ABSTRACT
The study explored (I) the concentration of heavy metals in water samples (II) their bioaccumulation in common carp Cyprinus carpio (III) and the subsequent genotoxicity in the selected organs of carp; from river Kabul, Khyber Pakhtunkhwa Pakistan. Except for Mercury (Hg) the water samples had all the heavy metals within permissible limits of recommended dietary allowance (RDA). Nonetheless a number of heavy metals (Zn, Ni, Cr, Cd, Pb and Hg) showed bioaccumulation at levels higher than permissible. Zinc (Zn) was the most while Cadmium (Cd) was the least accumulated metal in all tissue samples analyzed. The metal burden in different organs of C. carpio was in sequence of intestine > skin > liver > gills > muscle. The Comet assay established DNA damage in selected organs to be in accordance with metal burden; the most to least damage being in sequence of blood > intestine > skin > liver > gills > muscle. In conclusion assessment of DNA damage in the organs of C. carpio appears to be a useful bio-marker to evaluate genotoxic effects of heavy metal pollution.
Subject(s)
Carps/metabolism , DNA Damage , Environmental Monitoring/methods , Metals, Heavy/toxicity , Rivers/chemistry , Water Pollutants, Chemical/toxicity , Animals , Carps/genetics , Metals, Heavy/analysis , Metals, Heavy/metabolism , Pakistan , Tissue Distribution , Water Pollutants, Chemical/analysis , Water Pollutants, Chemical/metabolismABSTRACT
BACKGROUND: Large scale understanding of complex and dynamic alterations in cellular and subcellular levels during cancer in contrast to normal condition has facilitated the emergence of sophisticated systemic approaches like network biology in recent times. As most biological networks show modular properties, the analysis of differential modularity between normal and cancer protein interaction networks can be a good way to understand cancer more significantly. Two aspects of biological network modularity e.g. detection of molecular complexes (potential modules or clusters) and identification of crucial nodes forming the overlapping modules have been considered in this regard. METHODS: In the current study, the computational analysis of previously published protein interaction networks (PINs) has been conducted to identify the molecular complexes and crucial nodes of the networks. Protein molecules involved in ten major cancer signal transduction pathways were used to construct the networks based on expression data of five tissues e.g. bone, breast, colon, kidney and liver in both normal and cancer conditions. MCODE (molecular complex detection) and ModuLand methods have been used to identify the molecular complexes and crucial nodes of the networks respectively. RESULTS: In case of all tissues, cancer PINs show higher level of clustering (formation of molecular complexes) than the normal ones. In contrast, lower level modular overlapping is found in cancer PINs than the normal ones. Thus a proposition can be made regarding the formation of some giant nodes in the cancer networks with very high degree and resulting in reduced overlapping among the network modules though the predicted molecular complex numbers are higher in cancer conditions. CONCLUSION: The study predicts some major molecular complexes that might act as the important regulators in cancer progression. The crucial nodes identified in this study can be potential drug targets to combat cancer.
