Insilico evaluation on potential Mt-Sp1/matriptase inhibitors data: DFT and molecular modelling approaches.

Nine heterocyclic compounds were investigated using density functional theory, molecular operating environment software, material studio, swissparam (Swiss drug design) software. In this work, the descriptors generated from the optimized compounds proved to be efficient and explain the level of reactivity of the investigated compound. The developed quantitative structure activity relationship (QSAR) model was predictive and reliable. Also, compound 9 proved to be capable of inhibiting Mt-Sp1/Matriptase (pdb id: 1eax) than other examined heterocyclic compounds. Target prediction analysis was carried out on the compound with highest binding affinity (Compound 9) and the results were reported.

Resveratrol Inhibits Nasopharyngeal Carcinoma (NPC) by Targeting the MAPK Signaling Pathway.

BACKGROUND: With conventional cancer treatments facing limitations, interest in plant-derived natural products as potential alternatives is increasing. Although resveratrol has demonstrated antitumor effects in various cancers, its impact and mechanism on nasopharyngeal carcinoma remain unclear. OBJECTIVE: This study aimed to systematically investigate the anti-cancer effects of resveratrol on nasopharyngeal carcinoma using a combination of experimental pharmacology, network pharmacology, and molecular docking approaches. METHODS: CCK-8, scratch wound, and transwell assays were employed to confirm the inhibitory effect of resveratrol on the proliferation, migration, and invasion of nasopharyngeal carcinoma cells. H&E and TUNEL stainings were used to observe the morphological changes and apoptosis status of resveratrol-treated cells. The underlying mechanisms were elucidated using a network pharmacology approach. Immunohistochemistry and Western blotting were utilized to validate key signaling pathways. RESULTS: Resveratrol inhibited the proliferation, invasion, and migration of nasopharyngeal carcinoma cells, ultimately inducing apoptosis in a time- and dose-dependent manner. Network pharmacology analysis revealed that resveratrol may exert its anti-nasopharyngeal carcinoma effect mainly through the MAPK pathway. Immunohistochemistry results from clinical cases showed MAPK signaling activation in nasopharyngeal carcinoma tissues compared to adjacent tissues. Western blotting validated the targeting effect of resveratrol, demonstrating significant inhibition of the MAPK signaling pathway. Furthermore, molecular docking supported its multi-target role with MAPK, TP53, PIK3CA, SRC, etc. Conclusion: Resveratrol has shown promising potential in inhibiting human nasopharyngeal carcinoma cells by primarily targeting the MAPK pathway. These findings position resveratrol as a potential therapeutic agent for nasopharyngeal carcinoma.

Discovery of new anti-diabetic potential agents based on paracetamol incorporating sulfa-drugs: Design, synthesis, α-amylase, and α-glucosidase inhibitors with molecular docking simulation.

Uncontrolled diabetes can lead to hyperglycemia, which causes neuropathy, heart attacks, retinopathy, and nervous system damage over time, therefore, controlling hyperglycemia using potential drug target inhibitors is a promising strategy. This work focused on synthesizing new derivatives via the diazo group, using a hybridization strategy involving two approved drugs, paracetamol and several sulfonamides. The newly designed diazo-paracetamols 5-12 were fully characterized and then screened for in vitro α-amylase and α-glucosidase activities and exhibited inhibitory percentages (IP) = 92.5-96.5 % and 91.0-95.7 % compared to Acarbose IP = 96.5 and 95.8 %, respectively at 100 µg/mL. The IC50 values of the synthesized derivatives were evaluated against α-amylase and α-glucosidase enzymes, and the results demonstrated moderate to potent activity. Among the tested diazo-paracetamols, compound 11 was found to have the highest potency activity against α-amylase with IC50 value of 0.98 ± 0.015 µM compared to Acarbose IC50 = 0.43 ± 0.009 µM, followed by compound 10 (IC50 = 1.55 ± 0.022 µM) and compound 9 (IC50 = 1.59 ± 0.023 µM). On the other hand, for α-glucosidase, compound 10 with pyrimidine moiety demonstrated the highest inhibitory activity with IC50 = 1.39 ± 0.021 µM relative to Acarbose IC50 = 1.24 ± 0.029 µM and the order of the most active derivatives was 10 > 9 (IC50 = 2.95 ± 0.046 µM) > 11 (IC50 = 5.13 ± 0.082 µM). SAR analysis confirmed that the presence of 4,5-dimethyl-isoxazole or pyrimidine nucleus attached to the sulfonyl group is important for activity. Finally, the docking simulation was achieved to determine the mode of binding interactions for the most active derivatives in the enzyme's active site.

