutagenic negative in both experimental and predicted value. It’s three related compounds were mutagenic positive but rest six related compounds mutagenic negative in predicted value while in experimental data, seven compounds were found mutagenic positive and rest two mutagenic negative. In case of quercetin, both data were obtained mutagenic positive while in related compounds, seven compounds were found to be mutagenic positive and two compounds mutagenic

World Scientific News 48 (2016) 162-191 negative in predicted value. All were found to be mutagenic positive in experimental metadata. Such findings poses a curiosity that are there any possibilities of conversion or substitution in the position of aromatic ring of allelochemicals when present in soil? Because allelopathy depends upon several environmental stressors and mutagenicity may be induced by allelochemicals. It is suggesting for future research to detect metabolic pathway and mechanism of allelochemicals formation in A. tenella in presence of toxins in soil and to validate with other available 2D and 3D softwares. Keywords: Allelopathy; Allelochemicals; Alternanthera tenella; Invasive species; Terrestrial ecosystem; Predictive Ames mutagenicity; QSAR modeling; T.E.S.T. software

1. INTRODUCTION In 1996, International Allelopathy Society has defined the term allelopathy as any process involving secondary metabolites produced by plants, microorganisms, viruses and fungi that influence the growth and development of agricultural and biological systems. In recent years, interest is increasing in sustainable agriculture, aimed at detailed and far-sighted evaluation of the effects of allelochemicals which has been evaluated as an alternative strategy for controlling weeds and insects and diseases (Roshchina and Narwal, 2007). Allelochemicals are bioactive compounds mostly plant secondary metabolites. These are biochemically synthesized from the metabolism of carbohydrates, fats and amino acids and arise from acetate or the shikimic acid pathway. These are cinnamic acid derivatives, coumarins, simple phenols, benzoic acid derivatives flavonoids, condensed and hydrolysable tannins, terpenoids and steroid, simple unsaturated lactones, naphthoquinones, anthraquinones and complex quinones, alkaloids and cyanohydrins etc. (Rice 1984; Waller 1987; Inderjit and Duke 2003; Callaway and Ridenour 2004; Zhou and Yu 2006; Muller 2009). It has already been established that these allelochemicals are released from the plant cells through the process of volatilization, leaching root exudates and decomposition of biomass and these influence the organisms viz. plants, pathogens, insect pests etc., when they come in contact. Several researchers have already described that allelochemicals are either inhibitory or beneficial for the growth of other herbs (Putnam, 1988; Inderjit, 1996; Duke et al., 2000; Bhattacharjee et al., 2003; Bhakat et al., 2006; Macías et al., 2007; Itani et al., 2013; Mali, and Kanade, 2014; Mondal et al., 2015). According to Sampietro et al. (2009), researches on allelochemicals emphasize not only the need of structural elucidation of the isolated compounds, but also of their natural biological function and possible utility for human purposes. The ecological role of these allelochemicals as herbicides, insecticides and fungicides for ecological/organic sustainable agriculture has recently drawn great attention, due to increasing public concern against synthetic pesticides . These concern relevant research protocols for such experiments, which sometimes may not be available. It is very important to identify exact allelochemical(s) in the field of allelopathy research. Narwal and Sampietro (2009) have stated that majority of researchers do not identify such allelochemical(s) easily in developing countries, because of lack of compendium on the techniques and the findings obtained are not suitable to compare with other works. To know this large family of compounds using conventional experimental toxicological methods, would require huge investments in terms of both cost and time. In addition,

