Life Sciences
A New Strategy for Obesity Treatment: Revealing the Frontiers of Anti-obesity Medications
Obesity dramatically increases the risk of type 2 diabetes fatty liver hypertension cardiovascular disease and cancer causing both declines in quality of life and life expectancy which is a serious worldwide epidemic. At present more and more patients with obesity are choosing drug therapy. However given the high failure rate high cost and long design and testing process for discovering and developing new anti-obesity drugs drug repurposing could be an innovative method and opportunity to broaden and improve pharmacological tools in this context. Because different diseases share molecular pathways and targets in the cells anti-obesity drugs discovered in other fields are a viable option for treating obesity. Recently some drugs initially developed for other diseases such as treating diabetes tumors depression alcoholism erectile dysfunction and Parkinson's disease have been found to exert potential anti-obesity effects which provides another treatment prospect. In this review we will discuss the potential benefits and barriers associated with these drugs being used as obesity medications by focusing on their mechanisms of action when treating obesity. This could be a viable strategy for treating obesity as a significant advance in human health.
Dysregulated Long Non-coding RNAs in Myasthenia Gravis-A Mini-Review
Myasthenia gravis (MG) is an acquired autoimmune disease that is mediated by humoral immunity supplemented by cellular immunity along with participation of the complement system. The pathogenesis of MG is complex; although autoimmune dysfunction is clearly implicated the specific mechanism remains unclear. Long non-coding RNAs (lncRNAs) are a class of non-coding RNA molecules with lengths greater than 200 nucleotides with increasing evidence of their rich biological functions and high-level structure conservation. LncRNAs can directly interact with proteins and microRNAs to regulate the expression of target genes at the transcription and post-transcription levels. In recent years emerging studies have suggested that lncRNAs play roles in the differentiation of immune cells secretion of immune factors and complement production in the human body. This suggests the involvement of lncRNAs in the occurrence and progression of MG through various mechanisms. In addition the differentially expressed lncRNAs in peripheral biofluid may be used as a biomarker to diagnose MG and evaluate its prognosis. Moreover with the development of lncRNA expression regulation technology it is possible to regulate the differentiation of immune cells and influence the immune response by regulating the expression of lncRNAs which will provide a potential therapeutic option for MG. Here we review the research progress on the role of lncRNAs in different pathophysiological events contributing to MG focusing on specific lncRNAs that may largely contribute to the pathophysiology of MG which could be used as potential diagnostic biomarkers and therapeutic targets.
Amiodarone Advances the Apoptosis of Cardiomyocytes by Repressing Sigmar1 Expression and Blocking KCNH2-related Potassium Channels
Heart failure (HF) is the ultimate transformation result of various cardiovascular diseases. Mitochondria-mediated cardiomyocyte apoptosis has been uncovered to be associated with this disorder.
This study mainly delves into the mechanism of the anti-arrhythmic drug amiodarone on mitochondrial toxicity of cardiomyocytes.
The viability of H9c2 cells treated with amiodarone at 0.5 1 2 3 and 4 μM was determined by 3-(45-dimethylthiazol-2-yl)-25-diphenyltetrazolium bromide (MTT) assay and Sigmar1 expression was examined by quantitative real-time PCR (qRT-PCR). After transfection the viability apoptosis reactive oxygen species (ROS) level mitochondrial membrane potential (MMP) and potassium voltage-gated channel subfamily H member 2 (KCNH2) expression in H9c2 cells were assessed by MTT flow cytometry ROS assay kit mitochondria staining kit and Western blot.
Amiodarone at 1-4 μM notably weakened H9c2 cell viability with IC50 value of 2.62 ± 0.43 μM. Amiodarone at 0.5-4 μM also evidently suppressed the Sigmar1 level in H9c2 cells. Amiodarone repressed H9c2 cell viability and KCNH2 level and triggered apoptosis ROS production and mitochondrial depolarization while Sigmar1 up-regulation reversed its effects. Moreover KCNH2 silencing neutralized the effect of Sigmar1 up-regulation on H9c2 cell viability apoptosis and ROS production.