Explore new quinoxaline pharmacophore tethered sulfonamide fragments as in vitro α-glucosidase, α-amylase, and acetylcholinesterase inhibitors with ADMET and molecular modeling simulation.

A new series of quinoxaline-sulfonamide derivatives 3-12 were synthesized using fragment-based drug design by reaction of quinoxaline sulfonyl chloride (QSC) with different amines and hydrazines. The quinoxaline-sulfonamide derivatives were evaluated for antidiabetic and anti-Alzheimer's potential against α-glucosidase, α-amylase, and acetylcholinesterase enzymes. These derivatives showed good to moderate potency against α-amylase and α-glucosidase with inhibitory percentages between 24.34 ± 0.01%-63.09 ± 0.02% and 28.95 ± 0.04%-75.36 ± 0.01%, respectively. Surprisingly, bis-sulfonamide quinoxaline derivative 4 revealed the most potent activity with inhibitory percentages of 75.36 ± 0.01% and 63.09 ± 0.02% against α-glucosidase and α-amylase compared to acarbose (IP = 57.79 ± 0.01% and 67.33 ± 0.01%), respectively. Moreover, the quinoxaline derivative 3 exhibited potency as α-glucosidase and α-amylase inhibitory with a minute decline from compound 4 and acarbose with inhibitory percentages of 44.93 ± 0.01% and 38.95 ± 0.01%. Additionally, in vitro acetylcholinesterase inhibitory activity for designed derivatives exhibited weak to moderate activity. Still, sulfonamide-quinoxaline derivative 3 emerged as the most active member with inhibitory percentage of 41.92 ± 0.02% compared with donepezil (IP = 67.27 ± 0.60%). The DFT calculations, docking simulation, target prediction, and ADMET analysis were performed and discussed in detail.

A systematic method for solving data imbalance in CRISPR off-target prediction tasks.

Accurately identifying potential off-target sites in the CRISPR/Cas9 system is crucial for improving the efficiency and safety of editing. However, the imbalance of available off-target datasets has posed a major obstacle in enhancing prediction performance. Despite several prediction models have been developed to address this issue, there remains a lack of systematic research on handling data imbalance in off-target prediction. This article systematically investigates the data imbalance issue in off-target datasets and explores numerous methods to process data imbalance from a novel perspective. First, we highlight the impact of the imbalance problem on off-target prediction tasks by determining the imbalance ratios present in these datasets. Then, we provide a comprehensive review of various sampling techniques and cost-sensitive methods to mitigate class imbalance in off-target datasets. Finally, systematic experiments are conducted on several state-of-the-art prediction models to illustrate the impact of applying data imbalance solutions. The results show that class imbalance processing methods significantly improve the off-target prediction capabilities of the models across multiple testing datasets. The code and datasets used in this study are available at https://github.com/gzrgzx/CRISPR_Data_Imbalance.

Shuangdong Capsule Activates Insulin Signal Pathway to Improve Urinary Tract Infection in Diabetic Rats.

