Method for Ultrarapid High-Content Screening for Biologically Active Chemicals Using Plant Pollen.

Chemical genomics attracts considerable attention by offering crucial tools for plant biology to regulate plant growth and development. However, most chemical screens are time consuming and laborious, and require a high input of resources. Here we describe a broadly applicable method for the ultrarapid high-content phenotypic screening of small chemical compound libraries for new plant growth regulators. The assay is based on determination of pollen tube growth and can be completed in less than 8 h. Using this method, we identified novel inhibitors and modulators of plant growth and showed that compounds selected using a Nicotiana tabacum-based assay were biologically active in other plants, such as Arabidopsis thaliana.

Novel small molecule modulators of plant growth and development identified by high-content screening with plant pollen.

BACKGROUND: Small synthetic molecules provide valuable tools to agricultural biotechnology to circumvent the need for genetic engineering and provide unique benefits to modulate plant growth and development. RESULTS: We developed a method to explore molecular mechanisms of plant growth by high-throughput phenotypic screening of haploid populations of pollen cells. These cells rapidly germinate to develop pollen tubes. Compounds acting as growth inhibitors or stimulators of pollen tube growth are identified in a screen lasting not longer than 8 h high-lighting the potential broad applicability of this assay to prioritize chemicals for future mechanism focused investigations in plants. We identified 65 chemical compounds that influenced pollen development. We demonstrated the usefulness of the identified compounds as promotors or inhibitors of tobacco and Arabidopsis thaliana seed growth. When 7 days old seedlings were grown in the presence of these chemicals twenty two of these compounds caused a reduction in Arabidopsis root length in the range from 4.76 to 49.20 % when compared to controls grown in the absence of the chemicals. Two of the chemicals sharing structural homology with thiazolidines stimulated root growth and increased root length by 129.23 and 119.09 %, respectively. The pollen tube growth stimulating compound (S-02) belongs to benzazepin-type chemicals and increased Arabidopsis root length by 126.24 %. CONCLUSIONS: In this study we demonstrate the usefulness of plant pollen tube based assay for screening small chemical compound libraries for new biologically active compounds. The pollen tubes represent an ultra-rapid screening tool with which even large compound libraries can be analyzed in very short time intervals. The broadly applicable high-throughput protocol is suitable for automated phenotypic screening of germinating pollen resulting in combination with seed germination assays in identification of plant growth inhibitors and stimulators.

Computational insight into the chemical space of plant growth regulators.

An enormous technological progress has resulted in an explosive growth in the amount of biological and chemical data that is typically multivariate and tangled in structure. Therefore, several computational approaches have mainly focused on dimensionality reduction and convenient representation of high-dimensional datasets to elucidate the relationships between the observed activity (or effect) and calculated parameters commonly expressed in terms of molecular descriptors. We have collected the experimental data available in patent and scientific publications as well as specific databases for various agrochemicals. The resulting dataset was then thoroughly analyzed using Kohonen-based self-organizing technique. The overall aim of the presented study is to investigate whether the developed in silico model can be applied to predict the agrochemical activity of small molecule compounds and, at the same time, to offer further insights into the distinctive features of different agrochemical categories. The preliminary external validation with several plant growth regulators demonstrated a relatively high prediction power (67%) of the constructed model. This study is, actually, the first example of a large-scale modeling in the field of agrochemistry.