Foodomics: Fundamentals and...

Laboratory of Foodomics, CIAL

National Research Council of Spain (CSIC)

Madrid, Spain

[email protected]

eSeminar

Foodomics: Fundamentals and applications

Alejandro Cifuentes

Thank you!...

CURRENT & FUTURE CHALLENGES

IN FOOD SCIENCE AND NUTRITION

1. To produce new functional foods with scientifically proved claims

2. To detect food safety issues at early stage, before they become global!

3. To develop, produce and monitor new transgenic foods

4. To understand the effects of gene-food interaction on human health

(Nutrigenomics)

5. To explain the different answers from individuals to food (Nutrigenetics)

6. To establish the global role and functions of gut microbiome

7. To reduce through diet the impact of cardiovascular diseases, obesity and

cancer (discovering the molecular mechanisms behind).

8. To get a personalized nutrition: How far we are?

To get a personalized nutrition: How far we are?

OTHER CURRENT & FUTURE CHALLENGES

IN FOOD SCIENCE AND NUTRITION

9. To reduce food allergy and food allergens

10. To confirm food quality and traceability

11. Understand the stress adaptation responses of food-borne pathogens

12. To understand the molecular basis of biological processes essential for

improving agronomic and farm animal production

13. To understand postharvest phenomena through a global approach

(genetics linked to environmental responses: biological networks)

14. To carry out pangenomics of industrial starter cultures and probiotics

15. Bioinformatics (including data processing, clustering, dynamics, or

integration of the various ‘omics’ levels) will have to progress.

NEW CHALLENGES USUALLY REQUIRE NEW ANSWERS…

Transcriptomics

Proteomics

Metabolomics

Genomics & Epigenomics

Foodomics Foodomics has been defined by our group as:

A discipline that studies the Food and Nutrition domains through

the application and integration of advanced omics technologies to

improve consumer’s well-being, health, and knowledge

(Cifuentes et al.; J. Chromatogr. A 1216 (2009) 7109;

Electrophoresis 31 (2010) 205;

Mass Spec. Rev. 31 (2012) 49–69;

Anal. Chem. 84 (2012) 10150–10159).

).

Bioinformatics

Toxicity assays

In-vitro assays

In-vivo assays

Clinical trials

Foodomics: a new way to investigate Food

Science and Nutrition in the postgenomic era

To improve consumers

well-being, health

and knowledge

Transcriptomics

Proteomics

Metabolomics

Genomics & Epigenomics

Foodomics tools and applications

Bioactivity

Quality

Safety

Traceability

(impact factor: 3.303) “Foodomics and Advanced Food Analysis”

June 2014. Editor: A. Cifuentes

(impact factor: 5.856) “Foodomics” Cover and Feature Article

December 2012

(impact factor: 6.273) “Modern Food Analysis and Foodomics”

December 2013

Editors: A. Cifuentes D. Rutledge

March 2013

(Impact factor: 6.273)

REVIEW PAPERS ON:

“Modern Food Analysis and Foodomics”

or “Green Extraction

Techniques”

ARE WELCOME!

- Development of green extraction and fractionation processes based on

compressed fluids (sub- and supercritical fluids).

- Development of advanced analytical methods for food analysis.

- Development of new functional foods and ingredients.

- Food safety issues.

- Foodomics: genomics, transcriptomics, proteomics & metabolomics in food

science.

Foodomics Lab Research Lines

Natural source

extracts

characterization Selection of the

extracts with the

highest bioactivity

RNAs. proteins

and metabolites

fractions obtained

from control and

treated individuals

Data processing

Biomarkers identification and

confirmation. Pathway analysis and biological process understanding

Statistical analysis

Transcriptomics Proteomics Metabolomics

In vitro assays

In vivo assays

Clinical trials

Green

extraction

processes

Advanced

analytical

techniques

Foodomics evaluation

A typical work-flow in the Foodomics Lab:

