Altered Composition of Liver Proteasome Assemblies Contributes to Enhanced Proteasome Activity in the Exceptionally Long-Lived Naked Mole-Rat

The longest-lived rodent, the naked mole-rat (Bathyergidae; Heterocephalus glaber), maintains robust health for at least 75% of its 32 year lifespan, suggesting that the decline in genomic integrity or protein homeostasis routinely observed during aging, is either attenuated or delayed in this extraordinarily long-lived species. The ubiquitin proteasome system (UPS) plays an integral role in protein homeostasis by degrading oxidatively-damaged and misfolded proteins. In this study, we examined proteasome activity in naked mole-rats and mice in whole liver lysates as well as three subcellular fractions to probe the mechanisms behind the apparently enhanced effectiveness of UPS. We found that when compared with mouse samples, naked mole-rats had significantly higher chymotrypsin-like (ChT-L) activity and a two-fold increase in trypsin-like (T-L) in both whole lysates as well as cytosolic fractions. Native gel electrophoresis of the whole tissue lysates showed that the 20S proteasome was more active in the longer-lived species and that 26S proteasome was both more active and more populous. Western blot analyses revealed that both 19S subunits and immunoproteasome catalytic subunits are present in greater amounts in the naked mole-rat suggesting that the observed higher specific activity may be due to the greater proportion of immunoproteasomes in livers of healthy young adults. It thus appears that proteasomes in this species are primed for the efficient removal of stress-damaged proteins. Further characterization of the naked mole-rat proteasome and its regulation could lead to important insights on how the cells in these animals handle increased stress and protein damage to maintain a longer health in their tissues and ultimately a longer life

Design and application of a polyclonal peptide antiserum for the universal detection of leptin protein

An epitope-specific polyclonal antiserum was produced in rabbits immunized against a synthetic 15 amino acid peptide (QRVTGLDFIPGLHPV) derived from the coding sequence reported for the porcine leptin gene (GenBank Accession No. U59894). This peptide contains a core sequence comprised of eight amino acids (GLDFIPGL) that is totally conserved in all leptin proteins studied to date. Purified recombinant human, mouse, rat, pig, and chicken leptin proteins were separated by polyacrylamide gel electrophoresis (SDS-PAGE) and electro-blotted onto PVDF membranes. Western blots were developed employing the leptin-specific peptide antiserum with an alkaline-phosphatase-conjugated anti-rabbit IgG second antibody chromogenic system. The peptide antiserum was found to be highly specific for leptin which exhibited an estimated molecular weight of about 16 kDa for all species analyzed. The sensitivity of the Western blot assay was not sufficient to permit the direct detection of leptin in chicken serum or plasma. However, with this assay we were able to detect native leptin protein in an enriched fraction prepared from chicken plasma using a combination of gel filtration and ion exchange column chromatography. Slot blots indicated a potential application of the immunostaining technique for quantitative analysis of leptin protein. Finally, the peptide antiserum was successfully employed to localize leptin protein by immunohistochemical staining of thin sections prepared from adipose (chicken and pig) and liver (chicken) tissue samples. This study is the first to report a polyclonal peptide antiserum that apparently recognizes intact leptin protein, both native and recombinant, regardless of the species of origin

Machine Learning-Based Cyber-Attack Detection in Photovoltaic Farms

In this article, a machine learning technique is proposed for the detection of cyber-attacks in Photovoltaic (PV) farms using point of common coupling (PCC) sensors alone. A comprehensive cyber-attack model of a PV farm is first developed to consider operating conditions variability. The attack model specifically includes two types of cyber-attacks that are historically more difficult to detect. A Convolutional Neural Network (CNN) using $\mu$PMU plus figures of merit is proposed and compared with other machine learning techniques using raw electric waveform and micro-phase measurement units ($\mu$PMU), respectively. Finally, a cyber-physical security testbed of an IEEE 37-bus distributed grid with PV farms is developed. A real-time simulation, detection, and visualization framework is designed to demonstrate the feasibility of the proposed method in a real-world application. Results show that the proposed machine learning methods can achieve adequate detection accuracy and robustness under various attack scenarios