Next-generation plant science: putting big data to work

Next-generation plant science: putting big data to work

Updated: Jan 11, 2019

A Report on the Plant Genomes & Biotechnology: From Genes to Networks meeting, held at the Cold Spring Harbor Laboratories, USA, December 4–7, 2013.

Omics ApproachDrive Gene ExpressionNetwork MeetingRoot Stem CellStem Cell Dynamic

The introduction of next-generation sequencing has benefitted plant science because of a speedily increasing range of totally sequenced and annotated plant genomes. The availability of genomic information has enabled researchers to travel a step any and integrate massive information from completely different sorts of -omics analyses to deal with basic queries.

Over 120 participants gathered at the Plant Genomes & Biotechnology:

From Genes to Networks meeting to discuss novel findings in diverse areas of plant biology using one or multiple -omics techniques. Overall, the speakers underlined the benefits of multi-omics tools exploring the variety in physiological processes across plants, from model organisms and crops to carnivorous plants. While the multi-omics approach has several benefits, it could also present itself as overwhelming in both data quantity and complexity. Many talks bestowed at this meeting illustrated ways that to approach massive information to answer each general and elaborated queries in plant biology. Indeed, it had been evident that the queries that may be tackled mistreatment -omics approaches square measure immensely completely different from characterizing one sequence in an exceedingly specific biological process or immune pathway. Rather, -omics approaches facilitate viewing and understanding explicit biological processes as a part of a bigger enterprise: the total plant and its reference to the surroundings. The meeting offered recent insights from applying -omics to discovery, in the fields of an abiotic and biotic stress response, epigenetics and genetics, hormone signaling, growth and development, biodiversity and adaptation to the environment, and synthetic and network biology. Some of the foremost challenging uses of multi-omics techniques bestowed at the conference square measure mentioned below and embrace the characterization of intergenic regions, defining genes and pathways involved in specific processes, and measurement dynamic responses in tissues, whole plants or plant populations.

Beyond gene annotation understanding transcriptional control:

The number and quality of plant genomes made in recent years are staggering. Jason Williams (Cold Spring Harbor Laboratories, USA) according that between fifty and eighty plant genomes are sequenced in 2013 alone. This flux of genomic and transcriptomic knowledge is difficult to store and share between users, that is why Williams and associates have developed iPlant as a platform for knowledge storage and analysis. Mining genomic knowledge has already provided novel insights for understanding however and once plants activate genes. The keynote speaker, Joe Ecker (Salk Institute, USA), has made considerable contributions in understanding hormonal-mediated transcriptional regulation. By combining knowledge from transcription issue binding sites with methylation knowledge and transcriptomes, Ecker showed that gas induces waves of sequence induction, targeting all of the other plant hormone pathways. A number of alternative studies additionally integrated similar tools to expand our current understanding of an organic phenomenon.

For example, the complexness of sequence induction was made public by a study of twenty-six transcription factors conferred by Ken Heyndrickx (Ghent University, Belgium). Heyndrickx incontestible that transcription factors bind multiple motifs which binding doesn't correlate with expression, drawing in to question however organic phenomenon is regulated.

Further insights into the importance and complexness of intergenic regions were provided by Luis Herrera-Estrella (National technical school, Mexico) in his investigation of a carnivorous underwater plant known as plant genus gibba. The genome of U. gibba is tiny at solely eighty-two megabases, though it contains roughly similar numbers of genes as alternative plants. The distinction in size is especially because of reduced intergenic polymer, that begs the question: what square measure the necessities for intergenic polymer length to drive organic phenomenon in plants? Herrera-Estrella demonstrated that while U. gibba intergenic regions square measure tiny compared with alternative plants, they're sufficient to drive organic phenomenon.

Plants in relation to their environment

Several participants reportable the employment of multi-omics tools in understanding biological process processes or stress responses. A range of multi-omics approaches enclosed the combination of transcriptomics, metabolomics, phenomics or interactomics. In a significantly thorough study, Hilde Nelissen (Ghent University, Belgium) represented a study of the maize leaf transition zone to characterize the role of gibberellins in plant growth. Using the information on gene transcription, protein interactions, hormone accumulation and microscopy in growing sections of the maize leaf, Nelissen identified growth-related genes that when overexpressed were shown to increase plant growth. The power of phenomics in manufacturing fast and sensitive analysis of phenotypes over time was additionally spectacular.

Techniques describing the large-scale analysis of photosynthetic parameters, inflorescence design, root growth, and field growth performance were given. This approach, once combined with genetics and reverse biology, has the potential to quickly establish genes to blame for delicate phenotypes, which might otherwise be overlooked due to constraints of time and money. To understand wherever new genomic variation originates, Detlef Weigel (Max-Planck-Institute for biological process Biology, Germany) measured the mutation accumulation rate in thirty generations of dilleniid dicot genus. Weigel determined that whereas the mutation rate per generation is reduced, the speed becomes important once considering a complete plant population.

Studying the dynamics of life:

Plants square measure dynamic living systems whereby external and internal signals square measure integrated to supply changes over time. While valuable information can be gleaned from the functional role of a given gene at a one-time point, combining reverse genetics with multi-omics can better capture how biological processes work temporally. For example, Ross Sozzani (North geographic region State University, USA) developed a sublime system to image root vegetative cell dynamics in vivo. By combining visualization of auxin and SHORTROOTtranscript gradients, Sozzani described a system whereby the movement of the visualized transcripts could be related to cell division rates.

Detecting phenotypes in mutant lines may also take pleasure in temporal measurements, particularly within the context of environmental responsibility. David Kramer (Michigan State University, USA) was able to detect a photosynthetic phenotype in 20% of his screened Arabidopsis T-DNA insertion mutants during light fluctuations, while only 2% of these lines demonstrated a phenotype without light fluctuations. Methods to review macromolecule dynamics square measure essential for mapping plant interactomes. A high-throughput macromolecule advanced purification platform DEveloped by Geert de coastal diving bird (Ghent University, Belgium) has generated a dynamic map of protein-protein interactions assembled throughout the cell cycle, and generated novel insights into this critical process.

A method for the detection of macromolecule interactions in plant cells, presented by Dharmendra Singh (The Boyce Thompson Institute, USA), has shown a potential for discovering associations between plant and pathogen proteins on a large scale.

Predicting the future:

One of the most objectives in plant biology is to dependably predict plant responses given genomic data and environmental circumstances. The copious quantity of knowledge derived from multi-omics studies has the potential to contribute to the current goal. Indeed, plant modeling tools square measure already out there, and multi-omics techniques square measure already being employed to hurry up plant breeding. However, there's still abundant to be discovered, and the talks presented at this meeting underscore how multi-omics approaches are changing plant research.

The nature of -omics analyses shifts analysis time from taking measurements to analyzing massive knowledge and finding relevant trends. Big knowledge additionally expands the queries we will rise to incorporate niches or processes that have up to now been too advanced to review exhaustively.

A great example is that the characterization of the ecology of the foundation bacteriome across soil conditions given by Sarah Lebeis (University of North geographic region, USA). While the study of this method still needs intensive wet science laboratory analysis, Lebeis used big data as a platform to begin her investigation into root bacterial communities and their effect on plant growth. Thus, fastidiously generated massive knowledge will guide biological discovery and complement targeted approaches in dissecting plant processes. Multi-omics approaches unify knowledge on the function and regulation of individual genes/pathways with contextual information - in short, it is a step forward toward capturing the essence of a plant.