The Arabidopsis gene co-expression network

Identification of the SP gene family and transcription factor SlSP5G promotes the high-temperature tolerance of tomatoes

Some members of the SELF PRUNING (SP) gene family have been shown to play critical roles in developmental processes and stress responses across a wide range of plant species. The study identifies 13 members that can be divided into three subfamilies based on evolutionary analysis. Cis-Acting element analysis of the promoter regions indicated the presence of numerous stress- and hormone-responsive elements in the SlSP family. Subcellular localization analysis showed that the SlSP family proteins are localized in the cell membrane, nucleus, and chloroplasts. Notably, the expression of SELF PRUNING 5G (SlSP5G) was significantly induced by high-temperature stress. Silencing SlSP5G reduced tolerance to high-temperature stress. Conversely, its overexpression in stable transgenic lines enhanced heat tolerance, as demonstrated by improved membrane stability, elevated antioxidant enzyme activity, and reduced reactive oxygen species (ROS) accumulation. In contrast, SlSP5G knockout lines were more susceptible to high-temperature stress. This study provides a comprehensive analysis of the SlSP gene family, offering novel insights into the mechanism of SlSP5G-mediated heat stress tolerance in tomato.

Identifying hub genes and key functional modules in leaf tissue of Populus species based on WGCNA

As one of the most important parts of plants, the genetic mechanisms of photosynthesis or the response of leaf to a single abiotic and biotic stress have been well studied. However, few researches have involved in the integration of data analysis from system level in leaf tissue under multiple abiotic stresses by utilizing biological networks. In this study, the weighted gene co-expression network analysis (WGCNA) strategy was used to integrate multiple data in leaf tissue of Populus species under different sample treatments. The gene co-expression networks were constructed and functional modules were identified by selecting the suitable soft threshold power β in the procedure of WGCNA. The identified hub genes and gene modules were annotated by agriGO, NetAffx Analysis Center, The Plant Genome Integrative Explorer (PlantGenIE) and other annotation tools. The annotation results have displayed that the highly correlated modules and hub genes are involved in the important biological processes or pathways related to module traits. The efficiency of the WGCNA strategy can generate comprehensive understanding of gene module-traits associations in leaf tissue, which will provide novel insight into the genetic mechanism of Populus species.

StCoExpNet: a global co-expression network analysis facilitates identifying genes underlying agronomic traits in potatoes

KEY MESSAGE

We constructed a gene expression atlas and co-expression network for potatoes and identified several novel genes associated with various agronomic traits. This resource will accelerate potato genetics and genomics research. Potato (Solanum tuberosum L.) is the world's most crucial non-cereal food crop and ranks third in food production after wheat and rice. Despite the availability of several potato transcriptome datasets at public databases like NCBI SRA, an effort has yet to be put into developing a global transcriptome atlas and a co-expression network for potatoes. The objectives of our study were to construct a global expression atlas for potatoes using publicly available transcriptome datasets, identify housekeeping and tissue-specific genes, construct a global co-expression network and identify co-expression clusters, investigate the transcriptional complexity of genes involved in various essential biological processes related to agronomic traits, and provide a web server (StCoExpNet) to easily access the newly constructed expression atlas and co-expression network to investigate the expression and co-expression of genes of interest. In this study, we used data from 2299 publicly available potato transcriptome samples obtained from 15 different tissues to construct a global transcriptome atlas. We found that roughly 87% of the annotated genes exhibited detectable expression in at least one sample. Among these, we identified 281 genes with consistent and stable expression levels, indicating their role as housekeeping genes. Conversely, 308 genes exhibited marked tissue-specific expression patterns. We exemplarily linked some co-expression clusters to important agronomic traits of potatoes, such as self-incompatibility, anthocyanin biosynthesis, tuberization, and defense responses against multiple pathogens. The dataset compiled here constitutes a new resource (StCoExpNet), which can be accessed at https://stcoexpnet.julius-kuehn.de . This transcriptome atlas and the co-expression network will accelerate potato genetics and genomics research.

