Dissecting metabolic processes in plant mutualistic interactions by the application of systems biology strategies

Mutualism is a type of ecological interaction with an essential role in ecosystem function. Specifically, mutualism is the communications between two or more partnering species, where all partners receive benefits from these interactions. Two common mutualistic associations that occur with plants involve with either a fungus or an animal species. Plant-fungus interactions are known as a mycorrhizal system which provide net benefits for the photosynthetic plant and fungus. However, in uncommon cases, a non-photosynthetic plant can also be involved as a third interacting partner. This non-photosynthetic plant is dependent on the communications with fungus to obtain nutritional resources, hence these plants are considered myco-heterotrophic. Plant-animal interactions are of particular importance for pollination. In this study, a systems biology approach is applied to dissect the molecular programs that are involved in two examples of these mutualistic systems. The mycorrhizal system was investigated with the non-photosynthetic plant, Monotropa uniflora. The pollination system was investigated with the unisexual flowers of Cucurbita maxima, which are dependent on a bee pollinator to transfer pollen from the male to the female flower. The systems biology strategy integrated metabolomics and transcriptomics datasets coupled with the morphological evaluations to describe the forenamed molecular systems. GC-MS based analytical techniques with capability of identifying and quantifying small molecules (molecular weight <1000 Da) were applied to characterize the metabolome of M. uniflora aerial tissues, and C. maxima male and female flowers. RNA-sequencing was applied to comprehensively analyze the transcriptome of forenamed plants. Additionally, transmission and scanning electron microscopy and light microscopy were utilized in describing the ultrastructural characteristics of M. uniflora stem. The results revealed that M. uniflora has simplified morphological structures and the molecular program remain relatively similar during growth and development. In C. maxima, different floral organs, as well as the gender of the flowers, introduce significant variations in the molecular systems and distinct functionality of the male and female flowers during anthesis that has evolved to enable efficient pollination

Assessing data analysis techniques in a high-throughput meiosis-like induction detection system

Background Strategies to understand meiotic processes have relied on cytogenetic and mutant analysis. However, thus far in vitro meiosis induction is a bottleneck to laboratory-based plant breeding as factor(s) that switch cells in crops species from mitotic to meiotic divisions are unknown. A high-throughput system that allows researchers to screen multiple candidates for their meiotic induction role using low-cost microfluidic devices has the potential to facilitate the identification of factors with the ability to induce haploid cells that have undergone recombination (artificial gametes) in cell cultures. Results A data analysis pipeline and a detailed protocol are presented to screen for plant meiosis induction factors in a quantifiable and efficient manner. We assessed three data analysis techniques using spiked-in protoplast samples (simulated gametes mixed into somatic protoplast populations) of flow cytometry data. Polygonal gating, which was considered the “gold standard”, was compared to two thresholding methods using open-source analysis software. Both thresholding techniques were able to identify significant differences with low spike-in concentrations while also being comparable to polygonal gating. Conclusion Our study provides details to test and analyze candidate meiosis induction factors using available biological resources and open-source programs for thresholding. RFP (PE.CF594.A) and GFP (FITC.A) were the only channels required to make informed decisions on meiosis-like induction and resulted in detection of cell population changes as low as 0.3%, thus enabling this system to be scaled using microfluidic devices at low costs.This article is published as Cook, T.M., Biswas, E., Dutta, S. et al. Assessing data analysis techniques in a high-throughput meiosis-like induction detection system. Plant Methods 20, 7 (2024). https://doi.org/10.1186/s13007-023-01132-9. © The Author(s) 2024. This article is licensed under a Creative Commons Attribution 4.0 International License