Venomics for Therapeutics: Decoding the Pharmacological Web of Giant Asian Honeybee and Greater Banded Hornet Venoms
Ontology highlight
ABSTRACT:
ORGANISM(S): Vespa
SUBMITTER: Dr. M. Murugan
PROVIDER: PXD072298 | JPOST Repository | Mon Dec 22 00:00:00 GMT 2025
REPOSITORIES: jPOST
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| Vespa_Proteomics.xlsx | Xlsx | |||
| Vespa_R1.zip | Other | |||
| Vespa_R2.zip | Other | |||
| Vespa_R3.zip | Other | |||
| Vespa_R4.zip | Other |
Items per page: 5 1 - 5 of 5 |
Similar Datasets
Project description:One challenge of cancer precision medicine is the heterogeneity of genetic and non-genetic alterations that result in dysfunctional molecular pathways. As an emerging drug discovery effort, dysregulation in Hippo pathway signaling is known to drive oncogenesis across numerous cancer types but lacks recurrent mutation(s) that are often found in other canonical signaling pathways. Here, we use first principles approach to develop a machine-learning framework to identify a robust, lineage-independent gene expression signature to quantify Hippo pathway dependency in cancers. Through integrating data from multi-omics platforms, this data-driven approach has enabled identifying a proposed combination with MAPK inhibition for direct targeting of Hippo pathway dependent cancers for which we then elucidate the underlying molecular mechanism. The results underscore how a multifaceted approach, computational models combined with laboratory efforts, can accelerate precision medicine efforts toward co-targeting Hippo and MAPK pathways, an approach that can be generalized to other key cancer signaling pathways to define therapeutic strategies.
2021-01-26 | GSE161018 | GEO
Project description:One challenge of cancer precision medicine is the heterogeneity of genetic and non-genetic alterations that result in dysfunctional molecular pathways. As an emerging drug discovery effort, dysregulation in Hippo pathway signaling is known to drive oncogenesis across numerous cancer types but lacks recurrent mutation(s) that are often found in other canonical signaling pathways. Here, we use first principles approach to develop a machine-learning framework to identify a robust, lineage-independent gene expression signature to quantify Hippo pathway dependency in cancers. Through integrating data from multi-omics platforms, this data-driven approach has enabled identifying a proposed combination with MAPK inhibition for direct targeting of Hippo pathway dependent cancers for which we then elucidate the underlying molecular mechanism. The results underscore how a multifaceted approach, computational models combined with laboratory efforts, can accelerate precision medicine efforts toward co-targeting Hippo and MAPK pathways, an approach that can be generalized to other key cancer signaling pathways to define therapeutic strategies.
2021-01-26 | GSE161010 | GEO
Project description:We investigated the molecular mechanisms of exercise adaptations in human muscle by integrating genome, methylome, transcriptome, and proteome data from over 1,000 participants (2,340 muscle samples). We identified distinctive signatures associated with maximal oxygen consumption (VO 2max ), and multi-omics integration uncovered five key genes as robust exercise markers across layers, with transcription factors functioning as activators, synergizing with DNA methylation to regulate gene expression. Minimal sex-differences were observed, while modality-specific analysis highlighted distinct pathways for aerobic and resistance exercise, contrasting with muscle disuse patterns. Finally, we have created a webtool namely OMAx (https://sarah-voisin.shinyapps.io/OMAEx/) featuring our individual omics and integration analysis. These findings provide a comprehensive multi-omic framework for understanding exercise-induced molecular adaptations, offering insights into muscle health, cardiorespiratory fitness, and their roles in aging and disease prevention.
2025-05-07 | PXD063543 | Pride
Project description:We use multi-omics (ATAC-seq, single cell RNA-seq and differential bulk RNA-seq) to increase resolution of the sea urchin posterior gut GRN for the cells expressing the ParaHox gene Sp-Pdx1.
2024-08-06 | GSE262916 | GEO
Project description:Pulmonary fibrosis (PF) is a progressive disease with a poor prognosis and lack of effective treatments. Therefore, identifying effective therapeutic modalities is required to improve outcome. However, the lack of predictive progressive disease biomarkers in the clinic causes a significant gap in the preclinical to clinical translational process. Here, we assessed and identified progressive alterations in pulmonary function, transcriptomics, and metabolomics in mice lungs at 7, 14, 21, and 28 days after a single dose of oropharyngeal bleomycin. By integrating multi-omics data, we identified two central gene subnetworks associated with multiple critical pathological changes in transcriptomics and metabolomics as well as pulmonary function. We presented a multi-omics-based framework to establish a translational link between the bleomycin-induced PF model in mice and human idiopathic pulmonary fibrosis to identify druggable targets and test therapeutic candidates. Our study also indicated peripheral CB1R antagonism as a rational therapeutic target for clinical translation in PF.
2023-04-11 | GSE217814 | GEO
Project description:Pulmonary fibrosis (PF) is a progressive disease with a poor prognosis and lack of effective treatments. Therefore, identifying effective therapeutic modalities is required to improve outcome. However, the lack of predictive progressive disease biomarkers in the clinic causes a significant gap in the preclinical to clinical translational process. Here, we assessed and identified progressive alterations in pulmonary function, transcriptomics, and metabolomics in mice lungs at 7, 14, 21, and 28 days after a single dose of oropharyngeal bleomycin. By integrating multi-omics data, we identified two central gene subnetworks associated with multiple critical pathological changes in transcriptomics and metabolomics as well as pulmonary function. We presented a multi-omics-based framework to establish a translational link between the bleomycin-induced PF model in mice and human idiopathic pulmonary fibrosis to identify druggable targets and test therapeutic candidates. Our study also indicated peripheral CB1R antagonism as a rational therapeutic target for clinical translation in PF.
