Alterations in the mitochondrial proteome of neuroblastoma cells in response to complex 1 inhibition

Increasing evidence points to mitochondrial dysfunction in Parkinson's disease (PD) associated with complex I dysfunction, but the exact pathways which lead to cell death have not been resolved. 2D-gel electrophoresis profiles of isolated mitochondria from neuroblastoma cells treated with subcytotoxic concentrations of 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP), a well-characterized complex I inhibitor, were assessed to identify associated targets. Up to 27 differentially expressed proteins were observed, of which 16 were identified using peptide mass fingerprinting. Changes in protein levels were validated by immunoprobing 1D blots, confirming increases in heat shock cognate 71 kDa (Hsc70), 60 kDa heat shock protein (Hsp60), fumarase, glutamate oxaloacetate transaminase 2, ATP synthase subunit d, and voltage-dependent anion-channel 1 (VDAC1). Immunoprobing of 2D blots revealed isoform changes in Hsc70, Hsp60, and VDAC1. Subcytotoxic concentrations of MPTP modulated a host of mitochondrial proteins including chaperones, metabolic enzymes, oxidative phosphorylation-related proteins, an inner mitochondrial protein (mitofilin), and an outer mitochondrial membrane protein (VDAC1). Early changes in chaperones suggest a regulated link between complex 1 inhibition and protein folding. VDAC1, a multifunctional protein, may have a key role in signaling between mitochondria and the rest of the cell prior to cell death. Our work provides new important information of relevance to PD.     

Metabolic signatures of lung cancer in biofluids: NMR-based metabonomics of blood plasma

In this work, the variations in the metabolic profile of blood plasma from lung cancer patients and healthy controls were investigated through NMR-based metabonomics, to assess the potential of this approach for lung cancer screening and diagnosis. PLS-DA modeling of CPMG spectra from plasma, subjected to Monte Carlo Cross Validation, allowed cancer patients to be discriminated from controls with sensitivity and specificity levels of about 90%. Relatively lower HDL and higher VLDL + LDL in the patients' plasma, together with increased lactate and pyruvate and decreased levels of glucose, citrate, formate, acetate, several amino acids (alanine, glutamine, histidine, tyrosine, valine), and methanol, could be detected. These changes were found to be present at initial disease stages and could be related to known cancer biochemical hallmarks, such as enhanced glycolysis, glutaminolysis, and gluconeogenesis, together with suppressed Krebs cycle and reduced lipid catabolism, thus supporting the hypothesis of a systemic metabolic signature for lung cancer. Despite the possible confounding influence of age, smoking habits, and other uncontrolled factors, these results indicate that NMR-based metabonomics of blood plasma can be useful as a screening tool to identify suspicious cases for subsequent, more specific radiological tests, thus contributing to improved disease management.     

Quantifying the relative contributions of divisive and subtractive feedback to rhythm generation

Biological systems are characterized by a high number of interacting components. Determining the role of each component is difficult, addressed here in the context of biological oscillations. Rhythmic behavior can result from the interplay of positive feedback that promotes bistability between high and low activity, and slow negative feedback that switches the system between the high and low activity states. Many biological oscillators include two types of negative feedback processes: divisive (decreases the gain of the positive feedback loop) and subtractive (increases the input threshold) that both contribute to slowly move the system between the high- and low-activity states. Can we determine the relative contribution of each type of negative feedback process to the rhythmic activity? Does one dominate? Do they control the active and silent phase equally? To answer these questions we use a neural network model with excitatory coupling, regulated by synaptic depression (divisive) and cellular adaptation (subtractive feedback). We first attempt to apply standard experimental methodologies: either passive observation to correlate the variations of a variable of interest to system behavior, or deletion of a component to establish whether a component is critical for the system. We find that these two strategies can lead to contradictory conclusions, and at best their interpretive power is limited. We instead develop a computational measure of the contribution of a process, by evaluating the sensitivity of the active (high activity) and silent (low activity) phase durations to the time constant of the process. The measure shows that both processes control the active phase, in proportion to their speed and relative weight. However, only the subtractive process plays a major role in setting the duration of the silent phase. This computational method can be used to analyze the role of negative feedback processes in a wide range of biological rhythms.