Exploring the mechanistic insights of Cas scaffolding protein family member 4 with protein tyrosine kinase 2 in Alzheimer's disease by evaluating protein interactions through molecular docking and dynamic simulations

Cell-type specific epigenetic clocks to quantify biological age at cell-type resolution

The ability to accurately quantify biological age could help monitor and control healthy aging. Epigenetic clocks have emerged as promising tools for estimating biological age, yet they have been developed from heterogeneous bulk tissues, and are thus composites of two aging processes, one reflecting the change of cell-type composition with age and another reflecting the aging of individual cell-types. There is thus a need to dissect and quantify these two components of epigenetic clocks, and to develop epigenetic clocks that can yield biological age estimates at cell-type resolution. Here we demonstrate that in blood and brain, approximately 39% and 12% of an epigenetic clock's accuracy is driven by underlying shifts in lymphocyte and neuronal subsets, respectively. Using brain and liver tissue as prototypes, we build and validate neuron and hepatocyte specific DNA methylation clocks, and demonstrate that these cell-type specific clocks yield improved estimates of chronological age in the corresponding cell and tissue-types. We find that neuron and glia specific clocks display biological age acceleration in Alzheimer's Disease with the effect being strongest for glia in the temporal lobe. Moreover, CpGs from these clocks display a small but significant overlap with the causal DamAge-clock, mapping to key genes implicated in neurodegeneration. The hepatocyte clock is found accelerated in liver under various pathological conditions. In contrast, non-cell-type specific clocks do not display biological age-acceleration, or only do so marginally. In summary, this work highlights the importance of dissecting epigenetic clocks and quantifying biological age at cell-type resolution.

Network dynamics-based subtyping of Alzheimer's disease with microglial genetic risk factors

BACKGROUND

The potential of microglia as a target for Alzheimer's disease (AD) treatment is promising, yet the clinical and pathological diversity within microglia, driven by genetic factors, poses a significant challenge. Subtyping AD is imperative to enable precise and effective treatment strategies. However, existing subtyping methods fail to comprehensively address the intricate complexities of AD pathogenesis, particularly concerning genetic risk factors. To address this gap, we have employed systems biology approaches for AD subtyping and identified potential therapeutic targets.

METHODS

We constructed patient-specific microglial molecular regulatory network models by utilizing existing literature and single-cell RNA sequencing data. The combination of large-scale computer simulations and dynamic network analysis enabled us to subtype AD patients according to their distinct molecular regulatory mechanisms. For each identified subtype, we suggested optimal targets for effective AD treatment.

RESULTS

To investigate heterogeneity in AD and identify potential therapeutic targets, we constructed a microglia molecular regulatory network model. The network model incorporated 20 known risk factors and crucial signaling pathways associated with microglial functionality, such as inflammation, anti-inflammation, phagocytosis, and autophagy. Probabilistic simulations with patient-specific genomic data and subsequent dynamics analysis revealed nine distinct AD subtypes characterized by core feedback mechanisms involving SPI1, CASS4, and MEF2C. Moreover, we identified PICALM, MEF2C, and LAT2 as common therapeutic targets among several subtypes. Furthermore, we clarified the reasons for the previous contradictory experimental results that suggested both the activation and inhibition of AKT or INPP5D could activate AD through dynamic analysis. This highlights the multifaceted nature of microglial network regulation.

CONCLUSIONS

These results offer a means to classify AD patients by their genetic risk factors, clarify inconsistent experimental findings, and advance the development of treatments tailored to individual genotypes for AD.

