This document discusses the use of various "omics" technologies in crop breeding, including genomics, transcriptomics, proteomics, metabolomics, phenomics, and ionomics. It provides examples of each type of omics analysis in crop plants like potato and wheat. Integrating multi-omics datasets can provide a powerful tool for crop improvement by identifying genes and networks controlling important traits. However, future work is still needed to reduce costs and develop bioinformatic tools to fully leverage omics technologies in breeding programs.

1. “OMICS” In Crop Breeding“OMICS” In Crop Breeding
Poornima KN
Roll No: 9869

2. ContentsContents
Introduction
Omics Space
◦ Genomics
◦ Transcriptomics
◦ Proteomics
◦ Metabolomics
◦ Phenomics
Case Studies
Summary
Conclusion
Approaches and applications

3. INTRODUCTION

4. Molecular networks of cell controlling traits and phenotypes

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. Crop Breeding
Developmental biology
“Omics in plants”
Identify genes, promoters, mi RNAs, pathway
components

8. Omics Platforms

9. GenomicsGenomics
 Genomics – the comprehensive study of whole sets of genes &
their interactions (DNA microarrays)

10. Genome sequencing projectsGenome sequencing projects

11. 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

12. 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.

13. Comparative analysis and GenomeComparative analysis and Genome
evolution studyevolution study
Orthologous and paralogous
gene families
Genome duplication
Analysis of syntenic blocks

14. 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.

15. 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

16. Sources for genomicsSources for genomics
De novo sequencing
Re sequencing
Metagenomics
Epigenetics
RNA sequencing - Transcriptomics

17. TranscriptomicsTranscriptomics
 The study of the transcriptome, the complete set of RNA
transcripts produced by the genome at any one time.

18. Transcript profiling methodsTranscript profiling methods
Whole genome transcriptome
analysis
- Microarray
- SAGE
- MPSS
Target genome transcriptome
analysis
- Northern blot
- Dot blot
- RT-PCR
-

19. Application of transcriptomicsApplication of transcriptomics
Differential expression of genes
Co expression of genes
Gene interaction
Alternative splicing of genes

20. Hiremath et al, 2011. Plant Biotechnology Journal

21.  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.

22. Expression analysis GSL biosynthesisExpression analysis GSL biosynthesis
genesgenes
Mutant analysis Over expression analysis

23. Sources of transcriptomicsSources of transcriptomics
Expression arrays
Tilling arrays
MicroRNA arrays
Protein arrays - Proteomics

24. ProteomicsProteomics
The study of proteome, the structure and function of
complete set of protein in a cell at a given time.

25. 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.

26.  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

27. Proteome analysisProteome analysis
Frontiers in plant science, 12 september 2011

28. 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

30. Sources of proteomicsSources of proteomics
Protein mixtures
Post-translational modifications
Biomarker studies
Examination of metabolites - Metabolomics

31. MetabolomicsMetabolomics
Study of metabolome, collection of all
metabolites in a cell, tissue, organ or organism.

32. 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

33. •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.

34. Sources of metabolomicsSources of metabolomics
Toxicity assessment
Nutrigenomics
Forensic analysis
Petrochemical analysis
Phenotype analysis- (phenomics)

35. 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).

36. 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.

37. 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

38. 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

39. “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

40. 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.

41. 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.

42. 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

43. 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.

44. Ionome analysis of arabidopsis trichomes using
ICP-MS
Laser ablated inductively
coupled plasma- mass spectroscopy

45. SummarySummary
Integrated data set for quick and precise breeding

46. 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

47. 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.