Amiodarone facilitates the apoptosis of H9c2 cells by restraining Sigmar1 expression and blocking KCNH2-related potassium channels.
Histone Deacetylase 2 Stabilizes SPARC-related Modular Calcium Binding 2 to Promote Metastasis and Stemness in Gallbladder Cancer
We aimed to investigate the relationship between histone deacetylase 2 (HDAC2) and SPARC-related modular calcium binding 2 (SMOC2) and the role of SMOC2 in gallbladder cancer (GBC).
The expression of HDAC2 and SMOC2 in GBC and normal cells was detected by quantitative real-time reverse transcription polymerase chain reaction (qRT-PCR) which was also used to detect the mRNA stability of SMOC2. The combination between HDAC2 and SMOC2 was detected by Chromatin immunoprecipitation (ChIP) assay. After silencing and/or overexpressing HDAC2 and SMOC2 cell viability migration invasion and stemness were respectively tested by the Cell Counting Kit-8 (CCK-8) cell scratch transwell and sphere-formation assay.
In GBC cells HDAC2 and SMOC2 were highly expressed. HDAC2 combined with SMOC2 promoted mRNA stability of SMOC2. HDAC2 or SMOC2 overexpression promoted GBC cell metastasis and stemness. SMOC2 overexpression rescued the negative effects of silencing HDAC2 in GBC.
HDAC2 stabilizes SMOC2 to promote metastasis and stemness in gallbladder cancer.
IL-1β-Stimulated Bone Mesenchymal Stem Cell-Derived Exosomes Mitigate Sepsis through Modulation of HMGB1/AKT Pathway and M2 Macrophage Polarization
Sepsis is a life-threatening disease caused by infection and developing novel strategies against sepsis is still required. Exosomes derived from mesenchymal stem cells (MSCs) have shown promising therapeutic potential for various diseases. In this study we aimed to investigate the action and mechanism of exosomes derived from IL-1β-pre-conditioned bone marrow-derived mesenchymal stromal cells (BMSCs) in sepsis.
Exosomes were isolated from BMSCs that were pretreated with (IL-1β-BMSC/exos) or without IL-1β (BMSC/exos). In vitro a cell model of sepsis was induced by treating human umbilical vein endothelial cells (HUVECs) with lipopolysaccharide (LPS) while in vivo a sepsis model was established through cecal ligation and puncture (CLP) operation. Immunofluorescence staining was used to detect the uptake of exosomes by HUVECs. The effects of exosomes on the cellular function of HUVECs were determined through EDU proliferation assay migration assay and tube formation assay. Gene and protein expression were analyzed using qRT-PCR Western blot ELISA immunofluorescence staining and immuno-histochemistry staining.
IL-1β-BMSC/exos significantly enhanced the proliferation migration and tube formation of HUVECs. Treatment with LPS induced the expression of high mobility group box 1 (HMGB1) and the phosphorylation of AKT in HUVECs but these effects were counteracted by the treatment of IL-1β-BMSC/exos. The protective effect of IL-1β-BMSC/exos on the viability and tube formation ability of HUVECs was reversed by overexpression of HMGB1. Moreover IL-1β-BMSC/exos promoted the polarization of M2 macrophages and reduced the secretion of inflammatory chemokines. Additionally IL-1β-BMSC/exos alleviated cecal ligation and puncture (CLP)-induced sepsis in vivo.
IL-1β-BMSC/exos alleviates sepsis by modulating the HMGB1/AKT pathway and triggering M2 macrophage polarization.
Hypoxia Affects Mitochondrial Stress and Facilitates Tumor Metastasis of Colorectal Cancer Through Slug SUMOylation
Colorectal cancer (CRC) is a malignant tumor. Slug has been found to display a key role in diversified cancers but its relevant regulatory mechanisms in CRC development are not fully explored.