BACKGROUND AND AIM: Diabetes and Urinary Tract Infections (UTIs) are both common and serious health problems. Shuangdong capsule, a Chinese patent medicine, has been used to treat these conditions. This study assesses its efficacy and mechanism in treating diabetes combined with UTIs. METHODS: We induced diabetes in rats using streptozotocin and UTIs with Escherichia coli, dividing the rats into five groups: control, model, levofloxacin, Shuangdong capsule, and levofloxacin + Shuangdong capsule. After two weeks, we measured blood glucose, insulin, infection indicators, and bladder histology. We also detected the expression of insulin receptor substrate 1 (IRS1)-phosphoinositide 3-kinase (PI3K)-protein kinase B (Akt)-C-X-C motif chemokine ligand 2 (CXCL2) signaling pathway by Western Blot and the myeloperoxidase (MPO) levels by Enzyme-Linked Immunosorbent Assay (ELISA). Additionally, we conducted a Mendelian randomization study using genetic variants of the insulin receptor to assess its causal effect on UTI risk. RESULTS: Shuangdong capsule improved bladder pathology and infection indicators, similar to levofloxacin. It did not affect blood glucose or insulin levels. Moreover, it reversed the suppression of the IRS1-PI3K-Akt-CXCL2 pathway and MPO levels caused by UTI in diabetic rats. The Mendelian randomization study showed that increased insulin receptor expression reduced UTI risk, which was consistent with the results of the animal experiments. CONCLUSION: The Shuangdong capsule was effective in treating diabetes with UTIs. It may function by activating the IRS1-PI3K-Akt signaling pathway, thereby increasing CXCL2 and MPO levels, enhancing innate immunity, and promoting bacterial clearance. The Mendelian randomization study provided further evidence supporting the causal role of the insulin receptor in UTI prevention.

Exploring the putative microRNAs cross-kingdom transfer in Solanum lycopersicum-Meloidogyne incognita interactions.

Introduction: Plant-pathogen interaction is an inexhaustible source of information on how to sustainably control diseases that negatively affect agricultural production. Meloidogyne incognita is a root-knot nematode (RKN), representing a pest for many crops, including tomato (Solanum lycopersicum). RKNs are a global threat to agriculture, especially under climate change, and RNA technologies offer a potential alternative to chemical nematicides. While endogenous microRNAs have been identified in both S. lycopersicum and M. incognita, and their roles have been related to the regulation of developmental changes, no study has investigated the miRNAs cross-kingdom transfer during this interaction. Methods: Here, we propose a bioinformatics pipeline to highlight potential miRNA-dependent cross-kingdom interactions between tomato and M. incognita. Results: The obtained data show that nematode miRNAs putatively targeting tomato genes are mostly related to detrimental effects on plant development and defense. Similarly, tomato miRNAs putatively targeting M. incognita biological processes have negative effects on digestion, mobility, and reproduction. To experimentally test this hypothesis, an in vitro feeding assay was carried out using sly-miRNAs selected from the bioinformatics approach. The results show that two tomato miRNAs (sly-miRNA156a, sly-miR169f) soaked by juvenile larvae (J2s) affected their ability to infect plant roots and form galls. This was also coupled with a significant downregulation of predicted target genes (Minc11367, Minc00111), as revealed by a qRT-PCR analysis. Discussions: Therefore, the current study expands the knowledge related to the cross-kingdom miRNAs involvement in host-parasite interactions and could pave the way for the application of exogenous plant miRNAs as tools to control nematode infection.

[Development of green pesticides from traditional Chinese medicines based on artificial intelligence and compatibility of traditional Chinese medicines].

The food security of China as a big agricultural country is attracting increasing attention. With the progress in the traditional Chinese medicine industry, Chinese medicinal materials and their preparations have been gradually developed as agents for disease prevention and with antimicrobial and insecticidal functions in agriculture. Promoting pesticide innovation by interdisciplinary integration has become the trend in pesticide research globally. Considering the increasingly important roles of green pesticides from traditional Chinese medicines and artificial intelligence in pest target prediction, this paper proposed an innovative green control strategy in line with the concepts of ecological sustainable development and food security protection. CiteSpace was used for visual analysis of the publications. The results showed that artificial intelligence had been extensively applied in the pesticide field in recent years. This paper explores the application and development of biopesticides for the first time, with focus on the plant-derived pesticides. The thought of traditional Chinese medicine compatibility can be employed to creat a new promosing field: pesticides from traditional Chinese medicine. Moreover, artificial intelligence can be employed to build the formulation system of pesticides from traditional Chinese medicines and the target prediction system of diseases and pests. This study provides new ideas for the future development and market application of biopesticides, aiming to provide more healthy and safe agricultural products for human beings, promote the innovation and development of green pesticides in China, and protect the sustainable development of the environment and ecosystem. This may be the research hotspot and competition point for the green development of the pesticide industry chain in the future.

