Spatiotemporal Gene Expression Profiling and Network Inference: A Roadmap for Analysis, Visualization, and Key Gene Identification

Spurney, Ryan J.; Schwartz, Michael F.; Gobble, Mariah; Sozzani, Rosangela; Broeck, Lisa Van den

doi:10.1007/978-1-0716-1534-8_4

Cited by 3 publications

(3 citation statements)

References 32 publications

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“…KEGG pathway enrichment analysis has shown that the ECM-receptor interaction pathway, leukocyte transendothelial migration signaling pathway, PI3K-AKT signaling pathway, and actin cytoskeleton regulation pathways play certain roles in OS ( 28 ). Dysregulation of the PI3K-AKT signaling pathway is found in many diseases such as cancer, diabetes, cardiovascular diseases, and neurological diseases ( 29 ). Leukocyte transendothelial migration signaling pathway can activate the transcription factor 3 signal transduction pathway in OS cells, thereby promoting their proliferation and metastasis.…”

Section: Discussionmentioning

confidence: 99%

Screening and identification of susceptibility genes for osteosarcoma based on bioinformatics analysis

Liu¹,

Xu²,

Cao³

et al. 2023

Ann Transl Med

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Background Gene play an important role in malignant tumors. However, there is still insufficient research on genetic variations in osteosarcoma (OS) patients. Therefore, we aimed to analyze the gene expression profile of OS using bioinformatics and to explore the pathogenesis of OS at the molecular level. Methods The gene chip dataset of OS samples was downloaded from the Gene Expression Omnibus (GEO) database for screening differentially expressed genes (DEGs). The R language clusterProfiler software package was used to perform Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis for DEGs. The central node proteins of the protein interaction network were analyzed by the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database, Cytoscape, and its plug-ins cytoHubba and NetworkAnalyzer to find the key genes. Results A total of 631 DEGs were obtained, including 362 upregulated genes and 269 downregulated genes. DEGs were mainly involved in the regulation of leukocyte chemotaxis and migration, vascular development, and other biological processes (BPs); mediation of receptor ligand activity, growth factor binding, growth factor activity, integrin binding, and other molecular functions (MFs); and were enriched in the extracellular matrix (ECM). Conclusions DEGs in the ECM and growth factors play a key role in the development of OS. The leukocyte transendothelial migration pathway and the PI3K-AKT pathway are closely related to OS, and the related molecular mechanism is worthy of further study.

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Section: Discussionmentioning

confidence: 99%

Screening and identification of susceptibility genes for osteosarcoma based on bioinformatics analysis

Liu¹,

Xu²,

Cao³

et al. 2023

Ann Transl Med

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“…S8). For time-course datasets with smaller time steps, only the inclusion of time-lapse 1 is advised 22,23 .…”

Section: Analysis and Network Inference With Netphorcementioning

confidence: 99%

“…Several analytical tools, such as GENIE3 17 and ARACNE 19 , infer causal regulations among differentially expressed genes to, for example, identify key transcriptional regulators 20 . GENIST, a dynamic Bayesian network approach, leverages time series data to identify dependencies among the modeled genes [20][21][22][23] . However, such network inference approaches to identify dependencies among phosphorylated proteins is still a bottleneck.…”

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confidence: 99%

Functional annotation of proteins for signaling network inference in non-model species

et al. 2023

Self Cite

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Molecular biology aims to understand cellular responses and regulatory dynamics in complex biological systems. However, these studies remain challenging in non-model species due to poor functional annotation of regulatory proteins. To overcome this limitation, we develop a multi-layer neural network that determines protein functionality directly from the protein sequence. We annotate kinases and phosphatases in Glycine max. We use the functional annotations from our neural network, Bayesian inference principles, and high resolution phosphoproteomics to infer phosphorylation signaling cascades in soybean exposed to cold, and identify Glyma.10G173000 (TOI5) and Glyma.19G007300 (TOT3) as key temperature regulators. Importantly, the signaling cascade inference does not rely upon known kinase motifs or interaction data, enabling de novo identification of kinase-substrate interactions. Conclusively, our neural network shows generalization and scalability, as such we extend our predictions to Oryza sativa, Zea mays, Sorghum bicolor, and Triticum aestivum. Taken together, we develop a signaling inference approach for non-model species leveraging our predicted kinases and phosphatases.

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Machine Learning Inference of Gene Regulatory Networks in Developing Mimulus Seeds

Tucci,

Flores-Vergara,

Franks

2024

Plants

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The angiosperm seed represents a critical evolutionary breakthrough that has been shown to propel the reproductive success and radiation of flowering plants. Seeds promote the rapid diversification of angiosperms by establishing postzygotic reproductive barriers, such as hybrid seed inviability. While prezygotic barriers to reproduction tend to be transient, postzygotic barriers are often permanent and therefore can play a pivotal role in facilitating speciation. This property of the angiosperm seed is exemplified in the Mimulus genus. In order to further the understanding of the gene regulatory mechanisms important in the Mimulus seed, we performed gene regulatory network (GRN) inference analysis by using time-series RNA-seq data from developing hybrid seeds from a viable cross between Mimulus guttatus and Mimulus pardalis. GRN inference has the capacity to identify active regulatory mechanisms in a sample and highlight genes of potential biological importance. In our case, GRN inference also provided the opportunity to uncover active regulatory relationships and generate a reference set of putative gene regulations. We deployed two GRN inference algorithms—RTP-STAR and KBoost—on three different subsets of our transcriptomic dataset. While the two algorithms yielded GRNs with different regulations and topologies when working with the same data subset, there was still significant overlap in the specific gene regulations they inferred, and they both identified potential novel regulatory mechanisms that warrant further investigation.

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Spatiotemporal Gene Expression Profiling and Network Inference: A Roadmap for Analysis, Visualization, and Key Gene Identification

Cited by 3 publications

References 32 publications

Screening and identification of susceptibility genes for osteosarcoma based on bioinformatics analysis

Screening and identification of susceptibility genes for osteosarcoma based on bioinformatics analysis

Functional annotation of proteins for signaling network inference in non-model species

Machine Learning Inference of Gene Regulatory Networks in Developing Mimulus Seeds

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