Hence exploring the function and regulatory mechanisms of Slug is critical for the treatment of CRC.
Protein expressions of Slug N-cadherin E-cadherin Snail HIF-1α SUMO-1 Drp1 Opa1 Mfn1/2 PGC-1α NRF1 and TFAM were measured through western blot. To evaluate the protein expression of Slug and SUMO-1 an immunofluorescence assay was used. Cell migration ability was tested through transwell assay. The SUMOylation of Slug was examined through CO-IP assay.
Slug displayed higher expression and facilitated tumor metastasis in CRC. In addition hypoxia treatment was discovered to upregulate HIF-1α Slug and SUMO-1 levels as well as induce Slug SUMOylation. Slug SUMOylation markedly affected mitochondrial biosynthesis fusion and mitogen-related protein expression levels to trigger mitochondrial stress. Additionally the induced mitochondrial stress by hypoxia could be rescued by Slug inhibition and TAK-981 treatment.
Our study expounded that hypoxia affects mitochondrial stress and facilitates tumor metastasis of CRC through Slug SUMOylation.
Neferine Targeted the NLRC5/NLRP3 Pathway to Inhibit M1-type Polarization and Pyroptosis of Macrophages to Improve Hyperuricemic Nephropathy
Neferine (Nef) has a renal protective effect. This research intended to explore the impact of Nef on hyperuricemic nephropathy (HN).
Adenine and potassium oxonate were administered to SD rats to induce the HN model. Bone marrow macrophages (BMDM) and NRK-52E were used to construct a transwell co-culture system. The polarization of BMDM and apoptosis levels were detected using immunofluorescence and flow cytometry. Renal pathological changes were detected using hematoxylin-eosin (HE) and Masson staining. Biochemical methods were adopted to detect serum in rats. CCK-8 and EDU staining were used to assess cell activity and proliferation. RT-qPCR and western blot were adopted to detect NLRC5 NLRP3 pyroptosis proliferation and apoptosis-related factor levels.
After Nef treatment renal injury and fibrosis in HN rats were inhibited and UA concentration urinary protein BUN and CRE levels were decreased. After Nef intervention M1 markers pyroptosis-related factors and NLRC5 levels in BMDM stimulated with uric acid (UA) treatment were decreased. Meanwhile the proliferation level of NRK-52E cells co-cultured with UA-treated BMDM was increased but the apoptosis level was decreased. After NLRC5 overexpression Nef-induced regulation was reversed accompanied by increased NLRP3 levels. After NLRP3 was knocked down the levels of M1-type markers and pyroptosis-related factors were reduced in BMDM.
Nef improved HN by inhibiting macrophages polarized to M1-type and pyroptosis by targeting the NLRC5/NLRP3 pathway. This research provides a scientific theoretical basis for the treatment of HN.
ADGRL4 Promotes Cell Growth, Aggressiveness, EMT, and Angiogenesis in Neuroblastoma via Activation of ERK/STAT3 Pathway
Neuroblastoma (NB) is one of the most common pediatric solid tumors. Emerging evidence has indicated that ADGRL4 can act as a master regulator of tumor progression. In addition it is well documented that the ERK/STAT3 signaling pathway can promote the proliferation EMT angiogenesis and metastasis in tumors. The current study was formulated to elucidate the exact role of ADGRL4 in the malignant behaviors of NB cells and to investigate the intrinsic mechanism.
In this work expression differences of ADGRL4 in human NB cell lines and HUVECs were assessed via RT-qPCR and western blot analysis. For functional experiments sh-ADGRL4 was transfected into SK-N-SH cells to generate ADGRL4 knockdown stable cell line. Moreover ADGRL4 knockdown stable SK-N-SH cells were treated with LM22B-10 (an ERK activator) for rescue experiments. CCK-8 colony formation would healing and transwell assays determined NB cell growth migration and invasion. Meanwhile proliferation- metastasis- and EMT- associated proteins were also detected. Additionally a tube formation assay was employed to evaluate in vitro angiogenesis. VM-cadherin the marker of angiogenesis was assessed using immunofluorescence staining.
