Browse
Submit Data
Databases
API
Help

Dataset Information

10 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Drosophila RASopathy models identify disease subtype differences and biomarkers of drug efficacy.

ABSTRACT: RASopathies represent a family of mostly autosomal dominant diseases that are caused by missense variants in the rat sarcoma viral oncogene/mitogen activated protein kinase (RAS/MAPK) pathway including KRAS, NRAS, BRAF, RAF1, and SHP2. These variants are associated with overlapping but distinct phenotypes that affect the heart, craniofacial, skeletal, lymphatic, and nervous systems. Here, we report an analysis of 13 Drosophila transgenic lines, each expressing a different human RASopathy isoform. Similar to their human counterparts, each Drosophila line displayed common aspects but also important differences including distinct signaling pathways such as the Hippo and SAPK/JNK signaling networks. We identified multiple classes of clinically relevant drugs-including statins and histone deacetylase inhibitors-that improved viability across most RASopathy lines; in contrast, several canonical RAS pathway inhibitors proved less broadly effective. Overall, our study compares and contrasts a large number of RASopathy-associated variants including their therapeutic responses.

SUBMITTER: Das TK

PROVIDER: S-EPMC8026909 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Drosophila Cancer Models Identify Functional Differences between Ret Fusions.

Project description:We generated and compared Drosophila models of RET fusions CCDC6-RET and NCOA4-RET. Both RET fusions directed cells to migrate, delaminate, and undergo EMT, and both resulted in lethality when broadly expressed. In all phenotypes examined, NCOA4-RET was more severe than CCDC6-RET, mirroring their effects on patients. A functional screen against the Drosophila kinome and a library of cancer drugs found that CCDC6-RET and NCOA4-RET acted through different signaling networks and displayed distinct drug sensitivities. Combining data from the kinome and drug screens identified the WEE1 inhibitor AZD1775 plus the multi-kinase inhibitor sorafenib as a synergistic drug combination that is specific for NCOA4-RET. Our work emphasizes the importance of identifying and tailoring a patient's treatment to their specific RET fusion isoform and identifies a multi-targeted therapy that may prove effective against tumors containing the NCOA4-RET fusion.

| S-EPMC5858711 | biostudies-literature

Using FlyAtlas to identify better Drosophila models of human disease

Project description:A new online resource (flyatlas.org) provides the most comprehensive view yet of expression in multiple tissues of Drosophila. Meta-analysis of the data reveals that a significant fraction ofthe genome is expressed with great tissue specificity in the adult, demonstrating the need for the functional genomic community to embrace a wider range of functional phenotypes. Well-known developmental genes are often re-used in surprising tissues in the adult, suggesting new functions. The homologues of many human genetic disease loci show selective expression in Drosophila tissues with function analogous to the affected tissues in the cognate human disease, providing a useful filter for potential candidate genes.

2007-06-21 | GSE7763 | GEO

A standardised framework to identify optimal animal models for efficacy assessment in drug development.

Project description:IntroductionPoor translation of efficacy data derived from animal models can lead to clinical trials unlikely to benefit patients-or even put them at risk-and is a potential contributor to costly and unnecessary attrition in drug development.ObjectivesTo develop a tool to assess, validate and compare the clinical translatability of animal models used for the preliminary assessment of efficacy.Design and resultsWe performed a scoping review to identify the key aspects used to validate animal models. Eight domains (Epidemiology, Symptomatology and Natural History-SNH, Genetic, Biochemistry, Aetiology, Histology, Pharmacology and Endpoints) were identified. We drafted questions to evaluate the different facets of human disease simulation. We designed the Framework to Identify Models of Disease (FIMD) to include standardised instructions, a weighting and scoring system to compare models as well as factors to help interpret model similarity and evidence uncertainty. We also added a reporting quality and risk of bias assessment of drug intervention studies in the Pharmacological Validation domain. A web-based survey was conducted with experts from different stakeholders to gather input on the framework. We conducted a pilot study of the validation in two models for Type 2 Diabetes (T2D)-the ZDF rat and db/db mouse. Finally, we present a full validation and comparison of two animal models for Duchenne Muscular Dystrophy (DMD): the mdx mouse and GRMD dog. We show that there are significant differences between the mdx mouse and the GRMD dog, the latter mimicking the human epidemiological, SNH, and histological aspects to a greater extent than the mouse despite the overall lack of published data.ConclusionsFIMD facilitates drug development by serving as the basis to select the most relevant model that can provide meaningful data and is more likely to generate translatable results to progress drug candidates to the clinic.

| S-EPMC6563989 | biostudies-literature

Are subtype differences important in HIV drug resistance?

Project description:The diversity of human immunodeficiency virus type 1 (HIV-1) has given rise to multiple subtypes and recombinant strains. The majority of research into antiretroviral agents and drug resistance has been performed on subtype B viruses, yet non-subtype B strains are responsible for 90% of global infections. Although it seems that combination antiretroviral regimens are effective against all HIV-1 subtypes, there is emerging evidence of subtype differences in drug resistance, relevant to antiretroviral strategies in different parts of the world. For this purpose, extensive sampling of HIV genetic diversity, curation and analyses are required to inform antiretroviral strategies in different parts of the world.

| S-EPMC3951383 | biostudies-literature

Machine learning models identify ferroptosis-related genes as potential diagnostic biomarkers for Alzheimer's disease.

