Integrative omics approches

1. Doctoral Seminar - I : MBB- (691)
on
Integrative Omics Approaches
Presented by
Magar Sayali Ganesh
Ph. D. (Agri.) 1st year
(Agricultural Bio-Technology)
Submitted to
Seminar chairman: Dr. S.B. Sakhare
BIOTECHNOLOGY CENTRE
Department of Agricultural Botany, Post Graduate Institute,
Dr. Panjabrao Deshmukh Krishi Vidyapeeth, Akola (M.S.) – 444104

2. Introduction
Overview of Omics
Integrative Omics
Applications of Integrative Omics
Softwares for Integrative Omics
Case studies
Summary
Content
1
2
3
4
5
6
7

3. Introducing Facts of life
• Ever-growing population and decreasing natural resources
• By 2050, we will need to produce 70 % more food to feed population (Jha et al., 2018)
• Under tougher climate conditions
• One of humanity's greatest challenges
• Need to enhance food production
How can we do it?
• Improve crop yields
• Breed crops that can cope with climate change
• By improving upon qualitative and quantitative traits of crop plants
• Different biotechnological tools, omics tech., crop improvement

4. Why omics????
• A understanding of plant response to stress at the molecular level is a prerequisite for its
effective management
• The molecular mechanism of stress tolerance is complex and requires information at the
omics level (Rai et al., 2019)
• Omics technologies : determination of all genes, transcripts, proteins, or metabolites in a
biological sample using high-throughput technologies.
• Technological advances, high-throughput, reliable, and quick array-based genotyping
platforms
• Recent developments in bioinformatics have lowered the cost of omics in many folds
• All data obtained by omics technologies have recently started to be integrated into
systems biology through bioinformatics approaches (Gupta, et al., 2017)

5. 5
4
3
2
Omics
1
1. Genomics
2. Transcriptomics
3. Proteomics
4. Metabolomics
5. Phenomics
• The Latin suffix “-ome” was first used by Professor
Hans Winkler as “genome” to express all hereditary
material in different chromosomes and in the
following years became “-omics”
• The same suffix used to identify all cellular activities
such as
• These omics branches are equally important to get
clear picture of the biological system.

6. • The word “genomics’’ appears to have been coined by Thomas Roderick in 1986
• Genomics – the comprehensive study of whole sets of genes and their interactions
• Genomes: a haploid content of all of the hereditary information of an organism
• The aim of genomics:
• Sequence the entire genome
• Assemble the entire genome from the pieces (fragments)
• Understand the how the gene expression takes place
• Tools to study the gene sequences/genomes
• Genome wide association study (GWAS)
• Next Generation Sequencing
• Genetic profiling, etc.
GENOMOICS

7. • Gene expression profiling
The identification and characterization of the mixture of m-RNA that is present in specific
samples
• Application
• To identify genes differentially expressed among different conditions
• Leading to new understanding of the genes or pathways associated with the conditions
• For comparative analysis of gene expression (Nachtomy et al., 2007)
• Challenge
• The transcriptome in contrast to genome is highly variable over the time, between cell types
and environmental changes (Celis et al., 2000)
Transcriptomics

8. Transcriptome study (mRNA level)
Macroarrays
Real time PCR
qRT-PCR;
Hybridization on
Northern blots
Methods based
on hybridization
Methods based on PCR
Chips / microarrays
RNA-seq
Parallel NG sequencing
of cDNAs
DNA (Genome)
mRNAs
(Transcriptome)

9. • Proteomics: the study of proteomes and their functions.
• The proteome consist of all proteins present in specific cell types or tissue and highly variable
over time, between cell types and will change in response to changes in its environment (Fliser
et al., 2009).
• It provides insight into the role of proteins in biological system (Sellars et al., 2003)
• Tools for proteomics
Mass spectrometry and protein microarrays
• Major focuses
• The identification of protein and protein interacting in protein-complexes
• The quantification of protein abundance
• Specific protein abundance related to its role in cell function (fliser et al., 2009)
Proteomics