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World Scientific News 48 (2016) 162-191 European Union member states, United States Environmental Protection Agency (USEPA) are determined to move away from animal studies, in assessing the environmental safety of chemicals. Computational methods offer an attractive alternative to laborious and expensive experimental studies, with the potential for several studies on many compounds (Sullivan et al., 2014). Computational tools could evaluate numerous compounds for several toxicological endpoints. This is an easy screening method by using software, highlighting compounds for which experimental evaluation is necessary or recommendable and producing a system for predicting the toxicity and/or mutagenicity of allelochemicals. This system can also give additional information, since it can identify genotoxic properties in a fast, reproducible and convenient way on the basis of the simple chemical structure. These models integrate knowledge from different fields (chemistry, toxicology and computer science) that together can show a possible relationship to predict the property of a chemical from its structure, without any direct experimental measurement (Piparo et al., 2006). It is an interesting feature in which some of these bioactive compounds of plants may act on other important crop plants such as cereals, wheats etc. by the presence of natural toxins or mutagens (Leather, 1983; Adler and Chase, 2007; Mominul Islam and KatoNoguchi, 2013). Among several weeds, Alternanthera tenella, a profusely available typical invasive species in West Bengal belonging to the family Amaranthaceae was selected because it is ecologically well adapted due to its phenotypic plasticity, several co-evolving ecotypes, stress tolerant nature (Hunt et al., 1987). There are many allelochemicals in the leaves and stem as well as roots of A. tenella, which have been well documented in literatures out of which three established allelochemicals viz anthraquinone – a shikimic acid derivative, βsitosterol an alkaloid containing six isoprene units obtained from mevalonate pathway and quercetin a phenol obtained from cinnamic acid derivatives and their related compounds have been studied by researchers (Brown and Brown, 1976; Wolfreys and Hepburn, 2002; Materska, 2008). Generally mutagenicity is a direct indicator of allelopathic potential of an allelochemical. Researchers have documented that soil plays an important role as the biological environment where secondary metabolites or allelochemicals have easily been detoxified or toxified through conversion by microbes (Inderjit, 1998; Inderjit and Weiner, 2001; Ohno, 2001). The microbial activities in the soil is mainly based on moisture content, which is the main edaphic factor and helps to enhance activity of soil microorganisms (Rizvi and Rizvi, 1992; Reinhardt et al., 1996). This is due to the fact that all allelochemicals transportation is done through water, which serves as a solvent and carrier of leachates from all aerial plant parts in the soil. Serafimova et al. (2010) have documented Ames mutagenicity prediction to be with an accuracy of about 70-75%. Mutagenic activities is based on frame-shift mutations or base-pair substitutions that can only occurr on strains of Salmonella typhimurium which depend on histidine-rich medium for any test chemical (Ames, 1979; USEPA, 2012). The concept is reverse mutations caused by the functional ability of the bacterial strain to synthesize histidine dependent colony growth on the histidine deficient medium during mutagens exposure, called as revertant colony (USEPA, 2012). The test chemical is classified Ames mutagenic positive when it induces revertant colony growth in any one of out of five strains (TA97, TA98, TA100, TA102 and TA104) frequently used and vice versa for mutagenic negative (Mortelmans and Zeiger, 2000).

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World Scientific News 48 (2016) 162-191 The T.E.S.T. is a two-dimensional QSAR modeling software, which helps to detect mutagenicity prediction for major organic compounds. In the T.E.S.T., a dataset of 6512 chemicals was incorporated by Hansen and coworkers from several sources and from Toxicity Benchmark (Hansen et al. 2009). The final dataset have consisted of 5743 chemicals where excluding salts, mixtures, ambiguous organic materials and compounds having unavailable CAS numbers. It has been reported for external statistical validation, the consensus method has been achieved the best prediction accuracy, which is based on concordance value and prediction specificity (USEPA, 2012). Other methods like, single model and group contribution could not be used to this endpoint and average value of all of the methods has achieved a nice balance of prediction of a compound on the basis of concordance (accuracy), sensitivity and specificity (USEPA, 2012). Many researchers have already used T.E.S.T. software for mutagenicity prediction for different organic compounds (Aggrawal and Chaturvedi, 2014; Banerjee and Talapatra, 2015; Dhar et al., 2016). The predicted toxicity or mutagenicity is simply the average of the predicted values from the above mentioned QSAR methodologies (Zhu et al. 2009). In addition this method provides the highest prediction coverage because several methods with slightly different applicability domains are used to make a prediction (Ruiz et al., 2012). The present study aims to detect the predictive mutagenicity of similar chemicals in relation to the three common phytochemicals viz. anthraquinone, β-sitosterol and quercetin present in A. tenella by using QSAR modeling software T.E.S.T. and corroborate it with the obtained experimental value and validate the data as to how closely they are correlated.

