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MBio Jun 2024Merkel cell polyomavirus (MCPyV) is a double-stranded tumor virus that is the main causative agent of Merkel cell carcinoma (MCC). The MCPyV large T antigen (LT), an...
UNLABELLED
Merkel cell polyomavirus (MCPyV) is a double-stranded tumor virus that is the main causative agent of Merkel cell carcinoma (MCC). The MCPyV large T antigen (LT), an essential viral DNA replication protein, maintains viral persistence by interacting with host Skp1-Cullin 1-F-box (SCF) E3 ubiquitin ligase complexes, which subsequently induces LT's proteasomal degradation, restricting MCPyV DNA replication. SCF E3 ubiquitin ligases require their substrates to be phosphorylated to bind them, utilizing phosphorylated serine residues as docking sites. The MCPyV LT unique region (MUR) is highly phosphorylated and plays a role in multiple host protein interactions, including SCF E3 ubiquitin ligases. Therefore, this domain highly governs LT stability. Though much work has been conducted to identify host factors that restrict MCPyV LT protein expression, the kinase(s) that cooperates with the SCF E3 ligase remains unknown. Here, we demonstrate that casein kinase 1 alpha (CK1α) negatively regulates MCPyV LT stability and LT-mediated replication by modulating interactions with the SCF β-TrCP. Specifically, we show that numerous CK1 isoforms (α, δ, ε) localize in close proximity to MCPyV LT through proximity ligation assays (PLA) and CK1α overexpression mainly resulted in decreased MCPyV LT protein expression. Inhibition of CK1α using short hairpin RNA (shRNA) and treatment of a CK1α inhibitor or an mTOR inhibitor, TORKinib, resulted in decreased β-TrCP interaction with LT, increased LT expression, and enhanced MCPyV replication. The expression level of the gene transcripts is higher in MCPyV-positive MCC, suggesting a vital role of CK1α in limiting MCPyV replication required for establishing persistent infection.
IMPORTANCE
Merkel cell polyomavirus (MCPyV) large tumor antigen is a polyphosphoprotein and the phosphorylation event is required to modulate various functions of LT, including viral replication. Therefore, cellular kinase pathways are indispensable for governing MCPyV polyomavirus infection and life cycle in coordinating with the immunosuppression environment at disease onset. Understanding the regulation mechanisms of MCPyV replication by viral and cellular factors will guide proper prevention strategies with targeted inhibitors for MCPyV-associated Merkel cell carcinoma (MCC) patients, who currently lack therapies.
PubMed: 38940554
DOI: 10.1128/mbio.01117-24 -
Journal of Global Antimicrobial... Jun 2024Herein, we combined different bioinformatics tools and databases (BV-BRC, ResFinder, RAST, and KmerResistance) to perform a prediction of antimicrobial resistance (AMR)...
Herein, we combined different bioinformatics tools and databases (BV-BRC, ResFinder, RAST, and KmerResistance) to perform a prediction of antimicrobial resistance (AMR) in the genomic sequences of 107 Corynebacterium striatum isolates for which trustable antimicrobial susceptibility (AST) phenotypes could be retrieved. Then, the reliabilities of the AMR predictions were evaluated by different metrics: area under the ROC curve (AUC); Major Error Rates (MERs) and Very Major Error Rates (VMERs); Matthews Correlation Coefficient (MCC); F1-Score; and Accuracy. Out of 15 genes that were reliably detected in the C. striatum isolates, only tetW yielded predictive values for tetracycline resistance that were acceptable considering Food and Drug Administration (FDA)'s criteria for quality (MER < 3.0% and VMER with a 95% C.I. ≤1.5-≤7.5%); this was accompanied by a MCC score higher than 0.9 for this gene. Noteworthy, our results indicate that other commonly used metrics (AUC, F1-score, and Accuracy) may render overoptimistic evaluations of AMR-prediction reliabilities on imbalanced datasets. Accordingly, out of 10 genes tested by PCR on additional multidrug-resistant Corynebacterium spp. isolates (n = 18), the tetW gene rendered the best agreement values with AST profiles (94.11%). Overall, our results indicate that genome-based AMR prediction can still be challenging for MDR clinical isolates of emerging Corynebacterium spp.