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MiRNAs are a class of small non-coding RNAs, which regulate gene expression post-transcriptionally by partial complementary base pairing. Aberrant miRNA expressions have been reported in tumor tissues and peripheral blood of cancer patients. In recent years, artificial intelligence algorithms such as machine learning and deep learning have been widely used in bioinformatic research. Compared to traditional bioinformatic tools, miRNA target prediction tools based on artificial intelligence algorithms have higher accuracy, and can successfully predict subcellular localization and redistribution of miRNAs to deepen our understanding. Additionally, the construction of clinical models based on artificial intelligence algorithms could significantly improve the mining efficiency of miRNA used as biomarkers. In this article, we summarize recent development of bioinformatic miRNA tools based on artificial intelligence algorithms, focusing on the potential of machine learning and deep learning in cancer-related miRNA research.

Using Mendelian randomization provides genetic insights into potential targets for sepsis treatment.

Sepsis is recognized as a major contributor to the global disease burden, but there is a lack of specific and effective therapeutic agents. Utilizing Mendelian randomization (MR) methods alongside evidence of causal genetics presents a chance to discover novel targets for therapeutic intervention. MR approach was employed to investigate potential drug targets for sepsis. Pooled statistics from IEU-B-4980 comprising 11,643 cases and 474,841 controls were initially utilized, and the findings were subsequently replicated in the IEU-B-69 (10,154 cases and 454,764 controls). Causal associations were then validated through colocalization. Furthermore, a range of sensitivity analyses, including MR-Egger intercept tests and Cochran's Q tests, were conducted to evaluate the outcomes of the MR analyses. Three drug targets (PSMA4, IFNAR2, and LY9) exhibited noteworthy MR outcomes in two separate datasets. Notably, PSMA4 demonstrated not only an elevated susceptibility to sepsis (OR 1.32, 95% CI 1.20-1.45, p = 1.66E-08) but also exhibited a robust colocalization with sepsis (PPH4 = 0.74). According to the present MR analysis, PSMA4 emerges as a highly encouraging pharmaceutical target for addressing sepsis. Suppression of PSMA4 could potentially decrease the likelihood of sepsis.

TEC-miTarget: enhancing microRNA target prediction based on deep learning of ribonucleic acid sequences.

BACKGROUND: MicroRNAs play a critical role in regulating gene expression by binding to specific target sites within gene transcripts, making the identification of microRNA targets a prominent focus of research. Conventional experimental methods for identifying microRNA targets are both time-consuming and expensive, prompting the development of computational tools for target prediction. However, the existing computational tools exhibit limited performance in meeting the demands of practical applications, highlighting the need to improve the performance of microRNA target prediction models. RESULTS: In this paper, we utilize the most popular natural language processing and computer vision technologies to propose a novel approach, called TEC-miTarget, for microRNA target prediction based on transformer encoder and convolutional neural networks. TEC-miTarget treats RNA sequences as a natural language and encodes them using a transformer encoder, a widely used encoder in natural language processing. It then combines the representations of a pair of microRNA and its candidate target site sequences into a contact map, which is a three-dimensional array similar to a multi-channel image. Therefore, the contact map's features are extracted using a four-layer convolutional neural network, enabling the prediction of interactions between microRNA and its candidate target sites. We applied a series of comparative experiments to demonstrate that TEC-miTarget significantly improves microRNA target prediction, compared with existing state-of-the-art models. Our approach is the first approach to perform comparisons with other approaches at both sequence and transcript levels. Furthermore, it is the first approach compared with both deep learning-based and seed-match-based methods. We first compared TEC-miTarget's performance with approaches at the sequence level, and our approach delivers substantial improvements in performance using the same datasets and evaluation metrics. Moreover, we utilized TEC-miTarget to predict microRNA targets in long mRNA sequences, which involves two steps: selecting candidate target site sequences and applying sequence-level predictions. We finally showed that TEC-miTarget outperforms other approaches at the transcript level, including the popular seed match methods widely used in previous years. CONCLUSIONS: We propose a novel approach for predicting microRNA targets at both sequence and transcript levels, and demonstrate that our approach outperforms other methods based on deep learning or seed match. We also provide our approach as an easy-to-use software, TEC-miTarget, at https://github.com/tingpeng17/TEC-miTarget . Our results provide new perspectives for microRNA target prediction.

