RESUMO
Aim: This study investigates the altered expression and CpG methylation patterns of histone demethylase KDM8 in hepatocellular carcinoma (HCC), aiming to uncover insights and promising diagnostics biomarkers. Materials & methods: Leveraging TCGA-LIHC multi-omics data, we employed R/Bioconductor libraries and Cytoscape to analyze and construct a gene correlation network, and LASSO regression to develop an HCC-predictive model. Results: In HCC, KDM8 downregulation is correlated with CpGs hypermethylation. Differential gene correlation analysis unveiled a liver carcinoma-associated network marked by increased cell division and compromised liver-specific functions. The LASSO regression identified a highly accurate HCC prediction signature, prominently featuring CpG methylation at cg02871891. Conclusion: Our study uncovers CpG hypermethylation at cg02871891, possibly influencing KDM8 downregulation in HCC, suggesting these as promising biomarkers and targets.
Changes in gene function can play a role in causing cancer. In this study, we looked at how a specific gene called KDM8 behaves in liver cancer. By analyzing a large set of liver cancer samples, we investigated how gene interactions are different in this disease and if they can help predict liver cancer risk. Our results show that the KDM8 gene is less active, and its DNA gets chemically modified more often in liver cancer. We also found a group of genes and DNA changes, which are linked to the disease. Using this information, we identified 16 important markers and built a computer model that can accurately predict liver cancer. We found that DNA methylation at a specific spot called cg02871891 is especially important for predicting liver cancer. Overall, our study suggests that high levels of DNA methylation may lead to reduced KDM8 activity in liver cancer, which could be important for future research and better diagnostic tools.
RESUMO
Osteosarcoma (OS) is a common bone malignancy in children and adolescents. Although histological subtyping followed by improved OS treatment regimens have helped achieve favorable outcomes, a lack of understanding of the molecular subtypes remains a challenge to characterize its genetic heterogeneity and subsequently to identify diagnostic and prognostic biomarkers for developing effective treatments. In the present study, global analysis of DNA methylation, and mRNA and miRNA gene expression in OS patient samples were correlated with their clinical characteristics. The mucin family of genes, MUC6, MUC12, and MUC4, were found to be highly mutated in the OS patients. Results revealed the enrichment of molecular pathways including Wnt signaling, Calcium signaling, and PI3K-Akt signaling in the OS tumors. Survival analyses showed that the expression levels of several genes such as RAMP1, CRIP1, CORT, CHST13, and DDX60L, miRNAs and lncRNAs were associated with survival of OS patients. Molecular subtyping using Cluster-Of-Clusters Analysis (COCA) for mRNA, lncRNA, and miRNA expression; DNA methylation; and mutation data from the TARGET dataset revealed two distinct molecular subtypes, each with a distinctive gene expression profile. Between the two subtypes, three upregulated genes, POP4, HEY1, CERKL, and seven downregulated genes, CEACAM1, ABLIM1, LTBP2, ISLR, LRRC32, PTPRF, and GPX3, associated with OS metastasis were found to be differentially regulated. Thus, the molecular subtyping results provide a strong basis for classification of OS patients that could be used to develop better prognostic treatment strategies.
RESUMO
Kinases are a group of intracellular signaling molecules that play critical roles in various biological processes. Even though kinases comprise one of the most well-known therapeutic targets, many have been understudied and therefore warrant further investigation. DNA methylation is one of the key epigenetic regulators that modulate gene expression. In this study, the human kinome's DNA methylation and gene expression patterns were analyzed using the level-3 TCGA data for 32 cancers. Unsupervised clustering based on kinome data revealed the grouping of cancers based on their organ level and tissue type. We further observed significant differences in overall kinase methylation levels (hyper- and hypomethylation) between the tumor and adjacent normal samples from the same tissue. Methylation expression quantitative trait loci (meQTL) analysis using kinase gene expression with the corresponding methylated probes revealed a highly significant and mostly negative association (~92%) within 1.5 kb from the transcription start site (TSS). Several understudied (dark) kinases (PKMYT1, PNCK, BRSK2, ERN2, STK31, STK32A, and MAPK4) were also identified with a significant role in patient survival. This study leverages results from multi-omics data to identify potential kinase markers of prognostic and diagnostic importance and further our understanding of kinases in cancer.