PubMed: 38936471
DOI: 10.1016/j.jgar.2024.06.006 -
Genes May 2024Protein-DNA complex interactivity plays a crucial role in biological activities such as gene expression, modification, replication and transcription. Understanding the...
Protein-DNA complex interactivity plays a crucial role in biological activities such as gene expression, modification, replication and transcription. Understanding the physiological significance of protein-DNA binding interfacial hot spots, as well as the development of computational biology, depends on the precise identification of these regions. In this paper, a hot spot prediction method called EC-PDH is proposed. First, we extracted features of these hot spots' solid solvent-accessible surface area (ASA) and secondary structure, and then the mean, variance, energy and autocorrelation function values of the first three intrinsic modal components (IMFs) of these conventional features were extracted as new features via the empirical modal decomposition algorithm (EMD). A total of 218 dimensional features were obtained. For feature selection, we used the maximum correlation minimum redundancy sequence forward selection method (mRMR-SFS) to obtain an optimal 11-dimensional-feature subset. To address the issue of data imbalance, we used the SMOTE-Tomek algorithm to balance positive and negative samples and finally used cat gradient boosting (CatBoost) to construct our hot spot prediction model for protein-DNA binding interfaces. Our method performs well on the test set, with AUC, MCC and F1 score values of 0.847, 0.543 and 0.772, respectively. After a comparative evaluation, EC-PDH outperforms the existing state-of-the-art methods in identifying hot spots.
Topics: Machine Learning; DNA; Algorithms; Protein Binding; DNA-Binding Proteins; Computational Biology; Binding Sites
PubMed: 38927611
DOI: 10.3390/genes15060676 -
European Neuropsychopharmacology : the... Jun 2024Many individuals with autism spectrum disorder (ASD) experience various degrees of impairment in social interaction and communication, restricted, repetitive behaviours,... (Review)
Review
Many individuals with autism spectrum disorder (ASD) experience various degrees of impairment in social interaction and communication, restricted, repetitive behaviours, interests/activities. These impairments make a significant contribution to poorer everyday adaptive functioning. Yet, there are no pharmacological therapies to effectively treat the core symptoms of ASD. Since symptoms of ASD likely emerge from a complex interplay of vulnerabilities, environmental factors and compensatory mechanisms during the early developmental period, pharmacological interventions arguably would have the greatest impact to improve long-term outcomes when implemented at a young age. It is essential therefore, that clinical development programmes of investigational drugs in ASD include the paediatric population early on in clinical trials. Such trials need to offer the prospect of direct benefit (PDB) for participants. In most cases in drug development this prospect is supported by evidence of efficacy in adults. However, the effectiveness of treatment approaches may be age-dependent, so that clinical trials in adults may not provide sufficient evidence for a PDB in children. In this white paper, we consolidate recommendations from regulatory guidelines, as well as advice from the Food and Drug Administration, USA (FDA) and the Committee for Human Medicinal Products (CHMP) consultations on various development programmes on: 1) elements to support a PDB to participants in early paediatric clinical trials in ASD, including single-gene neurodevelopment disorders, 2) aspects of study design to allow for a PDB. This white paper is intended to be complementary to existing regulatory guidelines in guiding industry and academic sponsors in their conduct of early paediatric clinical trials in ASD.
PubMed: 38917772
DOI: 10.1016/j.euroneuro.2024.05.011 -
Aging Jun 2024The underlying mechanisms of gastric cancer (GC) remain unknown. Therefore, in this study, we employed a comprehensive approach, combining computational and experimental...
INTRODUCTION
The underlying mechanisms of gastric cancer (GC) remain unknown. Therefore, in this study, we employed a comprehensive approach, combining computational and experimental methods, to identify potential key genes and unveil the underlying pathogenesis and prognosis of GC.