2. MATERIALS AND METHODS In the present study, three phytochemicals viz. anthraquinone, β-sitosterol and quercetin, commonly available in A. tenella and nine structurally similar compounds for each phytochemical were selected. Anthraquinone and its similar molecular structure based compounds viz. anthrone, 2-methylanthraquinone, 1-aminoanthraquinone, anthrarufin, 1hydroxyanthraquinone, 9,10-anthracenedione,1-methoxy-, danthron, 1,5-diaminoanthraquinone and benzanthrone while β-sitosterol and its related compounds viz. 3β,5α,6β– trihydroxycholestane, lithocholic acid, deoxycholoic acid, solanidine, cholic acid, cholesterol 7-hydroperoxide, cholest-6-en-3-beta-ol, 5-alpha-hydroperoxide, 3-acetoxycholestanol(5,6b)(N-phthalimido)aziridine and lactandrate and also for quercetin and its related compounds viz. kaempferol, rhamnetin, 2-hydroxyemodin, quercitrin, citreorosein, 5-hydroxy-emodin, cynodontin, bellidifolin and demethylbellidifolin were selected for Ames mutagenicity prediction compared to experimental data that were obtained from Toxicity Benchmark through T.E.S.T. software. All three phytochemicals and their related organic compounds of similar molecular structure are exhibited in Table 1, 2 and 3. The prediction of Ames mutagenicity test was carried out by using T.E.S.T. software Ver 4.1. For individual phytochemical, another nine chemicals were selected on the basis of similar molecular structure and a total of ten chemicals were tabulated along with CAS no. obtained from ChemIDplus (Table 1, 2 and 3). There were total 30 chemicals studied for these three phytochemicals. These are predicted either mutagenic or antimutagenic organic compounds found in present plant species and their analogues are assumed to be present

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World Scientific News 48 (2016) 162-191 either in natural forms or derived from the natural ones by conversion or substitution in the position of aromatic ring when present in soil. It was recommended that T.E.S.T. software package estimates mutagenicity using several QSAR methodologies. These are hierarchical clustering, the Food and Drug Administration (FDA) MDL, nearest neighbor and a consensus. In T.E.S.T., the consensus method is the average predicted data of all QSAR methods, also obtained in present software based on the applicability domain for individual method (Zhu et al., 2009). Particular chemical structure can easily be observed after entering CAS no. in appropriate text box of T.E.S.T. software interface to obtain predictive mutagenicity or antimutagenicity data. All the experimental data were also gathered through T.E.S.T. According to User’s guide (USEPA, 2012), the Ames test has confirmed the frameshift mutations or base-pair substitutions and can only be simulated for any test chemical when exposed to Salmonella typhimurium of histidine-dependent strains. Statistical analysis were obtained for data of concordance (accuracy), sensitivity and specificity values from T.E.S.T. Concordance data revealed the fraction of all compounds for correct prediction, which described data of active compounds from experiment have predicted active while data of inactive compounds from experiment have predicted inactive. Sensitivity and specificity data also revealed experimentally active compounds have predicted active and experimentally active compounds have predicted active. All the data for concordance, sensitivity and specificity in each phytochemical and related compound were obtained through T.E.S.T. The FDA model prediction equation for individual compound was also obtained from T.E.S.T. and is described under results section. Statistical analysis with special reference to regression analysis to determine R 2 value (Correlation coefficient) was carried out by using software (Microsoft version 8.2, Excel with statistical data analysis add on tool pack) for individual chemical dataset obtained from FDA model fit results by similar chemicals through T.E.S.T. Table 1. Identification of similar chemicals analogous to anthraquinone present in Alternanthera tenella

Sl. No.

Parent chemical with molecular formula*

Similar chemical with molecular formula*

CAS No*

1.