Alzheimer's Disease: An Updated Overview of Its Genetics

Alzheimer's disease (AD) is the most common neurodegenerative disease in the world. It is classified as familial and sporadic. The dominant familial or autosomal presentation represents 1-5% of the total number of cases. It is categorized as early onset (EOAD; <65 years of age) and presents genetic mutations in presenilin 1 (PSEN1), presenilin 2 (PSEN2), or the Amyloid precursor protein (APP). Sporadic AD represents 95% of the cases and is categorized as late-onset (LOAD), occurring in patients older than 65 years of age. Several risk factors have been identified in sporadic AD; aging is the main one. Nonetheless, multiple genes have been associated with the different neuropathological events involved in LOAD, such as the pathological processing of Amyloid beta (Aβ) peptide and Tau protein, as well as synaptic and mitochondrial dysfunctions, neurovascular alterations, oxidative stress, and neuroinflammation, among others. Interestingly, using genome-wide association study (GWAS) technology, many polymorphisms associated with LOAD have been identified. This review aims to analyze the new genetic findings that are closely related to the pathophysiology of AD. Likewise, it analyzes the multiple mutations identified to date through GWAS that are associated with a high or low risk of developing this neurodegeneration. Understanding genetic variability will allow for the identification of early biomarkers and opportune therapeutic targets for AD.

Exploration of Potential Ewing Sarcoma Drugs from FDA-Approved Pharmaceuticals through Computational Drug Repositioning, Pharmacogenomics, Molecular Docking, and MD Simulation Studies

Novel drug development is a time-consuming process with relatively high debilitating costs. To overcome this problem, computational drug repositioning approaches are being used to predict the possible therapeutic scaffolds against different diseases. In the current study, computational drug repositioning approaches were employed to fetch the promising drugs from the pool of FDA-approved drugs against Ewing sarcoma. The binding interaction patterns and conformational behaviors of screened drugs within the active region of Ewing sarcoma protein (EWS) were confirmed through molecular docking profiles. Furthermore, pharmacogenomics analysis was employed to check the possible associations of selected drugs with Ewing sarcoma genes. Moreover, the stability behavior of selected docked complexes (drugs-EWS) was checked by molecular dynamics simulations. Taken together, astemizole, sulfinpyrazone, and pranlukast exhibited a result comparable to pazopanib and can be used as a possible therapeutic agent in the treatment of Ewing sarcoma.

The relationship of early- and late-onset Alzheimer’s disease genes with COVID-19

Individuals with Alzheimer’s disease and other neurodegenerative diseases have been exposed to excess risk by the COVID-19 pandemic. COVID-19’s main manifestations include high body temperature, dry cough, and exhaustion. Nevertheless, some affected individuals may have an atypical presentation at diagnosis but suffer neurological signs and symptoms as the first disease manifestation. These findings collectively show the neurotropic nature of SARS-CoV-2 virus and its ability to involve the central nervous system. In addition, Alzheimer’s disease and COVID-19 has a number of common risk factors and comorbid conditions including age, sex, hypertension, diabetes, and the expression of APOE ε4. Until now, a plethora of studies have examined the COVID-19 disease but only a few studies has yet examined the relationship of COVID-19 and Alzheimer’s disease as risk factors of each other. This review emphasizes the recently published evidence on the role of the genes of early- or late-onset Alzheimer’s disease in the susceptibility of individuals currently suffering or recovered from COVID-19 to Alzheimer’s disease or in the susceptibility of individuals at risk of or with Alzheimer’s disease to COVID-19 or increased COVID-19 severity and mortality. Furthermore, the present review also draws attention to other uninvestigated early- and late-onset Alzheimer’s disease genes to elucidate the relationship between this multifactorial disease and COVID-19.

A 3-Gene-Based Diagnostic Signature in Alzheimer's Disease

BACKGROUND

Alzheimer's disease (AD) is a chronic neurodegenerative disease. In this study, potential diagnostic biomarkers were identified for AD.

METHODS

All AD samples and healthy samples were collected from 2 datasets in the GEO database, in which differentially expressed genes (DEGs) were analyzed by using the limma package of R language. GO and KEGG pathway enrichment was conducted basing on the DEGs via the clusterProfiler package of R. And, the PPI network construction and gene prediction were performed using the STRING database and Cytoscape. Then, a logistic regression model was constructed to predict the sample type.

