Mild pentachlorophenol-mediated uncoupling of mitochondria depletes ATP but does not cause an oxidized redox state or dopaminergic neurodegeneration in Caenorhabditis elegans.

Aims: Mitochondrial dysfunction is implicated in several diseases, including neurological disorders such as Parkinson's disease. However, there is uncertainty about which of the many mechanisms by which mitochondrial function can be disrupted may lead to neurodegeneration. Pentachlorophenol (PCP) is an organic pollutant reported to cause mitochondrial dysfunction including oxidative stress and mitochondrial uncoupling. We investigated the effects of PCP exposure in Caenorhabditis elegans, including effects on mitochondria and dopaminergic neurons. We hypothesized that mild mitochondrial uncoupling by PCP would impair bioenergetics while decreasing oxidative stress, and therefore would not cause dopaminergic neurodegeneration. Results: A 48-hour developmental exposure to PCP causing mild growth delay (∼10 % decrease in growth during 48 h, covering all larval stages) reduced whole-organism ATP content > 50 %, and spare respiratory capacity âˆ¼ 30 %. Proton leak was also markedly increased. These findings suggest a main toxic mechanism of mitochondrial uncoupling rather than oxidative stress, which was further supported by a concomitant shift toward a more reduced cellular redox state measured at the whole organism level. However, exposure to PCP did not cause dopaminergic neurodegeneration, nor did it sensitize animals to a neurotoxic challenge with 6-hydroxydopamine. Whole-organism uptake and PCP metabolism measurements revealed low overall uptake of PCP in our experimental conditions (50 µM PCP in the liquid exposure medium resulted in organismal concentrations of < 0.25 µM), and no measurable production of the oxidative metabolites tetra-1,4-benzoquinone and tetrachloro-p-hydroquinone. Innovation: This study provides new insights into the mechanistic interplay between mitochondrial uncoupling, oxidative stress, and neurodegeneration in C. elegans. These findings support the premise of mild uncoupling-mediated neuroprotection, but are inconsistent with proposed broad "mitochondrial dysfunction"-mediated neurodegeneration models, and highlight the utility of the C. elegans model for studying mitochondrial and neurotoxicity. Conclusions: Developmental exposure to pentachlorophenol causes gross toxicological effects (growth delay and arrest) at high levels. At a lower level of exposure, still causing mild growth delay, we observed mitochondrial dysfunction including uncoupling and decreased ATP levels. However, this was associated with a more-reduced cellular redox tone and did not exacerbate dopaminergic neurotoxicity of 6-hydroxydopamine, instead trending toward protection. These findings may be informative of efforts to define nuanced mitochondrial dysfunction-related adverse outcome pathways that will differ depending on the form of initial mitochondrial toxicity.

Using a triple-quadrupole mass spectrometer in accurate mass mode and an ion correlation program to identify compounds.

Atomic masses and isotopic abundances are independent and complementary properties for discriminating among ion compositions. The number of possible ion compositions is greatly reduced by accurately measuring exact masses of monoisotopic ions and the relative isotopic abundances (RIAs) of the ions greater in mass by +1 Da and +2 Da. When both properties are measured, a mass error limit of 6-10 mDa (< 31 ppm at 320 Da) and an RIA error limit of 10% are generally adequate for determining unique ion compositions for precursor and fragment ions produced from small molecules (less than 320 Da in this study). 'Inherent interferences', i.e., mass peaks seen in the product ion mass spectrum of the monoisotopic [M+H]+ ion of an analyte that are -2, -1, +1, or +2 Da different in mass from monoisotopic fragment ion masses, distort measured RIAs. This problem is overcome using an ion correlation program to compare the numbers of atoms of each element in a precursor ion to the sum of those in each fragment ion and its corresponding neutral loss. Synergy occurs when accurate measurement of only one pair of +1 Da and +2 Da RIAs for the precursor ion or a fragment ion rejects all but one possible ion composition for that ion, thereby indirectly rejecting all but one fragment ion-neutral loss combination for other exact masses. A triple-quadrupole mass spectrometer with accurate mass capability, using atmospheric pressure chemical ionization (APCI), was used to measure masses and RIAs of precursor and fragment ions. Nine chemicals were investigated as simulated unknowns. Mass accuracy and RIA accuracy were sufficient to determine unique compositions for all precursor ions and all but two of 40 fragment ions, and the two corresponding neutral losses. Interrogation of the chemical literature provided between one and three possible compounds for each of the nine analytes. This approach for identifying compounds compensates for the lack of commercial ESI and APCI mass spectral libraries, which precludes making tentative identifications based on spectral matches.

Use of artificial neural networks for the accurate prediction of peptide liquid chromatography elution times in proteome analyses.

The use of artificial neural networks (ANNs) is described for predicting the reversed-phase liquid chromatography retention times of peptides enzymatically digested from proteome-wide proteins. To enable the accurate comparison of the numerous LC/MS data sets, a genetic algorithm was developed to normalize the peptide retention data into a range (from 0 to 1), improving the peptide elution time reproducibility to approximately 1%. The network developed in this study was based on amino acid residue composition and consists of 20 input nodes, 2 hidden nodes, and 1 output node. A data set of approximately 7000 confidently identified peptides from the microorganism Deinococcus radiodurans was used for the training of the ANN. The ANN was then used to predict the elution times for another set of 5200 peptides tentatively identified by MS/MS from a different microorganism (Shewanella oneidensis). The model was found to predict the elution times of peptides with up to 54 amino acid residues (the longest peptide identified after tryptic digestion of S. oneidensis) with an average accuracy of approximately 3%. This predictive capability was then used to distinguish with high confidence isobar peptides otherwise indistinguishable by accurate mass measurements as well as to uncover peptide misidentifications. Thus, integration of ANN peptide elution time prediction in the proteomic research will increase both the number of protein identifications and their confidence.