De novo transcriptome assemblies of C3 and C4 non-model grass species reveal key differences in leaf development

evolSOM: An R package for analyzing conservation and displacement of biological variables with self-organizing maps

Motivation

Unraveling the connection between genes and traits is crucial for solving many biological puzzles. Ribonucleic acid molecules and proteins, derived from these genetic instructions, play crucial roles in shaping cell structures, influencing reactions, and guiding behavior. This fundamental biological principle links genetic makeup to observable traits, but integrating and extracting meaningful relationships from this complex, multimodal data present a significant challenge.

Results

We introduce evolSOM, a novel R package that allows exploring and visualizing the conservation or displacement of biological variables, easing the integration of phenotypic and genotypic attributes. It enables the projection of multi-dimensional expression profiles onto interpretable two-dimensional grids, aiding in the identification of conserved or displaced genes/phenotypes across multiple conditions. Variables displaced together suggest membership to the same regulatory network, where the nature of the displacement may hold biological significance. The conservation or displacement of variables is automatically calculated and graphically presented by evolSOM. Its user-friendly interface and visualization capabilities enhance the accessibility of complex network analyses.

Availability and implementation

The package is open-source under the GPL ( ≥ 3) and is available at https://github.com/sanprochetto/evolSOM, along with a step-by-step vignette and a full example dataset that can be accessed at https://github.com/sanprochetto/evolSOM/tree/main/inst/extdata.

LSTrAP-denovo: Automated Generation of Transcriptome Atlases for Eukaryotic Species Without Genomes

Despite the abundance of species with transcriptomic data, a significant number of species still lack sequenced genomes, making it difficult to study gene function and expression in these organisms. While de novo transcriptome assembly can be used to assemble protein-coding transcripts from RNA-sequencing (RNA-seq) data, the datasets used often only feature samples of arbitrarily selected or similar experimental conditions, which might fail to capture condition-specific transcripts. We developed the Large-Scale Transcriptome Assembly Pipeline for de novo assembled transcripts (LSTrAP-denovo) to automatically generate transcriptome atlases of eukaryotic species. Specifically, given an NCBI TaxID, LSTrAP-denovo can (1) filter undesirable RNA-seq accessions based on read data, (2) select RNA-seq accessions via unsupervised machine learning to construct a sample-balanced dataset for download, (3) assemble transcripts via over-assembly, (4) functionally annotate coding sequences (CDS) from assembled transcripts and (5) generate transcriptome atlases in the form of expression matrices for downstream transcriptomic analyses. LSTrAP-denovo is easy to implement, written in Python, and is freely available at https://github.com/pengkenlim/LSTrAP-denovo/.

Photosynthesis promotion mechanisms of artificial humic acid depend on plant types: A hydroponic study on C3 and C4 plants

It is feasible to improve plant photosynthesis to address the global climate goals of carbon neutrality. The application of artificial humic acid (AHA) is a promising approach to promote plant photosynthesis, however, the associated mechanisms for C3 and C4 plants are still unclear. In this study, the real-time chlorophyll synthesis and microscopic physiological changes in plant leave cells with the application of AHA were first revealed using the real-time chlorophyll fluorescence parameters and Non-invasive Micro-test Technique. The transcriptomics suggested that the AHA application up-regulated the genes in photosynthesis, especially related to chlorophyll synthesis and light energy capture, in maize and the genes in photosynthetic vitality and carbohydrate metabolic process in lettuce. Structural equation model suggested that the photodegradable substances and growth hormones in AHA directly contributes to photosynthesis of C4 plants (0.37). AHA indirectly promotes the photosynthesis in the C4 plants by upregulating functional genes (e.g., Mg-CHLI and Chlorophyllase) involved in light capture and transformation (0.96). In contrast, AHA mainly indirectly promotes C3 plants photosynthesis by increasing chlorophyll synthesis, and the Rubisco activity and the ZmRbcS expression in the dark reaction of lettuce (0.55). In addition, Mg2+ transfer and flux in C3 plant leaves was significantly improved by AHA, indirectly contributes to plant photosynthesis (0.24). Finally, the AHA increased the net photosynthetic rate of maize by 46.50 % and that of lettuce by 88.00 %. Application of the nutrients- and hormone-rich AHA improves plant growth and photosynthesis even better than traditional Hoagland solution. The revelation of the different photosynthetic promotion mechanisms on C3 and C4 plant in this work guides the synthesis and efficient application of AHA in green agriculture and will propose the development of AHA technology to against climate change resulting from CO2 emissions in near future.

The Calvin-Benson-Bassham cycle in C4 and Crassulacean acid metabolism species

The Calvin-Benson-Bassham (CBB) cycle is the ancestral CO2 assimilation pathway and is found in all photosynthetic organisms. Biochemical extensions to the CBB cycle have evolved that allow the resulting pathways to act as CO2 concentrating mechanisms, either spatially in the case of C4 photosynthesis or temporally in the case of Crassulacean acid metabolism (CAM). While the biochemical steps in the C4 and CAM pathways are known, questions remain on their integration and regulation with CBB cycle activity. The application of omic and transgenic technologies is providing a more complete understanding of the biochemistry of C4 and CAM species and will also provide insight into the CBB cycle in these plants. As the global population increases, new solutions are required to increase crop yields and meet demands for food and other bioproducts. Previous work in C3 species has shown that increasing carbon assimilation through genetic manipulation of the CBB cycle can increase biomass and yield. There may also be options to improve photosynthesis in species using C4 photosynthesis and CAM through manipulation of the CBB cycle in these plants. This is an underexplored strategy and requires more basic knowledge of CBB cycle operation in these species to enable approaches for increased productivity.