new functional ingredients

Natural source

extracts


extracts with the

highest bioactivity

RNAs. proteins

and metabolites

fractions obtained

from control and

treated individuals

Data processing





In vitro assays

In vivo assays

Clinical trials

Green

extraction

processes

Advanced

analytical

techniques

A typical work-flow in the Foodomics Lab


SFE

PLE

SWE

GXL

CHALLENGE

EXTRACTION OF BIOACTIVE COMPOUNDS FROM

NATURAL SOURCES THROUGH GREEN

PROCESSES

COMPRESSED FLUIDS

Natural source

extracts


extracts with the

highest bioactivity

RNAs. proteins

and metabolites

fractions obtained

from control and

treated individuals

Data processing





In vitro assays

In vivo assays

Clinical trials

Green

extraction

processes

Advanced

analytical

techniques



Advanced analytical methods Advanced analytical methods

CE

HPLC

UPLC

LCxLC

SFC

GC

TOF-MS

IT-MS

QqQ-MS

UV

LIF

DAD

Q-MS

Characterization

of extracts and/or

bioactive compounds

Natural source

extracts


extracts with the

highest bioactivity

RNAs. proteins

and metabolites

fractions obtained

from control and

treated individuals

Data processing





In vitro assays

In vivo assays

Clinical trials

Green

extraction

processes

Advanced

analytical

techniques



Data

analysis

2DE MALDI-TOF-TOF

LC-MS

CE-MS

Control invididuals vs. treated with dietary ingredients

CGE-LIF

Microarrays (Serv)

RT-qPCR

CE-MS

LC-MS

FT-MS (Munich)

GC-MS

NMR (Bologna) Proved effects

and/or

Biomarkers discovery Health benefits

known and

scientifically

based

Legal issues:

Claims on new

functional foods

approval

SYSTEMS

BIOLOGY

BIOINFORMATICS

Protein

expression

GENOMICS/

TRANSCRIPTOMICS

Gene

expression

Nucleic acids Metabolites Proteins

METABOLOMICS

Metabolite

expression

FOODOMICS PLATFORM

PROTEOMICS

Data

analysis

Data

integration

Foodomics projects in our lab on:

Alzheimer Colon cancer Leukemia

Population study

Biological sample:

Cebrospinal fluid

(CSF)

Human cell lines

Animal models

Biological samples:

DNA, RNA,

proteins and

metabolites

Human cell lines

Biological samples:

DNA, RNA,

proteins and

metabolites

In collaboration with

Karolinska Institute

(Stockholm, Sweden)


Univ. Miguel Hernandez, Elche, Spain

University of Granda, Granada, Spain

GM corn, GM soya,

GM yeasts…

DNA, proteins and

metabolites


GSF

(Munich, Germany)

Safety, quality and

traceability of Bioactivity of food ingredients against:

Transgenic foods

Other foods & ingr

AD Healthy

• Most prevalent dementia among aged people.

Increasing incidence (WHO):

>20% older than 64 years old.

• Alzheimer’s disease (AD) description 100

years ago; however origin and causes are

unknown.

• Progressive destruction and atrophy of brain

cortex: neurofibrillary tangles and amyloid

plaques.

Amyloid plaques Neurofibrillary tangles

90% advanced cases of AD

1. Early diagnosis of AD is urgently needed

2. Is it possible to reduce AD impact through diet?

6000-10000 € / patient

MCI: Mild cognitive impairment cannot be detected

Metabolomics of CSF

M1 - Control aged subjects

M2 - Mild cognitive impairment without developing Alzheimer subjects

M3 - Mild cognitive impairment with developing Alzheimer subjects

M4 - Alzheimer disease diagnosed subjects

M5- Unknown (Blind or Test group)

In order to find new possible AD biomarkers and investigate the activity of new functional

ingredients on Alzheimer development, four different groups of study were selected, namely:

METABOLITES

EXTRACTION

CE-TOF-MS

ANALYSIS METABOLITES

SEPARATION

DATA

PROCESSING

CSF from M2

CSF from M3

Optimization

required

CSF from M1

CSF from M4

*MCI: Mild cognitive impairment AD: Alzheimer disease

ANALYTICAL PROCEDURE

CE-TOF-MS <3 (kDa) fraction IS addition*

*Internal Standards addition for a final concentration of:

Tyramine = 0.001 mg/mL

Methionine sulfone = 0.004 mg/mL

0 2 4 6 8 10 12 Time [min]