Assuntos
Enzima de Conversão de Angiotensina 2/metabolismo , Bromelaínas/farmacologia , Tratamento Farmacológico da COVID-19 , COVID-19 , SARS-CoV-2/metabolismo , Serina Endopeptidases/metabolismo , Glicoproteína da Espícula de Coronavírus/metabolismo , Animais , COVID-19/metabolismo , COVID-19/patologia , Chlorocebus aethiops , Humanos , Células VeroRESUMO
BACKGROUND: Cholangiocarcinoma (CCA) is a rare disease, but it is amongst the most lethal cancers with a median survival under 1 year. Variations in DNA methylation and gene expression have been extensively studied in other cancers for their role in pathogenesis and disease prognosis, but these studies are very limited in CCA. This study focusses on the identification of DNA methylation and gene expression prognostic biomarkers using multi-omics data of CCA tumors from The Cancer Genome Atlas (TCGA). METHOD: We have conducted a genome-wide analysis of differential DNA methylation and gene/miRNA expression using data from 36 CCA tumor and 9 normal samples from TCGA. The impact of DNA methylation in promoters and long-range distal enhancers on the regulation and expression of CCA-associated genes was examined using linear regression. Next, we conducted network analyses on genes which are regulated by DNA methylation as well as by miRNA. Finally, we performed Kaplan-Meier and Cox proportional hazards regression analyses in order to identify the role of selected methylation sites and specific genes and miRNAs in patient survival. We also performed real-time quantitative PCR (qPCR) to confirm the change in gene expression in CCA patients' tumor and adjacent normal samples. RESULTS: Altered DNA methylation was observed on 12,259 CpGs across all chromosomes, of which 78% were hypermethylated. We observed a strong negative relationship between promoter hypermethylation and corresponding gene expression in 92% of the CpGs. Differential expression analyses revealed altered expression patterns in 3,305 genes and 101 miRNAs. Finally, we identified 17 differentially methylated promoter CpGs, 72 differentially expressed genes, and two miRNAs that are likely associated with patient survival. Pathway analysis suggested that cell division, bile secretion, amino acid metabolism, PPAR signaling, hippo signaling were highly affected by gene expression and DNA methylation alterations. The qPCR analysis further confirmed that MDK, HNF1B, PACS1, and GLUD1 are differentially expressed in CCA. CONCLUSION: Based on the survival analysis, we conclude that DEPDC1, FUT4, MDK, PACS1, PIWIL4 genes, miR-22, miR-551b microRNAs, and cg27362525 and cg26597242 CpGs can strongly support their use as prognostic markers of CCA.
RESUMO
Coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). The initial interaction between Transmembrane Serine Protease 2 (TMPRSS2) primed SARS-CoV-2 spike (S) protein and host cell receptor angiotensin-converting enzyme 2 (ACE-2) is a pre-requisite step for this novel coronavirus pathogenesis. Here, we expressed a GFP-tagged SARS-CoV-2 S-Ectodomain in Tni insect cells. That contained sialic acid-enriched N- and O-glycans. Surface resonance plasmon (SPR) and Luminex assay showed that the purified S-Ectodomain binding to human ACE-2 and immunoreactivity with COVID-19 positive samples. We demonstrate that bromelain (isolated from pineapple stem and used as a dietary supplement) treatment diminishes the expression of ACE-2 and TMPRSS2 in VeroE6 cells and dramatically lowers the expression of S-Ectodomain. Importantly, bromelain treatment reduced the interaction between S-Ectodomain and VeroE6 cells. Most importantly, bromelain treatment significantly diminished the SARS-CoV-2 infection in VeroE6 cells. Altogether, our results suggest that bromelain or bromelain rich pineapple stem may be used as an antiviral against COVID-19. HIGHLIGHTS: Bromelain inhibits / cleaves the expression of ACE-2 and TMPRSS2Bromelain cleaves / degrades SARS-CoV-2 spike proteinBromelain inhibits S-Ectodomain binding and SARS-CoV-2 infection.
