lecture notes

1. Omics technology: Metabolomics and its
application
Zanariah Hashim, PhD
Department of Bioprocess & Polymer
Engineering
Faculty of Chemical & Energy Engineering
Universiti Teknologi Malaysia
MKKB1103 (Biotechnology for Engineers)

2.  What is “Metabolomics”?
 Methodology
 Application and research themes
 Case study: application in the study of yeast transcription
factor knockouts
Contents
2

3. The Omics Technology
• Use of high throughput screening and
highly sensitive analytical equipment such
as HPLC, GC-MS, LC-MS, MNR, FTIR etc
• Use of bioinformatics
• Use of analytical software, Multivariate
analysis approaches
• Genomics-Anaysis of genes
• Proteomic-Anaysis of proteins
• Metabolomics-Analysis of metabolites

4. Components of biological system
4
DNA
mRNA
Protein
Metabolite
Genome
Transcriptome
Proteome
Metabolome
Physiological phenomenon
downstream effects of
gene and protein regulation
Vital information of biological system!!
Media for
genome
information

5. Omics Flow

6. Omics Description Methods Applications
Genomics Comprehensive study of a
genome, including protein
coding genes, regulatory
elements and non-coding
sequences
・ Gene sequencer Genome sequence
information
Transcriptomics Quantitative study of
mRNA (transcript)
expression levels
・ Hybridization
arrays
(microarrays)
・ RNA sequencer
Analysis of differential
gene expression, gene-
gene network
Proteomics Analysis of protein content
and abundances
・ 2D-PAGE gels
・ Protein arrays
・ MALDI-TOF,
LC/MS
Identification of protein
function, protein-
protein interactions
Metabolomics Comprehensive study of
metabolites and metabolic
network
・ GC/MS, LC/MS
・ NMR
Identification and
quantification of key
metabolites, elucidation
of metabolic behavior
Omics technologies
6

7. Complexity of our biochemical
pathways

8. Bioinformatics

9. genomics

10. Cancer genome workflow

11. Chemical Genomics to
characterize diseases

12. proteomics

13. metabolomics

14. What is metabolomics?
14
Metabolite Omics Metabolomics
Metabolite
 Intermediates and products of metabolism
 Primary metabolite and secondary metabolite
 E.g.) antibiotics, amino acid, pigment, carbohydrate and fatty acid
Omics
 The academic field of studying comprehensively
 E.g.) Genomics, Proteomics
Metabolomics
The academic field to analyze specific molecules caused by the
activities of the cell (i.e. metabolite) comprehensively

15. Metabolomics workflow
15
② Sampling
③ Derivatization
④ Analysis
⑤ Data conversion
Metabolites
x1
･･･ Metabolites
xn
No.1 x11 ･･･ x1n
・
・
・
・
・
・
・
・
・
No.m xm1 ･･･ xmn
⑥ Data mining
sugar
organic acid
amino acid
lipid
terpenoid
alkaloid
① Cultivation,
cell culture
⑦ Data interpretation
Metabolomics is an interdisciplinary research between
Biology, Analytical chemistry, Bioinformatics

16. 16
Targeted analysis (Metabolic profiling)
• The metabolomics data are scanned for specific compounds
normally collected in a reference library.
 One major drawback is the relatively limited size of most
current reference libraries, thus preventing the use of the
whole amount of information present in the spectral data.
Non-targeted analysis (Metabolic fingerprinting)
• The compounds are not identified and the spectroscopic
features of all potential compounds are considered for further
analysis.
Strategy –Targeted and Non-targeted

17. 17
Separation Techniques
 Gas Chromatography (GC)
 Capillary Electrophoresis (CE)
 High Performance Liquid Chromatography (HPLC)
 Ultra Performance Liquid Chromatography (UPLC)
 Supercritical Fluid Chromatography(SFC)
Detection Techniques
 Nuclear Magnetic Resonance Spectroscopy (NMR)
 Mass Spectrometry (MS)
Combination of Techniques
 GC-MS
 HPLC-MS
Strategy –Data acquisition techniques

18. Xevo G2 QqQ
Benchtop MS
(Waters)
GCMS-QP2010 Ultra
(Shimadzu)
LCMS-8040
(Shimadzu)
GCMS-TQ8030
(Shimadzu)
TripleTOF® 5600
(AB Sciex)
4000 QTRAP
(AB Sciex)
Trace DSQ
(Thermo)
LCMS-8030
(Shimadzu)
Pegasus Ⅲ
(LECO)
Q Exactive
(Thermo)
LCMS-IT-TOF MS
(Shimadzu)
7000B Triple Quadrupole
GC/MS system
(Agilent)
18
Examples of analysis instrument

