Discovery of Small-Molecule Activators for Glucose-6-Phosphate Dehydrogenase (G6PD) Using Machine Learning Approaches

Delineation of the role of G6PD in Alzheimer's disease and potential enhancement through microfluidic and nanoparticle approaches

Alzheimer's disease (AD) is a neurodegenerative pathologic entity characterized by the abnormal presence of tau and macromolecular Aβ deposition that leads to the degeneration or death of neurons. In addition to that, glucose-6-phosphate dehydrogenase (G6PD) has a multifaceted role in the process of AD development, where it can be used as both a marker and a target. G6PD activity is dysregulated due to its contribution to oxidative stress, neuroinflammation, and neuronal death. In this context, the current review presents a vivid depiction of recent findings on the relationship between AD progression and changes in the expression or activity of G6PD. The efficacy of the proposed G6PD-based therapeutics has been demonstrated in multiple studies using AD mouse models as representative animal model systems for cognitive decline and neurodegeneration associated with this disease. Innovative therapeutic insights are made for the boosting of G6PD activity via novel innovative nanotechnology and microfluidics tools in drug administration technology. Such approaches provide innovative methods of surpassing the blood-brain barrier, targeting step-by-step specific neural pathways, and overcoming biochemical disturbances that accompany AD. Using different nanoparticles loaded with G6DP to target specific organs, e.g., G6DP-loaded liposomes, enhances BBB penetration and brain distribution of G6DP. Many nanoparticles, which are used for different purposes, are briefly discussed in the paper. Such methods to mimic BBB on organs on-chip offer precise disease modeling and drug testing using microfluidic chips, requiring lower sample amounts and producing faster findings compared to conventional techniques. There are other contributions to microfluid in AD that are discussed briefly. However, there are some limitations accompanying microfluidics that need to be worked on to be used for AD. This study aims to bridge the gap in understanding AD with the synergistic use of promising technologies; microfluid and nanotechnology for future advancements.

The Experimentalist's Guide to Machine Learning for Small Molecule Design

Initially part of the field of artificial intelligence, machine learning (ML) has become a booming research area since branching out into its own field in the 1990s. After three decades of refinement, ML algorithms have accelerated scientific developments across a variety of research topics. The field of small molecule design is no exception, and an increasing number of researchers are applying ML techniques in their pursuit of discovering, generating, and optimizing small molecule compounds. The goal of this review is to provide simple, yet descriptive, explanations of some of the most commonly utilized ML algorithms in the field of small molecule design along with those that are highly applicable to an experimentally focused audience. The algorithms discussed here span across three ML paradigms: supervised learning, unsupervised learning, and ensemble methods. Examples from the published literature will be provided for each algorithm. Some common pitfalls of applying ML to biological and chemical data sets will also be explained, alongside a brief summary of a few more advanced paradigms, including reinforcement learning and semi-supervised learning.

Substitution of arginine 219 by glycine compromises stability, dimerization, and catalytic activity in a G6PD mutant

Glucose-6-phosphate dehydrogenase (G6PD) deficiency is one of the most common enzymopathies in humans, present in approximately half a billion people worldwide. More than 230 clinically relevant G6PD mutations of different classes have been reported to date. We hereby describe a patient with chronic hemolysis who presents a substitution of arginine by glycine at position 219 in G6PD protein. The variant was never described in an original publication or characterized on a molecular level. In the present study, we provide structural and biochemical evidence for the molecular basis of its pathogenicity. When compared to the wild-type enzyme, the Arg219Gly mutation markedly reduces the catalytic activity by 50-fold while having a negligible effect on substrate binding affinity. The mutation preserves secondary protein structure, but greatly decreases stability at higher temperatures and to trypsin digestion. Size exclusion chromatography elution profiles show monomeric and dimeric forms for the mutant, but only the latter for the wild-type form, suggesting a critical role of arginine 219 in G6PD dimer formation. Our findings have implications in the development of small molecule activators, with the goal of rescuing the phenotype observed in this and possibly other related mutants.

The evolution of small molecule enzyme activators

There is a myriad of enzymes within the body responsible for maintaining homeostasis by providing the means to convert substrates to products as and when required. Physiological enzymes are tightly controlled by many signaling pathways and their products subsequently control other pathways. Traditionally, most drug discovery efforts focus on identifying enzyme inhibitors, due to upregulation being prevalent in many diseases and the existence of endogenous substrates that can be modified to afford inhibitor compounds. As enzyme downregulation and reduction of endogenous activators are observed in multiple diseases, the identification of small molecules with the ability to activate enzymes has recently entered the medicinal chemistry toolbox to afford chemical probes and potential therapeutics as an alternative means to intervene in diseases. In this review we highlight the progress made in the identification and advancement of non-kinase enzyme activators and their potential in treating various disease states.

A Novel Sesquiterpene Lactone Xanthatin-13-(pyrrolidine-2-carboxylic acid) Isolated from Burdock Leaf Up-Regulates Cells' Oxidative Stress Defense Pathway

The aim of our study was to identify novel molecules able to induce an adaptative response against oxidative stress during the first stages of metabolic syndrome. A cellular survival in vitro test against H₂O₂-based test was applied after pretreatment with various natural bitter Asteraceae extracts. This screening revealed potent protection from burdock leaf extract. Using chromatography and LC-MS—RMN, we then isolated and identified an original sesquiterpene lactone bioactive molecule: the Xanthatin-13-(pyrrolidine-2-carboxylic acid) (XPc). A real-time RT-qPCR experiment was carried out on three essential genes involved in oxidative stress protection: GPx, SOD, and G6PD. In presence of XPc, an over-expression of the G6PD gene was recorded, whereas no modification of the two others genes could be observed. A biochemical docking approach demonstrated that XPc had a high probability to directly interact with G6PD at different positions. One of the most probable docking sites corresponds precisely to the binding site of AG1, known to stabilize the G6PD dimeric form and enhance its activity. In conclusion, this novel sesquiterpene lactone XPc might be a promising prophylactic bioactive agent against oxidative stress and inflammation in chronic diseases such as metabolic syndrome or type 2 diabetes.