I

Groups of samples Samples in each group

1 - Healthy 19

2 - MCI* without development to AD 22

3 - MCI* with development to AD 9

4 - AD 23

5 - Unknown 12

TOTAL 73 + 12 = 85

Groups of samples

1 - Healthy 19

2 - MCI* without development to AD 22

3 - MCI* with development to AD 9

4 - AD 23

5 - Unknown 12

TOTAL 73 + 12 = 85

75 74 73 72 71 70 69 68 67 66 65 64 63 62 61 60 59 58 57 56 55 54 53 52 51 50 49 48 47 46 45 44 43 42 41 40 39 38 37 36 35 34 33 32 31 30

29 28

27

26 25

24 23 22 21 20

19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4

2 1

3

0 5 10 15 20 25 Time [min]

EIE, M4

CS

F m

etab

oli

tes

by C

E-T

OF

-MS

CSF pretreatment: Ultracentrifugation with 3KDa membrane.

Analysis of the fraction < 3 kDa

CE conditions: Bare silica capillary (87cm, 50 µm ID)

BGE, 0.5M formic acid buffer, pH 2

Running voltage +25 kV

Injection at 0.5 psi during 80 s.

MS conditions: Positive ion mode

Sheath liquid isopropanol-water (1:1 v/v)

Flow rate of 4 µL/min

Dry gas flow at 4 L/min

Nebulizer gas flow at 0.4 bar

Temperature 200°C

Mass scan: 50-500 m/z

DATA PROCESSING

0 2 4 6 8 10 12 Time [min]

1- Electropherogram calibration (sodium formate). 2- Retention time filtration (5-14 min). 3- Wavelet chromatogram building. 4- Deconvolution of electropherograms into individual peaks. 5- Deisotoping. 6- Adduct search. 7- Normalization of retention time in the replicates. 8- Alignement of replicates. 9- Area integration and not detected m/z values revision (manually integration if necesary). 10- Sample alignement. 11- Repeat step 9 and 10 sample by sample until all of them are aligned. 12- Extraction of all the ion electropherograms (manually detection and corroboration of automatically detected adducts) 13- Export data to a CSV file

213 features 71 metabolites

Area normalization

DATA PROCESSING

Next, statistical analysis was carried out with all the common features in all the samples.

CE-MS allowed the reproducible detection of 211 features. After removing adducts, isotopes, etc, 71 were confirmed in the CSF samples of which: 46 endogenous metabolites 22 unknown (high error) 3 exogenous (drugs) Galanthamine (AD treatment) Memantine (AD treatment) Perindoprilat (antihypertensive)

STATISTICAL ANALYSIS

- PCA 95% confidence (outliers detection): 2 samples (017 and 108) were considered outliers. 71

samples were studied.

Scatterplot (estadistica areas corregidas 19-07-11 170v *85c)

Include condition: v 1>3

PCA1(gr. 4)

PC

A2(g

r.4)

M039-Rep03

M037-Rep02M023-Rep01

M081-Rep03

M053-Rep02

M075-Rep11

M016-Rep03

M091-Rep04

M017-Rep01

M018-Rep04

M013-Rep01

M066-Rep05

M056-Rep01

M019-Rep11

M088-Rep01

M022-Rep03

M055-Rep08

M044-Rep03

M009-Rep03

M038-Rep03

M026-Rep06

M063-Rep02

M041-Rep01

-3 -2 -1 0 1 2 3 4

-2

-1

0

1

2


Include condition: v 1=3

PCA1 (gr. 3)

PC

A2 (

gr.

3)

M084-Rep02M082-Rep02

M107-Rep08

M101-Rep02

M085-Rep01

M093-Rep07

M110-Rep02

M062-Rep15

M061-Rep01

-4 -3 -2 -1 0 1 2 3 4

-4

-3

-2

-1

0

1

2

3

4


Include condition: v 1=2

PCA1 (gr. 2)

PC

A2 (

gr.