19. 19
 Data collected represented in a matrix
 Multivariate analysis
 Principle Component Analysis (PCA)
 Soft Independent Modeling of Class Analogy (SIMCA)
 Partial Least-Squares (PLS) Method by Projections to Latent Structures
 Orthogonal PLS (OPLS)
 Biological interpretation
Use metabolic pathway map, comparison with transcript/protein data, etc.
Strategy –Data analysis and interpretation

20. 20
 Finding bioactive markers in plant or herbal samples.
 Finding the bottleneck of a synthetic bioconversion
using microbes to improve productivity (e.g.
production of alcohols by genetically engineered yeast
or E. coli)
 Discrimination of plant samples according to
geographical areas or processing method (e.g.
authentication of Luwak coffee)
 Finding important metabolites associated with certain
diseases or different developmental stages
Some applications and research themes (in my previous lab)

21. 21
Case study: application of
metabolomics in the study of yeast
transcription factor knockouts

22. What are transcription factors (TFs)?
Classification by mode:
1) core machinery transcriptional
component (i.e. basal transcription
factors) that binds to DNA promoter
region, e.g. TATA-binding proteins
2) activator or repressor proteins (i.e.
sequence-specific binding proteins)
3) co-activator proteins that do not by
themselves bind to the DNA but instead
interact with other TFs to activate gene
expression machinery.
Classification based on DNA-binding
domain (DBD) motifs:
zinc-stabilized (A&B), zipper type (C&D)
and helix-turn-helix (E)
Non-binding proteins cannot be
classified Hahn, S.; Young, E. T. Genetics 189, 705–736 (2011).
Transcription factors (TFs): regulator proteins that
activate or suppress gene expression
22

23. TFs as a modulator of gene expression
Gene expression regulation by transcription factors (TFs):
Cues/Stimulus
(internal or
external) detected
Phenotypic
changes occurred
Transcription
factors activated
Genes expressed
DNA as a template, how and
when mRNAs or proteins are
expressed under a given
circumstance are tightly
controlled
Transcription factors as
controller of gene
expression
TF “shuffle” used in strain
engineering (Alper 2006)
Serve as drug targets (Sommer
2001, Delmore 2011)
Response element
Transcription
Gene transcribed
Transcription
factors
Nutrition,
drug, etc.
Gene
products
Enzymes, receptors,
biomarkers, etc.
NUCLEUS 23

24. Problem statement
Transcription factors are proteins involved in gene activation/repression
process. Thus, it is important to study their regulation (dysregulation can
lead to diseases).
However, the understanding of the roles of transcription factors is not
complete due to the complex interaction and co-regulation:
 Multiple-stage regulation, modularity nature of TFs
 Post-transcriptional and post-translational modifications
 Lack of information from transcript and protein to final phenotypic
change
Characterization of the most downstream product of gene
expression, i.e. metabolites, is necessary
Can metabolomics be used to enhance our
knowledge of TFs and their regulation?
24

25. Functionally similar TFs should
share similar metabolic (readout)
features
Metabolites are the final
readouts of gene expression!
mRNA
protein
metabolite
DNA
TF complex
genome
transcriptome
proteome
metabolome
*Yeast S. cerevisiae as a model organism:
 single gene knock-out mutant library
available
 TF well documented and widely
studied, genome and transcript data
available
Metabolites as readouts for TF perturbation
X
?
25

26. Repressed
mitochondrial
function
Butow, R. A. & Avadhani, N. G. Molecular
Cell 14, 1–15 (2004).
Rtg1 and Rtg3:
 Basic helix-loop-helix transcription factors
 Related to mitochondrial function (several diseases including Alzheimer’s
and Parkinson have been associated with mitochondrial dysfunction)
 Positive regulators of mitochondrial retrograde (RTG) pathway
RTG
target
genes
Model TF for metabolomics study
CIT2 Peroxisomal citrate synthase
CIT1 Mitochondrial citrate
synthase
ACO1 Aconitase
IDH1 and IDH2 Isocitrate dehydrogenase
RTG target genes:
26