10. • Metabolomics: study of the set of metabolome present within an organism
• The metabolome is made up of small molecule, intermediates and products of metabolism also
known as metabolites (Claudino et al., 2007).
• Involved in the energy transmission in cells, with metabolic pathways.
• The metabolome is highly variable and time dependent, chemical structures
• The metabolome is the most accessible and dynamically changing molecular phenotype
• It uses wide range of analytical techniques
• MS
• NMR
Metabolomics

11. • Phenotyping is analyzing plant’s phenotype
• Phenomics is analyzing way of speeding up phenotyping using high-tech imaging system
and computing power.
• Field phenomics: the measurement of phenotypes, cultivated and natural conditions
• Controlled environment phenomics research involves the use of glass houses, growth
chambers, and other systems
• Some phenomics techniques are:
• 3D imaging
• Infrared imaging
• Fluorescence imaging
• Magnetic resonance imaging
• Phenomics in field
• Phenomobile
• Phenotower
• Multicopter
Phenomics

12. 3 D Imaging Infrared Imaging Flurosense Imaging
Magnetic Resonance Imaging Phenomobile Multicopter

13. • Omics technologies produce rich data sets
• Microarrays / DNA chips / Sequencing
• Transcriptome profiling, QTL maps
• 2D-PAGE, Mass Sspectrometry , Protein microarray
…We need to integrate different omics to enable running the movie of all the
snapshots…
• Integrative omics unites the omics technologies used to dissect complexity in
large and small biomolecules.
Integrative omics

14. Integrative omics
• Multi-omics : more than one omics, provides the ‘genome to phenome’ biology.
• Single-layer omics, integrated multi-omics layers allow understanding of their
combined influence on the complex biological process.
• Effective and accurate solution of many problems in the living systems
• Integrative study is exercised for roughly two purposes:
• Prediction of gene functions
• Characterization of the systematic interaction of biological processes.
• Bioinformatics, the integration of omics fields to define the dynamicity of the process
involved in the biology and physiology of cell/ tissues/organ systems, and the pathophysiology
of diseases. (Rai et al., 2019)

15. The key requirements before integration of data
1. The data quality, appropriate experimental design;
2. Robust and reliable normalization;
3. Consistent data storage
4. Stage of data integration with multiple omics
(Gupta et al., 2017)

16. (Misra et al., 2020)

17. Phenomics and it’s integration with other omics
approaches
( Deshmukh et al., 2014)

18. (Jha et al., 2017)
• Stress adaptive/ tolerant
trait
• Candidate gene/ QTLs
• Proteins and metabolic
pathways

19. Software used in integrative omics
Sr. No Software Tool Omics
Integrated
Functionality
1 KaPPA-View Transcriptomics
Metabolomics
Integrates transcriptomics and metabolomics data
to map pathways
2 MetaboAnalyst Genomics
Transcriptomics
Proteomics
Metabolomics
Data processing and statistical analysis -
Pathway analysis - Multi-omics integration
3 Gaggle Variety of omics
platform
bioinformatics
solutions
Integration of data
Chemometric analysis (similarity/difference)
4 OmicsPLS Metagenomics
Transcriptomics
Proteomics
Integration of data - Chemometric analysis
(similarity/difference) - R-package with an open-
source implementation of two-way orthogonal
PLS
(Pinu et al., 2019)

20. Databases used in integrative omics
Sr. No Database Features Tools
1 SoyKB
Soybean Knowledge Base,
University of Missouri,
Columbia,http://soykb.org/
Multi-omics datasets,
Genes/proteins, miRNAs/sRNAs,
Metabolite profiling, Molecular
markers, information about plant
introduction lines and traits,
Graphical chromosome visualizer
Germplasm browser, QTL
and Trait browser, Fast
neutron mutant data,
Differential expression
analysis, Phosphorylation
data, Phylogeny, Protein
BioViewer, Heatmap and
hierarchical clustering, PI
and trait search, FTP/data
download capabilities
2 SGMD
The Soybean Genomics and
Microarray Database,
http://bioinformatics.towso
n.edu/SGMD/
Integrated view genomic Analytical tools allowing
correlation of soybean ESTs
with their gene expression
profiles
( Deshmukh et al., 2014)

