Ai-Drugnet: a Network-Based Deep Learning Model For Drug Repurposing and Combination therapy in Neurological Disorders

Discovering effective therapies is difficult for neurological and developmental disorders in that disease progression is often associated with a complex and interactive mechanism. Over the past few decades, few drugs have been identified for treating Alzheimer\u27s disease (AD), especially for impacting the causes of cell death in AD. Although drug repurposing is gaining more success in developing therapeutic efficacy for complex diseases such as common cancer, the complications behind AD require further study. Here, we developed a novel prediction framework based on deep learning to identify potential repurposed drug therapies for AD, and more importantly, our framework is broadly applicable and may generalize to identifying potential drug combinations in other diseases. Our prediction framework is as follows: we first built a drug-target pair (DTP) network based on multiple drug features and target features, as well as the associations between DTP nodes where drug-target pairs are the DTP nodes and the associations between DTP nodes are represented as the edges in the AD disease network; furthermore, we incorporated the drug-target feature from the DTP network and the relationship information between drug-drug, target-target, drug-target within and outside of drug-target pairs, representing each drug-combination as a quartet to generate corresponding integrated features; finally, we developed an AI-based Drug discovery Network (AI-DrugNet), which exhibits robust predictive performance. The implementation of our network model help identify potential repurposed and combination drug options that may serve to treat AD and other diseases

Ad-Syn-Net: Systematic Identification of alzheimer\u27s Disease-Associated Mutation and Co-Mutation Vulnerabilities Via Deep Learning

Alzheimer\u27s disease (AD) is one of the most challenging neurodegenerative diseases because of its complicated and progressive mechanisms, and multiple risk factors. Increasing research evidence demonstrates that genetics may be a key factor responsible for the occurrence of the disease. Although previous reports identified quite a few AD-associated genes, they were mostly limited owing to patient sample size and selection bias. There is a lack of comprehensive research aimed to identify AD-associated risk mutations systematically. to address this challenge, we hereby construct a large-scale AD mutation and co-mutation framework (\u27AD-Syn-Net\u27), and propose deep learning models named Deep-SMCI and Deep-CMCI configured with fully connected layers that are capable of predicting cognitive impairment of subjects effectively based on genetic mutation and co-mutation profiles. Next, we apply the customized frameworks to data sets to evaluate the importance scores of the mutations and identified mutation effectors and co-mutation combination vulnerabilities contributing to cognitive impairment. Furthermore, we evaluate the influence of mutation pairs on the network architecture to dissect the genetic organization of AD and identify novel co-mutations that could be responsible for dementia, laying a solid foundation for proposing future targeted therapy for AD precision medicine. Our deep learning model codes are available open access here: https://github.com/Pan-Bio/AD-mutation-effectors

D-GPM: A Deep Learning Method for Gene Promoter Methylation Inference

Whole-genome bisulfite sequencing generates a comprehensive profiling of the gene methylation levels, but is limited by a high cost. Recent studies have partitioned the genes into landmark genes and target genes and suggested that the landmark gene expression levels capture adequate information to reconstruct the target gene expression levels. This inspired us to propose that the methylation level of the promoters in landmark genes might be adequate to reconstruct the promoter methylation level of target genes, which would eventually reduce the cost of promoter methylation profiling. Here, we propose a deep learning model called Deep-Gene Promoter Methylation (D-GPM) to predict the whole-genome promoter methylation level based on the promoter methylation profile of the landmark genes from The Cancer Genome Atlas (TCGA). D-GPM-15%-7000 × 5, the optimal architecture of D-GPM, acquires the least overall mean absolute error (MAE) and the highest overall Pearson correlation coefficient (PCC), with values of 0.0329 and 0.8186, respectively, when testing data. Additionally, the D-GPM outperforms the regression tree (RT), linear regression (LR), and the support vector machine (SVM) in 95.66%, 92.65%, and 85.49% of the target genes by virtue of its relatively lower MAE and in 98.25%, 91.00%, and 81.56% of the target genes based on its relatively higher PCC, respectively. More importantly, the D-GPM predominates in predicting 79.86% and 78.34% of the target genes according to the model distribution of the least MAE and the highest PCC, respectively

