The Arabidopsis gene co‐expression network

Abstract

Identifying genes that interact to confer a biological function to an organism is one of the main goals of functional genomics. High‐throughput technologies for assessment and quantification of genome‐wide gene expression patterns have enabled systems‐level analyses to infer pathways or networks of genes involved in different functions under many different conditions. Here, we leveraged the publicly available, information‐rich RNA‐Seq datasets of the model plant Arabidopsis thaliana to construct a gene co‐expression network, which was partitioned into clusters or modules that harbor genes correlated by expression. Gene ontology and pathway enrichment analyses were performed to assess functional terms and pathways that were enriched within the different gene modules. By interrogating the co‐expression network for genes in different modules that associate with a gene of interest, diverse functional roles of the gene can be deciphered. By mapping genes differentially expressing under a certain condition in Arabidopsis onto the co‐expression network, we demonstrate the ability of the network to uncover novel genes that are likely transcriptionally active but prone to be missed by standard statistical approaches due to their falling outside of the confidence zone of detection. To our knowledge, this is the first A. thaliana co‐expression network constructed using the entire mRNA‐Seq datasets (>20,000) available at the NCBI SRA database. The developed network can serve as a useful resource for the Arabidopsis research community to interrogate specific genes of interest within the network, retrieve the respective interactomes, decipher gene modules that are transcriptionally altered under certain condition or stage, and gain understanding of gene functions.

Adaptation in Unstable Environments and Global Gene Losses: Small but Stable Gene Networks by the May–Wigner Theory

“…The gene losses avoid essential genes while simplifying GRNs. When mapping the mangrove gene losses to Arabidopsis thaliana , we found that 2,624 of the 10,480 loss events could be assigned to the reliably connected gene network that contains 6,411 of the 27,416 total genes ( Burks et al 2022 ; Fisher’s exact test; P = 2.1 × 10 −7 ). The gene losses are also enriched on large gene families.…”

Artificial selection reveals complex genetic architecture of shoot branching and its response to nitrate supply in Arabidopsis

“…Such regulatory networks are likely to be highly interconnected, and thus small-effect alleles across a wide range of loci, even those seemingly unrelated to the trait under study, may in fact have an impact on a trait such as shoot branching [ 75 ]. It will be interesting to investigate the overlap between our candidate QTL and their positioning in such regulatory networks, for example by exploring existing large-scale transcriptome networks [ 76 ] or by exploring co-expression patterns more specifically across a range of mutants in “core” shoot branching regulators (such as MAX , SMAXL and BRC genes) [ 24 , 77 ]. Furthermore, given the difference between LN and HN populations we observe, it may be fruitful to explore the transcriptomic changes in young buds from plastic and non-plastic genotypes growing under different nitrate conditions (i.e.…”

Identification of Novel Antimicrobial Resistance Genes Using Machine Learning, Homology Modeling, and Molecular Docking

“…One approach could be to assess their expression during resistance or use gene co-expression networks for understanding their role in resistance (see, for example, refs. [34,35] for such an approach that was used to characterize stress responsive genes). Novel resistance genes prioritized using computational approaches could be the candidates for experimental verification using wet lab assays, which may also provide insights into yet unknown mechanisms of resistance.…”