-
BMC Medical Genomics Mar 2024Mice are routinely utilized as animal models of drug-induced liver injury (DILI), however, there are significant differences in the pathogenesis between mice and humans....
OBJECTIVE
Mice are routinely utilized as animal models of drug-induced liver injury (DILI), however, there are significant differences in the pathogenesis between mice and humans. This study aimed to compare gene expression between humans and mice in acetaminophen (APAP)-induced liver injury (AILI), and investigate the similarities and differences in biological processes between the two species.
METHODS
A pair of public datasets (GSE218879 and GSE120652) obtained from GEO were analyzed using "Limma" package in R language, and differentially expressed genes (DEGs) were identified, including co-expressed DEGs (co-DEGs) and specific-expressed DEGS (specific-DEGs). Analysis of Gene Set Enrichment Analysis (GSEA), Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were performed analyses for specific-DEGs and co-DEGs. The co-DEGs were also used to construct transcription factor (TF)-gene network, gene-miRNA interactions network and protein-protein interaction (PPI) network for analyzing hub genes.
RESULTS
Mouse samples contained 1052 up-regulated genes and 1064 down-regulated genes, while human samples contained 1156 up-regulated genes and 1557 down-regulated genes. After taking the intersection between the DEGs, only 154 co-down-regulated and 89 co-up-regulated DEGs were identified, with a proportion of less than 10%. It was suggested that significant differences in gene expression between mice and humans in drug-induced liver injury. Mouse-specific-DEGs predominantly engaged in processes related to apoptosis and endoplasmic reticulum stress, while human-specific-DEGs were concentrated around catabolic process. Analysis of co-regulated genes reveals showed that they were mainly enriched in biosynthetic and metabolism-related processes. Then a PPI network which contains 189 nodes and 380 edges was constructed from the co-DEGs and two modules were obtained by Mcode. We screened out 10 hub genes by three algorithms of Degree, MCC and MNC, including CYP7A1, LSS, SREBF1, FASN, CD44, SPP1, ITGAV, ANXA5, LGALS3 and PDGFRA. Besides, TFs such as FOXC1, HINFP, NFKB1, miRNAs like mir-744-5p, mir-335-5p, mir-149-3p, mir-218-5p, mir-10a-5p may be the key regulatory factors of hub genes.
CONCLUSIONS
The DEGs of AILI mice models and those of patients were compared, and common biological processes were identified. The signaling pathways and hub genes in co-expression were identified between mice and humans through a series of bioinformatics analyses, which may be more valuable to reveal molecular mechanisms of AILI.
Topics: Humans; Animals; Mice; Acetaminophen; Chemical and Drug Induced Liver Injury, Chronic; Gene Expression Profiling; MicroRNAs; Gene Regulatory Networks; Computational Biology; Gene Expression
PubMed: 38549107
DOI: 10.1186/s12920-024-01848-0 -
Microorganisms Feb 2024Coxsackievirus-A6 (CV-A6) is responsible for more severe dermatological manifestations compared to other enteroviruses such as CV-A10, CV-A16, and EV-A71, causing HFMD...
Coxsackievirus-A6 (CV-A6) is responsible for more severe dermatological manifestations compared to other enteroviruses such as CV-A10, CV-A16, and EV-A71, causing HFMD in children and adults. Between 2005 and 2007, the recombinant subclade D3/RF-A started to expand globally, and a CV-A6 pandemic started. The study aimed to conduct whole-genome sequencing (WGS) of an isolated CV-A6 strain from currently circulating HFMD cases from India in 2022. Gene-specific RT-PCR and sequencing were used to perform molecular characterization of the isolated virus. Confirmation of these isolates was also performed by transmission electron microscopy and WGS. Among eleven positive clinical enterovirus specimens, eight CV-A6 strains were successfully isolated in the RD cell line. Isolates confirmed the presence of the CV-A6 strain based on VP1 and VP2 gene-specific RT-PCR. Sequences of isolates were clustered and identified as the novel CV-A6 strain of the D3/Y sub-genotype in India. The studies revealed that the D3/Y sub-genotype is being introduced into Indian circulation. The predicted putative functional loops found in VP1 of CV-A6 showed that the nucleotide sequences of the amino acid were a remarkably conserved loop prediction compatible with neutralizing linear epitopes. Therefore, this strain represents a potential candidate for vaccine development and antiviral studies.
PubMed: 38543541
DOI: 10.3390/microorganisms12030490 -
Journal of Cellular and Molecular... Apr 2024The present research focused on identifying necroptosis-related differentially expressed genes (NRDEGs) in spinal cord injury (SCI) to highlight potential therapeutic...
