Analysis of methionine enkephalin in human pituitary by multi-dimensional reversed-phase high-performance liquid chromatography, radioreceptor assay, radioimmunoassay, fast atom bombardment mass spectrometry, and mass spectrometry—mass spectrometry

Methionine enkephalin (ME = YGGFM) was measured in five individual human post-mortem pituitaries using four different analytical methods, with the objective of comparing the molecular specificities of the methods. Radioreceptor assay (RRA) used a receptor-rich preparation from brain and [³H]etorphine as radioligand to determine ME-like receptoractivity (ME-LR). Radioimmunoassay (RIA) measured ME-like immunoreactivity (ME-LI). Pituitary samples analyzed by RRA and RIA were purified first with a high-performance liquid chromatography (HPLC) gradient on a polymer analytical column. Fast atom bombardment mass spectrometry (FAB-MS) in two different detection modes quantified ME using the protonated molecular ion MH⁺ of ME at 574 a.m.u. and B/E linked-field selected reaction monitoring (SRM) to monitor the specific unimolecular metastable transition that produced the unique amino acid sequence-determining tetrapeptide fragment ion YGGF⁺ from the MH⁺ precursor ion. Both FAB-MS methods used the deuterated internal standard YGG[²H₅-F]M. Samples analyzed with FAB-MS were purified first with multi-dimensional reversed-phase HPLC. The first dimension was an ODS gradient, and the second dimension was a polymer isocratic elution. The following ME amounts were measured (mean ± standard error of the mean): ME-LR, 7.0 ± 1.9 μg g⁻¹ tissue; ME-LI, 1.8 ± 0.7 μg g⁻¹ tissue; MH⁺, 2.7 ± 0.6 μg g⁻¹ tissue; SRM, 3.0 ± 0.8 μg g⁻¹ tissue. The FAB SRM method provided the highest level of molecular specificity amount these four analytical methods used to measure picomole amounts of endogenous ME in a human pituitary.     

The EBM-DPSER Conceptual Model: Integrating Ecosystem Services into the DPSIR Framework

There is a pressing need to integrate biophysical and human dimensions science to better inform holistic ecosystem management supporting the transition from single species or single-sector management to multi-sector ecosystem-based management. Ecosystem-based management should focus upon ecosystem services, since they reflect societal goals, values, desires, and benefits. The inclusion of ecosystem services into holistic management strategies improves management by better capturing the diversity of positive and negative human-natural interactions and making explicit the benefits to society. To facilitate this inclusion, we propose a conceptual model that merges the broadly applied Driver, Pressure, State, Impact, and Response (DPSIR) conceptual model with ecosystem services yielding a Driver, Pressure, State, Ecosystem service, and Response (EBM-DPSER) conceptual model. The impact module in traditional DPSIR models focuses attention upon negative anthropomorphic impacts on the ecosystem; by replacing impacts with ecosystem services the EBM-DPSER model incorporates not only negative, but also positive changes in the ecosystem. Responses occur as a result of changes in ecosystem services and include inter alia management actions directed at proactively altering human population or individual behavior and infrastructure to meet societal goals. The EBM-DPSER conceptual model was applied to the Florida Keys and Dry Tortugas marine ecosystem as a case study to illustrate how it can inform management decisions. This case study captures our system-level understanding and results in a more holistic representation of ecosystem and human society interactions, thus improving our ability to identify trade-offs. The EBM-DPSER model should be a useful operational tool for implementing EBM, in that it fully integrates our knowledge of all ecosystem components while focusing management attention upon those aspects of the ecosystem most important to human society and does so within a framework already familiar to resource managers.     

The role of the voltage-dependent anion channels in the outer membrane of mitochondria in the regulation of cellular metabolism

The role of the voltage-dependent anion channels (VDAC) harbored in the outer membrane of mitochondria in the regulation of cellular metabolism was investigated using an experimental model of ethanol toxicity in cultured hepatocytes. It was demonstrated that ethanol inhibits State 3 and uncoupled mitochondrial respirations, decreases the accessibility of mitochondrial adenylate kinase localized in the intermembrane space of mitochondria, and suppresses ureagenic respiration and synthesis of urea in cultured hepatocytes. Increasing the permeability of the outer mitochondrial membrane with closed VDAC with high concentrations of digitonin (> 80 microM), which creates pores in the membrane, allowing the alternative bypass of closed VDAC, and restores all reactions suppressed with ethanol. It is concluded that the effect of ethanol in hepatocytes leads to global loss of mitochondrial functions due to the closure of VDAC, which limits the free diffusion of metabolites into the intermembrane space of mitochondria. Our studies demonstrated that ethanol affects the main mitochondrial functions and revealed the role of VDAC channels in the outer mitochondrial membrane in the regulation of liver specific intracellular processes such as ureagenesis. The data obtained can be used for the development of pharmaceutical drugs that prevent the closure of VDAC in mitochondria of ethanol oxidizing liver, thus protecting liver tissue from the hepatotoxic action of alcohol.     

<Emphasis Type="Italic">Scoredist</Emphasis>: A simple and robust protein sequence distance estimator

Background  

Distance-based methods are popular for reconstructing evolutionary trees thanks to their speed and generality. A number of methods exist for estimating distances from sequence alignments, which often involves some sort of correction for multiple substitutions. The problem is to accurately estimate the number of true substitutions given an observed alignment. So far, the most accurate protein distance estimators have looked for the optimal matrix in a series of transition probability matrices, e.g. the Dayhoff series. The evolutionary distance between two aligned sequences is here estimated as the evolutionary distance of the optimal matrix. The optimal matrix can be found either by an iterative search for the Maximum Likelihood matrix, or by integration to find the Expected Distance. As a consequence, these methods are more complex to implement and computationally heavier than correction-based methods. Another problem is that the result may vary substantially depending on the evolutionary model used for the matrices. An ideal distance estimator should produce consistent and accurate distances independent of the evolutionary model used.