Robert L. Copeland

Neuroprotective effects of nicotine against salsolinol-induced cytotoxicity: implications for Parkinson's disease.

Parkinson’s disease is associated with degeneration of dopaminergic cell bodies in the substantia nigra. It has been suggested that salsolinol, an endogenous metabolite of dopamine, may be involved in this process. An inverse relationship between Parkinson’s disease and smoking (nicotine intake) has been observed in epidemiological studies. Moreover, neuroprotective effects of nicotine in various experimental models have been observed. In this study we sought to determine whether salsolinol-induced cytotoxicity in SH-SY5Y human neuroblastoma cells, a cloned cell line which expresses dopaminergic activity, could also be prevented by nicotine pretreatment, and if so, which nicotinic receptors may mediate the actions of nicotine. Exposure of SH-SY5Y cells to 0.8 mM salsolinol for 24 hours resulted in approximately 80% cell death as determined by 3,[4,5-dimethylthiazol-2-yl]-2,5-diphenyl-tetrazolium bromide (MTT) assay. Pretreatment of cells with 0.1 mM nicotine resulted in inhibition of salsolinol-induced cytotoxicity. The effects of nicotine were blocked by mecamylamine, a non-selective nicotinic antagonist as well as conotoxins with selective antagonism against alpha3-containing nicotinic receptor subunits. The effects of nicotine were not affected by dihydro-beta-erythroidine or methyl-lycaconitine, selective antagonists against alpha4-beta2 or alpha7 nicotinic receptors, respectively. It is suggested that selective nicotinic agonists may be of therapeutic potential in at least a subpopulation of Parkinsonian patients.

Antiapoptotic effects of nicotine in its protection against salsolinol-induced cytotoxicity

Salsolinol (1-methyl-6,7-dihydroxy-1,2,3,4-tetrahydroisoquinoline), a metabolite of dopamine, may act as an endogenous neurotoxin and contribute to the etiology of Parkinson’s disease (PD). The inverse relationship between smoking and PD prompted our previous investigation and the report of protective effects of nicotine against salsolinol-induced toxicity in cultured SH-SY5Y cells (Copelandet al., Neurotox. Res. 8:289, 2005). These cells, derived from human neuroblastoma cells, express dopaminergic activity and are used as a model of nigral dopaminergic cells, the major site of pathology in PD. The purpose of the current study was to investigate whether apoptotic or antiapoptotic mechanisms were responsible for the observed effects of salsolinol and nicotine, respectively. Moreover, it was of interest to determine whether the actions of nicotine are mediated through nicotinic receptors. SH-SY5Y cells were exposed to 0.4 or 0.7 mM salsolinol with and without pretreatment in combination of 0.1 mM nicotine and 0.1 mM mecamylamine, and were exposed for 24 and 48 h. Various parameters including cell cycle perturbations (reflected in propidium iodide DNA staining); cell cycle regulator retinoblastoma protein (reflected in the Western blot), apoptosis (reflected in annexin V/propidium iodide staining followed by flow cytometry) were analyzed. Salsolinol caused an arrest of the cells in G1-phase of cell cycle and an increase in apoptotic indices, whereas pretreatment with nicotine attenuated or completely blocked the effects of salsolinol. Nicotine effects in turn, were totally blocked by mecamylamine (0.1 mM). The results suggest that apoptosis is a major mechanism for salsolinol-induced toxicity and that antiapoptotic effects of nicotine, mediated by nicotinic receptors, may play a primary role in its neuroprotective effects. Hence, nicotinic agonists in combination with other antiapoptotic agents may be of substantial benefit in at least a subpopulation of Parkinson patients.

Association of Cumulative Ultraviolet Radiation Exposure with Prostate Cancer Risk in a Case-control Study of African-American Men

It is well established that exposure to ultraviolet (UV) radiation has beneficial effects in reducing prostate cancer risk. To determine if there is a correlation between UV exposure and prostate cancer risk, we assessed sun exposure in a case-control study of 182 African-American men aged 40 years and older residing in the Metropolitan Washington, DC area. Using data on cumulative exposure per year and adult sunbathing scores derived from a validated questionnaire, analysis revealed significant difference in cumulative sun exposure between cases and controls (p=0.003). Additionally, the outdoor and recreation UV exposures were significantly higher in controls when compared to cases (p=0.003; p=0.03 in age-matched cases and controls). Although the results of conditional logistic regression analysis indicate that there was no association between total UV exposure and risk of prostate cancer after adjusting for age (OR=2.04, 95% CI 0.54-7.70, p=0.29), outdoor UV exposure was associated with decreased prostate cancer risk (OR= 0.31, 95% CI 0.14-0.65, p=0.002). Furthermore, a trend for reduced prostate cancer risk was found among men with early life high sun exposure during childhood ages 0-5 years (OR=0.17, 95% CI 0.03-0.74, p=0.02) and 6-11 years (OR= 0.28, 95% CI 0.07-1.05, p=0.06). Interestingly, this inverse association between prostate cancer risk and early life high sun exposure intensity was also observed among young men at ages 12-17 years although not statistically significant (OR=0.41, 95% CI 0.09-1.95, p=0.26). These findings indicate that UV exposure earlier in life may affect susceptibility to prostate cancer.