The present research focused on identifying necroptosis-related differentially expressed genes (NRDEGs) in spinal cord injury (SCI) to highlight potential therapeutic and prognostic target genes in clinical SCI. Three SCI-related datasets were downloaded, including GSE151371, GSE5296 and GSE47681. MSigDB and KEGG datasets were searched for necroptosis-related genes (NRGs). Differentially expressed genes (DEGs) and NRGs were intersected to obtain NRDEGs. The MCC algorithm was employed to select the first 10 genes as hub genes. A protein-protein interaction (PPI) network related to NRDEGs was developed utilizing STRING. Several databases were searched to predict interactions between hub genes and miRNAs, transcription factors, potential drugs, and small molecules. Immunoassays were performed to identify DEGs using CIBERSORTx. Additionally, qRT-PCR was carried out to verify NRDEGs in an animal model of SCI. Combined analysis of all datasets identified 15 co-expressed DEGs and NRGs. GO and KEGG pathway analyses highlighted DEGs mostly belonged to pathways associated with necroptosis and apoptosis. Hub gene expression analysis showed high accuracy in SCI diagnosis was associated with the expression of CHMP7 and FADD. A total of two hub genes, i.e. CHMP7, FADD, were considered potential targets for SCI therapy.
Topics: Animals; Necroptosis; Computational Biology; Gene Expression Profiling; MicroRNAs; Spinal Cord Injuries
PubMed: 38509743
DOI: 10.1111/jcmm.18219 -
Journal of Thoracic Disease Feb 2024var. (RW) is one of the traditional Chinese medicinal materials, which is used to treat angina pectoris (AP). However, the possible underlying mechanisms remains...
BACKGROUND
var. (RW) is one of the traditional Chinese medicinal materials, which is used to treat angina pectoris (AP). However, the possible underlying mechanisms remains unclear. The aim of this study was to explore RW in the treatment of AP and to identify the potential mechanism of the core compounds.
METHODS
In this study, systematic and comprehensive network pharmacology and molecular docking were used for the first time to explore the potential pharmacological mechanisms of RW on AP. First, the relative compounds were obtained by mining the literature, and potential targets of these compounds using target prediction were collected. We then built the AP target database using the DigSee and GeneCards databases. Based on the data, overlapping targets and hub genes were identified with Maximal Clique Centrality (MCC) algorithm in Cytoscape, cytoHubba. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses and protein-protein interaction (PPI) analysis were performed to screen the hub targets by topology. Molecular docking was utilized to investigate the receptor-ligand interactions on Autodock Vina and visualized in PyMOL.
RESULTS
A total of 218 known RW therapeutic targets were selected. Systematic analysis identified nine hub targets (, , , , , , , and ) mainly involved in the complex treatment effects associated with the protection of the vascular endothelium, as well as the regulation of glucose metabolism, cellular processes, inflammatory responses, and cellular signal transduction. Molecular docking indicated that the core compounds had good affinity with the core targets.
CONCLUSIONS
The results of this study preliminarily identify the potential targets and signaling pathways of RW in AP therapy and lay a promising foundation for further experimental studies and clinical trials.
PubMed: 38505080
DOI: 10.21037/jtd-23-1891 -
Human Cell May 2024Merkel cell carcinoma (MCC) is an aggressive skin cancer, with a propensity for early metastasis. Therefore, early diagnosis and the identification of novel targets...
Merkel cell carcinoma (MCC) is an aggressive skin cancer, with a propensity for early metastasis. Therefore, early diagnosis and the identification of novel targets become fundamental. The enzyme nicotinamide N-methyltransferase (NNMT) catalyzes the reaction of N-methylation of nicotinamide and other analogous compounds. Although NNMT overexpression was reported in many malignancies, the significance of its dysregulation in cancer cell phenotype was partly clarified. Several works demonstrated that NNMT promotes cancer cell proliferation, migration, and chemoresistance. In this study, we investigated the possible involvement of this enzyme in MCC. Preliminary immunohistochemical analyses were performed to evaluate NNMT expression in MCC tissue specimens. To explore the enzyme function in tumor cell metabolism, MCC cell lines have been transfected with plasmids encoding for short hairpin RNAs (shRNAs) targeting NNMT mRNA. Preliminary immunohistochemical analyses showed elevated NNMT expression in MCC tissue specimens. The effect of enzyme downregulation on cell proliferation, migration, and chemosensitivity was then evaluated through MTT, trypan blue, and wound healing assays. Data obtained clearly demonstrated that NNMT knockdown is associated with a decrease of cell proliferation, viability, and migration, as well as with enhanced sensitivity to treatment with chemotherapeutic drugs. Taken together, these results suggest that NNMT could represent an interesting MCC biomarker and a promising target for targeted anti-cancer therapy.