Anthraquinone (C14-H8-O2)

--

84-65-1

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Structure*

World Scientific News 48 (2016) 162-191

2.

3.

Anthrone (C14-H10-O)

90-44-8

2Methylanthraquinone (C15-H10-O2)

84-54-8

4.

1Aminoanthraquinone (C14-H9-N-O2)

82-45-1

5.

Anthrarufin (C14-H8-O4)

117-12-4

6.

1-Hydroxyanthraquinone (C14-H8-O3)

129-43-1

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7.

8.

9.

10.

9,10Anthracenedione, 1-methoxy(C15-H10-O3)

Danthron (C14-H8-O4)

82-39-3

117-10-2

1,5-Diaminoanthraquinone (C14-H10-N2-O2)

Benzanthrone (C17-H10-O)

129-44-2

82-05-3

* Source: ChemIDplus

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CAS No*

83-46-5

β-sitosterol (C29H50O)

1253-84-5

2. Similar chemical with molecular formula*


1.

3β,5α,6β –trihydroxy cholestane (C27H48O3)

Sl. No.


Table 2. Identification of similar chemicals analogous to β-sitosterol present in Alternanthera tenella

Structure*

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5. 80-78-4

4. 83-44-3

Deoxycholic acid (C24H40O4)

434-13-9

Lithocholic acid (C24H40O3)

3.

Solanidine (C27H43NO)


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7. Cholesterol 7-hydroperoxide (C27 H46 O3)

2846-29-9

8.

Cholest-6-en-3-beta-ol, 5-alpha-hydroperoxide (C27H46O3)

3328-25-4

6. 81-25-4

Cholic acid (C24 H40 O5)


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9. 3-Acetoxycholestanol(5,6-b) (N-phthalimido)aziridine (C37H52N2O4)

118517-20-7

10. Lactandrate (C31H44Cl2N2O3)

43000-65-3


* Source: ChemIDplus

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CAS No* 117-39-5 520-18-3

Similar chemical with molecular formula*

Quercetin (C15H10O7)

2.

---


1.

Kaempferol ( 15H10O6)

Sl. No.

Table 3. Identification of similar chemicals analogous to quercetin present in Alternanthera tenella.

Structure*

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4. 2-Hydroxyemodin (C15H10O6)

641-90-7

5. Quercitrin (C21H20O11)

522-12-3

3. 90-19-7

Rhamnetin (C16H12O7)


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Citreorosein (C15H10O6)

7. 5-Hydroxyemodin (C15H10O6)

20324-66-7

8. 476-43-7

481-73-2

6.

Cynodontin (C15H10O6)


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9.

Bellidifolin (C14H10O6)

2798-25-6

10.

Demethylbellidifolin (C13H8O6)

2980-32-7


3. RESULTS AND DISCUSSION The predictive mutagenicity results clearly indicated that phytochemical anthraquinone and its related eight compounds were mutagenic positive except benzanthrone, which is mutagenic negative. All the experimental values were found mutagenic positive (Table 4). Other phytochemical β-sitosterol showed mutagenic negative in both experimental and predicted value but six related compounds viz, 3β,5α,6β–trihydroxycholestane, lithocholic acid, deoxycholic acid, solanidine, cholic acid, 3-acetoxycholestanol(5,6-b)(Nphthalimido)aziridine were mutagenic negative after prediction by using T.E.S.T. but four out of these six viz. 3β,5α,6β–trihydroxycholestane, deoxycholic acid, cholic acid, 3acetoxycholestanol(5,6-b)(N-phthalimido)aziridine were mutagenic positive in experimental value. Thus in experimental data seven compounds were obtained mutagenic positive and rest three were mutagenic negative (Table 5). In case of quercetin, both experimental and predicted values were obtained mutagenic positive while in related analogous compounds, it was observed that seven compounds viz. 2-hydroxyemodin, quercitrin, citreorosein, 5hydroxyemodin, cynodontin, bellidifolin and demethylbellidifolin were predicted mutagenic positive and two compounds viz. kaempferol and rhamnetin, were predicted mutagenic negative using T.E.S.T. but all the experimental values obtained were mutagenic positive (Table 6). The software inbuilt external predictive validation analysis on the basis of concordance, sensitivity and specificity showed that the prediction was done on the basis of concordance, -176-