METHODS
Gene expression profiles from GEO databases (GSE118916, GSE79973, and GSE29272) were analyzed to identify DEGs between GC and normal tissues. A PPI network was constructed using STRING and Cytoscape, followed by hub gene identification with CytoHubba. Investigations included expression and promoter methylation analysis, survival modeling, mutational and miRNA analysis, gene enrichment, drug prediction, and assays for cellular behaviors.
RESULTS
A total of 83 DEGs were identified in the three datasets, comprising 41 up-regulated genes and 42 down-regulated genes. Utilizing the degree and MCC methods, we identified four hub genes that were hypomethylated and up-regulated: COL1A1, COL1A2, COL3A1, and FN1. Subsequent validation of their expression and promoter methylation on clinical GC samples through targeted bisulfite sequencing and RT-qPCR analysis further confirmed the hypomethylation and overexpression of these genes in local GC patients. Furthermore, it was observed that these hub genes regulate tumor proliferation and metastasis in and exhibited mutations in GC patients.
CONCLUSION
We found four potential diagnostic and prognostic biomarkers, including COL1A1, COL1A2, COL3A1, and FN1 that may be involved in the occurrence and progression of GC.
PubMed: 38913913
DOI: 10.18632/aging.205965 -
Heliyon Jun 2024Early cancer detection and treatment depend on the discovery of specific genes that cause cancer. The classification of genetic mutations was initially done manually....
Early cancer detection and treatment depend on the discovery of specific genes that cause cancer. The classification of genetic mutations was initially done manually. However, this process relies on pathologists and can be a time-consuming task. Therefore, to improve the precision of clinical interpretation, researchers have developed computational algorithms that leverage next-generation sequencing technologies for automated mutation analysis. This paper utilized four deep learning classification models with training collections of biomedical texts. These models comprise bidirectional encoder representations from transformers for Biomedical text mining (BioBERT), a specialized language model implemented for biological contexts. Impressive results in multiple tasks, including text classification, language inference, and question answering, can be obtained by simply adding an extra layer to the BioBERT model. Moreover, bidirectional encoder representations from transformers (BERT), long short-term memory (LSTM), and bidirectional LSTM (BiLSTM) have been leveraged to produce very good results in categorizing genetic mutations based on textual evidence. The dataset used in the work was created by Memorial Sloan Kettering Cancer Center (MSKCC), which contains several mutations. Furthermore, this dataset poses a major classification challenge in the Kaggle research prediction competitions. In carrying out the work, three challenges were identified: enormous text length, biased representation of the data, and repeated data instances. Based on the commonly used evaluation metrics, the experimental results show that the BioBERT model outperforms other models with an F1 score of 0.87 and 0.850 MCC, which can be considered as improved performance compared to similar results in the literature that have an F1 score of 0.70 achieved with the BERT model.
PubMed: 38912449
DOI: 10.1016/j.heliyon.2024.e32279 -
Identification of key genes as potential diagnostic biomarkers in sepsis by bioinformatics analysis.PeerJ 2024Sepsis, an infection-triggered inflammatory syndrome, poses a global clinical challenge with limited therapeutic options. Our study is designed to identify potential...
BACKGROUND
Sepsis, an infection-triggered inflammatory syndrome, poses a global clinical challenge with limited therapeutic options. Our study is designed to identify potential diagnostic biomarkers of sepsis onset in critically ill patients by bioinformatics analysis.
METHODS
Gene expression profiles of GSE28750 and GSE74224 were obtained from the Gene Expression Omnibus (GEO) database. These datasets were merged, normalized and de-batched. Weighted gene co-expression network analysis (WGCNA) was performed and the gene modules most associated with sepsis were identified as key modules. Functional enrichment analysis of the key module genes was then conducted. Moreover, differentially expressed gene (DEG) analysis was conducted by the "limma" R package. Protein-protein interaction (PPI) network was created using STRING and Cytoscape, and PPI hub genes were identified with the cytoHubba plugin. The PPI hub genes overlapping with the genes in key modules of WGCNA were determined to be the sepsis-related key genes. Subsequently, the key overlapping genes were validated in an external independent dataset and sepsis patients recruited in our hospital. In addition, CIBERSORT analysis evaluated immune cell infiltration and its correlation with key genes.
