Statistical Identification of Important Nodes in Biological Systems

Deciphering crucial salt-responsive genes in Brassica napus via statistical modeling and network analysis on dynamic transcriptomic data

Soil salinization severely impacts crop yields, threatening global food security. Understanding the salt stress response of Brassica napus (B. napus), a vital oilseed crop, is crucial for developing salt-tolerant varieties. This study aims to comprehensively characterize the dynamic transcriptomic response of B. napus seedlings to salt stress, identifying key genes and pathways involved in this process. RNA-sequencing on 43 B. napus seedling samples are performed, including 24 controls and 19 salt-stressed plants, at time points of 0, 1, 3, 6, and 12 h. Differential expression analysis using 33 control experiments (CEs) identified 39,330 differentially expressed genes (DEGs). Principal component analysis (PCA) and a novel penalized logistic regression with k-Shape clustering (PLRKSC) method identify 346 crucial DEGs. GO enrichment, differential co-expression network analysis, and functional validation through B. napus transformation verify the functional roles of the identified DEGs. The analysis reveals highly dynamic and tissue-specific expression patterns of DEGs under salt stress. The identified 346 crucial DEGs include those involved in leaf and root development, stress-responsive transcription factors, and genes associated with the salt overly sensitive (SOS) pathway. Specifically, Overexpression of RD26 (BnaC07g40860D) in B. napus significantly enhances salt tolerance, confirming its role in salt stress response. This study provides a comprehensive understanding of the B. napus salt stress response at the transcriptomic level and identifies key candidate genes, such as RD26, for developing salt-tolerant varieties. The methodologies established can be applied to other omics studies of plant stress responses.

Gene co-expression network construction and analysis for identification of genetic biomarkers associated with glioblastoma multiforme using topological findings

BACKGROUND

Glioblastoma multiforme (GBM) is one of the most malignant types of central nervous system tumors. GBM patients usually have a poor prognosis. Identification of genes associated with the progression of the disease is essential to explain the mechanisms or improve the prognosis of GBM by catering to targeted therapy. It is crucial to develop a methodology for constructing a biological network and analyze it to identify potential biomarkers associated with disease progression.

METHODS

Gene expression datasets are obtained from TCGA data repository to carry out this study. A survival analysis is performed to identify survival associated genes of GBM patient. A gene co-expression network is constructed based on Pearson correlation between the gene's expressions. Various topological measures along with set operations from graph theory are applied to identify most influential genes linked with the progression of the GBM.

RESULTS

Ten key genes are identified as a potential biomarkers associated with GBM based on centrality measures applied to the disease network. These genes are SEMA3B, APS, SLC44A2, MARK2, PITPNM2, SFRP1, PRLH, DIP2C, CTSZ, and KRTAP4.2. Higher expression values of two genes, SLC44A2 and KRTAP4.2 are found to be associated with progression and lower expression values of seven gens SEMA3B, APS, MARK2, PITPNM2, SFRP1, PRLH, DIP2C, and CTSZ are linked with the progression of the GBM.

CONCLUSIONS

The proposed methodology employing a network topological approach to identify genetic biomarkers associated with cancer.

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

Gene Differential Co-Expression Networks Based on RNA-Seq: Construction and Its Applications

Gene co-expression network (GCN) becomes an increasingly important tool in omics data analysis. A great challenge for GCN construction is that the sample size is far lower than the number of genes. Traditional methods rely on considerable samples. Moreover, association signals are likely weak, nonlinear and stochastic, which are difficult to be identified among thousands of candidates. In this paper, the gray correlation coefficient (GCC) is introduced, and a novel method to construct gene differential co-expression networks (GDCNs) is proposed. Based on the GDCNs, three measures are proposed to explore informative genes. The proposed method can make full use of the information provided by a handful of samples and overcome the shortages of GCNs, which can evaluate the changes of co-expression relationships that are possibly triggered by treatments. Based on RNA-seq data of Brassica napus, GDCNs under multiple experimental conditions are constructed and investigated. It is found that the GCC-based method is very robust to data processing. The GDCNs facilitate the inference of gene functions and the identification of informative genes that are responsible for stress responsiveness. The GDCN-based approaches integrate the 'guilt by association' and the 'guilt by rewiring' rules, which provide alternative tools for omics data analysis.