World Scientific News 48 (2016) 162-191 sensitivity and specificity. The value ranges from 80-100%, 88-100% and 62-100% respectively for anthraquinone and its structurally similar chemicals, except the compound anthrarufin, for which no data were obtained through T.E.S.T. (Table 7). In case of βsitosterol and related compounds, concordance, sensitivity and specificity value ranges 89100%, 50-100% and 94-100% respectively, except cholesterol 7-hydroperoxide for which no value or data was available (Table 8). For quercetin, concordance value range is 83%–97%, sensitivity and specificity value ranges from 89-100% and 50-83% respectively (Table 9). Generally external validation has been considered the best means for evaluating the predictive power and reliability of any QSAR model (Golbraikh and Tropsha, 2002; Perkins et al., 2003; Votano et al., 2004; Contrera et al., 2005). According to Snyder and Smith (2005), sensitivity and specificity values are most important to know exact predictive validity by QSAR modeling for mutagenicity testing. The individual test chemical prediction by T.E.S.T. with similar chemicals from inbuilt database were evaluated and R2 (correlation coefficient) value for each test compound were determined, which are depicted through scattered plots in Fig. 1 (A-I), Fig. 2 (A-I) and Fig. 3(A-G). Table 4. Predictive mutagenicity of anthraquinone and its similar compounds Ames mutagenicity estimation by T.E.S.T. Predicted value Experimental (Consensus value# method)

Sl No.

Compounds

CAS No.*

1.

Anthraquinone

84-65-1

1.00 (+)

0.74 (+)

2.

Anthrone

90-44-8

1.00 (+)

0.57 (+)

3.

2-Methylanthraquinone

84-54-8

0.00 (-)

0.76 (+)

4.

1-Aminoanthraquinone

82-45-1

1.00 (+)

0.96 (+)

5.

Anthrarufin

117-12-4

1.00 (+)

0.94 (+)

6.

1-Hydroxyanthraquinone

129-43-1

1.00 (+)

0.91 (+)

7.

9,10-Anthracenedione, 1-methoxy-

82-39-3

1.00 (+)

0.73 (+)

8.

Danthron

117-10-2

1.00 (+)

0.94 (+)

9.

1,5-Diaminoanthraquinone

129-44-2

1.00 (+)

1.07 (+)

10.

Benzanthrone

82-05-3

1.00 (+)

0.20 (-)

*Data obtained from ChemIDplus; # All data from Toxicity Benchmark through T.E.S.T; (+) = mutagenic positive; (_) = mutagenic negative