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

Although gene loss is common in evolution, it remains unclear whether it is an adaptive process. In a survey of seven major mangrove clades that are woody plants in the intertidal zones of daily environmental perturbations, we noticed that they generally evolved reduced gene numbers. We then focused on the largest clade of Rhizophoreae and observed the continual gene set reduction in each of the eight species. A great majority of gene losses are concentrated on environmental interaction processes, presumably to cope with the constant fluctuations in the tidal environments. Genes of the general processes for woody plants are largely retained. In particular, fewer gene losses are found in physiological traits such as viviparous seeds, high salinity, and high tannin content. Given the broad and continual genome reductions, we propose the May-Wigner theory (MWT) of system stability as a possible mechanism. In MWT, the most effective solution for buffering continual perturbations is to reduce the size of the system (or to weaken the total genic interactions). Mangroves are unique as immovable inhabitants of the compound environments in the land-sea interface, where environmental gradients (such as salinity) fluctuate constantly, often drastically. Extending MWT to gene regulatory network (GRN), computer simulations and transcriptome analyses support the stabilizing effects of smaller gene sets in mangroves vis-à-vis inland plants. In summary, we show the adaptive significance of gene losses in mangrove plants, including the specific role of promoting phenotype innovation and a general role in stabilizing GRN in unstable environments as predicted by MWT.

Aggregated gene co-expression networks predict transcription factor regulatory landscapes in grapevine

Gene co-expression networks (GCNs) have not been extensively studied in non-model plants. However, the rapid accumulation of transcriptome datasets in certain species represents an opportunity to explore underutilized network aggregation approaches. In fact, aggregated GCNs (aggGCNs) highlight robust co-expression interactions and improve functional connectivity. We applied and evaluated two different aggregation methods on public grapevine RNA-Seq datasets from three different tissues (leaf, berry, and 'all organs'). Our results show that co-occurrence-based aggregation generally yielded the best-performing networks. We applied aggGCNs to study several transcription factor gene families, showing their capacity for detecting both already-described and novel regulatory relationships between R2R3-MYBs, bHLH/MYC, and multiple specialized metabolic pathways. Specifically, transcription factor gene- and pathway-centered network analyses successfully ascertained the previously established role of VviMYBPA1 in controlling the accumulation of proanthocyanidins while providing insights into its novel role as a regulator of p-coumaroyl-CoA biosynthesis as well as the shikimate and aromatic amino acid pathways. This network was validated using DNA affinity purification sequencing data, demonstrating that co-expression networks of transcriptional activators can serve as a proxy of gene regulatory networks. This study presents an open repository to reproduce networks in other crops and a GCN application within the Vitviz platform, a user-friendly tool for exploring co-expression relationships.

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

Quantitative traits may be controlled by many loci, many alleles at each locus, and subject to genotype-by-environment interactions, making them difficult to map. One example of such a complex trait is shoot branching in the model plant Arabidopsis, and its plasticity in response to nitrate. Here, we use artificial selection under contrasting nitrate supplies to dissect the genetic architecture of this complex trait, where loci identified by association mapping failed to explain heritability estimates. We found a consistent response to selection for high branching, with correlated responses in other traits such as plasticity and flowering time. Genome-wide scans for selection and simulations suggest that at least tens of loci control this trait, with a distinct genetic architecture between low and high nitrate treatments. While signals of selection could be detected in the populations selected for high branching on low nitrate, there was very little overlap in the regions selected in three independent populations. Thus the regulatory network controlling shoot branching can be tuned in different ways to give similar phenotypes.

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

Antimicrobial resistance (AMR) threatens the healthcare system worldwide with the rise of emerging drug resistant infectious agents. AMR may render the current therapeutics ineffective or diminish their efficacy, and its rapid dissemination can have unmitigated health and socioeconomic consequences. Just like with many other health problems, recent computational advances including developments in machine learning or artificial intelligence hold a prodigious promise in deciphering genetic factors underlying emergence and dissemination of AMR and in aiding development of therapeutics for more efficient AMR solutions. Current machine learning frameworks focus mainly on known AMR genes and are, therefore, prone to missing genes that have not been implicated in resistance yet, including many uncharacterized genes whose functions have not yet been elucidated. Furthermore, new resistance traits may evolve from these genes leading to the rise of superbugs, and therefore, these genes need to be characterized. To infer novel resistance genes, we used complete gene sets of several bacterial strains known to be susceptible or resistant to specific drugs and associated phenotypic information within a machine learning framework that enabled prioritizing genes potentially involved in resistance. Further, homology modeling of proteins encoded by prioritized genes and subsequent molecular docking studies indicated stable interactions between these proteins and the antimicrobials that the strains containing these proteins are known to be resistant to. Our study highlights the capability of a machine learning framework to uncover novel genes that have not yet been implicated in resistance to any antimicrobials and thus could spur further studies targeted at neutralizing AMR.