Multi-organ protective effect of Costus afer on low concentration toxic metal mixture in albino rats

Abstract

Heavy metal mixture can induce multiple organ damage through oxidative stress and inflammatory processes. Dietary intervention using natural antidotes in resource poor countries where classical metal chelators are either not affordable or available can be explored as an alternative means of management of public health effects of chronic heavy metal exposure. The search for natural antidote against the deleterious effects of heavy metals gives the thrust for this study. Thus, the study investigated the effect of aqueous leaf extract of Costus afer on liver, kidney, brain and testis induced by low dose heavy metal mixture (LDHMM) of PbCl₂, CdCl₂ and HgCl₂ of concentrations of 20 mg/kg, 1.61 mg/kg and 0.40 mg/kg, respectively. Five groups of seven rats each (weight-matched) were used. First and second groups received deionized water and heavy metal mixture and served as normal and toxic controls, respectively. Groups 3, 4 and 5 received through oral gavage 750, 1500, 2250 mg/kg of the Costus afer extract respectively, with the metal mixture concurrently. All treatments were four times a week for 90 days (4/week/90 days). Hepatorenal, hormonal, oxidative stress markers, cytokines (interleukin-6 and interleukin-10), and heavy metals (Pb, Cd and Hg) concentrations were assayed. The one-way analysis of variance, agglomerative hierarchical clustering, parallel coordinates plot, principal component analysis and Bray Curtis dissimilarity were used to statistically analyze the data. LDHMM caused significant changes in these organs and however, the plant extract provided a protective effect against these pathological changes. The statistical analysis revealed that the kidney was the most affected organ, followed by the liver, then brain and testis, respectively. Costus afer may be an important nutraceutical in multi-organ deleterious effects of LDHMM following its regulation of oxidative stress markers, inflammatory cytokines and biometal chelation.

Keywords: Costus afer; brain; inflammation; kidney; liver; oxidative stress; testis.

Figure 1

A parallel coordinates plot showing comparison of inflammatory markers interaction in different groups; class 1 which include rats in groups 1 and 5, class 2 comprising of rats in group 2, and class 3 which include rats in groups 3 and 4. It was deduced that class 1 rats have high IL-10 in the organs with low IL-6 levels, while class 2 rats have low IL-10 in the organs with high IL-6 levels. Class 3 rats fit into the intermediate class showing that they have in-between values for the inflammatory cytokines. *L is liver, K is kidney, B is brain and T is testis.

Figure 2

Correlation plot of inflammatory parameters on male albino rats against the generated factors (F1 and F2) where IL-6T = testis interlukin-6, IL-6B = brain interlukin-6, IL-6K = kidney interlukin-6, IL-6L = liver interlukin-6, IL-10T = testis interlukin-10, IL-10B = brain interlukin-10, IL-10K = kidney interlukin-10, IL-10L = liver interlukin-10. Variables clustered together around the axes indicate component factor values with the highest correlation coefficient. Variables that form an acute angle have a very strong positive correlation between them, while variables at right angle have weak or no correlation between them. Also, any two variables that are opposite themselves have a negative correlation between them.

Figure 3

Score plot illustrating the differentiation of parameters associated with interactions among inflammatory markers where L represents the liver, K = kidney, B = Brain, T = testis, PC1 = principal component 1, PC2 = principal component 2, and PC3 = principal component 3. A three-dimensional component system explaining 98.84% of total variance was observed after the PC analysis.

Figure 4

Correlation plot of oxidative stress markers on male albino rats against the generated factors (F1 and F2) where SODT = testis superoxide dismutase, SODB = brain superoxide dismutase, SODK = kidney superoxide dismutase, SODL = liver superoxide dismutase, MDAT = testis malondialdehyde, MDAB = brain malondialdehyde, MDAK = kidney malondialdehyde, MDAL = liver malondialdehyde, CATT = testis catalase, CATB = brain catalase, CATK = kidney catalase, CATL = liver catalase, GSHT = testis reduced glutathione, GSHB = brain reduced glutathione, GSHK = kidney reduced glutathione and GSHL = liver reduced glutathione. Variables clustered together around the axes indicate component factor values with the highest correlation coefficient. Variables that form an acute angle have a very strong positive correlation between them, while variables at right angle have weak or no correlation between them. Also, any two variables that are opposite themselves have a negative correlation between them.

Figure 5

Score plot illustrating the differentiation of parameters associated with interactions amo- ng oxidative stress markers where CAT = catalase, SOD = superoxide dismutase, GSH = glutathione, MDA = Malaondialdehyde, L = liver, K = kidney, B = brain, T = testis. A three-dimensional component system explaining 83.89% of the total variance was observed after PC analysis.

Figure 6

Dendrogram showing clustering of toxic metals interaction in different organs; Liver (L), Kidney (K), Brain (Br), Testis (T), Blood (Bl), Cd = cadmium, Pb = lead and Hg = mercury. Three clusters were distinctively identified which consist of CdL, PbL, HgL and CdBr (cluster 1), HgK and CdT (cluster 2), CdK, HgBr, PbT, PbK, PbBr, PbBl, CdBl, HgT and HgBl (cluster 3).

Figure 7

A parallel coordinates plot showing comparison of toxic metals interaction in different groups where Liver (L), Kidney (K), Brain (Br), Testis (T), Blood (Bl), Cd = cadmium, Pb = lead and Hg = mercury. Four classes were distinctively identified which were made up of rats belonging to groups 1 and 5 (class 1), rats belonging to group 2 (class 2), rats belonging to group 3 (class 3) and rats belonging to group 4 (class 4). It was deduced that class 1 rats have the least values of the toxic metal contents, while class 2 has the maximum values for metal concentrations. Class 3 and 4 belong to the intermediate class showing that they have in-between values for the metal concentration with class 3 being higher than class 4.

Figure 8

Principal component analysis plot showing the Bray Curtis distance across the different organs where MM = metal mixture, CA = Costus afer, PC1 = principal component 1 and PC2 = principal component 2. The distance connecting the metal mixture treated group is farthest to the deionized water treated group in the kidney compared to other organs thereby making it the most affected organ by the metal mixture.