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World Scientific News 48 (2016) 162-191 It was observed R2 values (in % significant) for studied chemicals viz. anthraquinone is (98), anthrone (93), 2-methylanthraquinone (99), 1-aminoanthraquinone (100), 1hydroxyanthraquinone (100), 9,10-anthracenedione, 1methoxy (95), danthron (94), 1,5diaminoanthraquinone (100), benzanthrone (91), β-sitosterol (74), 3β,5α,6βtrihydroxycholestane (100), lithocholicacid (100), deoxycholoic acid (100), solanidine (98), cholic acid (99), cholest-6-en-3-betaol-5-alphahydroperoxide (100), 3-acetoxycholestanol(5,6b)-(Nphthalimido)aziridine (100), lactandrate (100), rhamnetin (100), 2-hydroxyemodin (100), quercitrin (100), citreorosein (100), 5hydroxyemodin (100), cynodontin (100) and Demethylbellidifolin (100). Two compounds anthrarufin, and cholesterol 7-hydroperoxide were unable to be determined due to unavailability of FDA model fit chemical data and quercetin, kaempferol and bellidifolin have not plotted because same predictive values were obtained in T.E.S.T. It has been documented that the secondary metabolite anthraquinone and its similar compounds as mentioned in the results section were potent mutagen on S. typhimurium in experimental study (Arcos and Argus, 1974; Tamaro et al., 1975; Brown and Brown, 1976). The present QSAR modeling prediction data were also obtained mutagenic positive except benzanthrone whose predicted value is (0.20) i.e. lowest than all other predicted values and much below (0.50) which is the least value to be mutagenic positive. The majority of natural anthraquinones and its analogues are weakly acidic because of the influence of electronic attraction of the carbonyl on hydroxyl because the hydrogen bond between hydroxyl and adjacent carbonyl limits the dissociation of the hydroxyl proton. Thus, they dissolve only in the sodium hydroxide solution. Thus an alkaline soil pH as a stress factor can promote their dissolution in the soil and their subsequent absorption into surrounding roots of several other plants inhibiting seed germination and primary root formation (Romagni et al., 2004). The phytochemical, quercetin has also showed to be mutagenic positive in purified form to Salmonella typhimurium (Bjeldanes and Chang, 1977; Stoewsand et al., 1984) and prediction by QSAR modeling also reported mutagenic positive with a value of (0.55) exceeding the barrier limit of (0.50) when studied without known mutagen (Edenharder and Tang, 1997; de Melo et al., 2010; Banerjee and Talapatra, 2015), which supports the present findings. Table 5. Predictive mutagenicity of β-sitosterol and its similar compounds. Ames mutagenicity estimation by T.E.S.T. Experimental Predicted value value# (Consensus method)

Sl. No.

Compounds

CAS No.*

1.

β-sitosterol

83-46-5

0.00 (-)

0.25 (-)

2.

3β,5α,6βtrihydroxycholestane

1253-84-5

1.00 (+)

0.43 (-)

3.

Lithocholic acid

434-13-9

0.00 (-)

0.28 (-)

4.

Deoxycholic acid

83-44-3

1.00 (+)

0.34 (-)

5.

Solanidine

80-78-4

0.00 (-)

0.17 (-)

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6. 7. 8. 9. 10.

Cholic acid Cholesterol 7-hydroperoxide Cholest-6-en-3-beta-ol, 5-alpha-hydroperoxide 3-Acetoxycholestanol(5,6b)(N-phthalimido)aziridine Lactandrate

81-25-4

1.00 (+)

0.41 (-)

2846-29-9

1.00 (+)

0.82 (+)

3328-25-4

1.00 (+)

0.62 (+)

1.00 (+)

0.06 (-)

1.00 (+)

0.82 (+)

11851720-7 43000-653


Table 6. Predictive mutagenicity of Quercetin and its similar compounds. Ames mutagenicity estimation by T.E.S.T. Experimental Predicted value value# (Consensus method)

Sl. No.

Compounds

CAS No.*

1.

Quercetin

117-39-5

1.00 (+)

0.55 (+)

2.

Kaempferol

520-18-3

1.00 (+)

0.39 (-)

3.

Rhamnetin

90-19-7

1.00 (+)

0.40 (-)

4.

2-hydroxyemodin

641-90-7

1.00 (+)

0.76 (+)

5.

Quercitrin

522-12-3

1.00 (+)

0.64 (+)

6.

Citreorosein

481-73-2

1.00 (+)

0.92 (+)

7.

5-Hydroxyemodin

20324-66-7

1.00 (+)

0.75 (+)

8.

Cynodontin

476-43-7

1.00 (+)

0.87 (+)

9.

Bellidifolin

2798-25-6

1.00 (+)

0..91 (+)

10.

