OmicVerse: A single pipeline for exploring the entire transcriptome universe

Zeng, Zehua; Ma, Yingmin; Hu, Lei; Xiong, Yuanyan; Du, Hongwu

doi:10.1101/2023.06.06.543913

Cited by 7 publications

(2 citation statements)

References 68 publications

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“…We then used the plot_network function in omicverse ( 29 ) to visualize the 73 components and for that matter the target proteins.…”

Section: Methodsmentioning

confidence: 99%

Network-based pharmacology and UHPLC-Q-Exactive-Orbitrap-MS reveal Jinhua Qinggan granule's mechanism in reducing cellular inflammation in COVID-19

Qian,

Zeng

2024

Front. Immunol.

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IntroductionThe outbreak of SARS-CoV-2, leading to COVID-19, poses a major global health threat. While specific treatments and vaccines are under development, Traditional Chinese Medicine (TCM) has historically been effective against pandemics, including viral pneumonias. Our study explores the efficacy and mechanisms of Jinhua Qinggan Granules (JHQG) in treating COVID-19.MethodsWe analyzed JHQG’s components using UHPLC-Q-Exactive-Orbitrap-MS, identifying 73 compounds. Network pharmacology and single-cell RNA sequencing (scRNA-seq) were used to assess JHQG’s effects on immune cells from peripheral blood mononuclear cells (PBMCs). Literature review supported the antiviral and anti-inflammatory effects of JHQG.ResultsJHQG targets were found to interact with immune cells, including neutrophils, monocytes, plasmablasts, and effector T cells, reducing their overactivation in severe COVID-19. JHQG’s modulation of these cells’ activity likely contributes to reduced inflammation and improved clinical outcomes.DiscussionOur findings provide insights into JHQG's mechanism of action, highlighting its potential in controlling the inflammatory response in COVID-19 patients. The study supports the use of JHQG as a safe and effective treatment for COVID-19 and similar viral infections, leveraging its ability to modulate immune cell activity and reduce inflammation.

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“…We then used the plot_network function in omicverse ( 29 ) to visualize the 73 components and for that matter the target proteins.…”

Section: Methodsmentioning

confidence: 99%

Network-based pharmacology and UHPLC-Q-Exactive-Orbitrap-MS reveal Jinhua Qinggan granule's mechanism in reducing cellular inflammation in COVID-19

Qian,

Zeng

2024

Front. Immunol.

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“…Clade-specific gene differential expression analyses were performed by DESeq2 method 82 in OmicVerse package 83 . The thresholds to identify significantly expressed genes were established at adjusted P value < 0.05 and absolute log2 fold-change > 2.…”

Section: Differential Expression and Kegg Pathway Enrichment Analysesmentioning

confidence: 99%

Yeast adapts to diverse ecological niches driven by genomics and metabolic reprogramming

Wang,

Nielsen,

Zhou

et al. 2024

Preprint

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The famous model organism -Saccharomyces cerevisiaeis widely present in a variety of natural and human-associated habitats. Despite extensive studies of this organism, the metabolic mechanisms driving its adaptation to varying niches remain elusive. We here gathered genomic resources from 1,807S. cerevisiaestrains and assembled them into a high-quality pan-genome, facilitating the comprehensive characterization of genetic diversity across isolates. Utilizing the pan-genome, 1,807 strain-specific genome-scale metabolic models (ssGEMs) were generated, which performed well in quantitative predictions of cellular phenotypes, thus helping to examine the metabolic disparities among allS. cerevisiaestrains. Integrative analyses of fluxomic and transcriptomics with ssGEMs showcased the ubiquitous transcriptional regulation in certain metabolic sub-pathways (i.e., amino acid synthesis) at a population level. Additionally, the gene/reaction loss analysis through the ssGEMs refined by transcriptomics showed thatS. cerevisiaestrains from various ecological niches had undergone reductive evolution at both the genomic and metabolic network levels when compared to wild isolates. Finally, the compiled analyses of the pan-genome, transcriptome, and metabolic fluxome revealed remarkable metabolic differences amongS. cerevisiaestrains originating from distinct oxygen-limited niches, including human gut and cheese environments, and identified convergent metabolic evolution, such as downregulation of oxidative phosphorylation pathways. Together, these results illustrate how yeast adapts to distinct niches modulated by genomic and metabolic reprogramming, and provide computational resources for translating yeast genotype to fitness in future studies.

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Subtype‐specific transcription factors affect polyamine metabolism and the tumor microenvironment in breast cancer

Song,

Wang,

Liu

2024

Cancer Innovation

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BackgroundPolyamines play important roles in cell growth and proliferation. Polyamine metabolism genes are dysregulated in various tumors. Some polyamine metabolism genes are regulated by transcription factors. However, the transcription factors that regulate polyamine metabolism genes have not been completely identified. Additionally, whether any of the transcriptional regulations depend on tumor heterogeneity and the tumor microenvironment has not been investigated.MethodsWe used bulk RNA‐seq data to identify dysregulated polyamine metabolism genes and their transcription factors across breast cancer subtypes. Genes highly correlated with polyamine changes were obtained, and their subtype‐specific expressions were checked in tumor microenvironment cells using single‐cell RNA (scRNA)‐seq data. Gene Ontology enrichment analysis was used to explore their molecular functions and biological processes, and survival analysis was used to examine the impact of these genes on therapeutic outcome.ResultsWe first analyzed the dysregulation of polyamine synthesis, catabolism, and transport in four breast cancer subtypes. Genes such as AGMAT and CAV1 were dysregulated across all subtypes, while APRT, SAT1, and other genes were dysregulated in the more lethal subtypes. Among the dysregulated genes of polyamine metabolism, we focused on three genes (SRM, APRT, and SAT1) and identified their transcription factors (SPI1 and IRF1 correspond to SAT1, and IRF3 corresponds to SRM and APRT). With scRNA‐seq data, we verified that these three transcription factors also regulated these three polyamine metabolism genes in the tumor microenvironment. Both bulk RNA‐seq and scRNA‐seq data indicated that these genes were specifically upregulated in high‐risk breast cancer subtypes, such as the basal‐like type. High expression of these genes corresponded to worse outcomes in the basal‐like subtype under chemotherapy and radiation treatment.ConclusionOur work identified three subtype‐specific transcription factors that regulate three polyamine metabolism genes in high‐risk breast cancer subtypes and the tumor microenvironment. Our results deepen the understanding of the role of polyamine metabolism in breast cancer and may help the clinical therapy of advanced breast cancer subtypes.

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OmicVerse: A single pipeline for exploring the entire transcriptome universe

Cited by 7 publications

References 68 publications

Network-based pharmacology and UHPLC-Q-Exactive-Orbitrap-MS reveal Jinhua Qinggan granule's mechanism in reducing cellular inflammation in COVID-19

Network-based pharmacology and UHPLC-Q-Exactive-Orbitrap-MS reveal Jinhua Qinggan granule's mechanism in reducing cellular inflammation in COVID-19

Yeast adapts to diverse ecological niches driven by genomics and metabolic reprogramming

Subtype‐specific transcription factors affect polyamine metabolism and the tumor microenvironment in breast cancer

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