Characteristics of anticancer activity of CBP/p300 inhibitors - Features of their classes, intracellular targets and future perspectives of their application in cancer treatment.

Due to the contribution of highly homologous acetyltransferases CBP and p300 to transcription elevation of oncogenes and other cancer promoting factors, these enzymes emerge as possible epigenetic targets of anticancer therapy. Extensive efforts in search for small molecule inhibitors led to development of compounds targeting histone acetyltransferase catalytic domain or chromatin-interacting bromodomain of CBP/p300, as well as dual BET and CBP/p300 inhibitors. The promising anticancer efficacy in in vitro and mice models led CCS1477 and NEO2734 to clinical trials. However, none of the described inhibitors is perfectly specific to CBP/p300 since they share similarity of a key functional domains with other enzymes, which are critically associated with cancer progression and their antagonists demonstrate remarkable clinical efficacy in cancer therapy. Therefore, we revise the possible and clinically relevant off-targets of CBP/p300 inhibitors that can be blocked simultaneously with CBP/p300 thereby improving the anticancer potential of CBP/p300 inhibitors and pharmacokinetic predicting data such as absorption, distribution, metabolism, excretion (ADME) and toxicity.

Baricitinib and tofacitinib off-target profile, with a focus on Alzheimer's disease.

INTRODUCTION: Janus kinase (JAK) inhibitors were recently identified as promising drug candidates for repurposing in Alzheimer's disease (AD) due to their capacity to suppress inflammation via modulation of JAK/STAT signaling pathways. Besides interaction with primary therapeutic targets, JAK inhibitor drugs frequently interact with unintended, often unknown, biological off-targets, leading to associated effects. Nevertheless, the relevance of JAK inhibitors' off-target interactions in the context of AD remains unclear. METHODS: Putative off-targets of baricitinib and tofacitinib were predicted using a machine learning (ML) approach. After screening scientific literature, off-targets were filtered based on their relevance to AD. Targets that had not been previously identified as off-targets of baricitinib or tofacitinib were subsequently tested using biochemical or cell-based assays. From those, active concentrations were compared to bioavailable concentrations in the brain predicted by physiologically based pharmacokinetic (PBPK) modeling. RESULTS: With the aid of ML and in vitro activity assays, we identified two enzymes previously unknown to be inhibited by baricitinib, namely casein kinase 2 subunit alpha 2 (CK2-α2) and dual leucine zipper kinase (MAP3K12), both with binding constant (K d) values of 5.8 µM. Predicted maximum concentrations of baricitinib in brain tissue using PBPK modeling range from 1.3 to 23 nM, which is two to three orders of magnitude below the corresponding binding constant. CONCLUSION: In this study, we extended the list of baricitinib off-targets that are potentially relevant for AD progression and predicted drug distribution in the brain. The results suggest a low likelihood of successful repurposing in AD due to low brain permeability, even at the maximum recommended daily dose. While additional research is needed to evaluate the potential impact of the off-target interaction on AD, the combined approach of ML-based target prediction, in vitro confirmation, and PBPK modeling may help prioritize drugs with a high likelihood of being effectively repurposed for AD. Highlights: This study explored JAK inhibitors' off-targets in AD using a multidisciplinary approach.We combined machine learning, in vitro tests, and PBPK modelling to predict and validate new off-target interactions of tofacitinib and baricitinib in AD.Previously unknown inhibition of two enzymes (CK2-a2 and MAP3K12) by baricitinib were confirmed using in vitro experiments.Our PBPK model indicates that baricitinib low brain permeability limits AD repurposing.The proposed multidisciplinary approach optimizes drug repurposing efforts in AD research.