Demethylbellidifolin

2980-32-7

1.00 (+)

1.03 (+)


Seven compounds viz. 2-hydroxyemodin, quercitrin, citreorosein, 5-hydroxy-emodin, cynodontin, bellidifolin and demethylbellidifolin were predicted mutagenic positive because of values ranging from 0.64 to 1.03 exceeding the barrier limit of (0.50). However, two compounds viz. kaempferol and rhamnetin, were predicted mutagenic negative because of

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World Scientific News 48 (2016) 162-191 values 0.39 and 0.40 respectively below (0.50), which is the least value to be mutagenic positive but all the experimental values obtained were mutagenic positive. Likewise in toxicity research with plant metabolites, the growth of lettuce seeds was impaired with a dose of 50 mg/l emodin (Izhaki, 2002). In soil, phosphate depletion occurred when emodin and hydroxyemodin were released by plants (Inderjit and Nishimura, 1999). It has been emphasized that a series of emodin analogs are reported to inhibit germination and primary root development (Romagni et al., 2004). Among all the phytochemicals, β-sitosterol was obtained non-mutagen in both experimental and prediction data with a value of (0.25). β-sitosterol was mutagenic negative after prediction by using T.E.S.T. but four out of its six analogs viz. 3β,5α,6β-trihydroxycholestane, deoxycholic acid, cholic acid, 3acetoxycholestanol-5,6-b-Nphtha-limidoaziridine were mutagenic positive in experimental value. Similar observations have been found in other research works in relation to several pesticides that few compounds have positive mutagenicity values for experimental results but same compounds have negative values for predicted values by using T.E.S.T. (Aggrawal and Chaturvedi, 2014). Lithocholic acid and solanidine were recorded non-mutagen in both experimental and predicted value (Riju et al., 2009). Table 7. External predictive validation of FDA cluster model fit results for anthraquinone and its similar compounds. Sl No.

Compounds

Concordance (%)

Sensitivity (%)

Specificity (%)

1.

Anthraquinone

90

100

67

2.

Anthrone

90

100

73

3.

2-Methylanthraquinone

97

96

100

4.

1-Aminoanthraquinone

91

97

62

5.

Anthrarufin

---

---

---

6.

1-Hydroxyanthraquinone

92

97

71

7.

9,10-Anthracenedione, 1-methoxy-

100

100

100

8.

Danthron

93

100

67

9.

1,5-Diaminoanthraquinone

95

100

62

10.

Benzanthrone

80

88

69

--- = values not obtained in T.E.S.T.

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World Scientific News 48 (2016) 162-191 Table 8. External predictive validation of FDA cluster model fit results for β-sitosterol and its similar compounds Sl No.

Compounds

Concordance (%)

Sensitivity (%)

Specificity (%)

1.

β-sitosterol

89

50

100

2.

3β,5α,6β-trihydroxycholestane

93

50

100

3.

Lithocholic acid

97

100

96

4.

Deoxycholoic acid

100

100

100

5.

Solanidine

97

83

100

6.

Cholic acid

93

62

100

7.

Cholesterol 7-hydroperoxide

---

---

---

90

71

94

100

100

100

91

71

96

Cholest-6-en-3-beta-ol, 5-alpha-hydroperoxide 3-Acetoxycholestanol(5,6-b)(Nphthalimido)aziridine

8. 9. 10.

Lactandrate


Table 9. External predictive validation of FDA cluster model fit results for quercetin and its similar compounds. Sl No.

Compounds

Concordance (%)

Sensitivity (%)

Specificity (%)

1.

Quercetin

83

91

67

2.

Kaempferol

83

89

75

3.

Rhamnetin

90

93

87

4.

2-hydroxyemodin

93

100

50

5.

Quercitrin

83

91

67

6.

Citreorosein

93

96

75

7.

5-Hydroxyemodin

93

96

75

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8.

Cynodontin

93

96

75

9.

Bellidifolin

93

100

60

10.

