Identification of Shoot Differentiation-Related Genes in Populus euphratica Oliv

Differential expression analysis using a model-based gene clustering algorithm for RNA-seq data

Background

RNA-seq is a tool for measuring gene expression and is commonly used to identify differentially expressed genes (DEGs). Gene clustering is used to classify DEGs with similar expression patterns for the subsequent analyses of data from experiments such as time-courses or multi-group comparisons. However, gene clustering has rarely been used for analyzing simple two-group data or differential expression (DE). In this study, we report that a model-based clustering algorithm implemented in an R package, MBCluster.Seq, can also be used for DE analysis.

Results

The input data originally used by MBCluster.Seq is DEGs, and the proposed method (called MBCdeg) uses all genes for the analysis. The method uses posterior probabilities of genes assigned to a cluster displaying non-DEG pattern for overall gene ranking. We compared the performance of MBCdeg with conventional R packages such as edgeR, DESeq2, and TCC that are specialized for DE analysis using simulated and real data. Our results showed that MBCdeg outperformed other methods when the proportion of DEG (P_DEG) was less than 50%. However, the DEG identification using MBCdeg was less consistent than with conventional methods. We compared the effects of different normalization algorithms using MBCdeg, and performed an analysis using MBCdeg in combination with a robust normalization algorithm (called DEGES) that was not implemented in MBCluster.Seq. The new analysis method showed greater stability than using the original MBCdeg with the default normalization algorithm.

Conclusions

MBCdeg with DEGES normalization can be used in the identification of DEGs when the P_DEG is relatively low. As the method is based on gene clustering, the DE result includes information on which expression pattern the gene belongs to. The new method may be useful for the analysis of time-course and multi-group data, where the classification of expression patterns is often required.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04438-4.

Modeling genome-wide by environment interactions through omnigenic interactome networks

How genes interact with the environment to shape phenotypic variation and evolution is a fundamental question intriguing to biologists from various fields. Existing linear models built on single genes are inadequate to reveal the complexity of genotype-environment (G-E) interactions. Here, we develop a conceptual model for mechanistically dissecting G-E interplay by integrating previously disconnected theories and methods. Under this integration, evolutionary game theory, developmental modularity theory, and a variable selection method allow us to reconstruct environment-induced, maximally informative, sparse, and casual multilayer genetic networks. We design and conduct two mapping experiments by using a desert-adapted tree species to validate the biological application of the model proposed. The model identifies previously uncharacterized molecular mechanisms that mediate trees' response to saline stress. Our model provides a tool to comprehend the genetic architecture of trait variation and evolution and trace the information flow of each gene toward phenotypes within omnigenic networks.

Protuberances are organized distinct regions of long-term callus: histological and transcriptomic analyses in kiwifruit

KEY MESSAGE

Macroscopic, ultrastructural, and molecular features-like a ball shape, the presence of starch granules, and the up-regulation of genes involved in carbohydrate metabolism and secondary metabolite biosynthesis-distinguish PT regions within a callus. The modification of the mass of pluripotent cells into de novo shoot bud regeneration is highly relevant to developmental biology and for agriculture and biotechnology. This study deals with protuberances (PT), structures that appear during the organogenic long-term culturing of callus (OC) in kiwifruit. These ball-shaped regions of callus might be considered the first morphological sign of the subsequent shoot bud development. Sections of PT show the regular arrangement of some cells, especially on the surface, in contrast to the regions of OC beyond the PT. The cells of OC possess chloroplasts; however, starch granules were observed only in PTs' plastids. Transcriptomic data revealed unique gene expression for each kind of sample: OC, PT, and PT with visible shoot buds (PT-SH). Higher expression of the gene involved in lipid (glycerol-3-phosphate acyltransferase 5 [GPAT5]), carbohydrate (granule-bound starch synthase 1 [GBSS1]), and secondary metabolite (beta-glucosidase 45 [BGL45]) pathways were detected in PT and could be proposed as the markers of these structures. The up-regulation of the regulatory associated protein of TOR (RAPTOR1) was found in PT-SH. The highest expression of the actinidain gene in leaves from two-year-old regenerated plants suggests that the synthesis of this protein takes place in fully developed organs. The findings indicate that PT and PT-SH are specific structures within OC but have more features in common with callus tissue than with organs.