Gene Target Prediction of Environmental Chemicals Using Coupled Matrix-Matrix Completion.

Human exposure to toxic chemicals presents a huge health burden. Key to understanding chemical toxicity is knowledge of the molecular target(s) of the chemicals. Because a comprehensive safety assessment for all chemicals is infeasible due to limited resources, a robust computational method for discovering targets of environmental exposures is a promising direction for public health research. In this study, we implemented a novel matrix completion algorithm named coupled matrix-matrix completion (CMMC) for predicting direct and indirect exposome-target interactions, which exploits the vast amount of accumulated data regarding chemical exposures and their molecular targets. Our approach achieved an AUC of 0.89 on a benchmark data set generated using data from the Comparative Toxicogenomics Database. Our case studies with bisphenol A and its analogues, PFAS, dioxins, PCBs, and VOCs show that CMMC can be used to accurately predict molecular targets of novel chemicals without any prior bioactivity knowledge. Our results demonstrate the feasibility and promise of computationally predicting environmental chemical-target interactions to efficiently prioritize chemicals in hazard identification and risk assessment.

SiteMine: Large-scale binding site similarity searching in protein structure databases.

Drug discovery and design challenges, such as drug repurposing, analyzing protein-ligand and protein-protein complexes, ligand promiscuity studies, or function prediction, can be addressed by protein binding site similarity analysis. Although numerous tools exist, they all have individual strengths and drawbacks with regard to run time, provision of structure superpositions, and applicability to diverse application domains. Here, we introduce SiteMine, an all-in-one database-driven, alignment-providing binding site similarity search tool to tackle the most pressing challenges of binding site comparison. The performance of SiteMine is evaluated on the ProSPECCTs benchmark, showing a promising performance on most of the data sets. The method performs convincingly regarding all quality criteria for reliable binding site comparison, offering a novel state-of-the-art approach for structure-based molecular design based on binding site comparisons. In a SiteMine showcase, we discuss the high structural similarity between cathepsin L and calpain 1 binding sites and give an outlook on the impact of this finding on structure-based drug design. SiteMine is available at https://uhh.de/naomi.

miTDS: Uncovering miRNA-mRNA interactions with deep learning for functional target prediction.

MicroRNAs (miRNAs) are vital in regulating gene expression through binding to specific target sites on messenger RNAs (mRNAs), a process closely tied to cancer pathogenesis. Identifying miRNA functional targets is essential but challenging, due to incomplete genome annotation and an emphasis on known miRNA-mRNA interactions, restricting predictions of unknown ones. To address those challenges, we have developed a deep learning model based on miRNA functional target identification, named miTDS, to investigate miRNA-mRNA interactions. miTDS first employs a scoring mechanism to eliminate unstable sequence pairs and then utilizes a dynamic word embedding model based on the transformer architecture, enabling a comprehensive analysis of miRNA-mRNA interaction sites by harnessing the global contextual associations of each nucleotide. On this basis, miTDS fuses extended seed alignment representations learned in the multi-scale attention mechanism module with dynamic semantic representations extracted in the RNA-based dual-path module, which can further elucidate and predict miRNA and mRNA functions and interactions. To validate the effectiveness of miTDS, we conducted a thorough comparison with state-of-the-art miRNA-mRNA functional target prediction methods. The evaluation, performed on a dataset cross-referenced with entries from MirTarbase and Diana-TarBase, revealed that miTDS surpasses current methods in accurately predicting functional targets. In addition, our model exhibited proficiency in identifying A-to-I RNA editing sites, which represents an aberrant interaction that yields valuable insights into the suppression of cancerous processes.

