Crop production today is threatened by severe biotic and abiotic stresses due to extreme weather condition The benefits in yield and food quality brought by “The Green Revolution” are not sufficient as forecasts an increase of 70% for food requirements by 2050 (Fischer et al. 2009 Only 2% of genetic gain in order to meet demands is questionable as, so far, annual gain in crop productivity is rated from 0.8 to 1.2%, which is considered insufficient (Li et al 2018 ) Sunflower has been recognized as a major source of high-quality edible oil and dietary fibers with high amount of oil (40–50%) and contain monounsaturated fatty acid (oleic acid) of potential health benefits Native to North America (ability for adaptability sunflower is grown around the globe as major oil seed crop)sunflower oil has a wide range of applications as a supplement in chemical and pharmaceutical industries. substantial progress in sunflower breeding by application of modern breeding apporachers substantial progress has been made in order to improve breeding process in sunflower by application of DNA markers, especially for disease resistance and tolerance to abiotic stress Further progress in sunflower improvement relies on combining all available cutting edge scientific tools, techniques and platforms in modern breeding such as molecular breeding, genomics and other functional omics through integrated approach with the aim to thoroughly decipher the complex mechanisms in the background of many important agronomic traits Integrated omics approach through the systems biology offers novel possibilities for deciphering the complex pathways, and molecular profiling provides important answers that can be utilized for more efficient breeding of sunflower. The genetic improvement of any crop, and thus of sunflower, largely depends on the acquisition of appropriate variability as the main cornerstone that allows successful progress as a response to new breeding challenges. Additionally, detailed information on genetic and phenotypic data of available germplasm is important from the breeding aspect in terms of correct selection of material for crossing although there is a very large collection of genetic material around the world, there is a lack in discovering beneficial alleles that can be utilized in breeding and transferred into elite genotypes Technological developments enabled detailed and complex omic-studies and provided valuable inferences generated by examining the functioning of the cellular system under different circumstances Such data can be utilized to formulate predictive models of behavior of important agronomic traits and integrate them within the concept of quantitative genetics omics data are expected to group interaction information within and between different biological layers and enhance the predictive ability of a particular trait An integrative approach, which includes data from different omics datasets, is known as systems biology

1. UNIVERSITY OF AGRICULTURAL SCIENCES,
RAICHUR
SREE VATHSA SAGAR U S
PG20AGR12071
DEPARTMENT OF GENETICS
AND PLANT BREEDING
2nd MASTER’S SEMINAR
1

2. • There is a scarcity of desirable genetic variation (undiscovered because
it has not been sufficiently researched) broad and deep resequencing
• Basic exploration of genomes is insufficient to find insight about important
physiological and molecular mechanisms unique to crops.
• So integrating information from genomics, epigenomics, transcriptomics,
proteomics, metabolomics and phenomics enables a comprehensive
understanding of the molecular mechanisms
• Integarting Omics technologies offer novel possibilities for deciphering the
complex pathways and molecular profiling through the level of systems
biology
2

3. Sunflower crop improvement through
Integrated Omic approach
3

4. SEMINAR FLOW :
IMPORTANCE OF INTEGRATED OMIC
SUNFLOWER
GENOMICS AND PANGENOMICS
EPIGENOMICS
TRANSCRIPTOMICS
PROTEOMICS
METABOLOMICS
PHENOMICS
INTEGRATED OMICS
RESEARCH STUDIES
CONCLUSION
4

5. Only 2% of genetic gain in order to meet demands is questionable as, so far,
annual gain in crop productivity is rated from 0.8 to 1.2%, which is considered
insufficient (Li et al 2018 )
The benefits in yield and food quality brought by “The Green Revolution” are
not sufficient as forecasts an increase of 70% for food requirements by 2050
(Fischer et al. 2009).
Crop production today is threatened by severe biotic and abiotic stresses due
to extreme weather conditions
5