Inhibitory Effect on the Hepatitis B Cells through the Regulation of miR-122-MAP3K2 signal pathway

Abstract: The aim of this study was to investigate the inhibitory effect of regulation of miR-122-MAP3K2 signal pathway on the hepatitis B cells. We detected the content of MAP3K2 from patients with HBV blood serum samples and analyzed the correlation between content of MAP3K2 and copies of HBV-DNA. Wound healing and Transwell assays were used to detect the function of cells from control group (wild type) and observer group (overexpresses miR-122). Secretion levels of HBsAg and MAP3K2 in the supernatant and level of MAP3K2 in cells were detected by ELISA and western blot, respectively. The results showed that there was a positive correlation between the copies of HBV-DNA and MAP3K2 in serum. In the assays involving detection of the number of HBV-DNA copies, the supernatant levels of HBsAg and MAP3K2, and the level of MAP3K2 in the cells, the rate of increase of these indicators significantly slowed as culture time. In conclusion, overexpression of miR-122 could inhibit the migration of hepatoblastoma cells; however, following transfection with miR-122, DNA synthesis and the secretion of HBsAg were inhibited. Overexpression of miR-122 can also downregulate MAP3K2. Consequently, we concluded that regulating the miR-122-MAP3K2 signaling pathway exerts an inhibitory effect in hepatitis B cells

AI-DrugNet: A network-based deep learning model for drug repurposing and combination therapy in neurological disorders

Discovering effective therapies is difficult for neurological and developmental disorders in that disease progression is often associated with a complex and interactive mechanism. Over the past few decades, few drugs have been identified for treating Alzheimer’s disease (AD), especially for impacting the causes of cell death in AD. Although drug repurposing is gaining more success in developing therapeutic efficacy for complex diseases such as common cancer, the complications behind AD require further study. Here, we developed a novel prediction framework based on deep learning to identify potential repurposed drug therapies for AD, and more importantly, our framework is broadly applicable and may generalize to identifying potential drug combinations in other diseases. Our prediction framework is as follows: we first built a drug-target pair (DTP) network based on multiple drug features and target features, as well as the associations between DTP nodes where drug-target pairs are the DTP nodes and the associations between DTP nodes are represented as the edges in the AD disease network; furthermore, we incorporated the drug-target feature from the DTP network and the relationship information between drug-drug, target-target, drug-target within and outside of drug-target pairs, representing each drug-combination as a quartet to generate corresponding integrated features; finally, we developed an AI-based Drug discovery Network (AI-DrugNet), which exhibits robust predictive performance. The implementation of our network model help identify potential repurposed and combination drug options that may serve to treat AD and other diseases

Unexpected O–H Insertion of Rhodium-Azavinylcarbenes with <i>N</i>‑Acylhydrazones: Divergent Synthesis of 3,6-Disubstituted- and 3,5,6-Trisubstituted-1,2,4-Triazines

A practical and efficient method for divergent synthesis of 3,6-disubstituted- and 3,5,6-trisubstituted-1,2,4-triazines via unexpected rhodium-catalyzed O–H insertion/rearrangement/conditions-controlled intramolecular cyclization and oxidation reaction under mild conditions has been developed. Notably, it is the first example for the synthesis of 1,2,4-triazines with different substituted-patterns via a common intermediate with excellent chemoselectivities by the reaction of <i>N</i>-acylhydrazones as aze-[3C] or [4C] synthons with <i>N</i>-sulfonyl-1,2,3-triazoles as aze-[2C] synthons. Furthermore, this method allows direct access to di­(het)­aryl ketone frameworks containing 1,2,4-triazine moiety for the first time, serving as a versatile building block for the synthesis of other useful heterocyclic skeletons, such as pyridine or pyridazinone-fused triazine in excellent yields

Unexpected O–H Insertion of Rhodium-Azavinylcarbenes with <i>N</i>‑Acylhydrazones: Divergent Synthesis of 3,6-Disubstituted- and 3,5,6-Trisubstituted-1,2,4-Triazines

