Co-expression Networks From Gene Expression Variability Between Genetically Identical Seedlings Can Reveal Novel Regulatory Relationships

Integrated Systems Biology Pipeline to Compare Co-Expression Networks in Plants and Elucidate Differential Regulators

To identify sets of genes that exhibit similar expression characteristics, co-expression networks were constructed from transcriptome datasets that were obtained from plant samples at various stages of growth and development or treated with diverse biotic, abiotic, and other environmental stresses. In addition, co-expression network analysis can provide deeper insights into gene regulation when combined with transcriptomics. The coordination and integration of all these complex networks to deduce gene regulation are major challenges for plant biologists. Python and R have emerged as major tools for managing complex scientific data over the past decade. In this study, we describe a reproducible protocol POTFUL (pant co-expression transcription factor regulators), implemented in Python 3, for integrating co-expression and transcription factor target protein networks to infer gene regulation.

Design, execution, and interpretation of plant RNA-seq analyses

Genomics has transformed our understanding of the genetic architecture of traits and the genetic variation present in plants. Here, we present a review of how RNA-seq can be performed to tackle research challenges addressed by plant sciences. We discuss the importance of experimental design in RNA-seq, including considerations for sampling and replication, to avoid pitfalls and wasted resources. Approaches for processing RNA-seq data include quality control and counting features, and we describe common approaches and variations. Though differential gene expression analysis is the most common analysis of RNA-seq data, we review multiple methods for assessing gene expression, including detecting allele-specific gene expression and building co-expression networks. With the production of more RNA-seq data, strategies for integrating these data into genetic mapping pipelines is of increased interest. Finally, special considerations for RNA-seq analysis and interpretation in plants are needed, due to the high genome complexity common across plants. By incorporating informed decisions throughout an RNA-seq experiment, we can increase the knowledge gained.

Melatonin Attenuates H2O2-Induced Oxidative Injury by Upregulating LncRNA NEAT1 in HT22 Hippocampal Cells

More research is required to understand how melatonin protects neurons. The study aimed to find out if and how long non-coding RNA (lncRNA) contributes to melatonin's ability to defend the hippocampus from H2O2-induced oxidative injury. LncRNAs related to oxidative injury were predicted by bioinformatics methods. Mouse hippocampus-derived neuronal HT22 cells were treated with H2O2 with or without melatonin. Viability and apoptosis were detected by Cell Counting Kit-8 and Hoechst33258. RNA and protein levels were measured by quantitative real-time PCR, Western blot, and immunofluorescence. Bioinformatics predicted that 38 lncRNAs were associated with oxidative injury in mouse neurons. LncRNA nuclear paraspeckle assembly transcript 1 (NEAT1) was related to H2O2-induced oxidative injury and up-regulated by melatonin in HT22 cells. The knockdown of NEAT1 exacerbated H2O2-induced oxidative injury, weakened the moderating effect of melatonin, and abolished the increasing effect of melatonin on the mRNA and protein level of Slc38a2. Taken together, melatonin attenuates H2O2-induced oxidative injury by upregulating lncRNA NEAT1, which is essential for melatonin stabilizing the mRNA and protein level of Slc38a2 for the survival of HT22 cells. The research may assist in the treatment of oxidative injury-induced hippocampal degeneration associated with aging using melatonin and its target lncRNA NEAT1.

Genetic dissection of nitrogen induced changes in the shoot and root biomass of spinach

Efficient partitioning of above and below-ground biomass in response to nitrogen (N) is critical to the productivity of plants under sub-optimal conditions. It is particularly essential in vegetable crops like spinach with shallow root systems, a short growth cycle, and poor nitrogen use efficiency. In this study, we conducted a genome-wide association study (GWAS) to explore N-induced changes using spinach accessions with diverse genetic backgrounds. We evaluated phenotypic variations as percent changes in the shoot and root biomass in response to N using 201 spinach accessions grown in randomized complete blocks design in a soilless media under a controlled environment. A GWAS was performed for the percent changes in the shoot and root biomass in response to N in the 201 spinach accessions using 60,940 whole-genome resequencing generated SNPs. Three SNP markers, chr4_28292655, chr6_1531056, and chr6_37966006 on chromosomes 4 and 6, were significantly associated with %change in root weight, and two SNP markers, chr2_18480277 and chr4_47598760 on chromosomes 2 and 4, were significantly associated with % change shoot weight. The outcome of this study established a foundation for genetic studies needed to improve the partitioning of total biomass and provided a resource to identify molecular markers to enhance N uptake via marker-assisted selection or genomic selection in spinach breeding programs.

Network Analysis Reveals Different Cellulose Degradation Strategies Across Trichoderma harzianum Strains Associated With XYR1 and CRE1

Trichoderma harzianum, whose gene expression is tightly controlled by the transcription factors (TFs) XYR1 and CRE1, is a potential candidate for hydrolytic enzyme production. Here, we performed a network analysis of T. harzianum IOC-3844 and T. harzianum CBMAI-0179 to explore how the regulation of these TFs varies between these strains. In addition, we explored the evolutionary relationships of XYR1 and CRE1 protein sequences among Trichoderma spp. The results of the T. harzianum strains were compared with those of Trichoderma atroviride CBMAI-0020, a mycoparasitic species. Although transcripts encoding carbohydrate-active enzymes (CAZymes), TFs, transporters, and proteins with unknown functions were coexpressed with cre1 or xyr1, other proteins indirectly related to cellulose degradation were identified. The enriched GO terms describing the transcripts of these groups differed across all strains, and several metabolic pathways with high similarity between both regulators but strain-specific differences were identified. In addition, the CRE1 and XYR1 subnetworks presented different topology profiles in each strain, likely indicating differences in the influences of these regulators according to the fungi. The hubs of the cre1 and xyr1 groups included transcripts not yet characterized or described as being related to cellulose degradation. The first-neighbor analyses confirmed the results of the profile of the coexpressed transcripts in cre1 and xyr1. The analyses of the shortest paths revealed that CAZymes upregulated under cellulose degradation conditions are most closely related to both regulators, and new targets between such signaling pathways were discovered. Although the evaluated T. harzianum strains are phylogenetically close and their amino acid sequences related to XYR1 and CRE1 are very similar, the set of transcripts related to xyr1 and cre1 differed, suggesting that each T. harzianum strain used a specific regulation strategy for cellulose degradation. More interestingly, our findings may suggest that XYR1 and CRE1 indirectly regulate genes encoding proteins related to cellulose degradation in the evaluated T. harzianum strains. An improved understanding of the basic biology of fungi during the cellulose degradation process can contribute to the use of their enzymes in several biotechnological applications and pave the way for further studies on the differences across strains of the same species.

OPT gene family analysis of potato (Solanum tuberosum) responding to heavy metal stress: Comparative omics and co-expression networks revealed the underlying core templates and specific response patterns

Oligopeptides transporter (OPT) can maintain intracellular metal homeostat, however, their evolutionary characteristics, as well as their expression patterns in heavy metal exposure, remain unclear. Compared with previous OPT family identification, we identified 94 OPT genes (including 21 in potato) in potato and 4 other plants by HMMER program based on OPT domain (PF03169) for the first time. Secondly, conserved and special OPTs were found through comprehensive analysis. Thirdly, spatio-temporal tissue specific expression patterns and co-expression frameworks of potato OPT genes under different heavy metal stress were constructed. These data can provide excellent gene resources for food security and soil remediation.