Omics in plant breeding


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Technologies for development of plant science and also crop improvement for sustainable agriculture

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Omics in plant breeding

  1. 1. “OMICS” In Crop Breeding“OMICS” In Crop Breeding Poornima KN Roll No: 9869
  2. 2. ContentsContents Introduction Omics Space ◦ Genomics ◦ Transcriptomics ◦ Proteomics ◦ Metabolomics ◦ Phenomics Case Studies Summary Conclusion Approaches and applications
  4. 4. Molecular networks of cell controlling traits and phenotypes
  5. 5. • That one gene encodes one protein, which catalyzes one reaction and determines one phenotype is no longer the case.  How to capture all molecules and their interactions, dynamics, regulations and turnover … ?  How to determine the rate-limiting molecule and step ? How to predict ?  Manipulating one gene can cause pleiotropic effects ? Large-scale biology – “OMICS” – Revolution in screening traits and develop novel improved organisms Concepts to be investigated and understood
  6. 6. Crop Breeding Developmental biology “Omics in plants” Identify genes, promoters, mi RNAs, pathway components
  7. 7. Omics Platforms
  8. 8. GenomicsGenomics  Genomics – the comprehensive study of whole sets of genes & their interactions (DNA microarrays)
  9. 9. Genome sequencing projectsGenome sequencing projects
  10. 10. Applications of Plant Genomics Gene identification and cloning Gene prediction/ discovery Genetic mapping and locating genes Genome manipulation QTLs analysis Molecular markers and MAS Comparative genomics Gene banks and chromosome stocks Understanding expression profiles, responses and interactions
  11. 11. Potato is the first sequenced genome of an asterid, a clade within eudicots that encompasses nearly 70,000 species characterized by unique morphological, developmental and compositional features. Autotetraploid- used a doubled monoploid line – Phureja DM 1-3 516 R44 (DM) Heterozygous diploid breeding line RH89-039-16 (RH) WGS- Illumina and Roche- 727 Mb which is 117Mb less than estimated genome size. Repetitive sequences account for 62.2% of 452.5MB assembled genome with 29.4% occupied by LTR retrotransposons. RNA seq data- 39,031 protein coding genes annotated and 9875 genes showed alternative splicing indicating more functional variation.
  12. 12. Comparative analysis and GenomeComparative analysis and Genome evolution studyevolution study Orthologous and paralogous gene families Genome duplication Analysis of syntenic blocks
  13. 13. Haplotype diversity and inbreedingHaplotype diversity and inbreeding depressiondepression Vigor Modality representing zygosity Inbreeding depression analysis Euchromatic and heterochromatic region analysis • Sequenced and assembled 1,644RH BAC clones generating 178 Mb of non-redundant sequence from both haplotypes • 3,018 SNPs induce PS in RH and 940 in DM 80 loci with FS in RH. • 246 genes specific to RH and 29 were DM specific.
  14. 14. Study of tuber biologyStudy of tuber biology KTI gene organisation across potato genome Phylogenetic tree and KTI gene expression heat map Starch synthesis enzymes and genes involved in carbohydrate metabolism
  15. 15. Sources for genomicsSources for genomics De novo sequencing Re sequencing Metagenomics Epigenetics RNA sequencing - Transcriptomics
  16. 16. TranscriptomicsTranscriptomics  The study of the transcriptome, the complete set of RNA transcripts produced by the genome at any one time.
  17. 17. Transcript profiling methodsTranscript profiling methods Whole genome transcriptome analysis - Microarray - SAGE - MPSS Target genome transcriptome analysis - Northern blot - Dot blot - RT-PCR -
  18. 18. Application of transcriptomicsApplication of transcriptomics Differential expression of genes Co expression of genes Gene interaction Alternative splicing of genes
  19. 19. Hiremath et al, 2011. Plant Biotechnology Journal
  20. 20.  Studied biosynthesis of glucosinolates (GSLs) from amino acids found in the family Brassicaceae.  Discovery of two TFs- Myb28 and Myb29 involved in aliphatic GSL production by integrated omics approach.  Combined transcriptome coexpression analysis, mutant transcriptome analysis and GSL analysis.
  21. 21. Expression analysis GSL biosynthesisExpression analysis GSL biosynthesis genesgenes Mutant analysis Over expression analysis
  22. 22. Sources of transcriptomicsSources of transcriptomics Expression arrays Tilling arrays MicroRNA arrays Protein arrays - Proteomics
  23. 23. ProteomicsProteomics The study of proteome, the structure and function of complete set of protein in a cell at a given time.
  24. 24. Applications of proteomicsApplications of proteomics  Protein Mining – catalog all the proteins present in a tissue, cell, organelle, etc.  Differential Expression Profiling – Identification of proteins in a sample as a function of a particular state: differentiation, stage of development, disease state, response to stimulus or environments.  Network Mapping – Identification of proteins in functional networks: biosynthetic pathways, signal transduction pathways, multiprotein complexes.  Mapping Protein Modifications – Characterization of posttranslational modifications: phosphorylation, glycosylation, oxidation, etc.
  25. 25.  Three Australian wheat cultivars (Triticum aestivum L. cv Kukri, Excalibur, and RAC875)  Shotgun proteomics study using iTRAQ (Isobaric tags for relative and absolute quantitation) approach. Frontiers in plant science, 12 september 2011
  26. 26. Proteome analysisProteome analysis Frontiers in plant science, 12 september 2011
