Robust Glyoxalase activity of Hsp31, a ThiJ/DJ-1/PfpI Family Member Protein, Is Critical for Oxidative Stress Resistance in Saccharomyces cerevisiae

Methylglyoxal (MG) is a reactive metabolic intermediate generated during various cellular biochemical reactions, including glycolysis. The accumulation of MG indiscriminately modifies proteins, including important cellular antioxidant machinery, leading to severe oxidative stress, which is implicated in multiple neurodegenerative disorders, aging, and cardiac disorders. Although cells possess efficient glyoxalase systems for detoxification, their functions are largely dependent on the glutathione cofactor, the availability of which is self-limiting under oxidative stress. Thus, higher organisms require alternate modes of reducing the MG-mediated toxicity and maintaining redox balance. In this report, we demonstrate that Hsp31 protein, a member of the ThiJ/DJ-1/PfpI family in Saccharomyces cerevisiae, plays an indispensable role in regulating redox homeostasis. Our results show that Hsp31 possesses robust glutathione-independent methylglyoxalase activity and suppresses MG-mediated toxicity and ROS levels as compared with another paralog, Hsp34. On the other hand, glyoxalase-defective mutants of Hsp31 were found highly compromised in regulating the ROS levels. Additionally, Hsp31 maintains cellular glutathione and NADPH levels, thus conferring protection against oxidative stress, and Hsp31 relocalizes to mitochondria to provide cytoprotection to the organelle under oxidative stress conditions. Importantly, human DJ-1, which is implicated in the familial form of Parkinson disease, complements the function of Hsp31 by suppressing methylglyoxal and oxidative stress, thus signifying the importance of these proteins in the maintenance of ROS homeostasis across phylogeny.

Comparison of Matching Pursuit Algorithm with Other Signal Processing Techniques for Computation of the Time-Frequency Power Spectrum of Brain Signals

Signals recorded from the brain often show rhythmic patterns at different frequencies, which are tightly coupled to the external stimuli as well as the internal state of the subject. In addition, these signals have very transient structures related to spiking or sudden onset of a stimulus, which have durations not exceeding tens of milliseconds. Further, brain signals are highly nonstationary because both behavioral state and external stimuli can change on a short time scale. It is therefore essential to study brain signals using techniques that can represent both rhythmic and transient components of the signal, something not always possible using standard signal processing techniques such as short time fourier transform, multitaper method, wavelet transform, or Hilbert transform. In this review, we describe a multiscale decomposition technique based on an over-complete dictionary called matching pursuit (MP), and show that it is able to capture both a sharp stimulus-onset transient and a sustained gamma rhythm in local field potential recorded from the primary visual cortex. We compare the performance of MP with other techniques and discuss its advantages and limitations. Data and codes for generating all time-frequency power spectra are provided.