A practical and efficient method for divergent synthesis of 3,6-disubstituted- and 3,5,6-trisubstituted-1,2,4-triazines via unexpected rhodium-catalyzed O–H insertion/rearrangement/conditions-controlled intramolecular cyclization and oxidation reaction under mild conditions has been developed. Notably, it is the first example for the synthesis of 1,2,4-triazines with different substituted-patterns via a common intermediate with excellent chemoselectivities by the reaction of <i>N</i>-acylhydrazones as aze-[3C] or [4C] synthons with <i>N</i>-sulfonyl-1,2,3-triazoles as aze-[2C] synthons. Furthermore, this method allows direct access to di­(het)­aryl ketone frameworks containing 1,2,4-triazine moiety for the first time, serving as a versatile building block for the synthesis of other useful heterocyclic skeletons, such as pyridine or pyridazinone-fused triazine in excellent yields

Activated carbon N-acetylcysteine microcapsule protects against nonalcoholic fatty liver disease in young rats via activating telomerase and inhibiting apoptosis.

Non-alcoholic fatty liver disease (NAFLD) is becoming one of the world's most common chronic liver diseases in childhood, yet no therapy is available that has been approved by the food and drug administration (FDA). Previous studies have reported that telomere and telomerase are involved the development and progression of NAFLD. This study was designed to investigate the potential beneficial effects of activated carbon N-acetylcysteine (ACNAC) microcapsules on the development of NAFLD in young rats as well as the underlying mechanism(s) involved. Three-week old male Sprague Dawley rats were given high-fat diet (HFD) with/without ACNAC treatment for 7 consecutive weeks. Liver pathologies were determined by hematoxylin and eosin (H&E) and Oil Red O staining, as well as by changes in biochemical parameters of plasma alanine transaminase (ALT) and aspartate transaminase (AST) levels, respectively. Glucose homeostasis was evaluated by the glucose tolerance test and the liver telomere length and activity were measured by real time PCR and telomeric repeat amplification protocol (TRAP). Western blot analysis was performed to determine the expression level of Bcl-2, Bax and Caspase-3. Our results demonstrated that ACNAC supplementation improved liver pathologies of rats that received long-term HFD feeding. ACNAC supplementation prevented HFD-induced telomere shortening and improved telomerase activity. Moreover, in comparison to HFD-fed rats, ACNAC supplementation markedly increased the expression of Bcl-2, but significantly decreased the expression of Bax and Caspase-3 in juvenile rats. Together, these results indicate that ACNAC may be a promising choice for preventing and treating NAFLD among children

DataSheet_1_Circulating metabolites associated with tumor hypoxia and early response to treatment in bevacizumab-refractory glioblastoma after combined bevacizumab and evofosfamide.docx

Glioblastomas (GBM) are the most common and aggressive form of primary malignant brain tumor in the adult population, and, despite modern therapies, patients often develop recurrent disease, and the disease remains incurable with median survival below 2 years. Resistance to bevacizumab is driven by hypoxia in the tumor and evofosfamide is a hypoxia-activated prodrug, which we tested in a phase 2, dual center (University of Texas Health Science Center in San Antonio and Dana Farber Cancer Institute) clinical trial after bevacizumab failure. Tumor hypoxic volume was quantified by 18F-misonidazole PET. To identify circulating metabolic biomarkers of tumor hypoxia in patients, we used a high-resolution liquid chromatography-mass spectrometry-based approach to profile blood metabolites and their specific enantiomeric forms using untargeted approaches. Moreover, to evaluate early response to treatment, we characterized changes in circulating metabolite levels during treatment with combined bevacizumab and evofosfamide in recurrent GBM after bevacizumab failure. Gamma aminobutyric acid, and glutamic acid as well as its enantiomeric form D-glutamic acid all inversely correlated with tumor hypoxia. Intermediates of the serine synthesis pathway, which is known to be modulated by hypoxia, also correlated with tumor hypoxia (phosphoserine and serine). Moreover, following treatment, lactic acid was modulated by treatment, likely in response to a hypoxia mediated modulation of oxidative vs glycolytic metabolism. In summary, although our results require further validation in larger patients’ cohorts, we have identified candidate metabolic biomarkers that could evaluate the extent of tumor hypoxia and predict the benefit of combined bevacizumab and evofosfamide treatment in GBM following bevacizumab failure.</p