Regulation of PCSK9 Expression and Function: Mechanisms and Therapeutic Implications

Proprotein convertase subtilisin/kexin type 9 (PCSK9) promotes degradation of low-density lipoprotein receptor (LDLR) and plays a central role in regulating plasma levels of LDL cholesterol levels, lipoprotein(a) and triglyceride-rich lipoproteins, increasing the risk of cardiovascular disease. Additionally, PCSK9 promotes degradation of major histocompatibility protein class I and reduces intratumoral infiltration of cytotoxic T cells. Inhibition of PCSK9 increases expression of LDLR, thereby reducing plasma levels of lipoproteins and the risk of cardiovascular disease. PCSK9 inhibition also increases cell surface levels of major histocompatibility protein class I in cancer cells and suppresses tumor growth. Therefore, PCSK9 plays a vital role in the pathogenesis of cardiovascular disease and cancer, the top two causes of morbidity and mortality worldwide. Monoclonal anti-PCSK9 antibody-based therapy is currently the only available treatment that can effectively reduce plasma LDL-C levels and suppress tumor growth. However, high expenses limit their widespread use. PCSK9 promotes lysosomal degradation of its substrates, but the detailed molecular mechanism by which PCSK9 promotes degradation of its substrates is not completely understood, impeding the development of more cost-effective alternative strategies to inhibit PCSK9. Here, we review our current understanding of PCSK9 and focus on the regulation of its expression and functions.

Cadherin and Wnt signaling pathways as key regulators in diabetic nephropathy

Aim A recent meta-analysis of genome-wide linkage studies (GWLS) has identified multiple genetic regions suggestive of linkage with DN harboring hundreds of genes. Moving this number of genetic loci forward into biological insight is truly the next step. Here, we approach this challenge with a gene ontology (GO) analysis in order to yield biological and functional role to the genes, an over-representation test to find which GO terms are enriched in the gene list, pathway analysis, as well as protein network analysis. Method GO analysis was performed using protein analysis through evolutionary relationships (PANTHER) version 14.0 software and P-values less than 0.05 were considered statistically significant. GO analysis was followed by over-representation test for the identification of enriched terms. Statistical significance was calculated by Fisher’s exact test and adjusted using the false discovery rate (FDR) for correction of multiple tests. Cytoscape with the relevant plugins was used for the construction of the protein network and clustering analysis. Results The GO analysis assign multiple GO terms to the genes regarding the molecular function, the biological process and the cellular component, protein class and pathway analysis. The findings of the over-representation test highlight the contribution of cell adhesion regarding the biological process, integral components of plasma membrane regarding the cellular component, chemokines and cytokines with regard to protein class, while the pathway analysis emphasizes the contribution of Wnt and cadherin signaling pathways. Conclusions Our results suggest that a core feature of the pathogenesis of DN may be a disturbance in Wnt and cadherin signaling pathways, whereas the contribution of chemokines and cytokines need to be studied in additional studies.

PANTHER version 16: a revised family classification, tree-based classification tool, enhancer regions and extensive API

PANTHER (Protein Analysis Through Evolutionary Relationships, http://www.pantherdb.org) is a resource for the evolutionary and functional classification of protein-coding genes from all domains of life. The evolutionary classification is based on a library of over 15,000 phylogenetic trees, and the functional classifications include Gene Ontology terms and pathways. Here, we analyze the current coverage of genes from genomes in different taxonomic groups, so that users can better understand what to expect when analyzing a gene list using PANTHER tools. We also describe extensive improvements to PANTHER made in the past two years. The PANTHER Protein Class ontology has been completely refactored, and 6101 PANTHER families have been manually assigned to a Protein Class, providing a high level classification of protein families and their genes. Users can access the TreeGrafter tool to add their own protein sequences to the reference phylogenetic trees in PANTHER, to infer evolutionary context as well as fine-grained annotations. We have added human enhancer-gene links that associate non-coding regions with the annotated human genes in PANTHER. We have also expanded the available services for programmatic access to PANTHER tools and data via application programming interfaces (APIs). Other improvements include additional plant genomes and an updated PANTHER GO-slim.

Transmembrane Alpha Helix Prediction Using Neural Networks

This paper presents a simple artificial neural network which classifies proteins into two classes from their sequences alone: the alpha helix transmembrane protein class and the non-alpha helix transmembrane protein class. The network described here has a simple feed-forward topology and a limited number of neurons which makes it very fast.

Arginine mimetic appended peptide-based probes for fluorescence turn-on detection of 14-3-3 proteins

Development of fluorescence markers for the 14-3-3 adapter protein class.