2)

M078-Rep03

M015-Rep01

M106-Rep08

M079-Rep03

M077-Rep01

M012-Rep01

M031-Rep07

M080-Rep03

M102-Rep03 M001-Rep02

M073-Rep02

M064-Rep04M105-Rep31M112-Rep02

M111-Rep08

M108-Rep03

M086-Rep02

M067-Rep01 M065-Rep04

M094-Rep05

M104-Rep05

M059-Rep01

-3 -2 -1 0 1 2 3

-3

-2

-1

0

1

2

3

4


Include condition: v 1<2

PCA1(gr 1)

PC

A2(g

r 1)

M003-Rep07

M030-Rep12

M069-Rep06

M040-Rep04

M054-Rep01

M070-Rep04

M074-Rep03

M072-Rep01

M025-Rep06

M050-Rep14

M043-Rep06

M042-Rep06

M007-Rep15

M049-Rep21

M045-Rep05

M047-Rep19

M011-Rep01

M051-Rep02

M052-Rep10

-4 -2 0 2 4

-3

-2

-1

0

1

2

3

1. General discriminant analysis considering all variables (select the variables to clasify all samples in the correct group). Forward stepwise. Selection one-by-one of main metabolites able to differentiate among different groups.

2. Linear discriminant analysis: confirmation of percentage of right classification using the metabolites selected in step 1.

3. LSD Fisher Test (least significant difference test): allows the study of the differences found in the normalized peak areas of each selected metabolite among the different groups.

4. Prediction of the unknown samples classification following the model provided by the general discriminant analysis and confirmed after the step 2.

STATISTICAL ANALYSIS

STATISTICAL RESULTS (four groups of samples)

86=245.007571 m/z 74= 162.103053 m/z 56= 128.032137 m/z

290= 106.993966 m/z 128= 101.103462 m/z 158= 150.057944 m/z 28= 102.055486 m/z 69= 106.084089 m/z

93= 138.054113 m/z 60= 140.066925 m/z

186= 308.125792 m/z 142= 123.597103 m/z 110= 84.0793484 m/z 177= 220.001822 m/z 33= 154.057694 m/z 16= 294.104034 m/z 77= 233.144461 m/z 71= 256.089351 m/z 94= 170.031800 m/z 49= 371.210107 m/z 64= 247.125790 m/z 74= 162.103053 m/z 95= 262.126175 m/z 88=123.046742 m/z 68=184.043228 m/z

114= 105.110228 m/z 92= 86.0951723 m/z

295= 134.104577 m/z 10= 293.143308 m/z 69= 106.084089 m/z 90= 197.024267 m/z 81= 203.14464 0 m/z 96= 112.040584 m/z

114= 105.110228 m/z 295= 134.104577 m/z 86= 245.007571 m/z

90.14% *

114=105.110228 m/z 86= 245.007571 m/z

129= 162.110396 m/z 112= 247.090537 m/z 60= 140.066925 m/z

142= 123.597103 m/z 178= 230.182471 m/z 34= 197.097509 m/z 81= 203.144640 m/z 21= 144.100951 m/z 156= 141.075667 m/z

*

129= 162.110396 m/z 112= 247.090537 m/z 82= 152.023092 m/z 70= 386.922559 m/z 63= 202.176766 m/z

7 SAMPLES WRONGLY CLASSIFIED

GROUP 1

GROUP 2 GROUP 3

GROUP 4

97.5 %

100 %

10

0 %

95

%

Control

Mild cognitive impairment No Alzheimer

Mild cognitive impairment developing Alzheimer

Alzheimer diagnosed subjects

STATISTICAL RESULTS (three groups of samples)

G1+G2

114= 105.110228 m/z 112= 247.090537 m/z 172= 212.099119 m/z 129= 162.110396 m/z 70= 386.922559 m/z 53= 318.935591 m/z 86= 245.007571 m/z

149= 132.078332 m/z

93= 138.054113 m/z 60= 140.066925 m/z

186= 308.125792 m/z 142= 123.597103 m/z 110= 84.0793484 m/z 177= 220.001822 m/z 33= 154.057694 m/z 16= 294.104034 m/z 77= 233.144461 m/z 71= 256.089351 m/z 94= 170.031800 m/z 49= 371.210107 m/z 64= 247.125790 m/z 74= 162.103053 m/z 95= 262.126175 m/z 88=123.046742 m/z 68=184.043228 m/z