21. Sr. No Database Features
3 Paintomics Joint pathway analysis of transcriptomics or
proteomics and metabolomics data that also performs
over-representation or enrichment analysis
4 3Omics Integrating multiple inter- or intra-transcriptomic,
proteomic, and metabolomic human data
5 Omics data integration tools
MapMan
Visualize and map gene expression, metabolite or
other data, displays large data sets onto diagrams of
metabolic pathways
6 MixOmics Provides a wide range of linear multivariate methods
for data exploration, integration, dimension
reduction and visualization of biological data sets
Databases used in integrative omics
(Misra et al., 2020)

22. Application of integrative omics

23. CASE STUDY I

24. OBJECTIVE:
• To explore the molecular mechanism of drought tolerance in ryegrass varieties,
• To identified differentially expressed metabolites and their corresponding proteins and transcripts that are
involved in drought treatment

25. Materials and methods
• Material:
• “Abundant 10” (drought-resistant) and “Adrenalin 11 (drought-susceptible)
• 16-h photoperiod (25°/18 °C day/night temperature), drought stress.
• Methods:
• Transcriptome sequencing
• Real-time quantitative -PCR
• HiSeq 2000 sequencing system (Analysis done using DESeq software)
• Protein identification
• The extractions were performed with Lysis Buffer
• Mass spectrometry (Proteome Discoverer 1.2 software; The Mascot 2.3.02 search engine
was used to identify and quantify proteins)
• Functional annotations of identified proteins were performed using the Blast2GO
program
• Metabolome profiling:
• Methanol extraction method;
• Gas chromatography–mass spectrometry (ANOVA was performed using the SPSS
Statistics 20.0 software)

26. Results
Drought stress induced growth and physiological changes in L. multiflorum
Fig A-B) Representative images of two L. multiflorum genotypes under long term drought
stress for 5 weeks
A B

27. Fig E): No significant changes were noted in SH when
subjected to drought stress
Fig C): The impact of drought stress on RWC was apparently
reduced among treated seedlings
C
Fig D): Chlorophyll (a + b) content of the susceptible
plants exhibited a dramatic reduction
D
E

28. Fig F-H): Higher levels of catalase (CAT), superoxide
dismutase (SOD), and ascorbic acid peroxidase (APX)
activity were observed among the tolerant genotype
exposed to long-term drought
G
F
H

29. Results
Metabolite profiling of two L. multiflorum genotypes revealed changes in
metabolites under drought stress
• Detected significant difference in lipids, amino acids, organic acids, amine
compounds, and pyridines when exposure of 24 h of drought stress
Comparative proteomics and transcriptomic profiling reveals differences in
the expression of proteins and genes regulating core metabolism
• A total of 26,189 unique peptides matching 8224 proteins were identified by
Mascot of which 1395 were differentially abundant between the drought-
susceptible and drought resistant genotypes

30. Results
• A strong correlation between the four datasets
was observed
• Clearly separated the drought stress sensitive
and resistant genotypes
Multiple co-inertia analysis to evaluate the integration of omics datasets

31. • In order to explore the molecular mechanism associated with drought
tolerance in two annual ryegrass genotypes, they identified
differentially expressed metabolites and their corresponding proteins
and transcripts that are involved in 23 core metabolic processes, in
response to short-term drought stress.
• The regulatory networks were inferred using MCoA (Multiple co-
inertia) and correlation analysis to reveal the relationships among the
expression of transcripts, proteins, and metabolites that highlight the
corresponding elements of these core metabolic pathways.
Conclusions

32. CASE STUDY II

33. Objective:
• To investigate contrasting salt tolerance properties through integrative analyses of transcriptomics and
metabolomics.