RESULTS

Bioinformatic analysis of GEO datasets revealed 2,063 and 108 DEGs in GSE5281 and GSE4226 datasets, separately, and 15 overlapping DEGs were found. GO and KEGG enrichment analysis revealed terms associated with neurodevelopment. Then, we built a logistic regression model based on the hub genes from the PPI network and optimized the model to 3 genes (ALDOA, ENC1, and NFKBIA). The values of area under the curve of the training set GSE5281 and testing set GSE4226 were 0.9647 and 0.7857, respectively, which implied the efficacy of this model.

CONCLUSION

The comprehensive bioinformatic analysis of gene expression in AD patients and the effective logistic regression model built in our study may provide promising research value for diagnostic methods of AD.

Mechanistic insights into TNFR1/MADD death domains in Alzheimer's disease through conformational molecular dynamic analysis

Proteins are tiny players involved in the activation and deactivation of multiple signaling cascades through interactions in cells. The TNFR1 and MADD interact with each other and mediate downstream protein signaling pathways which cause neuronal cell death and Alzheimer’s disease. In the current study, a molecular docking approach was employed to explore the interactive behavior of TNFR1 and MADD proteins and their role in the activation of downstream signaling pathways. The computational sequential and structural conformational results revealed that Asp400, Arg58, Arg59 were common residues of TNFR1 and MADD which are involved in the activation of downstream signaling pathways. Aspartic acid in negatively charged residues is involved in the biosynthesis of protein. However, arginine is a positively charged residue with the potential to interact with oppositely charged amino acids. Furthermore, our molecular dynamic simulation results also ensured the stability of the backbone of TNFR1 and MADD death domains (DDs) in binding interactions. This DDs interaction mediates some conformational changes in TNFR1 which leads to the activation of mediators proteins in the cellular signaling pathways. Taken together, a better understanding of TNFR1 and MADD receptors and their activated signaling cascade may help treat Alzheimer’s disease. The death domains of TNFR1 and MADD could be used as a novel pharmacological target for the treatment of Alzheimer’s disease by inhibiting the MAPK pathway.

Understand the Functions of Scaffold Proteins in Cell Signaling by a Mesoscopic Simulation Method

Scaffold proteins are central players in regulating the spatial-temporal organization of many important signaling pathways in cells. They offer physical platforms to downstream signaling proteins so that their transient interactions in a crowded and heterogeneous environment of cytosol can be greatly facilitated. However, most scaffold proteins tend to simultaneously bind more than one signaling molecule, which leads to the spatial assembly of multimeric protein complexes. The kinetics of these protein oligomerizations are difficult to quantify by traditional experimental approaches. To understand the functions of scaffold proteins in cell signaling, we developed a, to our knowledge, new hybrid simulation algorithm in which both spatial organization and binding kinetics of proteins were implemented. We applied this new technique to a simple network system that contains three molecules. One molecule in the network is a scaffold protein, whereas the other two are its binding targets in the downstream signaling pathway. Each of the three molecules in the system contains two binding motifs that can interact with each other and are connected by a flexible linker. By applying the new simulation method to the model, we show that the scaffold proteins will promote not only thermodynamics but also kinetics of cell signaling given the premise that the interaction between the two signaling molecules is transient. Moreover, by changing the flexibility of the linker between two binding motifs, our results suggest that the conformational fluctuations in a scaffold protein play a positive role in recruiting downstream signaling molecules. In summary, this study showcases the capability of computational simulation in understanding the general principles of scaffold protein functions.

Computational investigation of mechanistic insights of Aβ42 interactions against extracellular domain of nAChRα7 in Alzheimer's disease

AIM

Amyloid beta (Aβ) 1-42, which is a basic constituent of amyloid plaques, binds with extracellular transmembrane receptor nicotine acetylcholine receptor α7 (nAChRα7) in Alzheimer's disease.