Data showed notably upregulated ADGRL4 in NB cells especially in SK-N-SH cells. ADGRL4 knockdown inhibited NB cell growth migration invasion EMT and in vitro angiogenesis. ADGRL4 knockdown inactivated ERK/STAT3 signaling pathway. Activation of the ERK/STAT3 signaling pathway partially rescued the tumor suppression effects of ADGRL4 knockdown on NB cells.
To conclude the downregulation of ADGRL4 may inhibit cell growth aggressiveness EMT and angiogenesis in NB by inactivating the ERK/STAT3 signaling pathway.
A Mechanism Study on the Antioxidant Pathway of Keap1-Nrf2-ARE Inhibiting Ferroptosis in Dopaminergic Neurons
The pathology of Parkinson's disease (PD) indicates that iron deposition exists in dopaminergic neurons which may be related to the death of cellular lipid iron peroxide. The extracellular autophagy adaptor SQSTM1(p62) of dopamine (DA) neurons can activate the intracellular Keap1-Nrf2-ARE signaling pathway to inhibit ferroptosis which has a protective effect on DA neurons.
The objective of this study was to investigate the protective mechanism of the Keap1-Nrf2-ARE antioxidant pathway against iron death in dopaminergic neurons.
The experiment was divided into a control group (Control group) 1-methyl-4-phenylpyridiniumion control group (MPP+ Control group) p62 overexpression group (MPP+OV-p62) and p62 overexpression no-load group (MPP+ OV-P62-NC). The inhibitors brusatol and ZnPP inhibited the activation of NF-E2-related factor 2(Nrf2) and Heme oxygenase-1(HO-1) respectively and were divided into brusatol group (MPP+OV-p62+brusatol) and ZnPP group (MPP+OV-p62+ZnPP). RT-qPCR was used to detect transfection efficiency and Cell Counting Kit-8 (CCK8) was used to detect cell activity. FerroOrange 27-Dichlorodihydrofluorescein diacetate (DCFH-DA) and Liperfluo probes were used to detect intracellular iron reactive oxygen species (ROS) and lipid peroxidation (LPO) levels. Western Blotting detected the levels of Nrf2 HO-1 Kelch-like ECH-associated protein1 (Keap1) and their downstream Glutathione peroxidase 4(GPX4) and Acyl-CoA synthetase long-chain family member 4(ACSL4). The levels of L-Glutathione (GSH) and Malondialdehyde (MDA) were detected by GSH and MDA kits and the activation of Keap1-Nrf2-ARE pathway was verified at the cellular level to have an antioxidant protective effect on iron death in dopaminergic neurons.
(1) The results of RT-qPCR showed that compared with the control group the expression of the p62 gene was significantly increased in the MPP+OV-p62 groups (p = 0.039) and the p62 gene was significantly increased in the brusatol and ZnPP groups indicating successful transfection (p =0.002; p=0.008). (2) The immunofluorescence probe flow results showed that compared to the normal control group the contents of three kinds of probes in MPP+ model group were significantly increased (p =0.001; p <0.001; p<0.001) and the contents of three kinds of probes in MPP+OV-p62 group were decreased compared to the MPP+ model group (p =0.004). The results indicated that the levels of iron ROS and LPO were increased in the MPP+ group and decreased in the MPP+OV-p62 group. (3) Compared with the control group the expressions of Nrf2 HO-1 and GPX4 in the MPP+OV-p62 group were increased (p =0.007; p =0.004; p=0.010) and the expressions of Keap1 and ACSL4 in MPP+p62 overexpression group were decreased (p =0.017; p =0.005). Compared with the MPP+ control group Nrf2 and GPX4 were increased in the MPP+OV-p62 group and ACSL4 was decreased in the MPP+OV-p62 group (p =0.041; p <0.001; p <0.001). The results of the GSH and MDA kit showed that compared with the normal control group the content of GSH in the MPP+ control group was decreased (p < 0.01) and the content of MDA was increased (p < 0.01). Compared with the MPP+ model group GSH content was increased (P = 0.003) and MDA content was decreased in the MPP+OV-p62 group (p < 0.001). Nrf2 HO-1 and GPX4 increased in the MPP+p62 overexpression group but decreased in the brusatol group and ZnPP group (p < 0.001).