6. SUNFLOWER (Helianthus annuus L.)
• Sunflower has been recognized as a major source of high-quality edible oil
and dietary fibers with high amount of oil (40–50%) and contain
monounsaturated fatty acid (oleic acid) of potential health benefits
• Native to North America (ability for adaptability sunflower is grown around
the globe as major oil seed crop)
• sunflower oil has a wide range of applications as a supplement in chemical
and pharmaceutical industries.
6
Awatif et al., 2014

7. substantial progress in sunflower breeding by
application of modern breeding apporachers
Further improvement in sunflower breeding relies on
combining all available cutting edge scientific tools,
techniques
So integrated approach through system biology for
understanding the complex pathway and molecular
mechanisms.
7

8. Molecular Omics Profiling
Genomics—Pangenomics :
• Genetic improvement of crop requires acquisition of
appropriate of variability as the main cornerstone for breeding
• detailed information on genetic and phenotypic data of available
germplasm is important (for correct selection of material for crossing )
• although there is a very large collection of genetic material around the
world, there is a lack in discovering beneficial alleles
8
Milan et al., 2021

9. • Since the development of the first genetic map on wild sunflower in
1993, molecular markers enabled the successive addition of new markers
to the map and enabled the positioning and detection of desirable genes
on individual linking groups
• three high-quality sunflower reference genomes is available, two of them
covering genomes of inbred lines XRQ and HA412-HO and one of the
restorer line PSC8.
• detection of favorable genetic variation thorough genome sequencing
through broad and deep resequencing and construction of “pangenome”
9

10. Pangenome:
• To obtain more complete information on the
total genetic variability of a particular species,
the concept of pangenome has recently been disguised
• The total genetic variation of a population or species consists of :
• core genome that represents a set of genes that are common to all
individuals
• dispensable genome consisting of a small number of genes that
are absent in one or more individuals
Wild relatives usage will be easy.
10

11. 11

12. • a high quality reference for the sunflower genome that contains 3.6
gigabases allows more efficient exploitation of sunflower genetic
background.
12
Hubner et al., 2019

13. plants are exposed to different types of
challenges, among which various
environmental stresses have a severe
impact on phenotype development
Knowledge about these complex mechanisms
requires thorough studies about epigenetic
changes
These mechanisms, of course, represent genetic
and epigenetic modifications, helping them to
survive the challenges they are exposed to
Due to the significant influence of external
factors, plants have developed various
mechanisms that help them cope
Epigenomics
13

14. Transcriptomics
• Knowing the biological processes at the
molecular level in the life cycle of the plant to
understand the influence of various factors on
plant development.
• Advanced sequencing technologies allowed high throughput transcriptomic
analysis and decoding complex transcriptional changes in phenotype
(genotype x environment) development
14

15. It provides clearer knowledge
about the molecular mechanisms
of interaction and genetic basis of
many processes
• It is performed for analyzing diverse effects
of stresses that explain dynamic and
complex processes at molecular level, which
lead to modifications in plant tissues
• Data obtained from transcriptome analysis
allow identification of transcriptional
regulatory elements and mechanisms of
transcriptional regulation.
15

16. Proteomics
• Proteomic analysis is a powerful approach for more
comprehensive knowledge about gene expression and
their functional mechanisms during plant life cycle.
• mRNA levels : microarray ; it is necessary to examine the protein and
their level of the rate of protein translation and degradation
• The proteomics approach commonly utilizes : two-dimensional (2-D)
gel electrophoresis, mass spectrometry (MS), matrix-assisted laser
desorption ionization–time of flight (MALDI TOF), western blots, and
ELISA in combination with bioinformatics tools
16

17. The proteome size is
several folds higher than
the genome size with
the number of proteins.
Although proteomics
provides extensive
information about the
genotype, it provides
limited information
about phenotype.
17

18. Metabolomics :
Metabolomics, the comprehensive
profiling of all small molecules
within an organism, is at the
phenotypic end of the -omics
spectrum
It includes a diverse array of small
molecules, including peptides,
carbohydrates, lipids, nucleosides,
and catabolic products of
exogenous compounds
Metabolomics in plants uses high
throughput analysis for separation,
characterization and quantification
of metabolite mixtures
18