2023-04-11 | GSE213709 | GEO
Project description:<p> The mechanisms by which probiotics ameliorate neurodegenerative diseases have garnered significant scientific interest, yet elucidating their underlying pathways remains challenging. In this study, we establish an integrated multi-omics and network pharmacology strategy to unravel the protective mechanism of Lactobacillus reuteri DSM17938 and its supernatant in a Parkinson’s disease mice model. This work is intended as an initial step toward a more comprehensive understanding of how probiotics alleviate neurodegenerative diseases through gut microbiota and metabolism regulation. Given these findings, our research aligns well with the scope of Microbiome.</p><p> Based on the integrated approach incorporating gut microbiota analysis, untargeted metabolomics, network pharmacology, and molecular dynamics simulations, this study revealed that Lactobacillus reuteri DSM17938 and its supernatant treatment alleviate Parkinson's disease through the following aspects: (1) Improved motor function, reduced neuroinflammation and oxidative stress, and protection of dopaminergic neurons; (2) The treatment modulated the gut microbiota, promoted the accumulation of xanthurenic acid in the gut; (3) Furthermore, ADME analysis indicated that xanthurenic acid can cross the blood-brain barrier, and network pharmacology and molecular docking suggested that it may activate the aryl hydrocarbon receptor (AhR), thereby underlying its anti-PD effects; (4) Molecular dynamics simulations and free energy calculations confirmed the stable binding between xanthurenic acid and AhR, demonstrating high binding affinity with the key residue PHE-133.</p><p> The results of this article provide valuable insights by highlighting the crucial role of the gut-derived tryptophan metabolite xanthurenic acid in the neuroprotective effects of Lactobacillus reuteri DSM 17938 and its supernatant against Parkinson's disease. By integrating multi-omics, network pharmacology, molecular docking, and molecular dynamics simulations, we identified the AhR as a key target through which xanthurenic acid ameliorates PD.</p>
2025-10-18 | MTBLS13169 | MetaboLights
Project description:The gut microbiome has been implicated in multiple human chronic gastrointestinal (GI) disorders. Determining its mechanistic role in disease pathogenesis has been difficult due to the apparent disconnect between animal and human studies and a lack of an integrated multi-omics view in the context of disease-specific physiological changes. We integrated longitudinal multi-omics data from the gut microbiome, metabolome, host epigenome and transcriptome in the context of irritable bowel syndrome (IBS) host physiology. We identified IBS subtype-specific and symptom-related variation in microbial composition and function. A subset of identified changes in microbial metabolites correspond to host physiological mechanisms that are relevant to IBS. By integrating multiple data layers, we identified purine metabolism as a novel host-microbial metabolic pathway in IBS with translational potential. Our study highlights the importance of longitudinal sampling and integrating complementary multi-omics data to identify functional mechanisms that can serve as therapeutic targets in a comprehensive treatment strategy for chronic GI diseases.
2020-03-13 | GSE146853 | GEO
Project description:Modern omics technologies allow us obtaining global information on different types of biological networks. However, integrating these different types of analyzes into a coherent framework for a comprehensive biological interpretation remains challenging. Here, we present a conceptual framework that integrates protein interaction, phosphoproteomics and transcriptomics data. Applying this method to analyze HRAS signaling from different subcellular compartments shows that spatially-defined networks contribute specific functions to HRAS signaling. Changes in HRAS protein interactions at different sites lead to different kinase activation patterns that differentially regulate gene transcription. HRAS mediated signaling is the strongest from the plasma membrane, but it regulates the largest number of genes from the endoplasmic reticulum. The integrated networks provide a topologically and functionally resolved view of HRAS signaling. They reveal new HRAS functions including the control of cell migration from the endoplasmic reticulum and p53 dependent cell survival when signaling from the Golgi apparatus.
2019-03-12 | E-MTAB-7672 | biostudies-arrayexpress
Project description:<p>Environmental co-contamination presents significant challenges. To tackle these, while microbial consortia offer advantages over single-strain approaches, such as functional redundancy and synergistic degradation, rationally designing effective synthetic microbiomes specifically for complex co-contamination scenarios remains a major challenge. Here, we utilized our advanced genome-scale metabolic modeling (GSMM) tool, SuperCC, to simulate the metabolic behavior of communities consisting of six isolated key strains under single- and multi-carbon source conditions, mimicking single-pollutant or co-contamination scenarios respectively. By integrating multi-omics data with metabolic modeling of cultured consortia, we systematically elucidated key strain interaction networks and adaptive strategies under co-contamination. This revealed that the specific secretory products of broad-spectrum resource-utilizing bacteria serve as key metabolites driving cooperation and highlighted the pivotal role of indigenous keystone strains in stabilizing and enhancing community function. Consequently, we propose a novel and rational paradigm for consortium design: DHP-Com (Degrader-Helper-Potentiator). Synthetic microbiomes constructed based on this framework exhibited enhanced ecological fitness (survival and growth) and, most importantly, substantially improved remediation performance across diverse co-contamination scenarios. Our findings advance the practical application of GSMM predictions to decipher intricate multi-pollutant/multi-strain interaction networks, offering a powerful rational framework and robust methodological tools for engineering multi-functional and effective synthetic microbiomes for complex environmental remediation.</p>
2025-11-20 | MTBLS12809 | MetaboLights