RESUMO
Pancreatic ductal adenocarcinoma (PDAC) is the most common and among the deadliest of pancreatic cancers. Its 5-year survival is only â¼8%. Pancreatic cancers are a heterogeneous group of diseases, of which PDAC is particularly aggressive. Like many other cancers, PDAC also starts as a pre-invasive precursor lesion (known as pancreatic intraepithelial neoplasia, PanIN), which offers an opportunity for both early detection and early treatment. Even advanced PDAC can benefit from prognostic biomarkers. However, reliable biomarkers for early diagnosis or those for prognosis of therapy remain an unfulfilled goal for PDAC. In this study, we selected 153 PDAC patients from the TCGA database and used their clinical, DNA methylation, gene expression, and micro-RNA (miRNA) and long non-coding RNA (lncRNA) expression data for multi-omics analysis. Differential methylations at about 12,000 CpG sites were observed in PDAC tumor genomes, with about 61% of them hypermethylated, predominantly in the promoter regions and in CpG-islands. We correlated promoter methylation and gene expression for mRNAs and identified 17 genes that were previously recognized as PDAC biomarkers. Similarly, several genes (B3GNT3, DMBT1, DEPDC1B) and lncRNAs (PVT1, and GATA6-AS) are strongly correlated with survival, which have not been reported in PDAC before. Other genes such as EFR3B, whose biological roles are not well known in mammals are also found to strongly associated with survival. We further identified 406 promoter methylation target loci associated with patients survival, including known esophageal squamous cell carcinoma biomarkers, cg03234186 (ZNF154), and cg02587316, cg18630667, and cg05020604 (ZNF382). Overall, this is one of the first studies that identified survival associated genes using multi-omics data from PDAC patients.
RESUMO
The etiology of cerebral palsy (CP) is complex and remains inadequately understood. Early detection of CP is an important clinical objective as this improves long term outcomes. We performed genome-wide DNA methylation analysis to identify epigenomic predictors of CP in newborns and to investigate disease pathogenesis. Methylation analysis of newborn blood DNA using an Illumina HumanMethylation450K array was performed in 23 CP cases and 21 unaffected controls. There were 230 significantly differentially-methylated CpG loci in 258 genes. Each locus had at least 2.0-fold change in methylation in CP versus controls with a FDR p-value ≤ 0.05. Methylation level for each CpG locus had an area under the receiver operating curve (AUC) ≥ 0.75 for CP detection. Using Artificial Intelligence (AI) platforms/Machine Learning (ML) analysis, CpG methylation levels in a combination of 230 significantly differentially-methylated CpG loci in 258 genes had a 95% sensitivity and 94.4% specificity for newborn prediction of CP. Using pathway analysis, multiple canonical pathways plausibly linked to neuronal function were over-represented. Altered biological processes and functions included: neuromotor damage, malformation of major brain structures, brain growth, neuroprotection, neuronal development and de-differentiation, and cranial sensory neuron development. In conclusion, blood leucocyte epigenetic changes analyzed using AI/ML techniques appeared to accurately predict CP and provided plausible mechanistic information on CP pathogenesis.