Based on the transfection of P62 plasmid it was found that P62 plasmid can inhibit the lipid peroxidation of iron death in dopaminergic nerve cells by activating the Nrf2 signaling pathway thus playing a protective role in dopaminergic nerve cells.
MCHAN: Prediction of Human Microbe-drug Associations Based on Multiview Contrastive Hypergraph Attention Network
Complex and diverse microbial communities play a pivotal role in human health and have become a new drug target. Exploring the connections between drugs and microbes not only provides profound insights into their mechanisms but also drives progress in drug discovery and repurposing. The use of wet lab experiments to identify associations is time-consuming and laborious. Hence the advancement of precise and efficient computational methods can effectively improve the efficiency of association identification between microorganisms and drugs.
In this experiment we propose a new deep learning model a new multiview comparative hypergraph attention network (MCHAN) method for human microbe–drug association prediction.
First we fuse multiple similarity matrices to obtain a fused microbial and drug similarity network. By combining graph convolutional networks with attention mechanisms we extract key information from multiple perspectives. Then we construct two network topologies based on the above fused data. One topology incorporates the concept of hypernodes to capture implicit relationships between microbes and drugs using virtual nodes to construct a hyperheterogeneous graph. Next we propose a cross-contrastive learning task that facilitates the simultaneous guidance of graph embeddings from both perspectives without the need for any labels. This approach allows us to bring nodes with similar features and network topologies closer while pushing away other nodes. Finally we employ attention mechanisms to merge the outputs of the GCN and predict the associations between drugs and microbes.
To confirm the effectiveness of this method we conduct experiments on three distinct datasets. The results demonstrate that the MCHAN model surpasses other methods in terms of performance. Furthermore case studies provide additional evidence confirming the consistent predictive accuracy of the MCHAN model.
MCHAN is expected to become a valuable tool for predicting potential associations between microbiota and drugs in the future.
MFTP-Tool: A Wide & Deep Learning Framework for Multi-Functional Therapeutic Peptides Prediction
The identification and functional prediction of Multifunctional Therapeutic Peptides (MFTP) play a pivotal role in drug discovery particularly for conditions such as inflammation and hyperglycemia. Current computational methods exhibit limitations in their ability to accurately predict the multifunctionality of these peptides.
We propose a novel Wide and Deep Learning Framework that integrates both deep learning and machine learning approaches. The deep learning segment processes sequence vectors using a neural network model while the wide segment utilizes the physicochemical properties of peptides in a random forest-based model. This hybrid approach aims to enhance the accuracy of MFTP function prediction.
Our framework outperformed the existing PrMFTP predictor in terms of precision coverage accuracy and absolute true values. The evaluation was conducted on both training and independent testing datasets demonstrating the robustness and generalizability of our model.
The proposed Wide & Deep Learning Framework offers a significant advancement in the computational prediction of MFTP functions. The availability of our model through a user-friendly web interface at MFTP-Tool.m6aminer.cn provides a valuable tool for researchers in the field of therapeutic peptide-based drug discovery potentially accelerating the development of new treatments.