27. RTG maintains a continuous supply of 2-oxoglutarate when
mitochondrial function is repressed
 Elevated CIT2, increased 2-oxoglutarate levels
Liu & Butow. Annu. Rev. Genet. 40, 159–185 (2006).
Role of RTG pathway
Oxalacetate
Succinate
2-Oxoglutarate
Citrate
Isocitrate
CIT1
ACO1
IDH1/2
Citrate
Oxalacetate
2-Oxoglutarate
Glutamate
Oxalacetate
Mitochondria
Peroxisome
CIT2
Acetyl coA
Pyruvate
Phospholipids
Glucose
TCA cycle
Glyoxylate
cycle
RTG
response
X
27

28. ① Yeast culture and
metabolite extraction
② Metabolite
measurement (LC-MS)
③ Peak identification
④ Peak list table
Multivariate
data analysis
SC medium, 30 ˚C,
200 rpm (n=3)
Collect cells by fast filtration
Extract with MeOH/H2O/CHCl3=5/2/2
① Analysis of standard
metabolites
Retention time and m/z
MRM optimization
Linear range, LOD
③ Yeast metabolite
library
Using software to assign
peak RT and m/z
② Analysis of reference
strain
BY4742 (MATα leu2∆0
lys2∆0 ura3∆0 his3∆1) metabolites
strain
Analytical platform Metabolic profiling
e.g.
Pyruvate
Arginine
UMP
intensity
RT
m/z
Biological
interpretation
Experimental outline
28

29. Target metabolites: from central metabolism
29
)
)
IDH1,
IDH2
Gln, Arg)
Aromatic
family (Phe,
Tyr, Trp)

30. Add water, vortex, collect polar phase
Sampling
Culture in synthetic complete media with 2% glucose until desired optical
densities
Collect cells by filtering 5 OD of culture broth, insert into -30 oC precooled
extraction solvent (methanol/water/chloroform = 5/2/2) and store at -80 oC
Sample preparation
Filter-bound cells (~1 mg)
MeOH/H2O/CHCl3 extraction (at 4 oC, 1200 rpm for 30 min)
Filter, concentrate 5x
LC/MS/MS analysis
Materials and method
Ion pairing UHPLC-MS/MS
PFPP column UHPLC-MS/MS
30

31. ► Ion pairing UHPLC-MS/MS
Nexera UHPLC (Shimadzu, Kyoto, Japan)
Column: L-column2 ODS (150 mm x 2.1 mm, 3 µm)
Column temp: 35 oC
Injection volume: 3 µL
Gradient profile:
A: 10 mM TBA, 15 mM
acetic acid in water,
B: methanol
Flow rate: 0.3 mL/min
Time (min) B (%)
0.5 0
7.5 25
11 90
12 90
12.1 0
15 0
MS: LCMS8030 plus (Shimadzu, Kyoto, Japan)
MRM ESI negative mode
Probe position: +1.5 mm
DL temperature: 250 oC
Nebulizer gas flow: 2 L/min
Heat block temperature: 400 oC
Other MS parameters are determined by auto-tuning
► PFPP column UHPLC-MS/MS
Nexera UHPLC (Shimadzu, Kyoto, Japan)
Column: Discovery HS F5-3 (150 mm x 2.1 mm, 3 µm)
Column temp: 40 oC
Injection volume: 3 µL
Gradient profile:
A: water with 0.1%
formic acid, B: acetonitrile
with 0.1% formic acid
Flow rate: 0.3 mL/min
Time (min) B (%)
1 0
11 20
11.5 100
13 100
13.1 0
15 0
MS: LCMS8030 plus (Shimadzu, Kyoto, Japan)
MRM ESI positive mode
Probe position: +1.5 mm
DL temperature: 250 oC
Nebulizer gas flow: 2 L/min
Heat block temperature: 400 oC
Other MS parameters are determined by auto-tuning
Analytical conditions
31

32. Tandem mass spectrometry (MS/MS)
“Precursor ion m/z > Product ion m/z” (MRM transition)
m/z
Intensity
m/z
Intensity
Selection of
precursor ion
Fragmentation Monitoring
product ions
Multiple reaction
monitoring (MRM)
 High selectivity
 High sensitivity
Q1 Q2 Q3
Detector
Ion source
What is triple quadrupole mass spectrometer?
32

