Bioinformatics resources for pollen

Abstract

Bioinformatics for Pollen. Pollen plays a key role in crop production, and its development is the most delicate phase in reproduction. Different metabolic pathways are involved in pollen development, and changes in the level of some metabolites, as well as responses to stress, are correlated with the reduction in pollen viability, leading consequently to a decrease in the fruit production. However, studies on pollen may be hard because gamete development and fertilization are complex processes that occur during a short window of time. The rise of the so-called -omics sciences provided key strategies to promote molecular research in pollen tissues, starting from model organisms and moving to increasing number of species. An integrated multi-level approach based on investigations from genomics, transcriptomics, proteomics and metabolomics appears now feasible to clarify key molecular processes in pollen development and viability. To this aim, bioinformatics has a fundamental role for data production and analysis, contributing varied and ad hoc methodologies, endowed with different sensitivity and specificity, necessary for extracting added-value information from the large amount of molecular data achievable. Bioinformatics is also essential for data management, organization, distribution and integration in suitable resources. This is necessary to catch the biological features of the pollen tissues and to design effective approaches to identifying structural or functional properties, enabling the modeling of the major involved processes in normal or in stress conditions. In this review, we provide an overview of the available bioinformatics resources for pollen, ranging from raw data collections to complete databases or platforms, when available, which include data and/or results from -omics efforts on the male gametophyte. Perspectives in the fields will also be described.

Retained metabolic activity in honey bee collected pollen has implications for pollen digestion and effects on honey bee health

“…Our results are consistent with earlier studies b a that found that bee-collected pollen showed a significant increase in O 2 uptake relative to ungerminated pollen (Keularts and Linskens 1968;Verhoef and Hoekstra 2012), supporting retained metabolic activity despite absence of pollen tube formation. Future studies of early germination processes (e.g., through the use of Ca 2+ flux ((Steinhorst and Kudla 2013), structural cellular changes (Vogler et al 2015), and transcriptomics and proteomics (Ambrosino et al 2016)) may provide additional understanding of pollen digestion in this species. By investigating these earlystage cellular components and processes in the honey bee gut, future studies may yield additional information that cannot be captured in studies of pollen tube formation or fertilization potential alone.…”

Bioinformatics Resources for Plant Abiotic Stress Responses: State of the Art and Opportunities in the Fast Evolving -Omics Era

“…The list of the resources here presented (Table 6), in comparison to the variety of studies on different plants and stress types, and the limited number of integrated platforms dedicated to abiotic stresses, highlights that, although the spreading of advanced molecular technologies and novel challenging projects in the field (e.g., [353]), coordinated and completed efforts that could provide data and information resources for further investigations and mining in the field are still strongly required [354]. Table 6.…”

Pollen as a target of environmental changes

“…Mesihovic et al (2016) provide guidelines for terminologies and experimental designs, which are of importance to unify information and to perform future experiments in a more standardized fashion, and Ambrosino et al (2016) gives an overview at which places public available information can be found. Finally, what emerges is the need to integrate new large-scale datasets to extract key components of pollen vitality and to apply this knowledge to guide new breeding strategies, which is outlined in the article by Driedonks et al (2016).…”