114= 105.110228 m/z 92= 86.0951723 m/z

295= 134.104577 m/z 10= 293.143308 m/z 69= 106.084089 m/z 90= 197.024267 m/z 81= 203.14464 0 m/z 96= 112.040584 m/z 2 SAMPLES WRONGLY CLASSIFIED

60= 140.066925 m/z 112= 247.090537 m/z 129= 162.110396 m/z 142= 123.597103 m/z 49= 371.210107 m/z

169= 246.165686 m/z 114= 105.110228 m/z 175= 218.13445 m/z

186= 308.125792 m/z 75= 229.149251 m/z 295=134.104577 m/z 42= 198.084516 m/z

100 %

100 %

95

%

97.18% *

GROUP 3

GROUP 4

GROUP 0

114= 105.110228 m/z

60= 140.066925 m/z

49 = 371.210107 m/z

178= 230.182471 m/z

186 = 308.125792 m/z

57= 232.125786 m/z

75= 229.149251 m/z

129= 162.110396 m/z

86 = 245.007571 m/z

51 = 136.044175 m/z

171= 203.140810 m/z

293= 136.044984 m/z

56 = 128.032137 m/z

167= 178.055781 m/z

*

97.18%

BIOMARKERS VARIATION

0

0.0005

0.001

0.0015

0.002

0.0025

0.003

142 49 178 114 186 171 86 57

0

0.005

0.01

0.015

0.02

0.025

60 75 167 51 56 293 129

G0

G3

G4

An

An

An = normalized area with the corrected areas of tyramine and methionine sulfone

114= 105.110228 m/z

60= 140.066925 m/z

49 = 371.210107 m/z

178= 230.182471 m/z

186 = 308.125792 m/z

57= 232.125786 m/z

75= 229.149251 m/z

129= 162.110396 m/z

86 = 245.007571 m/z

51 = 136.044175 m/z

171= 203.140810 m/z

293= 136.044984 m/z

56 = 128.032137 m/z

167= 178.055781 m/z

BIOMARKERS IDENTIFICATION

97.18%

Mig

rati

on

tim

e

Metabolite m/z Formula Tentative compound Database link Error (ppm)

86 170.0795 C8H11NO3 Norepinephrine HMDB00216 -10.0

114 104.1072 C5H13NO Choline KEGG-C00114 2.6

49 175.1193 C6H14N4O2 L-Arginine HMDB00517 -12.4

167 156.0774 C6H9N3O2 L-Histidine HMDB00177 4.4

171 203.1475 C8H18N4O2 Dimethyl-L-arginine HMDB01539 -13.7

129 162.1124 C7H15NO3 L-Carnitine HMDB00062 -0.2

293 132.0762 C4H9N3O2 Creatine HMDB00064 -4.5

186 308.1274 C11H21N3O5S Tripeptide

(G,T,M; V,S,C; M,S,A)

METLIN-18567 ; 18572; 19031; 18394; 17151; 16307; 16796; 17053; 19196; 19418; 22195; 22497; 22618; 20697; 20621 ; 20458; 20515, 23583

-0.3

75 229.1542 C11H20N2O3 Dipeptide (L,P; I,P) HMDB11175; HMDB06695 -2.2

178 198.0814 C9H11NO4 L-Dopa HMDB00181 17.2

57 232.1221 C10H17NO5 Suberylglycine HMDB00953 18.0 56 106.0506 C3H7NO3 L-Serine HMDB00187 7.2

60 118.0870 C5H11NO2 Betaine HMDB00043 6.6

51 136.0442 C4H9NO2S Homocysteine HMDB00742 11.2

114= 105.110228 m/z

60= 140.066925 m/z

49 = 371.210107 m/z

178= 230.182471 m/z

186 = 308.125792 m/z

57= 232.125786 m/z

75= 229.149251 m/z

129= 162.110396 m/z

86 = 245.007571 m/z

51 = 136.044175 m/z

171= 203.140810 m/z

293= 136.044984 m/z

56 = 128.032137 m/z

167= 178.055781 m/z

POSITIVE CONFIRMATION WITH COMMERCIAL STANDARDS

EIE Choline (choline concentration with the sample 0.04 mg/mL)

EIE Arginine (arginine concentration with the sample 0.04 mg/mL)

EIE Histidine (histidine concentration with the sample 0.04 mg/mL)

EIE Dimethylarginine (dimethylarginine concentration with the sample 0.08 mg/mL)

EIE Carnitine (carnitine concentration with the sample 0.04 mg/mL)

2

4

6

8

2

4

6

8

Intens.

