Nox1 and Nox4, homologues of the leukocyte NADPH oxidase subunit Nox2 (gp91phox) mediate superoxide anion formation in various cell types. However, their interactions with other components of the NADPH oxidase are poorly defined. We determined whether a direct interaction of Nox1 and Nox4 with the p22phox subunit of the NADPH oxidase occurs. Using confocal microscopy, co-localization of p22phox with Nox1, Nox2, and Nox4 was observed in transiently transfected vascular smooth muscle cells (VSMC) and HEK293 cells. Plasmids coding for fluorescent fusion proteins of p22phox and the Nox proteins with cyan-and yellowfluorescent protein (cfp and yfp, respectively) were constructed and expressed in VSMC and HEK293 cells. The cfp-tagged p22phox expression level increased upon cotransfection with Nox1 or Nox4. Protein-protein interaction between the fluorescent fusion proteins of p22phox and the Nox partners was observed using the fluorescence resonance energy transfer technique. Immunoprecipitation of native Nox1 from human VSMC revealed co-precipitation of p22phox. Immunoprecipitation from transfected HEK293 cells revealed co-precipitation of native p22phox with yfp-tagged Nox1, Nox2, and Nox4. Following mutation of a histidine (corresponding to the position 115 in human Nox2) to leucine, this interaction was abolished. Transfection of rat p22phox (but not Noxo1 and Noxa1) increased the radical generation in cells expressing Nox4. We provide evidence that p22phox directly interacts with Nox1 and Nox4, to form an superoxide-generating NADPH oxidase and demonstrate that mutation of the potential heme binding site in the Nox proteins disrupts the complex formation of Nox1 and Nox4 with p22phox.

Graphic abstract Drug designing and development is an important area of research for pharmaceutical companies and chemical scientists. However, low efficacy, off-target delivery, time consumption, and high cost impose a hurdle and challenges that impact drug design and discovery. Further, complex and big data from genomics, proteomics, microarray data, and clinical trials also impose an obstacle in the drug discovery pipeline. Artificial intelligence and machine learning technology play a crucial role in drug discovery and development. In other words, artificial neural networks and deep learning algorithms have modernized the area. Machine learning and deep learning algorithms have been implemented in several drug discovery processes such as peptide synthesis, structure-based virtual screening, ligand-based virtual screening, toxicity prediction, drug monitoring and release, pharmacophore modeling, quantitative structure–activity relationship, drug repositioning, polypharmacology, and physiochemical activity. Evidence from the past strengthens the implementation of artificial intelligence and deep learning in this field. Moreover, novel data mining, curation, and management techniques provided critical support to recently developed modeling algorithms. In summary, artificial intelligence and deep learning advancements provide an excellent opportunity for rational drug design and discovery process, which will eventually impact mankind. The primary concern associated with drug design and development is time consumption and production cost. Further, inefficiency, inaccurate target delivery, and inappropriate dosage are other hurdles that inhibit the process of drug delivery and development. With advancements in technology, computer-aided drug design integrating artificial intelligence algorithms can eliminate the challenges and hurdles of traditional drug design and development. Artificial intelligence is referred to as superset comprising machine learning, whereas machine learning comprises supervised learning, unsupervised learning, and reinforcement learning. Further, deep learning, a subset of machine learning, has been extensively implemented in drug design and development. The artificial neural network, deep neural network, support vector machines, classification and regression, generative adversarial networks, symbolic learning, and meta-learning are examples of the algorithms applied to the drug design and discovery process. Artificial intelligence has been applied to different areas of drug design and development process, such as from peptide synthesis to molecule design, virtual screening to molecular docking, quantitative structure–activity relationship to drug repositioning, protein misfolding to protein–protein interactions, and molecular pathway identification to polypharmacology. Artificial intelligence principles have been applied to the classification of active and inactive, monitoring drug release, pre-clinical and clinical development, primary and secondary drug screeni...

The C-terminus Hsp70 interacting protein (CHIP) has dual function as both co-chaperone and ubiquitin ligase. CHIP is increasingly implicated in the biology of polyglutamine expansion disorders, Parkinson's disease and tau protein in Alzheimer's disease. We investigated the involvement of CHIP in the metabolism of the beta-amyloid precursor protein and its derivative beta-amyloid (Abeta). Using immunoprecipitation, fluorescence localization and crosslinking methods, endogenous CHIP and betaAPP interact in brain and cultured skeletal myotubes as well as when they are expressed in stable HEK cell lines. Their interaction is confined to Golgi and ER compartments. In the presence of the proteasome inhibitor with MG132, endogenous and expressed betaAPP levels are significantly increased and accordingly, the interaction with CHIP enhanced. Concurrently, levels of Hsp70 were most consistently induced by proteasome inhibition among the various heat shock proteins (HSPs) tested. Thus, complexes of CHIP, Hsp70 and holo-betaAPP (as well as C-terminal fragments) were stabilized by the action of MG132. Moreover, CHIP itself is shown to both increase cellular holo-betaAPP levels and protect it from oxidative stress and degradation. Interestingly, CHIP also promotes the association of ubiquitin with betaAPP, implying that a smaller pool of betaAPP is destined for proteasomal processing. In neuronal cultures, CHIP and Hsp70/90 expression reduce steady-state cellular Abeta levels and hasten its degradation in pulse-chase experiments. The functional significance of CHIP and HSP interactions, especially with Hsp70, was tested using siRNA and in neuronal cells where protection from Abeta-induced toxicity is shown. We conclude that CHIP, as a bimolecular switch, interacts with HSP to stabilize normal holo-betaAPP on the one hand while also assisting in the ubiquitination of a subpopulation of betaAPP molecules that are destined for proteasome degradation. CHIP also hastens the clearance of Abeta in a manner consistent with its known neuroprotective properties.

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Mounting evidence suggests a link between metabolic syndrome (MetS) such as diabetes, obesity, non-alcoholic fatty liver disease in the progression of Alzheimer's disease (AD), Parkinson's disease (PD) and other neurodegenerative diseases (NDDs). For instance, accumulated Aβ oligomer is enhancing neuronal Ca release and neural NO where increased NO level in the brain through post translational modification is modulating the level of insulin production. It has been further confirmed that irrespective of origin; brain insulin resistance triggers a cascade of the neurodegeneration phenomenon which can be aggravated by free reactive oxygen species burden, ER stress, metabolic dysfunction, neuorinflammation, reduced cell survival and altered lipid metabolism. Moreover, several studies confirmed that MetS and diabetic sharing common mechanisms in the progression of AD and NDDs where mitochondrial dynamics playing a critical role. Any mutation in mitochondrial DNA, exposure of environmental toxin, high-calorie intake, homeostasis imbalance, glucolipotoxicity is causative factors for mitochondrial dysfunction. These cumulative pleiotropic burdens in mitochondria leads to insulin resistance, increased ROS production; enhanced stress-related enzymes that is directly linked MetS and diabetes in neurodegeneration. Since, the linkup mechanism between mitochondrial dysfunction and disease phenomenon of both MetS and NDDs is quite intriguing, therefore, it is pertinent for the researchers to identify and implement the therapeutic interventions for targeting MetS and NDDs. Herein, we elucidated the pertinent role of MetS induced mitochondrial dysfunction in neurons and their consequences in NDDs. Further, therapeutic potential of well-known biomolecules and chaperones to target altered mitochondria has been comprehensively documented. This article is part of a Special Issue entitled: Oxidative Stress and Mitochondrial Quality in Diabetes/Obesity and Critical Illness Spectrum of Diseases - edited by P. Hemachandra Reddy.