J24335 exerts neuroprotective effects against 6-hydroxydopamine-induced lesions in PC12 cells and mice.

Parkinson's disease is the second most prevalent age-related neurodegenerative disease and disrupts the lives of people aged >60 years. Meanwhile, single-target drugs becoming inapplicable as PD pathogenesis diversifies. Mitochondrial dysfunction and neurotoxicity have been shown to be relevant to the pathogenesis of PD. The novel synthetic compound J24335 (11-Hydroxy-1-(8-methoxy-5-(trifluoromethyl)quinolin-2-yl)undecan-1-one oxime), which has been researched similarly to J2326, has the potential to be a multi-targeted drug and alleviate these lesions. Therefore, we investigated the mechanism of action and potential neuroprotective function of J24335 against 6-OHDA-induced neurotoxicity in mice, and in PC12 cell models. The key target of action of J24335 was also screened. MTT assay, LDH assay, flow cytometry, RT-PCR, LC-MS, OCR and ECAR detection, and Western Blot analysis were performed to characterize the neuroprotective effects of J24335 on PC12 cells and its potential mechanism. Behavioral tests and immunohistochemistry were used to evaluate behavioral changes and brain lesions in mice. Moreover, bioinformatics was employed to assess the drug-likeness of J24335 and screen its potential targets. J24335 attenuated the degradation of mitochondrial membrane potential and enhanced glucose metabolism and mitochondrial biosynthesis to ameliorate 6-OHDA-induced mitochondrial dysfunction. Animal behavioral tests demonstrated that J24335 markedly improved motor function and loss of TH-positive neurons and dopaminergic nerve fibers, and contributed to an increase in the levels of dopamine and its metabolites in brain tissue. The activation of both the CREB/PGC-1α/NRF-1/TFAM and PKA/Akt/GSK-3ß pathways was a major contributor to the neuroprotective effects of J24335. Furthermore, bioinformatics predictions revealed that J24335 is a low toxicity and highly BBB permeable compound targeting 8 key genes (SRC, EGFR, ERBB2, SYK, MAPK14, LYN, NTRK1 and PTPN1). Molecular docking suggested a strong and stable binding between J24335 and the 8 core targets. Taken together, our results indicated that J24335, as a multi-targeted neuroprotective agent with promising therapeutic potential for PD, could protect against 6-OHDA-induced neurotoxicity via two potential pathways in mice and PC12 cells.

Uncovering the mechanism of Naoxintong capsule against hypertension based on network analysis and in vitro experiments.

Naoxintong capsule (NXT) is a clinical drug for the treatment of cardiovascular diseases, but its pharmacological mechanism against hypertension remains unclear. Data concerning the compounds and targets of NXT were obtained from the TCMSP and DrugBank, whereas data concerning hypertension-related genes were obtained from DisGeNET. The network was analyzed and established by STRING and Cytoscape, and function enrichment was analyzed by GO and KEGG analysis. Molecular docking was performed to analyze the interaction between ingredients and targets, cellular activity was evaluated by MTT assay, and RT-qPCR and western blot were used to evaluate the expressions of related genes. The results showed that 146 active therapeutic components can target hypertension-related genes, and we found that core genes were mainly involved in the metabolism of lipids, lipopolysaccharides, the inflammatory signaling pathway, and the oxidative stress pathway. In addition, there was high affinity between the components of NXT and targets of hypertension, where the former can increase cell viability and reduce the expressions of NOX4, MCP-1, BAX, TNF-α and IL-1ß. Moreover, NXT inhibited the expressions of IL-6 and Fis1, as well as increased the expression of MCL-1. These results revealed the active compounds, hypertension targets, signaling pathways, and molecular mechanisms of NXT for treating hypertension, offering references for the clinical application of NXT and the treatment of hypertension.

LncRTPred: Predicting RNA-RNA mode of interaction mediated by lncRNA.