Topics: Humans; Nicotinamide N-Methyltransferase; Carcinoma, Merkel Cell; Drug Resistance, Neoplasm; Cell Proliferation; Skin Neoplasms; RNA, Small Interfering
PubMed: 38504052
DOI: 10.1007/s13577-024-01047-0 -
Health Data Science 2024Gemcitabine is a first-line chemotherapy for pancreatic adenocarcinoma (PAAD), but many PAAD patients do not respond to gemcitabine-containing treatments. Being able to...
Gemcitabine is a first-line chemotherapy for pancreatic adenocarcinoma (PAAD), but many PAAD patients do not respond to gemcitabine-containing treatments. Being able to predict such nonresponders would hence permit the undelayed administration of more promising treatments while sparing gemcitabine life-threatening side effects for those patients. Unfortunately, the few predictors of PAAD patient response to this drug are weak, none of them exploiting yet the power of machine learning (ML). Here, we applied ML to predict the response of PAAD patients to gemcitabine from the molecular profiles of their tumors. More concretely, we collected diverse molecular profiles of PAAD patient tumors along with the corresponding clinical data (gemcitabine responses and clinical features) from the Genomic Data Commons resource. From systematically combining 8 tumor profiles with 16 classification algorithms, each of the resulting 128 ML models was evaluated by multiple 10-fold cross-validations. Only 7 of these 128 models were predictive, which underlines the importance of carrying out such a large-scale analysis to avoid missing the most predictive models. These were here random forest using 4 selected mRNAs [0.44 Matthews correlation coefficient (MCC), 0.785 receiver operating characteristic-area under the curve (ROC-AUC)] and XGBoost combining 12 DNA methylation probes (0.32 MCC, 0.697 ROC-AUC). By contrast, the hENT1 marker obtained much worse random-level performance (practically 0 MCC, 0.5 ROC-AUC). Despite not being trained to predict prognosis (overall and progression-free survival), these ML models were also able to anticipate this patient outcome. We release these promising ML models so that they can be evaluated prospectively on other gemcitabine-treated PAAD patients.
PubMed: 38486621
DOI: 10.34133/hds.0108 -
Nature Communications Mar 2024Developing clinically predictive model systems for evaluating gene transfer and gene editing technologies has become increasingly important in the era of personalized...
Developing clinically predictive model systems for evaluating gene transfer and gene editing technologies has become increasingly important in the era of personalized medicine. Liver-directed gene therapies present a unique challenge due to the complexity of the human liver. In this work, we describe the application of whole human liver explants in an ex situ normothermic perfusion system to evaluate a set of fourteen natural and bioengineered adeno-associated viral (AAV) vectors directly in human liver, in the presence and absence of neutralizing human sera. Under non-neutralizing conditions, the recently developed AAV variants, AAV-SYD12 and AAV-LK03, emerged as the most functional variants in terms of cellular uptake and transgene expression. However, when assessed in the presence of human plasma containing anti-AAV neutralizing antibodies (NAbs), vectors of human origin, specifically those derived from AAV2/AAV3b, were extensively neutralized, whereas AAV8- derived variants performed efficiently. This study demonstrates the potential of using normothermic liver perfusion as a model for early-stage testing of liver-focused gene therapies. The results offer preliminary insights that could help inform the development of more effective translational strategies.
Topics: Humans; Genetic Vectors; Dependovirus; Antibodies, Neutralizing; Liver; Perfusion
PubMed: 38485924
DOI: 10.1038/s41467-024-46194-y -
Mathematical Biosciences and... Jan 2024Lung adenocarcinoma, a chronic non-small cell lung cancer, needs to be detected early. Tumor gene expression data analysis is effective for early detection, yet its...