Relational Graph Convolution Network with Multi Features for Anti-COVID-19 Drugs Discovery using 3CLpro Potential Target
The potential of graph neural networks (GNNs) to revolutionize the analysis of non-Euclidean data has gained attention recently making them attractive models for deep machine learning. However insufficient compound or molecular graphs and feature representations might significantly impair and jeopardize their full potential. Despite the devastating impacts of ongoing COVID-19 across the globe for which there is no drug with proven efficacy that has been shown to be effective. As various stages of drug discovery and repositioning require the accurate prediction of drug-target interactions (DTI) here we propose a relational graph convolution network (RGCN) using multi-features based on the developed drug compound-coronavirus target graph data representation and combination of features. During the implementation of the model we further introduced the use of not only the feature module to understand the topological structure of drugs but also the structure of the proven drug target (i.e. 3CLpro) for SARS-CoV-2 that shares a genome sequence similar to that of other members of the betacoronavirus group such as SARS-CoV MERS-CoV bat coronavirus. Our feature comprises topological information in molecular SMILES and local chemical context in the SMILES sequence for the drug compound and drug target. Our proposed method prevailed with high and compelling performance accuracy of 97.30% which could be prioritized as the potential and promising prediction route for the development of novel oral antiviral medicine for COVID-19 drugs.
Forecasting DTI stands as a pivotal aspect of drug discovery. The focus on computational methods in DTI prediction has intensified due to the considerable expense and time investment associated with conducting extensive in vitro and in vivo experiments. Machine learning (ML) techniques particularly deep learning have found broad applications in DTI prediction. We are convinced that this study could be prioritized and utilized as the promising predictive route for the development of novel oral antiviral treatments for COVID-19 and other variants of coronaviruses.
This study addressed the problem of COVID-19 drugs using proposed RGCN with multi-features as an attractive and potential route. This study focused mainly on the prediction of novel antiviral drugs against coronaviruses using graph-based methodology namely RGCN. This research further utilized the features of both drugs and common potential drug targets found in betacoronaviruses group to deepen understanding of their underlying relation.
Our suggested approach prevailed with a high and convincing performance accuracy of 97.30% which may be utilized as a top priority to support and advance this field in the prediction and development of novel antiviral treatments against coronaviruses and their variants.
We recursively performed experiments using the proposed method on our constructed DCC-CvT graph dataset from our collected dataset with various single and multiple combinations of features and found that our model had achieved comparable best-averaged accuracy performance on T7 features followed by a combination of T7 R6 and L8. The proposed model implemented in this investigation turns out to outperform the previous related works.
Research on the Mechanism of Traditional Chinese Medicine Treatment for Diseases caused by Human Coronavirus COVID-19
Human coronaviruses are a large group of viruses that exist widely in nature and multiply through self-replication. Due to its suddenness and variability it poses a great threat to global human health and is a major problem currently faced by the medical and health fields.
COVID-19 is the seventh known coronavirus that can infect humans. The main purpose of this paper is to analyze the effective components and action targets of the Longyi Zhengqi formula and Lianhua Qingwen formula study their mechanism of action in the treatment of new coronavirus pneumonia (new coronavirus pneumonia) compare the similarities and differences of their pharmacological effects and obtain the pharmacodynamic mechanism of the two traditional Chinese medicine compounds.
Obtain the effective ingredients and targets of Longyi-Zhengqi Formula and Lianhua-Qingwen Formula from ETCM (Encyclopedia of Traditional Chinese Medicine) and other traditional Chinese medicine databases use GeneCards database to obtain the relevant targets of COVID-19 and use Cytoscape software to build the component COVID-19 target network of Longyi-Zhengqi Formula and the component COVID-19 target network of Lianhua-Qingwen Formula. STRING was used to construct a protein interaction network and screen key targets. GO (Gene Ontology) was used for enrichment analysis and KEGG (Kyoto Encyclopedia of Genes and Genomes) was used for pathways to find out the targets and pathways related to the treatment of COVID-19.