Assuntos
Inteligência Artificial , Ácidos Nucleicos Livres , Paralisia Cerebral/genética , Aprendizado Profundo , Epigênese Genética , Estudos de Casos e Controles , Paralisia Cerebral/sangue , Paralisia Cerebral/metabolismo , Ilhas de CpG , Metilação de DNA , Epigenômica/métodos , Perfilação da Expressão Gênica , Redes Reguladoras de Genes , Humanos , Recém-Nascido , Curva ROCRESUMO
Pancreatic cancer (PC) is the fourth leading cause of cancer deaths in the United States with a five-year patient survival rate of only 6%. Early detection and treatment of this disease is hampered due to lack of reliable diagnostic and prognostic markers. Recent studies have shown that dynamic changes in the global DNA methylation and gene expression patterns play key roles in the PC development; hence, provide valuable insights for better understanding the initiation and progression of PC. In the current study, we used DNA methylation, gene expression, copy number, mutational and clinical data from pancreatic patients. We independently investigated the DNA methylation and differential gene expression profiles between normal and tumor samples and correlated methylation levels with gene expression patterns. We observed a total of ~23-thousand differentially methylated CpG sites (Δß≥0.1) between normal and tumor samples, where majority of the CpG sites are hypermethylated in PC, and this phenomenon is more prominent in the 5'UTRs and promoter regions compared to the gene bodies. Differential methylation is observed in genes associated with the homeobox domain, cell division and differentiation, cytoskeleton, epigenetic regulation and development, pancreatic development and pancreatic signaling and pancreatic cancer core signaling pathways. Correlation analysis suggests that methylation in the promoter region and 5'UTR has mostly negative correlations with gene expression while gene body and 3'UTR associated methylation has positive correlations. Regulatory element analysis suggests that HOX cluster and histone core proteins are upstream regulators of hypomethylation, while SMAD4, STAT4, STAT5B and zinc finger proteins (ZNF) are upstream regulators of hypermethylation. Non-negative matrix factorization (NMF) clustering of differentially methylated sites generated three clusters in PCs suggesting the existence of distinct molecular subtypes. Cluster 1 and cluster 2 showed samples enriched with clinical phenotypes like neoplasm histological grade and pathologic T-stage T3, respectively, while cluster 3 showed the enrichment of samples with neoplasm histological grade G1. To the best of our knowledge, this is the first genome-scale methylome analysis of PC data from TCGA. Our clustering analysis provides a strong basis for future work on the molecular subtyping of epigenetic regulation in pancreatic cancer.
Assuntos
Ilhas de CpG/genética , Metilação de DNA , Epigênese Genética , Regulação Neoplásica da Expressão Gênica , Neoplasias Pancreáticas/genética , Neoplasias Pancreáticas/patologia , Regiões 3' não Traduzidas/genética , Regiões 5' não Traduzidas/genética , Análise por Conglomerados , Variações do Número de Cópias de DNA , Proteínas de Ligação a DNA/metabolismo , Genes Homeobox/genética , Genoma Humano , Histonas/genética , Histonas/metabolismo , Proteínas de Homeodomínio/genética , Proteínas de Homeodomínio/metabolismo , Humanos , Análise de Sequência com Séries de Oligonucleotídeos , Regiões Promotoras Genéticas/genética , Fator de Transcrição STAT4/metabolismo , Fator de Transcrição STAT5/metabolismo , Proteína Smad4/metabolismoRESUMO
BACKGROUND: Mannose binding proteins (MBPs) play a vital role in several biological functions such as defense mechanisms. These proteins bind to mannose on the surface of a wide range of pathogens and help in eliminating these pathogens from our body. Thus, it is important to identify mannose interacting residues (MIRs) in order to understand mechanism of recognition of pathogens by MBPs. RESULTS: This paper describes modules developed for predicting MIRs in a protein. Support vector machine (SVM) based models have been developed on 120 mannose binding protein chains, where no two chains have more than 25% sequence similarity. SVM models were developed on two types of datasets: 1) main dataset consists of 1029 mannose interacting and 1029 non-interacting residues, 2) realistic dataset consists of 1029 mannose interacting and 10320 non-interacting residues. In this study, firstly, we developed standard modules using binary and PSSM profile of patterns and got maximum MCC around 0.32. Secondly, we developed SVM modules using composition profile of patterns and achieved maximum MCC around 0.74 with accuracy 86.64% on main dataset. Thirdly, we developed a model on a realistic dataset and achieved maximum MCC of 0.62 with accuracy 93.08%. Based on this study, a standalone program and web server have been developed for predicting mannose interacting residues in proteins (http://www.imtech.res.in/raghava/premier/). CONCLUSIONS: Compositional analysis of mannose interacting and non-interacting residues shows that certain types of residues are preferred in mannose interaction. It was also observed that residues around mannose interacting residues have a preference for certain types of residues. Composition of patterns/peptide/segment has been used for predicting MIRs and achieved reasonable high accuracy. It is possible that this novel strategy may be effective to predict other types of interacting residues. This study will be useful in annotating the function of protein as well as in understanding the role of mannose in the immune system.