Kernel-Based Microfluidic Constriction Assay for Tumor Sample Identification

A high-throughput multiconstriction microfluidic channels device can distinguish human breast cancer cell lines (MDA-MB-231, HCC-1806, MCF-7) from immortalized breast cells (MCF-10A) with a confidence level of ∼81-85% at a rate of 50-70 cells/min based on velocity increment differences through multiconstriction channels aligned in series. The results are likely related to the deformability differences between nonmalignant and malignant breast cells. The data were analyzed by the methods/algorithms of Ridge, nonnegative garrote on kernel machine (NGK), and Lasso using high-dimensional variables, including the cell sizes, velocities, and velocity increments. In kernel learning based methods, the prediction values of 10-fold cross-validations are used to represent the difference between two groups of data, where a value of 100% indicates the two groups are completely distinct and identifiable. The prediction value is used to represent the difference between two groups using the established algorithm classifier from high-dimensional variables. These methods were applied to heterogeneous cell populations prepared using primary tumor and adjacent normal tissue obtained from two patients. Primary breast cancer cells were distinguished from patient-matched adjacent normal cells with a prediction ratio of 70.07%-75.96% by the NGK method. Thus, this high-throughput multiconstriction microfluidic device together with the kernel learning method can be used to perturb and analyze the biomechanical status of cells obtained from small primary tumor biopsy samples. The resultant biomechanical velocity signatures identify malignancy and provide a new marker for evaluation in risk assessment.

Abstract 3603: Metabolic profiles distinguish breast cancer progression in African American women

Breast cancer (BCa) is one of the most common malignancies in women and the incidence, distribution, clinical outcome and mortality rates vary widely among women of different ethnic backgrounds. Because tissue from African American (AA) women is difficult to obtain for biomarker studies, new population-relevant biomarkers that enable earlier detection and novel therapeutic intervention development are critical. To identify new biomarkers and targets that have the potential to be leveraged for earlier detection, classification of disease progression and development of improved therapeutics for AA patients, plasma and tissue samples were selected from two AA BCa case and control cohorts at the Tissue, Plasma and Clinical Bank at the Howard University Cancer Center (HUCC). Samples were from women either diagnosed with BCa, screened for potential BCa lesions or undergoing reduction mammoplasty surgery at both Howard University and Providence Hospitals. Samples were analyzed by untargeted metabolomics using 1 H nuclear magnetic resonance (NMR) spectroscopy. Multivariate and statistical analyses determined bins important to differentiating BCa by progressive stage and grade from control reduction mammoplasty tissues and fibrocystic fibroadenoma. Significant bins were library-matched to identify corresponding metabolites and distinguish common and unique metabolites between tissue groups and compare tissue profiles to plasma samples. Metabolites from each study group were also correlated with other known clinicopathologic BCa risk factors, including age, BMI, and smoking status, to determine their influence on disease progression. Several metabolites were found to distinguish nonmalignant reduction mammoplasty tissues from fibrocystic fibroadenomas, and from Grade (G) I-II estrogen receptor (ER)-positive or GI-II ER-negative tumors and GIII ER-positive or GIII ER-negative tumors. For example, in three comparisons using orthogonal partial least squares discriminant analyses (OPLS-DA) between reduction mammoplasty, fibrocystic fibroadenomas and the malignant tissues, we found 8 unique metabolites when comparing reduction mammoplasty versus fibrocystic fibroadenomas (4-hydroxybenzoate, dimethylamine, formate, glutamine, glutathione, histidine, methionine and UDP-N-acetylglucosamine); 2 unique metabolites comparing reduction mammoplasty versus GI-II (ER-positive and -negative) tumors (2-phenylpropionate and succinate); and 6 unique metabolites comparing reduction mammoplasty versus GIII (ethanolamine, glycine, hypoxanthine, maltose, sucrose and uridine). Our results demonstrate the continued usefulness of metabolomics-based research and the potential for these findings to identify early detection or disease staging biomarkers in a population that experiences a disparate burden of this disease. Citation Format: Delisha A. Stewart, Wimal W. Pathmasiri, Susan L. McRitchie, Lance Buckley, Tammey J. Naab, Robert L. DeWitty, Vikisha T. Fripp, Estelle Cooke-Sampson, Desta A. Beyene, Luisel Ricks-Santi, Robert L. Copeland, Susan J. Sumner, Yasmine M. Kanaan. Metabolic profiles distinguish breast cancer progression in African American women [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3603.