34. Materials and methods
• Material:
• Foxtail millet cultivars of Yugu2 and An04
• 150 mM NaCl solutions for salt stress treatment
• 30/25 °C day/night cycle with a 14-h photoperiod for seven days, the roots
were sampled after been treated for 24 h and 48 h
• Methods:
• Transcriptomics
RNA library construction and sequencing
Validation of DEGs using qRT-PCR.
• Metabolomics analysis
LC–MS
• Histochemical detection of H2O2 and O2 - , antioxidant enzyme activity

35. • In the transcriptomics results, 8887 and 12,249 DEGs were identified in Yugu2
and An04 in response to salinity, respectively, and 3149 of which were overlapped
between two varieties.
• These salinity-responsive genes indicated that ion transport, redox homeostasis,
phytohormone metabolism, signalling and secondary metabolism were enriched in
Yugu2 (analysis using GO and KEGG analyses)
• The integrative omics analysis implied that phenylpropanoid, flavonoid and lignin
biosynthesis pathways, and lysophospholipids were vital in determining the foxtail
millet salinity tolerance
Results

36. • In order to create the regulatory network of salinity response, the salt-tolerance of
different foxtail millet varieties were screened and based on the phenotypic
alteration and physiological indexes determination under 150 mM NaCl treatment,
Yugu2 was defined as salt tolerant variety and An04 was identified as salt sensitive.
• Integrative analyses of transcriptomics and metabolomics demonstrated that several
key biological processes and metabolites, such as ion transport, redox homeostasis,
secondary metabolism were vital for Yugu2 salt tolerance
Conclusions

37. Summary
• The biology is shifting from observational to predictive
• To understand biological systems we must consider data from multi-omics, bridging the
gap from genotype to phenotype
• Omics approaches helps conventional breeding in achieving important advances in the
breeding of crops in the view of genetic improvement.
• The new genomic tools are of great value for genetic dissection and breeding of complex
traits.
• Due to reduced cost on sequencing and genotyping technologies combined with
bioinformatics we envisage a bright future in the breeding programmes

38. References
Celis, Yichun Qian, Shao-shan Carol Huang, 2002. Improving plant gene regulatory network inference by integrative analysis of multiomics and
highresolution datasets. DOI: https://doi.org/10.1016/j.coisb.2020.07.010
Deshmukh Rupesh, Sohan Humaria, Patil Gunvant, 2014. Integrating omics approaches for biotic stress tolerance in soybean. Frontiers in plant science,
plant genetics and genomics.
Gupta, Fudota B. Bhaskar, Shreedharan Sriram, Po-Hao Wang, 2017. Integration of omics approaches to understand oil/protein content during seed
development in oilseed crops. Plant Cell Rep 36:637–652.
Jha Uday Chand, Abhishek Bohra,· Rintu Jha, Swarup Kumar Parida, 2019. Salinity stress response and ‘omics’ approaches for improving salinity stress
tolerance in major grain legumes. Plant Cell Reports 38:255–277.
Misra Biswapriya, Carl Langefeld, Michael Olivier and Laura A Cox, 2020. Integrated omics: tools, advances and future approaches. Journal of Molecular
62:1 Endocrinology, 62:1.
Nachtomy, Kazuki Saito, Ramesh Setia, 2007.The omics technologies and crop improvement. Crop Improvement: Strategies and Applications. 15:58–67.
Pan iaowen, Zhen Li, Shaojun Dai and Hanfeng Ding, 2020. I Integrative analyses of transcriptomics and metabolomics upon seed germination of foxtail
millet in response to salinity. Scientific Reports, 10:13660. | https://doi.org/10.1038/s41598-020-70520-1.
Pan Ling, Chen Meng , Jianping Wang , Xiao Ma, Xiaomei Fan4 , Zhongfu Yan and Meiliang Zhou, 2018. Integrated omics data of two annual ryegrass
(Lolium multiflorum L.) genotypes reveals core metabolic processes under drought stress. Pan et al. BMC Plant Biology, 18:26.
Pinu, David J. Beale, Amy M. Paten and Konstantinos Kouremenos, 2019. Systems Biology and Multi-Omics Integration: Viewpoints from the
Metabolomics Research Community. Metabolites, 9, 76; doi:10.3390/metabo9040076
Rai Amit , Mami Yamazaki and Kazuki Saito, 2019. A new era in plant functional genomics. Current Opinion in Systems Biology, 15:58–67.

39. THANK
YOU….