19. • Metabolic markers are a recent development in science. Applications for
medicine is recent and its application to crop improvement is very recent.
• A huge number of metabolites are known in the plant metabolome pool,
which exceeds 200,000.
19

20. Phenomics
• phenomics is a study for high-throughput and high-dimensional analysis of
phenotypic variation
• In order to better understand the genetic basis of a complex trait, a
detailed and precise assessment of the phenotype is necessary
• phenotyping has progressed more slowly, leading to limited
characterization of complex quantitative traits compared to genomics.
20

21. Integrated Omics Approach—Systems Biology
21

22. 22
Yang et al., 2021

23. • An integrative approach, which includes data from different omics
datasets, is known as systems biology
• omics data are expected to group interaction information within and
between different biological layers and enhance the predictive ability of a
particular trait
• In the system biology the object of observation is observed as process that
occurs as result of genetics and interaction with various factors and its
result
23

24. • One flaw is confusion with the correct interpretation of huge amount of
omic data, often without clear connection
• To overcome incorrect interpretation of data, systematic multi-omics
integration (MOI) with a well-defined scheme for linking different data was
proposed.
24

25. Multi-omics integration (MOI) :
• It is impossible to manually associate hundred thousands of transcripts to
their respective proteins or metabolic pathways
• Plants are especially challenging due to large poorly annotated genomes,
multi-organelles, and diverse secondary metabolites
• we need is a well-defined methodological scheme for multi-omics integration
(MOI) to extract, combine, and critically associate different data sets to allow
researchers to decipher the seemingly complex biological results at hand
• MOI is based on three levels that differ in complexity 25
Jamil et al., 2020

26. 26

27. Pitfalls of system biology :
• Selection of combination of omics
• Still the chemical compound database is in the nascent phase
• More involvement of software tools for the data analysis
• Lack of more specific data base for some omics
• Still there is requirement of collaborative approach to understand the
different complex physiological and biochemical response (stress)
• Metabolite annotation is typical obstacle as far as metabolomics is
concerned
• Cost of estimation or analysis……..
27

28. • The aim of the present study was to characterize transcriptional and
metabolic pathways related to the triggering and progression of leaf
senescence in sunflower, at different developmental stages..
28

29. • Leaf senescence is a complex process, controlled by multiple genetic and
environmental variables, which has strong impact on crop yield (Gregersen
et al., 2013).
• A delay in leaf senescence : more yield : more photosynthetic leaf area
especially during the reproductive stage
• So systems biology approach is crucial in understanding the leaf
senescence process which is very complex.
29

30. • Material and methods :
• sunflower hybrid VDH 487 was used
• Transcriptomic and metabolic profiles were performed using the leaf 10
(numbered from the bottom to the top of the plant)
• At 3 stages : T-0 : young leaf
T-1 : pre-anthesis leaf
T-2 : post anthesis leaf, with senescence symptoms
• Time was expressed on a thermal time basis by daily integration of air
temperature with a threshold temperature of 6 °C and considering plant
emergence as thermal time origin (°CDAE: °C days after emergence)
• Transcriptomic and metabolic integration was done by MapMan software
(Thimm et al., 2004) was used to integrate transcriptomic and metabolic
profiles.
30

31. Results :
• Physiological measurements during leaf senescence.
• (a) Chlorophyll content in mg/cm2 under field (solid line) and glasshouse
• conditions (dotted line);
• (b) total soluble carbohydrates in mg/cm² under field conditions and
• (c) total nitrogen percentage under field conditions.
• The red line indicates anthesis time under field conditions and the orange line
indicates anthesis time under glasshouse conditions
31

32. • Senescence-associated genes (SAGs)
analysis:
• 369 candidate unigenes had high
sequence similarity.
• Of these candidates, 167 and 103
putative SAGs were differentially
expressed during leaf senescence in the
field and glasshouse experiments,
respectively.
32

