Multi-omics analysis defines 5-fluorouracil drug resistance in 3D HeLa carcinoma cell model
Ontology highlight
ABSTRACT:
SUBMITTER: Wang L
PROVIDER: S-EPMC10991626 | biostudies-literature | 2021 Dec
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:BackgroundGenetic aberrations in hepatocellular carcinoma (HCC) are well known, but the functional consequences of such aberrations remain poorly understood.ResultsHere, we explored the effect of defined genetic changes on the transcriptome, proteome and phosphoproteome in twelve tumors from an mTOR-driven hepatocellular carcinoma mouse model. Using Network-based Integration of multi-omiCS data (NetICS), we detected 74 'mediators' that relay via molecular interactions the effects of genetic and miRNA expression changes. The detected mediators account for the effects of oncogenic mTOR signaling on the transcriptome, proteome and phosphoproteome. We confirmed the dysregulation of the mediators YAP1, GRB2, SIRT1, HDAC4 and LIS1 in human HCC.ConclusionsThis study suggests that targeting pathways such as YAP1 or GRB2 signaling and pathways regulating global histone acetylation could be beneficial in treating HCC with hyperactive mTOR signaling.
| S-EPMC8340535 | biostudies-literature
Project description:Cancer cells often become resistant to chemotherapy, and induction of the ABC transporter Multi-drug Resistance gene-1 (MDR1) is a major cause. We established a tool for high-throughput screening of substrates and inhibitors of MDR1, using transformed HeLa cells that over-express MDR1. The cDNA for human MDR1 was subcloned into the eukaryotic expression vector pBK-CMV to produce an MDR1 expression vector, pBK-CMV/MDR1. HeLa cells were transfected with pBK-CMV/MDR1 or the empty vector pBK-CMV. Transfection of the vector sequence for MDR1 and its expression were evaluated by genomic PCR and western blotting, respectively. The efficiency of the MDR1 transporter for pumping a substrate out of the transformed cells was evaluated using rhodamine123 (R-123), a mitochondrial dye that is also an MDR1 substrate. After treatment of the MDR1-expressing HeLa cells with MDR1 substrate vinblastin or inhibitors cyclosporin A and verapamil, the amount of R-123 retained in the cells was increased to 2 to 2.3 times the level in untreated MDR1-expressing HeLa cells. The transfection of empty pBK-CMV had no effect on the R-123 retention in HeLa cells, regardless of drug treatment. In conclusion, we have established a model human carcinoma cell line that expresses functional MDR1 and can be used to screen for substrates and inhibitors of MDR1.
| S-EPMC4370427 | biostudies-literature
Project description:Epithelial cell transforming 2 (ECT2) is a potential oncogene and a number of recent studies have correlated it with the progression of several human cancers. Despite this elevated attention for ECT2 in oncology-related reports, there is no collective study to combine and integrate the expression and oncogenic behavior of ECT2 in a panel of human cancers. The current study started with a differential expression analysis of ECT2 in cancerous versus normal tissue. Following that, the study asked for the correlation between ECT2 upregulation and tumor stage, grade, and metastasis, along with its effect on patient survival. Moreover, the methylation and phosphorylation status of ECT2 in tumor versus normal tissue was assessed, in addition to the investigation of the ECT2 effect on the immune cell infiltration in the tumor microenvironment. The current study revealed that ECT2 was upregulated as mRNA and protein levels in a list of human tumors, a feature that allowed for the increased filtration of myeloid-derived suppressor cells (MDSC) and decreased the level of natural killer T (NKT) cells, which ultimately led to a poor prognosis survival. Lastly, we screened for several drugs that could inhibit ECT2 and act as antitumor agents. Collectively, this study nominated ECT2 as a prognostic and immunological biomarker, with reported inhibitors that represent potential antitumor drugs.
| S-EPMC10135641 | biostudies-literature
Project description:Obesity is a multifactorial disease with many complications and related diseases and has become a global epidemic. To thoroughly understand the impact of obesity on whole organism homeostasis, it is helpful to utilize a systems biological approach combining gene expression and metabolomics across tissues and biofluids together with metagenomics of gut microbial diversity. Here, we present a multi-omics study on liver, muscle, adipose tissue, urine, plasma, and feces on mice fed a high-fat diet (HFD). Gene expression analyses showed alterations in genes related to lipid and energy metabolism and inflammation in liver and adipose tissue. The integration of metabolomics data across tissues and biofluids identified major differences in liver TCA cycle, where malate, succinate and oxaloacetate were found to be increased in HFD mice. This finding was supported by gene expression analysis of TCA-related enzymes in liver, where expression of malate dehydrogenase was found to be decreased. Investigations of the microbiome showed enrichment of Lachnospiraceae, Ruminococcaceae, Streptococcaceae and Lactobacillaceae in the HFD group. Our findings help elucidate how the whole organism metabolome and transcriptome are integrated and regulated during obesity.
| S-EPMC7143281 | biostudies-literature
Project description:Modern oncology offers a wide range of treatments and therefore choosing the best option for particular patient is very important for optimal outcome. Multi-omics profiling in combination with AI-based predictive models have great potential for streamlining these treatment decisions. However, these encouraging developments continue to be hampered by very high dimensionality of the datasets in combination with insufficiently large numbers of annotated samples. Here we proposed a novel deep learning-based method to predict patient-specific anticancer drug response from three types of multi-omics data. The proposed DeepInsight-3D approach relies on structured data-to-image conversion that then allows use of convolutional neural networks, which are particularly robust to high dimensionality of the inputs while retaining capabilities to model highly complex relationships between variables. Of particular note, we demonstrate that in this formalism additional channels of an image can be effectively used to accommodate data from different omics layers while implicitly encoding the connection between them. DeepInsight-3D was able to outperform other state-of-the-art methods applied to this task. The proposed improvements can facilitate the development of better personalized treatment strategies for different cancers in the future.