33. Dataset construction and data analysis
► LC-MS
LabSolutions (Shimadzu, Kyoto, Japan) with manual inspection
Peak picking parameters:
integration: auto, max peak: 3, width: 5 sec; smoothing: standard, counts: 5, width: 1
sec; identification: absolute RT & closest peak, target window: 5%, reference window:
5%, process time: ± 1 min
Data analysis
Pretreatment: normalized to IS + autoscaled, or normalized to WT BY4742 + log2
PCA and Hotelling’s T2: SIMCA-P+ver. 13
Pathway analysis: MetaboAnalyst 2.0
Statistical analysis (t-test): MS Excel
Confirmation of metabolites:
Spike-in experiment
Checking YMDB (Yeast
Metabolome Database)
33

34. Score plot
1. Multi-dimensional space (x1,
x2, x3: metabolites)
x3
x2
x1
x3
x2
x1
2. Observations are plotted into
the space
3. Principal component 1 (the
largest variation)
x3
x2
x1
PC1
x3
x2
x1
PC1
PC2
4. Principal component 2
x3
x2
x1
PC1
PC2
5. A planar surface composed
by PC1 and PC2
Score t1
6. Projected into two
dimensional space
PCA score plot (t1 vs. t2)
Score t2
PC1
PC2
t2
t1
About PCA (Principal Component Analysis)
34

35. Loading value = cosine of the
angle the component makes with
the original variable axis
A high value (max=1) means that
the component is aligned with the
original variable, a close to zero
value shows that it has no
influence. A low value (min -1)
indicates an opposite influence.
x3
x2
x1
PC1
PC2
Loading also
describes the
correlation
between variables
(positive or
negative)
x3
x2
x1
PC1
α1 α2
α3
Loading plot
PCA loading plot (p[Comp. 1])
p[Comp.
1]
X2
X3
X1
XK
X5
X4
...
A loading describes
the correlation that the
principal component
has with the original
variable.
35
About PCA (Principal Component Analysis)

36. PCA score plot
(SIMCA-P+ 13)
Sampling:
4 sampling points (n=3)*
5 h: mid-log phase,
9 h: late-log phase,
26 h: post-diauxic phase,
76 h: stationary phase
*cell amount is adjusted
to 5x OD (~5 x 107 cells)
Data pretreatment:
96 metabolites from LC-
MS/MS
Normalized to IS (PIPES)
Scaling: unit variance
(late) Growth phase (early)
Mutants
vs.
wild-type
strain
Discrimination of RTG knockouts on PCA
(wild-type/WT)
The difference between
RTG intact (wild-type) and
RTG deficient strains can be
seen from metabolic profile
36
RTG mutant strain

37. (late) Growth phase (early)
Mutants
vs.
wild-type
strain TCA and glyoxylate
cycle intermediates
Nucleotide
monophosphates,
ribonucleosides
Polyamine
biosynthesis-
related
PCA loading plot
(SIMCA-P+ 13)
Sampling:
4 sampling points (n=3)*
5 h: mid-log phase,
9 h: late-log phase,
26 h: post-diauxic phase,
76 h: stationary phase
*cell amount is adjusted
to 5x OD (~5 x 107 cells)
Data pretreatment:
96 metabolites from LC-
MS/MS
Normalized to IS (PIPES)
Scaling: unit variance
PCA screens important metabolites
Amino acids,
glycolysis
intermediates
37

38. 50 most important metabolites that were
affected by RTG1/RTG3 deletion:
-higher loading value: higher impact
-negative loading: negative correlation with RTG
Pathway Analysis
(MetaboAnalyst 2.0)
High levels of TCA and glyoxylate cycle
intermediates (2-oxoglutarate, glyoxylate,
malate, isocitrate, citrate, succinate) positively
correlate with RTG-genes (increased in WT
and decreased when RTG-genes were deleted)
PC2 separates
between WT-RTG
mutants
High levels of polyamine biosynthetic
intermediates (putrescine, ornithine,
spermidine) negatively correlate with
RTG-genes (increased when RTG-genes
were deleted)
Metabolites associated with RTG deletion
38

39. Pathway Analysis (MetaboAnalyst 2.0)
Amino acid metabolism was largely affected by RTG
deletion, besides TCA cycle and glyoxylate/ dicarboxylate
metabolism
Color intensity: significance of the pathway
Size: pathway impact score (centrality of its
involved metabolites).
Pathways associated with RTG deletion
39
Arginine and proline metabolism
Alanine, aspartate and
glutamate metabolism
Glutathione metabolism
Beta-alanine metabolism
TCA cycle
Glyoxylate and dicarboxylate metabolism
Glycine, serine and
threonine metabolism