RESULTS
By WGCNA, the greenyellow module showed the highest positive correlation with sepsis (0.7, = 2 - 19). 293 DEGs were identified in the merged datasets. The PPI network was created, and the CytoHubba was used to calculate the top 20 genes based on four algorithms (Degree, EPC, MCC, and MNC). Ultimately, LTF, LCN2, ELANE, MPO and CEACAM8 were identified as key overlapping genes as they appeared in the PPI hub genes and the key module genes of WGCNA. These sepsis-related key genes were validated in an independent external dataset (GSE131761) and sepsis patients recruited in our hospital. Additionally, the immune infiltration profiles differed significantly between sepsis and non-sepsis critical illness groups. Correlations between immune cells and these five key genes were assessed, revealing that plasma cells, macrophages M0, monocytes, T cells regulatory, eosinophils and NK cells resting were simultaneously and significantly associated with more than two key genes.
CONCLUSION
This study suggests a critical role of LTF, LCN2, ELANE, MPO and CEACAM8 in sepsis and may provide potential diagnostic biomarkers and therapeutic targets for the treatment of sepsis.
Topics: Humans; Sepsis; Computational Biology; Biomarkers; Protein Interaction Maps; Gene Expression Profiling; Gene Regulatory Networks; Databases, Genetic
PubMed: 38912048
DOI: 10.7717/peerj.17542 -
BMC Gastroenterology Jun 2024Hepatocellular carcinoma (HCC) is one of the most common cancers worldwide. Hepatitis B virus (HBV) is one of the major causes of liver cirrhosis (LC) and HCC....
BACKGROUND
Hepatocellular carcinoma (HCC) is one of the most common cancers worldwide. Hepatitis B virus (HBV) is one of the major causes of liver cirrhosis (LC) and HCC. Therefore, the discovery of common markers for hepatitis B or LC and HCC is crucial for the prevention of HCC.
METHODS
Expressed genes for to chronic active hepaititis B (CAH-B), LC and HCC were obtained from the GEO and TCGA databases, and co-expressed genes were screened using Protein-protein interaction (PPI) networks, least absolute shrinkage and selection operator (LASSO), random forest (RF) and support vector machine - recursive feature elimination (SVM-RFE). The prognostic value of genes was assessed using Kaplan-Meier (KM) survival curves. Columnar line plots, calibration curves and receiver operating characteristic (ROC) curves of individual genes were used for evaluation. Validation was performed using GEO datasets. The association of these key genes with HCC clinical features was explored using the UALCAN database ( https://ualcan.path.uab.edu/index.html ).
RESULTS
Based on WGCNA analysis and TCGA database, the co-expressed genes (565) were screened. Moreover, the five algorithms of MCODE (ClusteringCoefficient, MCC, Degree, MNC, and DMNC) was used to select one of the most important and most closely linked clusters (the top 50 genes ranked). Using, LASSO regression model, RF model and SVM-RFE model, four key genes (UBE2T, KIF4A, CDCA3, and CDCA5) were identified for subsequent research analysis. These 4 genes were highly expressed and associated with poor prognosis and clinical features in HCC patients.
CONCLUSION
These four key genes (UBE2T, KIF4A, CDCA3, and CDCA5) may be common biomarkers for CAH-B and HCC or LC and HCC, promising to advance our understanding of the molecular basis of CAH-B/LC/HCC progression.