Demethylbellidifolin

97

100

83


Why plant species Alternanthera tenella? Due to its phenotypic plasticity, several co evolving ecotypes, stress tolerant nature and because it is a photosynthetically C3-C4 intermediate suggesting a tendency for dominant evolution, showing partial Kranz anatomy in its leaves (Hunt et al., 1987). Several researchers have investigated that the confinement of decarboxylation capacity of the leaf depend upon bundle sheath cells along with the physical proximity of mitochondria and chloroplasts in these cells ,which is proposed to form the basis for efficient light dependent recapture of photorespired CO2 in leaves (Hunt et al.; 1987; Rawsthorne et al., 1988; Raghavendra and Das 1993). This physiological advantage gives it an allelopathic edge over other adjoining species, which is the main reason for us selecting this species. In past research works, it has already been studied that this species contained several secondary metabolites, which have allelopathic impacts on other weeds and important crops (Reddy and Raju, 1997; Guerra et al., 2003; Reddy et al, 2008; Poonguzhali and Ramani, 2012). In the present results, out of three allelochemicals, two have potent mutagenic activity on S. typhimurium and also out of twenty seven related compounds nineteen similar compounds were predicted mutagens by T.E.S.T. Thus, the present study is emphasized with a curiosity that if substitutes change on their position in these secondary metabolites viz. anthraquinone, β-sitosterol and quercetin of A. tenella producing its related analogus compounds by environmental stress in presence of other elemental toxins exposed in the soil and water as solvent either dissolves or changes their configuration, what is the fate of available phytochemical and its molecular structure in soil?

….

A. Anthraquinone

B. Anthrone

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C. 2-Methylanthraquinone

E. 1-Hydroxyanthraquinone

D. 1-Aminoanthraquinone

F. 9,10-Anthracenedione, 1-methoxy-

G. Danthron

H. 1,5-Diaminoanthraquinone

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I. Benzanthrone Fig. 4. Scattered plot for regression analysis of FDA model fit chemical data for anthraquinone and similar chemicals (A-I)

A. β-sitosterol

B. 3β,5α,6β–trihydroxycholestane

C. Lithocholic acid

D. Deoxycholoic acid

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E. Solanidine

F. Cholic acid

G. Cholest-6-en-3-beta-ol, 5-alpha-hydroperoxide

H. 3-Acetoxycholestanol(5,6-b) (N-phthalimido)aziridine

I. Lactandrate Fig. 5. Scattered plot for regression analysis of FDA model fit chemical data for β-sitosterol and similar chemicals (A-I) -185-


A. Rhamnetin

B. 2-hydroxyemodin

C. Quercitrin

D. Citreorosein

E. 5-Hydroxyemodin

F. Cynodontin

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G. Demethylbellidifolin Fig. 6. Scattered plot for regression analysis of FDA model fitchemical data for quercetin and similar chemicals (A-I)

4. CONCLUSIONS In conclusion, the present study was emphasized on three allelochemicals viz. anthraquinone, β-sitosterol and quercetin from A. tenella and nine structurally similar compounds for each allelochemical. Pertinently the question arises – Are there any possibilities of conversion or substitution in the position of aromatic ring when these chemicals release in soil from this plant? It has already been documented that the plant allelopathy is fully dependent upon several abiotic and biotic factors and the power of degradation can only be achieved by soil microorganisms (Inderjit, 1998; Inderjit and Weiner, 2001; Ohno, 2001). According Li et al. (2010) and Soltys et al. (2013), also solar irradiation, temperature gradient, soil types and properties like salinity, moisture content, nutrients and biological factors viz. insect and diseases in the soil are several other causes of allelopathy (Putnam, 1988; El-Darier and Youssef, 2007; El-Darier et al., 2014). Generally phenolic rich extracts have proved to confirm DNA breaks and mutation thus contributing significantly to genotoxicity while flavonoids like quercetin, quercitrin, citrorosein inhibit topoisomerase I and II enzyme, which has capacity to interfere with the replication and transcription process and inhibiting the process of the religation of DNA double strand breaks and enhancing the formation of cleavable DNA- enzyme complexes (Matthews et al., 2006; Srividya et al. 2013). The present study might be a possible indication in future to detect metabolic pathway and also to know the mechanism of allelochemicals formation in Alternanthera tenella in presence of organic and/or elemental toxins in soil. It should be investigated in future that whether the studied chemicals may enhance the toxic impact on weeds as herbicides but no effect to crops growth due to substitute alternations by the metabolic mechanisms and also by abiotic and biotic influence in soil? The present study was carried out with 2D molecular descriptor based software (T.E.S.T.) but suggested to validate with other available 2D and 3D softwares.

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( Received 09 May 2016; accepted 31 May 2016 )

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