In the GO enrichment analysis results there are 106 biological processes 31 cell localization and 28 molecular functions of the intersection PPI network targets of Longyi-Zhengqi Formula-COVID-19 224 biological processes 51 cell localization and 55 molecular functions of the intersection PPI network targets of Lianhua-Qingwen Formula-COVID-19. In the KEGG pathway analysis results the number of targets of Longyi-Zhengqi Formula on the COVID-19 pathway is 7 and the number of targets of Lianhua-Qingwen Formula on the COVID-19 pathway is 19; In the regulation analysis results Longyi-Zhengqi Formula achieves the effect of treating COVID-19 by regulating IL-6 and Lianhua-Qingwen Formula achieves the effect of treating pneumonia by regulating TLR4.
This paper explores the mechanism of action of Longyi-Zhengqi Formula and Lianhua-Qingwen Formula in treating COVID-19 based on the method of network pharmacology and provides a theoretical basis for traditional Chinese medicine to treat sudden diseases caused by human coronavirus in terms of drug targets and disease interactions. It has certain practical significance.
DiffSeqMol: A Non-Autoregressive Diffusion-Based Approach for Molecular Sequence Generation and Optimization
The application of deep generative models for molecular discovery has witnessed a significant surge in recent years. Currently the field of molecular generation and molecular optimization is predominantly governed by autoregressive models regardless of how molecular data is represented. However an emerging paradigm in the generation domain is diffusion models which treat data non-autoregressively and have achieved significant breakthroughs in areas such as image generation.
The potential and capability of diffusion models in molecular generation and optimization tasks remain largely unexplored. In order to investigate the potential applicability of diffusion models in the domain of molecular exploration we proposed DiffSeqMol a molecular sequence generation model underpinned by diffusion process.
DiffSeqMol distinguishes itself from traditional autoregressive methods by its capacity to draw samples from random noise and direct generating the entire molecule. Through experiment evaluations we demonstrated that DiffSeqMol can achieve even surpass the performance of established state-of-the-art models on unconditional generation tasks and molecular optimization tasks.
Taken together our results show that DiffSeqMol can be considered a promising molecular generation method. It opens new pathways to traverse the expansive chemical space and to discover novel molecules.
A Novel Machine-learning Model to Classify Schizophrenia Using Methylation Data Based on Gene Expression
The recent advancement in artificial intelligence has compelled medical research to adapt the technologies. The abundance of molecular data and AI technology has helped in explaining various diseases even cancers. Schizophrenia is a complex neuropsychological disease whose etiology is unknown. Several gene-wide association studies attempted to narrow down the cause of the disease but did not successfully point out the mechanism behind the disease. There are studies regarding the epigenetic changes in the schizophrenia disease condition and a classification machine-learning model has been trained using the blood methylation data.
In this study we have demonstrated a novel approach to elucidating the molecular cause of the disease. We used a two-step machine-learning approach to determine the causal molecular markers. By doing so we developed classification models using both gene expression microarray and methylation microarray data.
Our models because of our novel approach achieved good classification accuracy with the available data size. We analyzed the important features and they add up as evidence for the glutamate hypothesis of schizophrenia.
In this way we have demonstrated explaining a disease through machine learning models.
An Effective Method to Identify Cooperation Driver Gene Sets
In cancer genomics research identifying driver genes is a challenging task. Detecting cancer-driver genes can further our understanding of cancer risk factors and promote the development of personalized treatments. Gene mutations show mutual exclusivity and co-occur and most of the existing methods focus on identifying driver pathways or driver gene sets through the study of mutual exclusivity that is functionally redundant gene sets. Moreover less research on cooperation genes with co-occurring mutations has been conducted.
We propose an effective method that combines the two characteristics of genes co-occurring mutations and the coordinated regulation of proliferation genes to explore cooperation driver genes.
This study is divided into three stages: (1) constructing a binary gene mutation matrix; (2) combining mutation co-occurrence characteristics to identify the candidate cooperation gene sets; and (3) constructing a gene regulation network to screen the cooperation gene sets that perform synergistically regulating proliferation.
The method performance is evaluated on three TCGA cancer datasets and the experiments showed that it can detect effective cooperation driver gene sets. In further investigations it was determined that the discovered set of co-driver genes could be used to generate prognostic classifications which could be biologically significant and provide complementary information to the cancer genome.