Treatment strategies for glucose-6-phosphate dehydrogenase deficiency: past and future perspectives

Glucose-6-phosphate dehydrogenase (G6PD) maintains redox balance in a variety of cell types and is essential for erythrocyte resistance to oxidative stress. G6PD deficiency, caused by mutations in the G6PD gene, is present in ~400 million people worldwide, and can cause acute hemolytic anemia. Currently, there are no therapeutics for G6PD deficiency. We discuss the role of G6PD in hemolytic and nonhemolytic disorders, treatment strategies attempted over the years, and potential reasons for their failure. We also discuss potential pharmacological pathways, including glutathione (GSH) metabolism, compensatory NADPH production routes, transcriptional upregulation of the G6PD gene, highlighting potential drug targets. The needs and opportunities described here may motivate the development of a therapeutic for hematological and other chronic diseases associated with G6PD deficiency.

Antiparkinsonian activity of Cucurbita pepo seeds along with possible underlying mechanism

Cucurbita pepo is used as a vegetable in Pakistan and its seeds are also rich in tocopherol. Data showed the pivotal role of tocopherol in the treatment of Parkinson's disease (PD). The current study was designed to probe into the antiparkinson activity of methanolic extract of C. pepo (MECP) seeds in the haloperidol-induced Parkinson rat model. Behavioral studies showed improvement in motor functions. The increase in catalase, superoxide dismutase, glutathione levels whereas the decreases in the malondialdehyde and nitrite levels were noted in a dose-dependent manner. Acetylcholine-esterase (AchE) activity was increased. Molecular docking results revealed significant binding interaction of selected phytoconstituents within an active site of target protein AchE (PDB ID: 4EY7). Furthermore, α-synuclein was up regulated with down regulation of TNF-α and IL-1β in the qRT-PCR study. Subsequently, ADMET results on the basis of structure to activity predictions in terms of pharmacokinetics and toxicity estimations show that selected phytochemicals exhibited moderately acceptable properties. These properties add knowledge towards the structural features which could improve the bioavailability of selected phytochemicals before moving towards the initial phase of the drug development. Our integrated drug discovery scheme concluded that C. pepo seeds could ameliorate symptoms of PD and may prove a lead remedy for the treatment of PD.

RNA-Seq reveals placental growth factor regulates the human retinal endothelial cell barrier integrity by transforming growth factor (TGF-β) signaling

Placental growth factor (PlGF or PGF) is a member of the VEGF (vascular endothelial growth factor) family. It plays a pathological role in inflammation, vascular permeability, and pathological angiogenesis. The molecular signaling by which PlGF mediates its effects in non-proliferative diabetic retinopathy (DR) remains elusive. This study aims to characterize the transcriptome changes of human retinal endothelial cells (HRECs) with the presence and the absence of PlGF signaling. Primary HRECs were treated with the PlGF antibody (ab) to block its activity. The total RNA was isolated and subjected to deep sequencing to quantify the transcripts and their changes in both groups. We performed transcriptome-wide analysis, gene ontology, pathway enrichment, and gene-gene network analyses. The results showed that a total of 3760 genes were significantly differentially expressed and were categorized into cell adhesion molecules, cell junction proteins, chaperone, calcium-binding proteins, and membrane traffic proteins. Functional pathway analyses revealed that the TGF-β pathway, pentose phosphate pathway, and cell adhesion pathway play pivotal roles in the blood-retina barrier and antioxidant defense system. Collectively, the data provide new insights into the molecular mechanisms of PlGF's biological functions in HRECs relevant to DR and diabetic macular edema (DME). The newly identified genes and pathways may act as disease markers and target molecules for therapeutic interventions for the patients with DR and DME refractory to the current anti-VEGF therapy.

Prediction Model of Aryl Hydrocarbon Receptor Activation by a Novel QSAR Approach, DeepSnap-Deep Learning

The aryl hydrocarbon receptor (AhR) is a ligand-dependent transcription factor that senses environmental exogenous and endogenous ligands or xenobiotic chemicals. In particular, exposure of the liver to environmental metabolism-disrupting chemicals contributes to the development and propagation of steatosis and hepatotoxicity. However, the mechanisms for AhR-induced hepatotoxicity and tumor propagation in the liver remain to be revealed, due to the wide variety of AhR ligands. Recently, quantitative structure–activity relationship (QSAR) analysis using deep neural network (DNN) has shown superior performance for the prediction of chemical compounds. Therefore, this study proposes a novel QSAR analysis using deep learning (DL), called the DeepSnap–DL method, to construct prediction models of chemical activation of AhR. Compared with conventional machine learning (ML) techniques, such as the random forest, XGBoost, LightGBM, and CatBoost, the proposed method achieves high-performance prediction of AhR activation. Thus, the DeepSnap–DL method may be considered a useful tool for achieving high-throughput in silico evaluation of AhR-induced hepatotoxicity.