Disentangling plant response to biotic and abiotic stress using HIVE, a novel tool to perform unpaired multi-transcriptomics integration DOI Creative Commons

Giulia Calia,

Sophia Marguerit,

Ana Paula Zotta Mota

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: March 5, 2024

Abstract All organisms are subjected to multiple stresses usually occurring at the same time, requiring activation of appropriate signalling pathways respond all or by prioritizing response one stress factor. Plants, as sessile organisms, particularly impacted constantly changing environment that is often unfavourable even hostile. Because experimental complexity studying organism stressors simultaneously, experiments conducted considering individual factor time. An alternative consists in performing silico integration those data on single response. Currently used methods integrate unpaired consist meta-analysis finding differentially expressed genes for each condition separately and then selecting commonly regulated ones. Although these approaches allowed find valuable results, they mainly identify specific signatures very few signature responding lack modulated differently condition. For this purpose, we developed HIVE (Horizontal Integration analysis using Variational AutoEncoders) single-stress transcriptomics datasets composed experiments. Briefly, coupled a variational autoencoder, alleviates batch effects, with random forest regression SHAP explainer select relevant specifically stresses. We illustrate functionality study transcriptional changes several different plants namely Arabidopsis thaliana , rice, maize, wheat, grapevine peanut collecting publicly available stress, either biotic and/or abiotic, jointly analyse them. performed better than differential expression analysis, state-of-the-art tool horizontal allowing novel promising candidates responsible triggering effective defence responses

Language: Английский

SignalingProfiler 2.0 a network-based approach to bridge multi-omics data to phenotypic hallmarks DOI Creative Commons
Veronica Venafra, Francesca Sacco, Livia Perfetto

et al.

npj Systems Biology and Applications, Journal Year: 2024, Volume and Issue: 10(1)

Published: Aug. 23, 2024

Abstract Unraveling how cellular signaling is remodeled upon perturbation crucial for understanding disease mechanisms and identifying potential drug targets. In this pursuit, computational tools generating mechanistic hypotheses from multi-omics data have invaluable potential. Here, we present a newly implemented version (2.0) of SignalingProfiler , multi-step pipeline to draw on the events impacting phenotypes. 2.0 derives context-specific networks by integrating proteogenomic with prior knowledge-causal network. This freely accessible flexible tool that incorporates statistical, footprint-based, graph algorithms accelerate integration interpretation data. Through benchmarking process three proof-of-concept studies, demonstrate tool’s ability generate hierarchical recapitulating novel known perturbed phenotypic outcomes, in both human mice contexts. summary, S ignalingProfiler addresses emergent need derive biologically relevant information complex extracting interpretable networks.

Language: Английский

Citations

1

Correlative analysis of transcriptome and 16S rDNA in Procambarus clarkii reveals key signaling pathways are involved in Chlorantraniliprole stress response by phosphoinositide 3-kinase (PI3K) DOI

Dan‐Dan Bian,

Xin Liu, Xue Jun Zhang

et al.

International Journal of Biological Macromolecules, Journal Year: 2024, Volume and Issue: unknown, P. 135966 - 135966

Published: Sept. 1, 2024

Language: Английский

Citations

1

Transcriptomic and Metabolomics Joint Analyses Reveal the Influence of Gene and Metabolite Expression in Blood on the Lactation Performance of Dual-Purpose Cattle (Bos taurus) DOI Open Access
Shengchao Ma, Dan Wang, Menghua Zhang

et al.

International Journal of Molecular Sciences, Journal Year: 2024, Volume and Issue: 25(22), P. 12375 - 12375

Published: Nov. 18, 2024

Blood is an important component for maintaining animal lives and synthesizing sugars, lipids, proteins in organs. Revealing the relationship between genes metabolite expression milk somatic cell count (SCC), fat percentage, protein lactose percentage blood helpful understanding molecular regulation mechanism of formation. Therefore, we separated buffy coat plasma from Xinjiang Brown cattle (XJBC) Chinese Simmental (CSC), which exhibit high low SCC/milk percentage/milk percentage/lactose percentages, respectively. The metabolites was detected via RNA-Seq LC-MS/MS, Based on weighted gene coexpression network analysis (WGCNA) functional enrichment differentially expressed (DEGs), further found that mainly affected SCC percentage. Immune or inflammatory-response-related pathways were involved SCC, joint metabolome transcriptome indicated that, blood, metabolism purine, glutathione, glycerophospholipid, glycine, arginine, proline are also associated with while lipid amino-acid-related Finally, related DEGs DEMs identified blood.

Language: Английский

Citations

1

Decoding Drug Discovery: Exploring A-to-Z In Silico Methods for Beginners DOI
Hezha O. Rasul,

Dlzar D. Ghafour,

Bakhtyar K. Aziz

et al.

Applied Biochemistry and Biotechnology, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 4, 2024

Language: Английский

Citations

1

Disentangling plant response to biotic and abiotic stress using HIVE, a novel tool to perform unpaired multi-transcriptomics integration DOI Creative Commons

Giulia Calia,

Sophia Marguerit,

Ana Paula Zotta Mota

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: March 5, 2024

Abstract All organisms are subjected to multiple stresses usually occurring at the same time, requiring activation of appropriate signalling pathways respond all or by prioritizing response one stress factor. Plants, as sessile organisms, particularly impacted constantly changing environment that is often unfavourable even hostile. Because experimental complexity studying organism stressors simultaneously, experiments conducted considering individual factor time. An alternative consists in performing silico integration those data on single response. Currently used methods integrate unpaired consist meta-analysis finding differentially expressed genes for each condition separately and then selecting commonly regulated ones. Although these approaches allowed find valuable results, they mainly identify specific signatures very few signature responding lack modulated differently condition. For this purpose, we developed HIVE (Horizontal Integration analysis using Variational AutoEncoders) single-stress transcriptomics datasets composed experiments. Briefly, coupled a variational autoencoder, alleviates batch effects, with random forest regression SHAP explainer select relevant specifically stresses. We illustrate functionality study transcriptional changes several different plants namely Arabidopsis thaliana , rice, maize, wheat, grapevine peanut collecting publicly available stress, either biotic and/or abiotic, jointly analyse them. performed better than differential expression analysis, state-of-the-art tool horizontal allowing novel promising candidates responsible triggering effective defence responses

Language: Английский

Citations

0