33. Analysis of transcription factors :
• They identified sunflower TFs by comparing approximately 23 000
sequences of TFs from Arabidopsis iyrata, A. thaliana, Oryza sativa, Vitis
vinifera and Zea mays
• A total of 687 candidate TFs with high sequence similarity to TFs were
identified. The expression analysis of these candidate TFs revealed that 140
and 123 were differentially expressed along leaf senescence.
• most of these TF families showed a high ratio of up-/down-regulated genes
throughout the senescence process
33

34. 34

35. 35

36. • Metbolic analysis :
Primary metabolite analysis:
• By GC-TOF-MS analysis, we detected around 60 primary metabolites during
leaf development in both conditions, including different amino acids,
organic acids, sugars and sugar alcohols.
• As in the transcriptomic analysis, these changes were considerably stronger
in the field experiment, with higher fold changes during the progress of
leaf development
36

37. CHO :
TCA
Metabolites :
37

38. • Ionic nutrient analysis :
• Four anionic nutrients (chloride, nitrate, sulphate and phosphate) and
five cationic nutrients (sodium, ammonium, potassium, magnesium
and calcium) were detected and quantified by ion chromatography in
samples
38

39. • Data integration :
• They performed a transcriptomic and metabolic data integration using
“MapMan” (Thimm et al., 2004)
39

40. Glycolysis genes
Lipid degradation
Glyox
cycle
Genes and
metabolites
for nutrient
recycling.
Sucrose
degradation
40

41. • Integrative analysis of sunflower leaf senescence :
• Leaf senescence is complex cellular process ,system biology attempt to
elucidate it
• In annual crops, senescence in the whole plant after flowering
• Foliar nutrients are remobilized and translocated into the fruit, and cell
death (the source– sink relationships as onset of senescence)
• chlorophyll and nitrogen content sharply decreased after flowering
• Early stage metabolism changes were also observed in sunflower leaves (T1
vs. T0),
41

42. • The genes and metabolites related to the photosynthetic processes were
down-regulated during leaf development
• a decrease in sugar levels during senescence, in contrast to what happens in
A. thaliana
• The genes related to sucrose degradation, such as invertases and
fructokinases, displayed high levels of expression.
• A critical component in senescence process is the protein degradation
Genes for protein processing and degradation such as kinases,
phosphatases, cysteine protease (SAG12), F-box protein, and heat-shock
proteins (Hsp70 and Hsp90) also displayed high expression levels during
sunflower senescence
42

43. • identified candidate genes associated with the senescence process,
especially NAC TFs that could act as triggers for senescence.
• This is the first study that uses an integrated approach related to the
senescence process in sunflower. Thus this study provides an important
starting point for future analysis.
• Understanding the process of senescence that could help for crop
improvement as it controls the grain filling process that increases yield.
43

44. • In this study, they characterized transcriptional and metabolic pathways
related to drought conditions in sunflower and identified candidate TFs
and key metabolic pathways involved in the response to early water
deficit.
44

45. • Materials and methods :
• The sunflower hybrid VDH 487 was used
• Two experimental conditions were implemented :
• A control condition in which plants were grown without water and
nutritional limitations
• The other condition was a medium-intensive drought
• Transcriptomic and metabolic profiles were performed using the 10th leaf
(numbered from the bottom to the top of the plant)
• Samples taken at three satges :
• T1 : young leaf
• T2 : middle age leaf pre-anthesis
• T3 : old leaf, post-anthesis
45

46. RESULTS:
• Physiological measurement
46

47. • Transcriptomic analysis :
• They analyzed 9684 non-redundant genes from the microarray experiment
and detected 3434 differentially expressed genes between drought vs.
control conditions at the three sampling time points (T1=512; T2=1845 and
T3=2250 genes).
47

48. Functional analysis :
• Through gene set analysis methodology, we detected differentially
enriched functional categories represented on the microarray
48

49. Transcription factors analysis :
• They identified Sunflower Transcription Factors (TFs) by comparision
• Their expression analysis revealed that 42 TFs were differentially
expressed under drought conditions
• In downregulated TFs, they found that most of them correspond to the
AP2/EREBP, WRKY, MYB and NAC TF families. Furthermore, members of
zf-HD, AP2/DREB and Sigma70-like TF families were upregulated during
drought conditions.
49

