Quality control autophagy: a joint effort of ubiquitin, protein deacetylase and actin cytoskeleton.

Autophagy has been predominantly studied as a nonselective self-digestion process that recycles macromolecules and produces energy in response to starvation. However, autophagy independent of nutrient status has long been known to exist. Recent evidence suggests that this form of autophagy enforces intracellular quality control by selectively disposing of aberrant protein aggregates and damaged organelles--common denominators in various forms of neurodegenerative diseases. By definition, this form of autophagy, termed quality-control (QC) autophagy, must be different from nutrient-regulated autophagy in substrate selectivity, regulation and function. We have recently identified the ubiquitin-binding deacetylase, HDAC6, as a key component that establishes QC. HDAC6 is not required for autophagy activation per se; rather, it is recruited to ubiquitinated autophagic substrates where it stimulates autophagosome-lysosome fusion by promoting F-actin remodeling in a cortactin-dependent manner. Remarkably, HDAC6 and cortactin are dispensable for starvation-induced autophagy. These findings reveal that autophagosomes associated with QC are molecularly and biochemically distinct from those associated with starvation autophagy, thereby providing a new molecular framework to understand the emerging complexity of autophagy and therapeutic potential of this unique machinery.

Magnetic resonance water proton relaxation in protein solutions and tissue: T(1rho) dispersion characterization.

BACKGROUND: Image contrast in clinical MRI is often determined by differences in tissue water proton relaxation behavior. However, many aspects of water proton relaxation in complex biological media, such as protein solutions and tissue are not well understood, perhaps due to the limited empirical data. PRINCIPAL FINDINGS: Water proton T(1), T(2), and T(1rho) of protein solutions and tissue were measured systematically under multiple conditions. Crosslinking or aggregation of protein decreased T(2) and T(1rho), but did not change high-field T(1). T(1rho) dispersion profiles were similar for crosslinked protein solutions, myocardial tissue, and cartilage, and exhibited power law behavior with T(1rho)(0) values that closely approximated T(2). The T(1rho) dispersion of mobile protein solutions was flat above 5 kHz, but showed a steep curve below 5 kHz that was sensitive to changes in pH. The T(1rho) dispersion of crosslinked BSA and cartilage in DMSO solvent closely resembled that of water solvent above 5 kHz but showed decreased dispersion below 5 kHz. CONCLUSIONS: Proton exchange is a minor pathway for tissue T(1) and T(1rho) relaxation above 5 kHz. Potential models for relaxation are discussed, however the same molecular mechanism appears to be responsible across 5 decades of frequencies from T(1rho) to T(1).

Neuropeptide Y Y(1) receptor regulates protein turnover and constitutive gene expression in hypertrophying cardiomyocytes.

Increased levels of neuropeptide Y correlate with severity of left ventricular hypertrophy in vivo. At cardiomyocyte level, hypertrophy is characterised by increased mass and altered phenotype. The aims were to determine the contributions of increased synthesis and reduced degradation of protein to neuropeptide Y-mediated increase in mass, assess effects on gene expression, and characterise neuropeptide Y Y receptor subtype involvement. Neuropeptide Y (10 nM) increased protein mass of adult rat ventricular cardiomyocytes maintained in culture (24 h) (16%>basal) and de novo protein synthesis (incorporation of [14C]phenylalanine) (18%>basal). Neuropeptide Y (100 nM) prevented degradation of existing protein at 8 h. Actinomycin D (5 µM) attenuated increases in protein mass to neuropeptide Y (=1 nM) but not to neuropeptide Y (10 nM). [Leu31, Pro34]neuropeptide Y (10 nM), an agonist at neuropeptide Y Y1 receptors, increased protein mass (25%>basal) but did not stimulate protein synthesis. Neuropeptide Y-(3–36) (10 nM), an agonist at neuropeptide Y Y2 receptors, increased protein mass (29%>basal) and increased protein synthesis (13%>basal), respectively. Actinomycin D (5 µM) abolished the increase in protein mass elicited by neuropeptide Y-(3–36) but not that by [Leu31, Pro34]neuropeptide Y. BIBP3226 [(R)-N2-(diphenylacetyl)-N-(4-hydroxyphenylmethyl)-d-arginine amide] (1 µM), a neuropeptide Y Y1 receptor subtype-selective antagonist, and T4 [neuropeptide Y-(33–36)]4, a neuropeptide Y Y2 receptor subtype-selective antagonist, attenuated the increase in protein mass to 100 nM neuropeptide Y by 68% and 59%, respectively. Neuropeptide Y increased expression of the constitutive gene, myosin light chain-2 (MLC-2), maximally at 12 h (4.7-fold>basal) but did not induce (t=36 h) expression of foetal genes (atrial natriuretic peptide (ANP), skeletal-a-actin and myosin heavy chain-ß). This increase was attenuated by 86% and 51%, respectively, by BIBP3226 (1 µM) and T4 [neuropeptide Y-(33–36)]4 (100 nM). [Leu31, Pro34]neuropeptide Y (100 nM) (2.4-fold>basal) and peptide YY-(3–36) (100 nM) (2.3 fold>basal) increased expression of MLC-2 mRNA at 12 h. In conclusion, initiation of cardiomyocyte hypertrophy by neuropeptide Y requires activation of both neuropeptide Y Y1 and neuropeptide Y Y2 receptors and is associated with enhanced synthesis and attenuated degradation of protein together with increased expression of constitutive genes but not reinduction of foetal genes.

Custom coprocessor based matrix algorithms for image and signal processing

Matrix algorithms are important in many types of applications including image and signal processing. A close examination of the algorithms used in these, and related, applications reveals that many of the fundamental actions involve matrix algorithms such as matrix multiplication. This paper presents an investigation into the design and implementation of different matrix algorithms such as matrix operations, matrix transforms and matrix decompositions using a novel custom coprocessor system for MATrix algorithms based on Reconfigurable Computing (RCMAT). The proposed RCMAT architectures are scalable, modular and require less area and time complexity with reduced latency when compared with existing structures.