| S-EPMC9922304 | biostudies-literature
Project description:Pheochromocytomas and paragangliomas (PCCs/PGLs) are neural crest-derived tumours with a very strong genetic component. Here we report the first integrated genomic examination of a large collection of PCC/PGL. SNP array analysis reveals distinct copy-number patterns associated with genetic background. Whole-exome sequencing shows a low mutation rate of 0.3 mutations per megabase, with few recurrent somatic mutations in genes not previously associated with PCC/PGL. DNA methylation arrays and miRNA sequencing identify DNA methylation changes and miRNA expression clusters strongly associated with messenger RNA expression profiling. Overexpression of the miRNA cluster 182/96/183 is specific in SDHB-mutated tumours and induces malignant traits, whereas silencing of the imprinted DLK1-MEG3 miRNA cluster appears as a potential driver in a subgroup of sporadic tumours. Altogether, the complete genomic landscape of PCC/PGL is mainly driven by distinct germline and/or somatic mutations in susceptibility genes and reveals different molecular entities, characterized by a set of unique genomic alterations.
| S-EPMC4354166 | biostudies-literature
Project description:Emerging studies demonstrate that inflammation plays a crucial role in the pathogenesis of bipolar disorder (BD), but the underlying mechanism remains largely unclear. Given the complexity of BD pathogenesis, we performed high-throughput multi-omic profiling (metabolomics, lipidomics, and transcriptomics) of the BD zebrafish brain to comprehensively unravel the molecular mechanism. Our research proved that in BD zebrafish, JNK-mediated neuroinflammation altered metabolic pathways involved in neurotransmission. On one hand, disturbed metabolism of tryptophan and tyrosine limited the participation of the monoamine neurotransmitters serotonin and dopamine in synaptic vesicle recycling. On the other hand, dysregulated metabolism of the membrane lipids sphingomyelin and glycerophospholipids altered the synaptic membrane structure and neurotransmitter receptors (chrnα7, htr1b, drd5b, and gabra1) activity. Our findings revealed that disturbance of serotonergic and dopaminergic synaptic transmission mediated by the JNK inflammatory cascade was the key pathogenic mechanism in a zebrafish model of BD, provides critical biological insights into the pathogenesis of BD.
| S-EPMC10189518 | biostudies-literature
Project description:17 samples (7 chemorefractory and 10 chemosensitive) of DLBLC patients were analyzed by Ampliseq whole transcriptome assay (Thermofisher).
2018-03-29 | E-MTAB-6597 | biostudies-arrayexpress
Project description:Comprehensive molecular characterization of cancer subtypes is essential for predicting clinical outcomes and searching for personalized treatments. We present bnClustOmics, a statistical model and computational tool for multi-omics unsupervised clustering, which serves a dual purpose: Clustering patient samples based on a Bayesian network mixture model and learning the networks of omics variables representing these clusters. The discovered networks encode interactions among all omics variables and provide a molecular characterization of each patient subgroup. We conducted simulation studies that demonstrated the advantages of our approach compared to other clustering methods in the case where the generative model is a mixture of Bayesian networks. We applied bnClustOmics to a hepatocellular carcinoma (HCC) dataset comprising genome (mutation and copy number), transcriptome, proteome, and phosphoproteome data. We identified three main HCC subtypes together with molecular characteristics, some of which are associated with survival even when adjusting for the clinical stage. Cluster-specific networks shed light on the links between genotypes and molecular phenotypes of samples within their respective clusters and suggest targets for personalized treatments.
| S-EPMC9481159 | biostudies-literature
Project description:BackgroundWith the expansion of animal production, parasitic helminths are gaining increasing economic importance. However, application of several established deworming agents can harm treated hosts and environment due to their low specificity. Furthermore, the number of parasite strains showing resistance is growing, while hardly any new anthelminthics are being developed. Here, we present a bioinformatics workflow designed to reduce the time and cost in the development of new strategies against parasites. The workflow includes quantitative transcriptomics and proteomics, 3D structure modeling, binding site prediction, and virtual ligand screening. Its use is demonstrated for Acanthocephala (thorny-headed worms) which are an emerging pest in fish aquaculture. We included three acanthocephalans (Pomphorhynchus laevis, Neoechinorhynchus agilis, Neoechinorhynchus buttnerae) from four fish species (common barbel, European eel, thinlip mullet, tambaqui).ResultsThe workflow led to eleven highly specific candidate targets in acanthocephalans. The candidate targets showed constant and elevated transcript abundances across definitive and accidental hosts, suggestive of constitutive expression and functional importance. Hence, the impairment of the corresponding proteins should enable specific and effective killing of acanthocephalans. Candidate targets were also highly abundant in the acanthocephalan body wall, through which these gutless parasites take up nutrients. Thus, the candidate targets are likely to be accessible to compounds that are orally administered to fish. Virtual ligand screening led to ten compounds, of which five appeared to be especially promising according to ADMET, GHS, and RO5 criteria: tadalafil, pranazepide, piketoprofen, heliomycin, and the nematicide derquantel.ConclusionsThe combination of genomics, transcriptomics, and proteomics led to a broadly applicable procedure for the cost- and time-saving identification of candidate target proteins in parasites. The ligands predicted to bind can now be further evaluated for their suitability in the control of acanthocephalans. The workflow has been deposited at the Galaxy workflow server under the URL tinyurl.com/yx72rda7 .
| S-EPMC9523657 | biostudies-literature