Excessive generation of reactive oxygen species (ROS) is considered to play an important role in arsenic-induced carcinogenicity in the liver, lungs, and urinary bladder. However, little is known about the mechanism of ROS-based carcinogenicity, including where the ROS are generated, and which arsenic species are the most effective ROS inducers. In order to better understand the mechanism of arsenic toxicity, rat liver RLC-16 cells were exposed to arsenite (iAs(III)) and its intermediate metabolites [i.e., monomethylarsonous acid (MMA(III)) and dimethylarsinous acid (DMA(III))]. MMA(III) (IC(50) = 1 μM) was found to be the most toxic form, followed by DMA(III) (IC(50) = 2 μM) and iAs(III) (IC(50) = 18 μM). Following exposure to MMA(III), ROS were found to be generated primarily in the mitochondria. DMA(III) exposure resulted in ROS generation in other organelles, while no ROS generation was seen following exposures to low levels of iAs(III). This suggests the mechanisms of induction of ROS are different among the three arsenicals. The effects of iAs(III), MMA(III), and DMA(III) on activities of complexes I-IV in the electron transport chain (ETC) of rat liver submitochondrial particles and on the stimulation of ROS production in intact mitochondria were also studied. Activities of complexes II and IV were significantly inhibited by MMA(III), but only the activity of complexes II was inhibited by DMA(III). Incubation with iAs(III) had no inhibitory effects on any of the four complexes. Generation of ROS in intact mitochondria was significantly increased following incubation with MMA(III), while low levels of ROS generation were observed following incubation with DMA(III). ROS was not produced in mitochondria following exposure to iAs(III). The mechanism underlying cell death is different among As(III), MMA(III), and DMA(III), with mitochondria being one of the primary target organelles for MMA(III)-induced cytotoxicity.

Three minor sulfur-containing arsenic metabolites: monomethylmonothioarsonic acid (MMMTA(V)), dimethylmonothioarsinic acid (DMMTA(V)), and dimethyldithioarsinic acid (DMDTA(V)) were recently found in human and animal urine after exposure to inorganic arsenic. However, it remains unclear how the thioarsenicals are formed in the body and then excreted into the urine. It is hypothesized that the generation of thioarsenicals occurs during enterohepatic circulation. To address this hypothesis, male Sprague Dawley (SD) rats and Eisai hyperbilirubinuric (EHB) rats (with deficiency of multidrug resistance-associated protein 2) were orally administered a single dose of inorganic arsenite (iAs(III)) at 3.0 mg kg(-1) of body weight. Five hours after dosing, less than 1.0% of the dose was recovered in the bile of EHB rats, while more than 27% of the dose was recovered in the bile of SD rats, with the majority being monomethylarsinodiglutathione [MMA(SG)(2)] with a small amount of arsenic triglutathione [iAs(SG)(3)]. During the early time periods (3 h and 6 h) the arsenic levels in the liver, red blood cells (RBCs) and plasma of EHB rats were higher than those of SD rats, and approximately 76% and 87% of the dose was recovered in the RBCs of SD and EHB rats, respectively, at day 5 after dosing. However, there were no significant differences in arsenic concentration in urine between the two types of animal. Regarding the arsenic species in the urine of both types of rat, significant levels of thiolated arsenicals MMMTA(V) and DMMTA(V) were detected in SD rat urine, however in EHB rat urine only low levels of DMMTA(V) were detected. The present result of the metabolic balance and speciation study suggests that the formation of MMMTA(V) and DMMTA(V) in rats is dependent on enterohepatic circulation. In addition, in vitro experiments indicated that arsenicals excreted from bile may be transformed by gastrointestinal microbiota into MMMTA(V) and DMMTA(V), which are then absorbed into the bloodstream and finally excreted into the urine.

Arsenic trioxide (As(2)O(3)) is established as one of the most effective drugs for treatment of patients with acute promyelocytic leukemia, as well as other types of malignant tumors. However, HL-60 cells are resistant to As(2)O(3), and little is known about the underlying resistance mechanism for As(2)O(3) and its biomethylation products, namely, monomethylarsonous acid (MMA(III)) on the treatment of tumors. In the present study, we investigated the molecular mechanisms underlying iAs(III) and its intermediate metabolite MMA(III)-induced anticancer effects in the HL-60 cells. Here, we show that the HL-60 cells exhibit resistance to inorganic iAs(III) (IC(50) = 10 μM), but are relatively sensitive to its intermediate MMA(III) (IC(50) = 3.5 μM). Moreover, we found that the multidrug resistance protein 1 (MRP1), but not MRP2, is expressed in HL-60 cells, which reduced the intracellular arsenic accumulation, and conferred resistance to inorganic iAs(III) and MMA(III). Pretreatment of HL-60 with MK571, an inhibitor of MRP1, significantly increased iAs(III) and MMA(III)-induced cytotoxicity and arsenic accumulations, suggesting that the expression of MRP1/4 may lead to HL-60 cells resistance to trivalent arsenic compounds.

Distributed beamforming uses nodes in a wireless sensor network to transmit signals in different phases with controllable delay, to obtain coherent output signals with a gain after superposition. However, a wireless sensor network has a large topological area and wide distribution range, and it is difficult for distributed beamforming to obtain a highly directional beam as centralized beamforming does, which will cause interference to the non-target base stations. To solve this problem, a discrete adaptive dual-population cooperative differential evolution (DPCDE) algorithm is proposed, which can effectively reduce the interference by selecting nodes suitable for participating in distributed beamforming in a wireless sensor network. Simulation results show that the proposed algorithm can optimize the node set participating in distributed beamforming to minimize the interference of the wireless sensor network to the non-target base stations, and the effect is better than other classic intelligent optimization algorithms.