Our approach is effective in identifying sets of cancer cooperation driver genes and the results can be used as clinical markers to stratify patients.
Echinochrome-A Attenuates Arterial Thrombosis Complications in the Liver and Kidney in Rats
Arterial thrombosis represents the most commonly feared consequence of cardiovascular disease and a leading cause of death globally. Cardiovascular disease liver and kidney are closely linked conditions and disease in one organ can lead to dysfunction in the other.
The current research aims to examine the therapeutic impact of Ech-A on arterial thrombosis induced by FeCl3 complications on liver and kidney function.
Twenty-four rats were assigned into four sham groups (n= 6) and thrombotic model groups were orally administered 2% DMSO while the other groups were treated with two dosages of Ech-A (1 and 10 mg/kg body weight). After seven days of administration the left common carotid arteries of all groups were exposed to 50% ferric chloride for 10 min except those of the sham group rats exposed to normal saline.
The oral administration of Ech-A caused a significant increase in partial thromboplastin time prothrombin time glutathione catalase nitric oxide and glutathione S-transferase. While aspartate aminotransferase alkaline phosphatase and alanine aminotransferase activities as well as creatinine uric acid urea and malondialdehyde concentrations were significantly decreased (p< 0.05). The histological examination revealed a definite improvement in the liver and kidney tissues in the Ech-A groups.
The current investigation revealed that arterial thrombosis induced by FeCl3 in rats causes complications in the kidneys and liver. Additionally it demonstrates the beneficial impact of Ech-A on coagulation parameters and liver and kidney function. Despite this the current study has few limitations. Firstly the molecular mechanism regarding the protective effect of Ech-A on liver and kidney complications caused by arterial thrombosis has not been investigated. Secondly no reference drug has been utilised to compare with Ech-A.
Influence of Chromium(VI) on the Environment and Metabolic Processes in the Body
Cr(VI) is a heavy metal characterized by potent toxic carcinogenic mutagenic and prooxidant properties. Cr(VI) is one of the eight metals that are among the most toxic compounds and are of great concern to scientists due to the global risk to human health. In recent years Cr(VI) has attracted the attention of environmental researchers due to the increased level of ecosystem contamination by Cr compounds in many countries. The toxic and carcinogenic effects of Cr(VI) in cells of living organisms are realized through the activation of three main mechanisms: oxidative stress direct damage to cellular DNA and disruption of epigenetic mechanisms of genome regulation. The review brings together updated data on the main mechanisms of Cr(VI) toxicity and the protective role of antioxidants in cells of living organisms poisoned by the corresponding heavy metal. The review also summarizes the currently available information on the negative impact of Cr(VI) compounds on the environment and Cr(VI)-induced disorders of pro/antioxidant status hematological profile and lipid and protein metabolism.
Integrating Deep Learning and Molecular Dynamics to Identify GPR17 Ligands for Glioblastoma Therapy
Guanine Protein-coupled Receptor 17 (GPR17) plays pivotal roles in various physiological processes and diseases. However the discovery of ligands binding to GPR17 remains an active area of research.
In this study we utilized our recently published GPCR-specific deep learning approach molecular docking and molecular dynamics simulations. Specifically the DeepGPCR model employing graph convolutional networks was used to screen the extensive ZINC database for potential ligands.
This computational pipeline identified three highly promising lead compounds ZINC000044404209 ZINC000229938097 and ZINC000005158963. Molecular dynamics simulations confirmed the stability of the protein-ligand complexes while binding free energy calculations highlighted the crucial molecular forces stabilizing these interactions. Notably ZINC000229938097 exhibited particularly favorable binding energy values among the compounds assessed.
Our study underscores the efficacy of computational methodologies in identifying potential drug candidates targeting GPR17. Understanding the molecular mechanisms underlying GPR17 activation holds significant promise for developing tailored therapies for Glioblastoma Multiforme.