Project description:Alzheimer's disease (AD) is a complex, and multifactorial neurodegenerative disease. Previous studies have revealed that oxidative stress, synaptic toxicity, autophagy, and neuroinflammation play crucial roles in the progress of AD, however, its pathogenesis is still unclear. Recent researches have indicated that ferroptosis, an iron-dependent programmed cell death, might be involved in the pathogenesis of AD. Therefore, we aim to screen correlative ferroptosis-related genes (FRGs) in the progress of AD to clarify insights into the diagnostic value. Interestingly, we identified eight FRGs were significantly differentially expressed in AD patients. 10,044 differentially expressed genes (DEGs) were finally identified by differential expression analysis. The following step was investigating the function of DEGs using gene set enrichment analysis (GSEA). Weight gene correlation analysis was performed to explore ten modules and 104 hub genes. Subsequently, based on machine learning algorithms, we constructed diagnostic classifiers to select characteristic genes. Through the multivariable logistic regression analysis, five features (RAF1, NFKBIA, MOV10L1, IQGAP1, FOXO1) were then validated, which composed a diagnostic model of AD. Thus, our findings not only developed genetic diagnostics strategy, but set a direction for further study of the disease pathogenesis and therapy targets.

| S-EPMC9575464 | biostudies-literature

Uncovering the subtype-specific disease module and the development of drug response prediction models for glioma

Project description: Not available

| S-EPMC10926146 | biostudies-literature

Aptamer-based detection of disease biomarkers in mouse models for chagas drug discovery.

Project description:Drug discovery initiatives, aimed at Chagas treatment, have been hampered by the lack of standardized drug screening protocols and the absence of simple pre-clinical assays to evaluate treatment efficacy in animal models. In this study, we used a simple Enzyme Linked Aptamer (ELA) assay to detect T. cruzi biomarker in blood and validate murine drug discovery models of Chagas disease. In two mice models, Apt-29 ELA assay demonstrated that biomarker levels were significantly higher in the infected group compared to the control group, and upon Benznidazole treatment, their levels reduced. However, biomarker levels in the infected treated group did not reduce to those seen in the non-infected treated group, with 100% of the mice above the assay cutoff, suggesting that parasitemia was reduced but cure was not achieved. The ELA assay was capable of detecting circulating biomarkers in mice infected with various strains of T. cruzi parasites. Our results showed that the ELA assay could detect residual parasitemia in treated mice by providing an overall picture of the infection in the host. They suggest that the ELA assay can be used in drug discovery applications to assess treatment efficacy in-vivo.

| S-EPMC4287562 | biostudies-literature

Tumor-derived biomarkers predict efficacy of B7H3 antibody-drug conjugate treatment in metastatic prostate cancer models.

Project description:Antibody-drug conjugates (ADCs) are a promising targeted cancer therapy; however, patient selection based solely on target antigen expression without consideration for cytotoxic payload vulnerabilities has plateaued clinical benefits. Biomarkers to capture patients who might benefit from specific ADCs have not been systematically determined for any cancer. We present a comprehensive therapeutic and biomarker analysis of a B7H3-ADC with pyrrolobenzodiazepine(PBD) payload in 26 treatment-resistant, metastatic prostate cancer (mPC) models. B7H3 is a tumor-specific surface protein widely expressed in mPC, and PBD is a DNA cross-linking agent. B7H3 expression was necessary but not sufficient for B7H3-PBD-ADC responsiveness. RB1 deficiency and/or replication stress, characteristics of poor prognosis, and conferred sensitivity were associated with complete tumor regression in both neuroendocrine (NEPC) and androgen receptor positive (ARPC) prostate cancer models, even with low B7H3 levels. Non-ARPC models, which are currently lacking efficacious treatment, demonstrated the highest replication stress and were most sensitive to treatment. In RB1 WT ARPC tumors, SLFN11 expression or select DNA repair mutations in SLFN11 nonexpressors governed response. Importantly, WT TP53 predicted nonresponsiveness (7 of 8 models). Overall, biomarker-focused selection of models led to high efficacy of in vivo treatment. These data enable a paradigm shift to biomarker-driven trial designs for maximizing clinical benefit of ADC therapies.

| S-EPMC10645377 | biostudies-literature

Drosophila Lung Cancer Models Identify Trametinib plus Statin as Candidate Therapeutic.

Project description:We have developed a Drosophila lung cancer model by targeting Ras1(G12V)--alone or in combination with PTEN knockdown--to the Drosophila tracheal system. This led to overproliferation of tracheal tissue, formation of tumor-like growths, and animal lethality. Screening a library of FDA-approved drugs identified several that improved overall animal survival. We explored two hits: the MEK inhibitor trametinib and the HMG-CoA reductase inhibitor fluvastatin. Oral administration of these drugs inhibited Ras and PI3K pathway activity, respectively; in addition, fluvastatin inhibited protein prenylation downstream of HMG-CoA reductase to promote survival. Combining drugs led to synergistic suppression of tumor formation and rescue lethality; similar synergy was observed in human A549 lung adenocarcinoma cells. Notably, fluvastatin acted both within transformed cells and also to reduce whole-body trametinib toxicity in flies. Our work supports and provides further context for exploring the potential of combining statins with MAPK inhibitors such as trametinib to improve overall therapeutic index.

| S-EPMC4904304 | biostudies-literature

Drosophila Models of Sporadic Parkinson's Disease.

Project description:Parkinson's disease (PD) is the most common cause of movement disorders and is characterized by the progressive loss of dopaminergic neurons in the substantia nigra. It is increasingly recognized as a complex group of disorders presenting widely heterogeneous symptoms and pathology. With the exception of the rare monogenic forms, the majority of PD cases result from an interaction between multiple genetic and environmental risk factors. The search for these risk factors and the development of preclinical animal models are in progress, aiming to provide mechanistic insights into the pathogenesis of PD. This review summarizes the studies that capitalize on modeling sporadic (i.e., nonfamilial) PD using Drosophila melanogaster and discusses their methodologies, new findings, and future perspectives.

| S-EPMC6275057 | biostudies-other

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data