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Travis J Worst

Publications and source records attributed to Travis J Worst.

7 recordsLinked to original sources
PubMed2006

Apo-AII is an elevated biomarker of chronic non-human primate ethanol self-administration.

AIMS: Serum protein profiles were examined in naïve, ethanol self-administering and ethanol abstinent cynomolgus monkeys (Macaca fasicularis) to search for differences in protein expression which could possibly serve as biomarkers of heavy ethanol consumption. METHODS: Surface-enhanced laser desorption ionization time-of-flight (SELDI-ToF) mass spectrometry was used for proteomic profiling of serum. RESULTS: Two proteins were identified by SELDI-ToF to be increased in ethanol self-administering compared with abstinent animals. These proteins were identified to be apolipoprotein AI (Apo-AI) and apolipoprotein AII (Apo-AII) by peptide mass fingerprinting and comparison with spectra of purified human Apo-AI and AII proteins. Immunoblot analysis of Apo-AI and Apo-AII was performed on a separate group of animals (within-animal ethanol-naïve and self-administering) and confirmed a statistically significant increase in Apo-AII, while Apo-AI was unchanged. CONCLUSIONS: An open proteomic screen of serum and confirmation in a separate set of animals found Apo-AII to be increased in the serum of ethanol self-administering monkeys. These results are consistent with previous clinical studies of human ethanol consumption and serum apolipoprotein expression. Moreover, these results validate the use of non-human primates as a model organism for proteomic analysis of ethanol self-administration biomarkers.

Alcohol Drinking↗
PubMed2005

Transcriptome analysis of frontal cortex in alcohol-preferring and nonpreferring rats.

Although it is widely accepted that alcohol abuse and alcoholism have a significant genetic component of risk, the identities of the genes themselves remain obscure. To illuminate such potential genetic contributions, DNA macroarrays were used to probe for differences in normative cortical gene expression between rat strains genetically selected for alcohol self-administration preference, AA (Alko, alcohol) and P (Indiana, preferring), or avoidance, ANA (Alko, nonalcohol) and NP (Indiana, nonpreferring). Among 1,176 genes studied, six demonstrated confirmable, differential expression following comparison of ethanol-naive AA and ANA rats. Specifically, the mRNA level for metabotropic glutamate receptor 3 (mGluR3) was down-regulated in the AA vs. ANA lines. In contrast, calcium channel subunit alpha2delta1 (cacna2d1), vesicle-associated membrane protein 2 (VAMP2), syntaxin 1 (both syntaxin 1a and 1b; STX1a and STX1b), and syntaxin binding protein (MUNC-18) mRNAs were found to be increased in frontal cortex following comparison of AA with ANA animals. Bioinformatic analysis of these molecular targets showed that mGluR3 and cacna2d1 fall within chromosomal locations reported to be alcohol-related by the Collaborative Study on the Genetics of Alcoholism (COGA) as well as quantitative trait loci (QTL) studies. To determine further whether these differences were strain specific, the above-mentioned genes were compared in ethanol-preferring (P) and -nonpreferring (NP) selected lines. VAMP2 was the only gene that displayed statistically different mRNA levels in a comparison of P and NP rats. In conclusion, the altered cortical gene expression illuminated here would have the effect of altering neurotransmitter release in AA rats (compared with ANA rats). Such alterations, however, might not be a universal characteristic of all animal models of alcohol abuse and will also require further investigation in post-mortem human samples.

Alcohol Drinking↗
PubMed2004

Alcohol and gene expression in the central nervous system.

AIMS: To describe recent research focusing on the analysis of gene and protein expression relevant to understanding ethanol consumption, dependence and effects, in order to identify common themes. METHODS: A selective literature search was used to collate the relevant data. RESULTS: Over 160 genes have been individually assessed before or after ethanol administration, as well as in genetically selected lines. Techniques for studying gene expression include northern blots, differential display, real time reverse transcriptase-polymerase chain reaction (RT-PCR) and in situ hybridization. More recently, high throughput functional genomic technology, such as DNA microarrays, has been used to examine gene expression. Recent gene expression analyses have dramatically increased the number of candidate genes (nine array papers have illuminated 600 novel gene transcripts that may contribute to alcohol abuse and alcoholism). CONCLUSIONS: Although functional genomic experiments (transcriptome analysis) have failed to identify a single alcoholism gene, they have illuminated important pathways and gene products that may contribute to the risk of alcohol abuse and alcoholism.

Alcoholism↗
PubMed2004

Manganese-induced cytotoxicity in dopamine-producing cells.

Manganese (Mn) is an essential metal that, at excessive levels in the brain, produces extrapyramidal symptoms similar to those in patients with Parkinson's disease (PD). In the present study, Mn toxicity was characterized in a human neuroblastoma (SK-N-SH) cell line and in a mouse catecholaminergic (CATH.a) cell line. Mn was demonstrated to be more toxic in the catecholamine-producing CATH.a cells (EC50 = 60 microM) than in non-catecholaminergic SK-N-SH cells (EC50 = 200 microM). To test the hypothesis that the sensitivity of CATH.a cells to Mn is associated with their dopamine (DA) content, DA concentrations were suppressed in these cells by pretreatment with alpha-methyl-para-tyrosine (AMPT). Treatment for 24 h with 100 microM AMPT decreased intracellular DA, but offered no significant protection from Mn exposure (EC50 = 60 microM). Additional studies were carried out to assess if Mn toxicity was dependent on glutathione (GSH) levels. CATH.a cells were significantly protected by the addition of 5mM GSH (Mn EC50 = 200 microM) and 10mM N-acetyl cysteine (NAC) (Mn EC50 = 300 microM), therefore, indirectly identifying intracellular ROS formation as a mechanism for Mn neurotoxicity. Finally, apoptotic markers of Mn-induced cell death were investigated. DNA fragmentation, caspase-3 activation, and apoptosis-related gene expression were studied in CATH.a cells. No internucleosomal fragmentation or caspase activation was evident, even in the presence of "supraphysiological" Mn concentrations. cDNA hydridization array analysis with two differing Mn concentrations and time points, identified no noteworthy mRNA inductions of genes associated with programmed cell death. In conclusion, DA content was not responsible for the enhanced sensitivity of CATH.a cells to Mn toxicity, but oxidative stress was implicated as a probable mechanism of cytotoxicity.

Animals↗
PubMed2004

Functional genomic analysis in pain research using hybridization arrays.

Hybridization array technology makes it possible to compare global gene-expression patterns in any experimental context for which good-quality RNA can be generated. To date, DNA arrays have been used as a tool to compare functional genomic changes (differences in wholesale gene expression) in studies that cover an impressive variety of research disciplines including cancer, yeast genomics, and, more recently, neuroscience and behavior. The basic premise of the array experiment is that one interrogates a panel of probes (gene-specific cDNA fragments or gene-specific oligonucleotides that have been immobilized on a solid support) with RNAs (targets) from control and treated experimental samples that have been either radioactively or fluorescently labeled. Signal derived from either competitive (both samples on a single chip) or differential (one sample/one chip) hybridization is used to calculate relative gene expression. There are three widely used platforms available to perform array experiments (Affymetrix GeneChips, oligonucleotide arrays, and membrane-based cDNA arrays) and each platform offers advantages and limitations. The experimental description in this chapter explains, in detail, how to perform a hybridization array using the macroarray platform.

Animals↗