50. • They performed WGCNA analysis to find co-expressed genes related to
biological processes that we demonstrated to be involved in the drought
response
• Then they searched for highly connected transcription factors related to
differentially expressed genes and biological processes of interest for this
they correlated each module (gene sets) with metabolite levels
• The brown and blue modules were positively correlated with sugar
metabolites and contained upregulated transcription factors (T2)
• the turquoise module contained most of the downregulated transcription
factors and showed a negative correlation with sugars and a corresponding
high positive correlation with amino acid
50

51. • They exported the modules and visualized them by using Cytoscape
(Shannon et al. 2003) to find highly connected TFs associated to drought
in sunflower
• This analysis showed 12 upregulated and 19 downregulated TFs with high
numbers of connections (degree of >15 and >20 respectively) and
therefore are potentially acting as hubs in the gene network
51

52. 52

53. these transcription factors as potential hub genes
regulated during drought in sunflower.
53

54. •Metabolic analysis :
• Primary metabolite analysis :
• By GC–TOF-MS analysis, they detected 54 primary metabolites,
including different amino acids, organic acids, sugars and sugar alcohols
54

55. Glycolysis and tricarboxylic
acid cycle (TCA)
metabolites and all the
detected carbohydrates
showed higher levels under
drought conditions.
55

56. Ionic nutrient analysis :
• By ion chromatography, we detected four anionic (chloride, nitrate,
sulphate and phosphate) and cationic (sodium, ammonium,
potassium, magnesium and calcium) nutrients
56

57. Integrative analysis :
• we characterized the sunflower drought response by integrating
transcriptomic and metabolic data and using MapMan (Thimm et al. 2004)
• We detected higher expression levels at T2 (middle age leaf, pre-anthesis)
• This finding evidences an early activation of drought tolerance mechanisms
before anthesis
57

58. demonstrating
an active
detoxification
process under
drought
conditions in
sunflower.
sugar synthesis and
starch degradation
Glycolysis related
genes and TCA cycle
metabolites
58

59. Discussion :
• Sunflower is susceptible to low temperatures and salinity, but shows a
relatively high tolerance to drought (highly explorative root system).
• As a result, senescence was delayed and chlorophyll content showed high
levels under drought
• Sugar accumulation is an important mechanism of drought tolerance, by
preventing water loss and protecting membranes, enzymes and other
cellular structures (osmotic adjustment)
59

60. • Sugar accumulation mechanism avoids cellular dehydration, by maintaining
leaf turgor to improve stomatal conductance and promoting water uptake in
roots
• These results suggest a mechanism of drought tolerance in sunflower
involving an increase of photosynthesis related genes and higher sugar
levels during droughts
• the higher levels of different amino acids and derivatives
• Carbon accumulated under drought conditions promotes the synthesis of
secondary metabolites
• flavonoid accumulation : tolerance to both oxidative and drought stresses
• terpene accumulation : growth, development and resistance to environmental
stresses
60

61. • identified 12-candidate hub TFs with high expression levels under drought
conditions
• These TFs were there in gene network models with positive correlation to
sugar metabolites
• HeAn_C_419, a sunflower zf-HD TF : high transcription levels at a very early
stage : this TF as a promising candidate gene for drought response in plants
• delay in the senescence process activation under drought conditions, thus
highlighting this event as a tolerance strategy in sunflower
• In sunflower, in addition to its highly explorative root system, the osmotic
adjustment mechanism seems to play a very important role in drought
tolerance
61

62. An increase in the expression level of
photosynthesis related genes
lead to an accumulation of sugars
Increase in osmoprotectant amino acids….
an increase of ionic nutrients
a delay in senescence process under drought
Mechanism of osmoprotectant accumulation may act by preventing water loss and
protecting membranes, enzymes and other cellular structures :
62

63. 63

64. 64