Assuntos
Biologia Computacional/métodos , Lectina de Ligação a Manose/química , Lectina de Ligação a Manose/metabolismo , Manose/metabolismo , Sequência de Aminoácidos , Bases de Dados de Proteínas , Evolução Molecular , Internet , Lectina de Ligação a Manose/genética , Anotação de Sequência Molecular , Dados de Sequência Molecular , Máquina de Vetores de SuporteRESUMO
INTRODUCTION: Drug development is a time-consuming and cost-intensive process. On average, it takes around 12 - 15 years and approximately 800 billion to bring a new drug to the market. Despite introduction of combinatorial chemistry and establishment of high-throughput screening (HTS), the number of new drug entities is limited. In fact, a number of established drug entities have been withdrawn from the market because of drug-drug interactions (DDIs) and adverse drug reactions (ADRs). AREAS COVERED: This review covers the advancements in cytochrome P450 (CYP450) modeling using different computational/machine learning (ML) tools over the past decade. A computational model for identifying non-toxic drug molecule from the pool of small chemical molecules is always welcome in the drug industry. Any computational tool that identifies the toxic molecule at early stage reduces the economic burden by slashing the number of molecules to be screened. This review covers all issues related to CYP-mediated toxicity such as specificity, inhibition, induction and regioselectivity. EXPERT OPINION: Several computational methods for CYP-mediated toxicity are available, which are popular in computer-aided drug designing (CADD). These models may become helpful in toxicity prediction during early stages and can reduce high failure rates in preclinical and clinical trials. There is an urgent need to improve the accuracy, interpretability and confidence of the computation models used in drug discovery pathways.
Assuntos
Simulação por Computador , Sistema Enzimático do Citocromo P-450/química , Sistema Enzimático do Citocromo P-450/metabolismo , Algoritmos , Animais , Inteligência Artificial , Desenho de Fármacos , Avaliação Pré-Clínica de Medicamentos/métodos , Interações Medicamentosas , Efeitos Colaterais e Reações Adversas Relacionados a Medicamentos/genética , Efeitos Colaterais e Reações Adversas Relacionados a Medicamentos/metabolismo , Determinação de Ponto Final , Humanos , Modelos Animais , Modelos Químicos , Preparações Farmacêuticas/química , Preparações Farmacêuticas/metabolismoRESUMO
Glutathione S-transferase (GST) proteins play vital role in living organism that includes detoxification of exogenous and endogenous chemicals, survivability during stress condition. This paper describes a method developed for predicting GST proteins. We have used a dataset of 107 GST and 107 non-GST proteins for training and the performance of the method was evaluated with five-fold cross-validation technique. First a SVM based method has been developed using amino acid and dipeptide composition and achieved the maximum accuracy of 91.59% and 95.79% respectively. In addition we developed a SVM based method using tripeptide composition and achieved maximum accuracy 97.66% which is better than accuracy achieved by HMM based searching (96.26%). Based on above study a web-server GSTPred has been developed (http://www.imtech.res.in/raghava/gstpred/).