Topics: Carcinoma, Hepatocellular; Liver Neoplasms; Humans; Kinesins; Liver Cirrhosis; Computational Biology; Cell Cycle Proteins; Prognosis; Hepatitis B, Chronic; Biomarkers, Tumor; Protein Interaction Maps; Male; Kaplan-Meier Estimate; Support Vector Machine
PubMed: 38890649
DOI: 10.1186/s12876-024-03288-7 -
Annals of Human Biology Feb 2024Mitophagy and ferroptosis occur in intracerebral haemorrhage (ICH) but our understanding of mitophagy and ferroptosis-related genes remains incomplete.
BACKGROUND
Mitophagy and ferroptosis occur in intracerebral haemorrhage (ICH) but our understanding of mitophagy and ferroptosis-related genes remains incomplete.
AIM
This study aims to identify shared ICH genes for both processes.
METHODS
ICH differentially expressed mitophagy and ferroptosis-related genes (DEMFRGs) were sourced from the GEO database and literature. Enrichment analysis elucidated functions. Hub genes were selected via STRING, MCODE, and MCC algorithms in Cytoscape. miRNAs targeting hubs were predicted using miRWalk 3.0, forming a miRNA-hub gene network. Immune microenvironment variances were assessed with MCP and TIMER. Potential small molecules for ICH were forecasted CMap database.
RESULTS
64 DEMFRGs and ten hub genes potentially involved in various processes like ferroptosis, TNF signalling pathway, MAPK signalling pathway, and NF-kappa B signalling pathway were discovered. Several miRNAs were identified as shared targets of hub genes. The ICH group showed increased infiltration of monocytic lineage and myeloid dendritic cells compared to the Healthy group. Ten potential small molecule drugs (e.g. Zebularine, TWS-119, CG-930) were predicted CMap.
CONCLUSION
Several shared genes between mitophagy and ferroptosis potentially drive ICH progression TNF, MAPK, and NF-kappa B pathways. These results offer valuable insights for further exploring the connection between mitophagy, ferroptosis, and ICH.
Topics: Mitophagy; Ferroptosis; Cerebral Hemorrhage; Humans; Computational Biology; MicroRNAs; Gene Regulatory Networks
PubMed: 38863372
DOI: 10.1080/03014460.2024.2334719 -
China CDC Weekly May 2024Coxsackievirus A6 (CVA6) has emerged as a significant pathogen responsible for severe cases of hand, foot, and mouth disease (HFMD). This study aims to delineate the...
INTRODUCTION
Coxsackievirus A6 (CVA6) has emerged as a significant pathogen responsible for severe cases of hand, foot, and mouth disease (HFMD). This study aims to delineate the demographic characteristics and analyze the viral evolution of severe HFMD associated with CVA6, thereby assisting in its surveillance and management.
METHODS
In this investigation, 74 strains of CVA6 were isolated from samples collected from severe HFMD cases between 2012 and 2023. The gene sequences of CVA6 were amplified and analyzed to assess population historical dynamics and evolutionary characteristics using BEAST, DnaSP6, and PopART.
RESULTS
A significant portion (94.4%) of severe CVA6-associated HFMD cases (51 out of 54, with 20 lacking age information) were children under 5 years old. Among the 74 CVA6 strains analyzed, 72 belonged to the D3a sub-genotype, while only two strains were D2 sub-genotype. The average genetic distance between sequences prior to 2015 was 0.027, which increased to 0.051 when compared to sequences post-2015. Historical population dynamics analysis indicated three significant population expansions of severe CVA6-associated HFMD during 2012-2013, 2013-2014, and 2019-2020, resulting in the formation of 65 distinct haplotypes. Consistent with the MCC tree findings, transitioning between regional haplotypes required multiple base substitutions, showcasing an increase in population diversity during the evolutionary process (from 14 haplotypes in 2013 to 55 haplotypes over the subsequent decade).
CONCLUSIONS
CVA6, associated with severe HFMD, is evolving and presents a risk of outbreak occurrence. Thus, enhanced surveillance of severe HFMD is imperative.
PubMed: 38846357
DOI: 10.46234/ccdcw2024.086