Long non-coding RNAs (lncRNAs) play a significant role in various biological processes. Hence, it is utmost important to elucidate their functions in order to understand the molecular mechanism of a complex biological system. This versatile RNA molecule has diverse modes of interaction, one of which constitutes lncRNA-mRNA interaction. Hence, identifying its target mRNA is essential to understand the function of an lncRNA explicitly. Existing lncRNA target prediction tools mainly adopt thermodynamics approach. Large execution time and inability to perform real-time prediction limit their usage. Further, lack of negative training dataset has been a hindrance in the path of developing machine learning (ML) based lncRNA target prediction tools. In this work, we have developed a ML-based lncRNA-mRNA target prediction model- 'LncRTPred'. Here we have addressed the existing problems by generating reliable negative dataset and creating robust ML models. We have identified the non-interacting lncRNA and mRNAs from the unlabelled dataset using BLAT. It is further filtered to get a reliable set of outliers. LncRTPred provides a cumulative_model_score as the final output against each query. In terms of prediction accuracy, LncRTPred outperforms other popular target prediction protocols like LncTar. Further, we have tested its performance against experimentally validated disease-specific lncRNA-mRNA interactions. Overall, performance of LncRTPred is heavily dependent on the size of the training dataset, which is highly reflected by the difference in its performance for human and mouse species. Its performance for human species shows better as compared to that for mouse when applied on an unknown data due to smaller size of the training dataset in case of mouse compared to that of human. Availability of increased number of lncRNA-mRNA interaction data for mouse will improve the performance of LncRTPred in future. Both webserver and standalone versions of LncRTPred are available. Web server link: http://bicresources.jcbose.ac.in/zhumur/lncrtpred/index.html. Github Link: https://github.com/zglabDIB/LncRTPred.

Screening of anti-COPD quality biomarkers of Gnaphalium affine / 中国药房

OBJECTIVE To screen the quality biomarkers of Gnaphalium affine with anti-chronic obstructive pulmonary disease （COPD） effect and determine their contents. METHODS The effective components and targets of “G. affine” with anti- COPD effect were predicted by using network pharmacology as a search criterion. HPLC fingerprints for 10 batches of G. affine were established by using Similarity Evaluation System of TCM Chromatographic Fingerprint （2012 edition）； common peak identification and similarity evaluation were conducted； cluster analysis （CA）， principal component analysis （PCA）， and orthogonal partial least squares-discriminant analysis （OPLS-DA） were performed to screen differential components as quality maker that affected the quality of G. affine using variable importance projection （VIP）＞1 as the standard. The same HPLC method was adopted to determine the contents of the differential components in 10 batches of samples. RESULTS A total of 10 flavonoids （such as quercetin， luteolin， and chlorogenic acid） and organic acid components， were identified through network pharmacology search， with 91 targets closely related to anti-COPD. A total of 9 common peaks were identified in 10 batches of samples， with similarity greater than 0.90. Among them， the differential components included chlorogenic acid， caffeic acid， 1，3-O- dicaffeoylquinic acid and apigenin 7-O-β-D-glucopyranoside； S3， S4， S6， S7 and S10 were clustered into one category， S2， S5， S8 and S9 clustered into one category， and S1 clustered into one category. The contents of chlorogenic acid， caffeic acid， 1，3-O- dicaffeoylquinic acid， and apigenin 7-O-β-D-glucopyranoside in 10 batches of G. affine ranged 0.070-7.653， 0.010-0.097， 0.001- 0.036， 0.508-6.627 mg/g， respectively. CONCLUSIONS Chlorogenic acid， caffeic acid， 1，3-O-dicaffeoylquinic acid， apigenin 7- O-β-D-glucopyranoside can serve as the potential quality marker for the anti-COPD effect of G. affine， with the highest content of chlorogenic acid in G. affine produced in Ji’an， Jiangxi province， and the highest content of caffeic acid in G. affine produced in Ji’an， Jiangxi province and Sanming， Fujian province. The contents of the last two components are highest in G. affine produced in Chaoshan， Guangdong province.