MATERIALS AND METHODS

In the current study, a computational approach was employed to explore the active binding sites of nAChRα7 through Aβ 1-42 interactions and their involvement in the activation of downstream signalling pathways. Sequential and structural analyses were performed on the extracellular part of nAChRα7 to identify its core active binding site.

RESULTS

Results showed that a conserved residual pattern and well superimposed structures were observed in all nAChRs proteins. Molecular docking servers were used to predict the common interactive residues in nAChRα7 and Aβ1-42 proteins. The docking profile results showed some common interactive residues such as Glu22, Ala42 and Trp171 may consider as the active key player in the activation of downstream signalling pathways. Moreover, the signal communication and receiving efficacy of best-docked complexes was checked through DynOmic online server. Furthermore, the results from molecular dynamic simulation experiment showed the stability of nAChRα7. The generated root mean square deviations and fluctuations (RMSD/F), solvent accessible surface area (SASA) and radius of gyration (Rg) graphs of nAChRα7 also showed its backbone stability and compactness, respectively.

CONCLUSION

Taken together, our predicted results intimated the structural insight on the molecular interactions of beta amyloid protein involved in the activation of nAChRα7 receptor. In future, a better understanding of nAChRα7 and their interconnected proteins signalling cascade may be consider as target to cure Alzheimer's disease.

The exploration of novel Alzheimer's therapeutic agents from the pool of FDA approved medicines using drug repositioning, enzyme inhibition and kinetic mechanism approaches

Novel drug development is onerous, time consuming and overpriced process with particularly low success and relatively high enfeebling rates. To overcome this burden, drug repositioning approach is being used to predict the possible therapeutic effects of FDA approved drugs in different diseases. Herein, we designed a computational and enzyme inhibitory mechanistic approach to fetch the promising drugs from the pool of FDA approved drugs against AD. The binding interaction patterns and conformations of screened drugs within active region of AChE were confirmed through molecular docking profiles. The possible associations of selected drugs with AD genes were predicted by pharmacogenomics analysis and confirmed through data mining. The stability behaviour of docked complexes (Drugs-AChE) were checked by MD simulations. The possible therapeutic potential of repositioned drugs against AChE were checked by in vitro analysis. Taken together, Cinitapride displayed a comparable results with standard and can be used as possible therapeutic agent in the treatment of AD.

Exploration of synthetic multifunctional amides as new therapeutic agents for Alzheimer's disease through enzyme inhibition, chemoinformatic properties, molecular docking and dynamic simulation insights

A new series of multifunctional amides has been synthesized having moderate enzyme inhibitory potentials and mild cytotoxicity. 2-Furyl(1-piperazinyl)methanone (1) was coupled with 3,5-dichloro-2-hydroxybenzenesulfonyl chloride (2) to form {4-[(3,5-dichloro-2-hydroxyphenyl)sulfonyl]-1-piperazinyl}(2-furyl)methanone (3). Different elecrophiles were synthesized by the reaction of various un/substituted anilines (4a-o) with 2-bromoacetylbromide (5), 2‑bromo‑N-(un/substituted-phenyl)acetamides (6a-o). Further, equimolar ratios of 3 and 6a-o were allowed to react in the presence of K₂CO₃ in acetonitrile to form desired multifunctional amides (7a-o). The structural confirmation of all the synthesized compounds was carried out by their EI-MS, IR, ¹H NMR and ¹³C NMR spectral data. Enzyme inhibition activity was performed against acetyl and butyrylcholinestrase enzymes, whereby 7e showed very good activity having IC₅₀ value of 5.54 ± 0.03 and 9.15 ± 0.01 μM, respectively, relative to eserine, a reference standard. Hemolytic activity of the molecules was checked to asertain their cytotoxicity towards red blood cell membrance and it was observed that most of the compounds were not toxic up to certain range. Moreover, chemoinformatic protepties and docking simulation results also showed the significance of 7e as compared to other compounds. Based on in vitro and in silico analysis 7e could be used as a template for the development of new drugs against Alzheimer's disease.