Co-expression Networks in Predicting Transcriptional Gene Regulation

Transcriptome Analysis of Sesame (Sesamum indicum L.) Reveals the LncRNA and mRNA Regulatory Network Responding to Low Nitrogen Stress

Nitrogen is one of the important factors restricting the development of sesame planting and industry in China. Cultivating sesame varieties tolerant to low nitrogen is an effective way to solve the problem of crop nitrogen deficiency. To date, the mechanism of low nitrogen tolerance in sesame has not been elucidated at the transcriptional level. In this study, two sesame varieties Zhengzhi HL05 (ZZ, nitrogen efficient) and Burmese prolific (MD, nitrogen inefficient) in low nitrogen were used for RNA-sequencing. A total of 3964 DEGs (differentially expressed genes) and 221 DELs (differentially expressed lncRNAs) were identified in two sesame varieties at 3d and 9d after low nitrogen stress. Among them, 1227 genes related to low nitrogen tolerance are mainly located in amino acid metabolism, starch and sucrose metabolism and secondary metabolism, and participate in the process of transporter activity and antioxidant activity. In addition, a total of 209 pairs of lncRNA-mRNA were detected, including 21 pairs of trans and 188 cis. WGCNA (weighted gene co-expression network analysis) analysis divided the obtained genes into 29 modules; phenotypic association analysis identified three low-nitrogen response modules; through lncRNA-mRNA co-expression network, a number of hub genes and cis/trans-regulatory factors were identified in response to low-nitrogen stress including GS1-2 (glutamine synthetase 1-2), PAL (phenylalanine ammonia-lyase), CHS (chalcone synthase, CHS), CAB21 (chlorophyll a-b binding protein 21) and transcription factors MYB54, MYB88 and NAC75 and so on. As a trans regulator, lncRNA MSTRG.13854.1 affects the expression of some genes related to low nitrogen response by regulating the expression of MYB54, thus responding to low nitrogen stress. Our research is the first to provide a more comprehensive understanding of DEGs involved in the low nitrogen stress of sesame at the transcriptome level. These results may reveal insights into the molecular mechanisms of low nitrogen tolerance in sesame and provide diverse genetic resources involved in low nitrogen tolerance research.

iHerd: an integrative hierarchical graph representation learning framework to quantify network changes and prioritize risk genes in disease

Different genes form complex networks within cells to carry out critical cellular functions, while network alterations in this process can potentially introduce downstream transcriptome perturbations and phenotypic variations. Therefore, developing efficient and interpretable methods to quantify network changes and pinpoint driver genes across conditions is crucial. We propose a hierarchical graph representation learning method, called iHerd. Given a set of networks, iHerd first hierarchically generates a series of coarsened sub-graphs in a data-driven manner, representing network modules at different resolutions (e.g., the level of signaling pathways). Then, it sequentially learns low-dimensional node representations at all hierarchical levels via efficient graph embedding. Lastly, iHerd projects separate gene embeddings onto the same latent space in its graph alignment module to calculate a rewiring index for driver gene prioritization. To demonstrate its effectiveness, we applied iHerd on a tumor-to-normal GRN rewiring analysis and cell-type-specific GCN analysis using single-cell multiome data of the brain. We showed that iHerd can effectively pinpoint novel and well-known risk genes in different diseases. Distinct from existing models, iHerd's graph coarsening for hierarchical learning allows us to successfully classify network driver genes into early and late divergent genes (EDGs and LDGs), emphasizing genes with extensive network changes across and within signaling pathway levels. This unique approach for driver gene classification can provide us with deeper molecular insights. The code is freely available at https://github.com/aicb-ZhangLabs/iHerd. All other relevant data are within the manuscript and supporting information files.

A Novel Role of Medicago truncatula KNAT3/4/5-like Class 2 KNOX Transcription Factors in Drought Stress Tolerance

Class 2 KNOX homeobox transcription factors (KNOX2) play a role in promoting cell differentiation in several plant developmental processes. In Arabidopsis, they antagonize the meristematic KNOX1 function during leaf development through the modulation of phytohormones. In Medicago truncatula, three KNOX2 genes belonging to the KNAT3/4/5-like subclass (Mt KNAT3/4/5-like or MtKNOX3-like) redundantly works upstream of a cytokinin-signaling module to control the symbiotic root nodule formation. Their possible role in the response to abiotic stress is as-of-yet unknown. We produced transgenic M. truncatula lines, in which the expression of four MtKNOX3-like genes was knocked down by RNA interference. When tested for response to water withdrawal in the soil, RNAi lines displayed a lower tolerance to drought conditions compared to the control lines, measured as increased leaf water loss, accelerated leaf wilting time, and faster chlorophyll loss. Reanalysis of a transcriptomic M. truncatula drought stress experiment via cluster analysis and gene co-expression networks pointed to a possible role of MtKNOX3-like transcription factors in repressing a proline dehydrogenase gene (MtPDH), specifically at 4 days after water withdrawal. Proline measurement and gene expression analysis of transgenic RNAi plants compared to the controls confirmed the role of KNOX3-like genes in inhibiting proline degradation through the regulation of the MtPDH gene.

Data-driven analysis and druggability assessment methods to accelerate the identification of novel cancer targets

Over the past few decades, drug discovery has greatly improved the outcomes for patients, but several challenges continue to hinder the rapid development of novel drugs. Addressing unmet clinical needs requires the pursuit of drug targets that have a higher likelihood to lead to the development of successful drugs. Here we describe a bioinformatic approach for identifying novel cancer drug targets by performing statistical analysis to ascertain quantitative changes in expression levels between protein-coding genes, as well as co-expression networks to classify these genes into groups. Subsequently, we provide an overview of druggability assessment methodologies to prioritize and select the best targets to pursue.