Similar Datasets
Project description:Multi-omics comparisons of different forms of centronuclear myopathies and the effects of several therapeutic strategies
| PRJNA671751 | ENA
Project description:Research strategies that combine molecular data from multiple levels of genome expression (i.e., multi-omics data), often referred to as a systems biology strategy, has been advocated as a route to discovering gene functions. In this study we conducted an evaluation of this strategy by combining lipidomics, metabolite mass-spectral imaging and transcriptomics data from leaves and roots in response to mutations in two AuTophaGy-related (ATG) genes of Arabidopsis. Autophagy is an essential cellular process that degrades and recycles macromolecules and organelles, and this process is blocked in the atg7 and atg9 mutants that were the focus of this study. Specifically, we quantified abundances of ~100 lipids and imaged the cellular locations of ~15 lipid molecular species and the relative abundance of ~26,000 transcripts from leaf and root tissues of WT, atg7 and atg9 mutant plants, grown either in normal (nitrogen-replete) and autophagy-inducing conditions (nitrogen-deficient). The multi-omics data enabled detailed molecular depiction of the effect of each mutation, and a comprehensive physiological model to explain the consequence of these genetic and environmental changes in autophagy is greatly facilitated by the a priori knowledge of the exact biochemical function of the ATG7 and ATG9 proteins.
| S-EPMC9978356 | biostudies-literature
Project description: Not available
| S-EPMC10325664 | biostudies-literature
Project description:Genetically matched human iPSCs from different origins were generated using bone marrow stromal cells and dermal fibroblasts of the same donor, and the global gene expression profile were analyzed.
2013-01-01 | GSE41202 | GEO
Project description:Lohmann Brown (LB) and Lohmann Selected Leghorn (LSL) are two commercially important laying hen strains due to their high egg production and excellent commercial suitability. The present study integrated multiple data sets along the genotype-phenotype map to better understand how the genetic background of the two strains influences their molecular pathways. In total, 71 individuals were analyzed (LB, n = 36; LSL, n = 35). Data sets include gut miRNA and mRNA transcriptome data, microbiota composition, immune cells, inositol phosphate metabolites, minerals, and hormones from different organs of the two hen strains. All complex data sets were pre-processed, normalized, and compatible with the mixOmics platform. The most discriminant features between two laying strains included 20 miRNAs, 20 mRNAs, 16 immune cells, 10 microbes, 11 phenotypic traits, and 16 metabolites. The expression of specific miRNAs and the abundance of immune cell types were related to the enrichment of immune pathways in the LSL strain. In contrast, more microbial taxa specific to the LB strain were identified, and the abundance of certain microbes strongly correlated with host gut transcripts enriched in immunological and metabolic pathways. Our findings indicate that both strains employ distinct inherent strategies to acquire and maintain their immune and metabolic systems under high-performance conditions. In addition, the study provides a new perspective on a view of the functional biodiversity that emerges during strain selection and contributes to the understanding of the role of host-gut interaction, including immune phenotype, microbiota, gut transcriptome, and metabolome.
| S-EPMC9010826 | biostudies-literature
Project description:Background In the last few years, multi-omics data, that is, datasets containing different types of high-dimensional molecular variables for the same samples, have become increasingly available. To date, several comparison studies focused on feature selection methods for omics data, but to our knowledge, none compared these methods for the special case of multi-omics data. Given that these data have specific structures that differentiate them from single-omics data, it is unclear whether different feature selection strategies may be optimal for such data. In this paper, using 15 cancer multi-omics datasets we compared four filter methods, two embedded methods, and two wrapper methods with respect to their performance in the prediction of a binary outcome in several situations that may affect the prediction results. As classifiers, we used support vector machines and random forests. The methods were compared using repeated fivefold cross-validation. The accuracy, the AUC, and the Brier score served as performance metrics. Results The results suggested that, first, the chosen number of selected features affects the predictive performance for many feature selection methods but not all. Second, whether the features were selected by data type or from all data types concurrently did not considerably affect the predictive performance, but for some methods, concurrent selection took more time. Third, regardless of which performance measure was considered, the feature selection methods mRMR, the permutation importance of random forests, and the Lasso tended to outperform the other considered methods. Here, mRMR and the permutation importance of random forests already delivered strong predictive performance when considering only a few selected features. Finally, the wrapper methods were computationally much more expensive than the filter and embedded methods. Conclusions We recommend the permutation importance of random forests and the filter method mRMR for feature selection using multi-omics data, where, however, mRMR is considerably more computationally costly. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-022-04962-x.
| S-EPMC9533501 | biostudies-literature
Project description:In gliomas, casein kinase 2 (CK2) plays a dominant role in cell survival and tumour invasiveness and is upregulated in many brain tumours. Among CK2 inhibitors, benzimidazole and isothiourea derivatives hold a dominant position. While targeting glioma tumour cells, they show limited toxicity towards normal cells. Research in recent years has shown that these compounds can be suitable as components of combined therapies with hyperbaric oxygenation. Such a combination increases the susceptibility of glioma tumour cells to cell death via apoptosis. Moreover, researchers planning on using any other antiglioma investigational pharmaceutics may want to consider using these agents in combination with CK2 inhibitors. However, different compounds are not equally effective when in such combination. More research is needed to elucidate the mechanism of treatment and optimize the treatment regimen. In addition, the role of CK2 in gliomagenesis and maintenance seems to have been challenged recently, as some compounds structurally similar to CK2 inhibitors do not inhibit CK2 while still being effective at reducing glioma viability and invasion. Furthermore, some newly developed inhibitors specific for CK2 do not appear to have strong anticancer properties. Further experimental and clinical studies of these inhibitors and combined therapies are warranted.
| S-EPMC8877581 | biostudies-literature
Project description:Adult patients with metabolic myopathies typically present with exercise-induced pain, cramps, fatigue, and myoglobinuria. The current therapeutic options of glycogen and lipid storage myopathies include dietary treatments, excersise training, and pharmacological supplementations. Herein is a review of evidence from randomized controlled trials in McArdle disease (glycogen storage disease type V, muscle phosphorylase deficiency) and carnitine palmitoyltransferase (CPT) 2 deficiency. A brief overview on current treatment options in rhabdomyolysis is also included because patients with McArdle disease and CPT 2 often experience such potentially life-threatening complications.
| S-EPMC4514707 | biostudies-other
Project description:We characterize different tumour types in search for multi-tumour drug targets, in particular aiming for drug repurposing and novel drug combinations. Starting from 11 tumour types from The Cancer Genome Atlas, we obtain three clusters based on transcriptomic correlation profiles. A network-based analysis, integrating gene expression profiles and protein interactions of cancer-related genes, allows us to define three cluster-specific signatures, with genes belonging to NF-κB signaling, chromosomal instability, ubiquitin-proteasome system, DNA metabolism, and apoptosis biological processes. These signatures have been characterized by different approaches based on mutational, pharmacological and clinical evidences, demonstrating the validity of our selection. Moreover, we define new pharmacological strategies validated by in vitro experiments that show inhibition of cell growth in two tumour cell lines, with significant synergistic effect. Our study thus provides a list of genes and pathways that could possibly be used, singularly or in combination, for the design of novel treatment strategies.
| S-EPMC6207774 | biostudies-literature
Project description: Not available
| S-EPMC9363465 | biostudies-literature