  27. 27. Completion of the drought regime RAC 875 (tolerant) had the most number of protein changes (206) with Excalibur (tolerant) intermediate (177) and Kukri (intolerant; 168) the least. RAC875 has the highest capacity of the three cultivars for a cellular protein response to drought. Down regulation of proteins involved in photosynthesis and the Calvin cycle, consistent with avoidance of ROS generation in all three cultivars was observed. Known drought responsive proteins, including dehydrins, were also significantly up-regulated. The findings from this proteomic study support the physiological and yield data (Izanloo et al., 2008) previously reported between the three wheat cultivars (Kukri, Excalibur, RAC875) in response to cyclic drought stress. This highlights the importance of proteomics as a complementary tool for identifying candidate genes in abiotic stress tolerance in cereals. Protein changes during drought stress
  28. 28. Sources of proteomicsSources of proteomics Protein mixtures Post-translational modifications Biomarker studies Examination of metabolites - Metabolomics
  29. 29. MetabolomicsMetabolomics Study of metabolome, collection of all metabolites in a cell, tissue, organ or organism.
  30. 30. Applications of metabolomicsApplications of metabolomics Characterization of metabolism Identification of regulated key sites in network. Biofortification and genetic modification Investigation of gene function under stress conditions
  31. 31. •Evaluation of metabolite concentrations of fruit pericarp alongside whole-plant parameters in an IL population in which marker-defined regions of the wild species S. pennellii are replaced with homologous regions of the cultivated variety M82 (S. lycopersicum). •Harvest index, the measure of efficiency in partitioning of assimilated photosynthate to harvestable product, as the chief pleiotropic hub in the combined network of metabolic and whole-plant phenotypic traits. •The combination of marker-assisted selection and metabolite profiling therefore represents a viable alternative to genetic modification strategies for metabolic engineering.
  32. 32. Sources of metabolomicsSources of metabolomics Toxicity assessment Nutrigenomics Forensic analysis Petrochemical analysis Phenotype analysis- (phenomics)
  33. 33. PhenomicsPhenomics  Phenomics, the study of the phenome, where phenotypes are characterized in a rigorous and formal way, and link these traits to the associated genes and gene variants (alleles).
  34. 34. Why Phenomics ?Why Phenomics ? The genotype−phenotype map  Essential for assessing pleiotropic effects of genetic variation .  Study the fitness to understand evolution – Pleiotropic effects on phenotype and their interaction with environment .  Ideally identify relationships between genotype and phenotype as well as reveal correlations between seemingly unrelated phenotypes.
  35. 35. Traits measured on HTP phenotypingTraits measured on HTP phenotyping platformsplatforms  Leaf area  Chlorophyll content  Stem diameter  Plant height / width  Growth rate  Transpiration rate  Canopy temperature  Biomass  Root mass/growth  Rate of soil drying  Internode length  Pigmentation  Leaf rolling  Leaf angle  Leaf senescence/necrosis  Photosynthetic efficiency  Forage quality/digestability  Tissue water content  Ear/panicle size/number  Salinity/drought/heat /frost tolerance
  36. 36. Criteria for traits amenable to highCriteria for traits amenable to high throughput analysisthroughput analysis  Measurements must be made rapidly, cheaply  High genetic correlation with key target • Yield • Quality • Resource use efficiency • Abiotic/biotic stress resistance  High heritability • Minimise error variation • Minimise unwanted environmental variation
  37. 37. “High throughput” field phenotyping systems • Infra red cameras to scan temperature profiles • Spectroscopes for measuring photosynthetic rates • Lidar to guage growth rates • MRI for study of root physiology
  38. 38. Maize leaf, laser confocal microscopy reveals a clear distinction between high activity of photosystem II in mesophyll cells (pink fluorescence) and low activity in bundle sheath cells (purple)—a distinction typical of C4 plants. Phenomics provide snapshots of cellular structure – Required to understand the contrasting cellular features among C3 and C4 plants. IRRI- screening rice varieties with a cellular architecture best suited to house C4 enzyme assembly and those with muted photosystem II in bundle sheath cells.
  39. 39. Chlorophyll fluorescence, a measure of photosynthesis, in Arabidopsis seedlings and a wheat ear ( inset ) using a car engine dynamometer The emerging discipline of phenomics will help foment the next green revolution. We now have the tools “to make quantum leaps in crop breeding,” says plant physiologist Robert Furbank, director of HRPPC.
  40. 40. IonomicsIonomics  Ionomics is the study of the ionome, involving quantitative and simultaneous measurement of the elemental composition of living organisms and changes in this composition in response to physiological stimuli, developmental state, and genetic modifications. Inductively coupled plasma mass spectrometry
  41. 41. ApplicationsApplications Identification of genes and gene networks that regulate the ionome. Precise large-scale mutant screens for study of genetic variation. Ionomic biomarkers in assessment of particular physiological or biochemical state of plants.
  42. 42. Ionome analysis of arabidopsis trichomes using ICP-MS Laser ablated inductively coupled plasma- mass spectroscopy
  43. 43. SummarySummary Integrated data set for quick and precise breeding
  44. 44. The power of ‘omics’ approachesThe power of ‘omics’ approaches Ionomics Phenomics Metabolomics Proteomics Transcriptomics Genomics Conclusion OMICS “These are the tools we need to feed and fuel the world.” – E.Finkel
  45. 45. Future concernsFuture concerns Reduction in cost of technology usage. Development of bioinformatic tools for data analysis and storage of databases. Human resource development for an overall purview of technology to apply in crop breeding.
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