Prioritizing transcriptional factors in gene regulatory networks with PageRank

Multi-omics analysis uncovers the transcriptional regulatory mechanism of magnesium Ions in the synthesis of active ingredients in Sophora tonkinensis

Magnesium (Mg) plays a pivotal role as an essential component of plant chlorophyll and functions as a critical coenzyme. However, research exploring the regulatory mechanisms of magnesium ions on the synthesis of secondary metabolites is still in its early stages. Sophora tonkinensis is a widely utilized medicinal plant in China, recognized for its diverse secondary metabolites with active properties. This study investigates variations in these ingredients in tissue-cultured seedlings under varying magnesium concentrations. Simultaneously, an omics data analysis was conducted on tissue-cultured seedlings subjected to treatments with magnesium and low magnesium. These comprehensive omics analyses aimed to elucidate the mechanisms through which magnesium influences active components, growth, and development. Magnesium exerts a pervasive influence on various metabolic pathways, forming an intricate network. Research findings indicate that magnesium impacts diverse metabolic processes, including the absorption of potassium and calcium, as well as photosynthetic activity. Consequently, these influences lead to discernible changes in the levels of pharmacologically active compounds and the growth and developmental status.This study is the first to employ a multi-omics data analysis in S. tonkinensis. This methodology allows us to uncover the overarching impact of metabolic networks on the levels of various active ingredients and specific phenotypes.

PRRGO: A Tool for Visualizing and Mapping Globally Expressed Genes in Public Gene Expression Omnibus RNA-Sequencing Studies to PageRank-scored Gene Ontology Terms

We herein report PageRankeR Gene Ontology (PRRGO), a downloadable web application that can integrate differentially expressed gene (DEG) data from the gene expression omnibus (GEO) GEO2R web tool with the gene ontology (GO) database [1]. Unlike existing tools, PRRGO computes the PageRank for the entire GO network and can generate both interactive GO networks on the web interface and comma-separated values (CSV) files containing the DEG statistics categorized by GO term. These hierarchical and tabular GO-DEG data are especially conducive to hypothesis generation and overlap studies with the use of PageRank data, which can provide a metric of GO term centrality. We verified the tool for accuracy and reliability across nine independent heat shock (HS) studies for which the RNA-seq data was publicly available on GEO and found that the tool produced increasing concordance between study DEGs, GO terms, and select HS-specific GO terms.

Delving into gene-set multiplex networks facilitated by a k-nearest neighbor-based measure of similarity

Gene sets are functional units for living cells. Previously, limited studies investigated the complex relations among gene sets, but documents about their altering patterns across biological conditions still need to be prepared. In this study, we adopted and modified a classical k-nearest neighbor-based association function to detect inter-gene-set similarities. Based on this method, we built multiplex networks of gene sets for the first time; these networks contain layers of gene sets corresponding to different populations of cells. The context-based multiplex networks can capture meaningful biological variation and have considerable differences from knowledge-based networks of gene sets built on Jaccard similarity, as demonstrated in this study. Furthermore, at the scale of individual gene sets, the structural coefficients of gene sets (multiplex PageRank centrality, clustering coefficient, and participation coefficient) disclose the diversity of gene sets from the perspective of structural properties and make it easier to identify unique gene sets. In gene set enrichment analysis (GSEA), each gene set is treated independently, and its contextual and relational attributes are ignored. The structural coefficients of gene sets can supplement GSEA with information about the overall picture of gene sets, promoting the constructive reorganization of the enriched terms and helping researchers better prioritize and select gene sets.

Genetic and pharmacologic inhibition of ALDH1A3 as a treatment of β-cell failure

Type 2 diabetes (T2D) is associated with β-cell dedifferentiation. Aldehyde dehydrogenase 1 isoform A3 (ALHD1A3) is a marker of β-cell dedifferentiation and correlates with T2D progression. However, it is unknown whether ALDH1A3 activity contributes to β-cell failure, and whether the decrease of ALDH1A3-positive β-cells (A+) following pair-feeding of diabetic animals is due to β-cell restoration. To tackle these questions, we (i) investigated the fate of A+ cells during pair-feeding by lineage-tracing, (ii) somatically ablated ALDH1A3 in diabetic β-cells, and (iii) used a novel selective ALDH1A3 inhibitor to treat diabetes. Lineage tracing and functional characterization show that A+ cells can be reconverted to functional, mature β-cells. Genetic or pharmacological inhibition of ALDH1A3 in diabetic mice lowers glycemia and increases insulin secretion. Characterization of β-cells following ALDH1A3 inhibition shows reactivation of differentiation as well as regeneration pathways. We conclude that ALDH1A3 inhibition offers a therapeutic strategy against β-cell dysfunction in diabetes.

Exploring drought-responsive crucial genes in Sorghum

Drought severely affects global food production. Sorghum is a typical drought-resistant model crop. Based on RNA-seq data for Sorghum with multiple time points and the gray correlation coefficient, this paper firstly selects candidate genes via mean variance test and constructs weighted gene differential co-expression networks (WGDCNs); then, based on guilt-by-rewiring principle, the WGDCNs and the hidden Markov random field model, drought-responsive crucial genes are identified for five developmental stages respectively. Enrichment and sequence alignment analysis reveal that the screened genes may play critical functional roles in drought responsiveness. A multilayer differential co-expression network for the screened genes reveals that Sorghum is very sensitive to pre-flowering drought. Furthermore, a crucial gene regulatory module is established, which regulates drought responsiveness via plant hormone signal transduction, MAPK cascades, and transcriptional regulations. The proposed method can well excavate crucial genes through RNA-seq data, which have implications in breeding of new varieties with improved drought tolerance.

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We design a method that unites gene rewiring network and Markov random field model

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Drought-responsive genes for five developmental stages of Sorghum are explored

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A multilayer network reveals that Sorghum is very sensitive to pre-flowering drought

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A drought-responsive crucial gene regulatory module is established for Sorghum

Identification of Transcription Factors Regulating SARS-CoV-2 Tropism Factor Expression by Inferring Cell-Type-Specific Transcriptional Regulatory Networks in Human Lungs

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the virus that caused the coronavirus disease 2019 (COVID-19) pandemic. Though previous studies have suggested that SARS-CoV-2 cellular tropism depends on the host-cell-expressed proteins, whether transcriptional regulation controls SARS-CoV-2 tropism factors in human lung cells remains unclear. In this study, we used computational approaches to identify transcription factors (TFs) regulating SARS-CoV-2 tropism for different types of lung cells. We constructed transcriptional regulatory networks (TRNs) controlling SARS-CoV-2 tropism factors for healthy donors and COVID-19 patients using lung single-cell RNA-sequencing (scRNA-seq) data. Through differential network analysis, we found that the altered regulatory role of TFs in the same cell types of healthy and SARS-CoV-2-infected networks may be partially responsible for differential tropism factor expression. In addition, we identified the TFs with high centralities from each cell type and proposed currently available drugs that target these TFs as potential candidates for the treatment of SARS-CoV-2 infection. Altogether, our work provides valuable cell-type-specific TRN models for understanding the transcriptional regulation and gene expression of SARS-CoV-2 tropism factors.