Lung adenocarcinoma, a chronic non-small cell lung cancer, needs to be detected early. Tumor gene expression data analysis is effective for early detection, yet its challenges lie in a small sample size, high dimensionality, and multi-noise characteristics. In this study, we propose a lung adenocarcinoma convolutional neural network (LATCNN), a deep learning model tailored for accurate lung adenocarcinoma prediction and identification of key genes. During the feature selection stage, we introduce a hybrid algorithm. Initially, the fast correlation-based filter (FCBF) algorithm swiftly filters out irrelevant features, followed by applying the k-means-synthetic minority over-sampling technique (k-means-SMOTE) method to address category imbalance. Subsequently, we enhance the particle swarm optimization (PSO) algorithm by incorporating fast-decay dynamic inertia weights and utilizing the classification and regression tree (CART) as the fitness function for the second stage of feature selection, aiming to further eliminate redundant features. In the classifier construction stage, we present an attention convolutional neural network (atCNN) that incorporates an attention mechanism. This improved model conducts feature selection post lung adenocarcinoma gene expression data analysis for classification and prediction. The results show that LATCNN effectively reduces the feature dimensions and accurately identifies 12 key genes with accuracy, recall, F1 score, and MCC of 99.70%, 99.33%, 99.98%, and 98.67%, respectively. These performance metrics surpass those of other comparative models, highlighting the significance of this research for advancing lung adenocarcinoma treatment.
Topics: Humans; Carcinoma, Non-Small-Cell Lung; Lung Neoplasms; Neural Networks, Computer; Adenocarcinoma of Lung; Algorithms
PubMed: 38454716
DOI: 10.3934/mbe.2024133 -
BMC Genomics Mar 20245-Methylcytosine (5mC) plays a very important role in gene stability, transcription, and development. Therefore, accurate identification of the 5mC site is of key...
BACKGROUND
5-Methylcytosine (5mC) plays a very important role in gene stability, transcription, and development. Therefore, accurate identification of the 5mC site is of key importance in genetic and pathological studies. However, traditional experimental methods for identifying 5mC sites are time-consuming and costly, so there is an urgent need to develop computational methods to automatically detect and identify these 5mC sites.
RESULTS
Deep learning methods have shown great potential in the field of 5mC sites, so we developed a deep learning combinatorial model called i5mC-DCGA. The model innovatively uses the Convolutional Block Attention Module (CBAM) to improve the Dense Convolutional Network (DenseNet), which is improved to extract advanced local feature information. Subsequently, we combined a Bidirectional Gated Recurrent Unit (BiGRU) and a Self-Attention mechanism to extract global feature information. Our model can learn feature representations of abstract and complex from simple sequence coding, while having the ability to solve the sample imbalance problem in benchmark datasets. The experimental results show that the i5mC-DCGA model achieves 97.02%, 96.52%, 96.58% and 85.58% in sensitivity (Sn), specificity (Sp), accuracy (Acc) and matthews correlation coefficient (MCC), respectively.
CONCLUSIONS
The i5mC-DCGA model outperforms other existing prediction tools in predicting 5mC sites, and it is currently the most representative promoter 5mC site prediction tool. The benchmark dataset and source code for the i5mC-DCGA model can be found in https://github.com/leirufeng/i5mC-DCGA .
Topics: 5-Methylcytosine; Benchmarking; Promoter Regions, Genetic; Research Design; Software
PubMed: 38443802
DOI: 10.1186/s12864-024-10154-z -
Cancers Feb 2024Merkel cell carcinoma (MCC) and small cell lung cancer (SCLC) can be histologically similar. Immunohistochemistry (IHC) for cytokeratin 20 (CK20) and thyroid...
Merkel cell carcinoma (MCC) and small cell lung cancer (SCLC) can be histologically similar. Immunohistochemistry (IHC) for cytokeratin 20 (CK20) and thyroid transcription factor 1 (TTF-1) are commonly used to differentiate MCC from SCLC; however, these markers have limited sensitivity and specificity. To identify new diagnostic markers, we performed differential gene expression analysis on transcriptome data from MCC and SCLC tumors. Candidate markers included atonal BHLH transcription factor 1 (ATOH1) and transcription factor AP-2β (TFAP2B) for MCC, as well as carcinoembryonic antigen cell adhesion molecule 6 (CEACAM6) for SCLC. Immunostaining for CK20, TTF-1, and new candidate markers was performed on 43 MCC and 59 SCLC samples. All three MCC markers were sensitive and specific, with CK20 and ATOH1 staining 43/43 (100%) MCC and 0/59 (0%) SCLC cases and TFAP2B staining 40/43 (93%) MCC and 0/59 (0%) SCLC cases. TTF-1 stained 47/59 (80%) SCLC and 1/43 (2%) MCC cases. CEACAM6 stained 49/59 (83%) SCLC and 0/43 (0%) MCC cases. Combining CEACAM6 and TTF-1 increased SCLC detection sensitivity to 93% and specificity to 98%. These data suggest that ATOH1, TFAP2B, and CEACAM6 should be explored as markers to differentiate MCC and SCLC.
PubMed: 38398178
DOI: 10.3390/cancers16040788