40. RTG affects TCA/glyoxylate cycle
40
Glycolysis Pentose
phosphate
pathway
Fatty acid
metabolism
Nitrogen
metabolism
Glutathione
metabolism
Arg, Pro
metabolism
CIT1
CIT2
ACO1
IDH1
IDH2
Citrate
Isocitrate
2-Oxoglutarate
Succinate
Purine,
pyrimidine
metabolism
Malate
Glyoxylate
FUM1
SDH1-
SDH4
LSC1
LSC2
MDH1-
MDH3
ICL1
MLS1
ICL1
• TCA and glyoxylate cycles were down-
regulated in RTG mutant strains
• Decrease of 2-oxoglutarate precedes the
decreases of other TCA/glyoxylate cycle
intermediates
Key metabolite?

41. RTG affects polyamine biosynthesis
41
Ala, Asp, Glu
metabolism
Arg, Pro
metabolism
Pyrimidine
metabolism
Beta-Ala
metabolism
Glutathione
metabolism
Cys, Met
metabolism
Spermidine
Putrescine
Ornithine
Citrulline
SPE1
SPE3 SPE4
SPE4
SPE2
• Polyamine putrescine and spermidine were
higher in RTG mutant strains
Polyamines protect cells when
RTG is inactive?

42. Differential metabolic expression in BY4742, rtg1∆ and rtg3∆
strains at four different time points. Metabolite intensities
were normalized to internal standard and relative to those of
wild-type BY4742 at time 5 h (OD600=1), averaged and log-2
transformed. Metabolite clustering was based on Pearson’s
correlation and average linkage.
Metabolic expression in WT and RTG deletion strains
42

43. TCA cycle enzymes: can see similar result with metabolome data
Enzyme expression is easy to interpret, only transcript data is
enough
Comparison with transcript data
43
Kemmeren et.al. Cell 157, 740–752 (2014).
GAP1: general amino acid permease
Which amino acid is influenced?  Need to see
metabolite (amino acid expression) data
Increased GAP1 expression  Increased glutamine
uptake from the growth media
Glutamine seems to be preferred over glutamate
(this info cannot be extracted from transcript data
alone!)

44. Metabolite comes from various sources.
Enzyme: can be easily interpreted (substrate-product relation)
Transporter, receptor, regulator: not straightforward
Only transcript data sometimes not enough
Actual metabolite abundance should also be investigated
44
Metabolite
Synthesis
Degradation Interorganelle
transport
Taken from
nutrient/
environment
Export
Others??
Can be obtained from transcriptomics (gene expression levels)
Transcriptomics vs. metabolomics

45. Transcriptomics vs. metabolomics
► Transcriptomics
Protocol already established
Single extraction method and single analysis (microarray) can
measure all transcripts for the whole genome
45
► Metabolomics
Protocol not yet established (each lab has their own protocol)
It is not possible to measure all metabolites simultaneously!
(diverse chemical properties, requires several analytical
platforms and depends on extraction method, limitation in
standard metabolites for identification)
The idea is to gather as much data as possible!
Both transcriptomics and metabolomics data are important

46. Metabolomics is the comprehensive study of metabolites. It is an
interdisciplinary research between biology, analytical chemistry and
bioinformatics.
Typical workflow in a metabolomics research includes sample
preparation, metabolites extraction, sample analysis and data
acquisition, data analysis and interpretation.
Mitochondrial retrograde response is the signaling pathway that
maintains a continuous supply of 2-oxoglutarate under glyoxylate
cycle when mitochondrial function is repressed.
Metabolic changes caused by RTG deletion can effectively be
captured using metabolomics approach, enhancing our knowledge
of RTG regulation.
1) The characteristic decrease in 2-oxoglutarate preceding other
TCA cycle intermediates suggests a key role of 2-oxoglutarate in
balancing TCA/glyoxylate cycle
2) RTG involvement in polyamine biosynthesis
Summary
46

47. Future challenges
Large-scale metabolomics for genome-wide analysis
Challenges in
large-scale
metabolomics
studies
reliable and
reproducible
high-
throughput
analysis
platform
reproducible
and stable
sample
extraction
protocol
robust peak-
picking and
alignment
algorithm
sophisticated
data analysis
software and
curated
databases
47

48. 48
Thank You
Questions? Email me zanariahhashim@utm.my