1

2

3

1

2

3

0.5

1.0

1.5

0.5

1.0

1.5

1

2

3

1

2

3

0.2

0.4

0.6

0.8

0.2

0.4

0.6

0.8

x104

EIE Creatine (creatine concentration with the sample 0.04 mg/mL)

EIE Serine (serine concentration with the sample 0.04 mg/mL)

EIE Proline (proline concentration with the sample 0.08 mg/mL)

1

2

3

4

1

2

3

4

0.5

1.0

1.5

0.5

1.0

1.5

1

3

5

1

3

5

0

2

4

0

2

4

x104

x104

x105

0 2 4 6 8 10 12 0 2 4 6 8 10 12

Time [min]

x105

x105

x104

x105

x105

EIE Betaine (betaine concentration with the sample 0.04 mg/mL)

NEGATIVE CONFIRMATION WITH COMMERCIAL STANDARDS

EIE Norepinephrine

0.25

0.75

1.25

5 x10

Intens.

2 4 6 8 10 12 14 16 18 Time [min]

0.25

0.75

1.25

2 4 6 8 10 12 14 16 18 Time [min]

NOT INCREASED WITH THE ADDITION OF

THE COMMERCIAL STANDARD

x105

SAMPLE

SAMPLE+ Norepinephrine

EIE DOPA

2

4

6

Intens.

2 4 6 8 10 12 Time [min] 0

2

4

6

2 4 6 8 10 12 Time [min]

4 x10

4 x10

NOT INCREASED WITH THE ADDITION OF

THE COMMERCIAL STANDARD SAMPLE

SAMPLE+ DOPA

CLASSIFICATION OF THE UNKOWN SAMPLES FROM TEST GROUP 71 KNOWN SAMPLES (2 OUTLIERS) + 12 UNKNOWN TEST SAMPLES

SAMPLE p G0 p G3 p G4 CLASIFICATION

T002 0.929034 0.001162 0.069804 G0 Y

T035 0.999686 0.000025 0.000289 G0 Y

T096 0.514988 0.005227 0.479785 G0 Y

T109 0.986890 0.013108 0.000001 G0 Y

T029 0.066190 0.933810 0.000000 G3 N-MCI

T092* 0.000004 0.999996 0.000000 G3 N-MCI*

T097 0.001071 0.998929 0.000000 G3 Y

T098 0.125679 0.874321 0.000000 G3 Y

T103 0.212705 0.697683 0.089612 G3 Y

T113 0.020583 0.979417 0.000000 G3 Y

T008 0.089401 0.001842 0.908757 G4 Y

T076 0.000000 0.000000 1.000000 G4 Y

83% of the test samples were correctly assigned

(the value could go till 92% since patient T092*

is a young man with diagnosed MCI who still can develop to AD)

Future work:

A Foodomics project towards nutrition and cognitive health

is now under preparation.

Conclusion:

Combining metabolites from energy provision, oxidation process

and products of methylation as biomarkers, the diagnosis and

monitoring of the progression of Alzheimer's disease could be

improved.


Population study

Biological sample:

Cebrospinal fluid

(CSF)

Human cell lines

Animal models

Biological samples:

DNA, RNA,

proteins and

metabolites

Human cell lines

Biological samples:

DNA, RNA,

proteins and

metabolites



(Stockholm, Sweden)




GM corn, GM soya,

GM yeasts…

DNA, proteins and

metabolites


GSF

(Munich, Germany)

Safety, quality and



Transgenic foods

Other foods & ingr

The score scatter plot underlines a different

pattern for the transgenic (blue color)

and wild maize lines (red color). The different

properties of the discriminative masses

(represented in blue and red in the loading plot)

were investigated with MassTRIX.

Statistical analysis of the data from non-target

metabolomics (based on FT-ICR-MS and CE-

TOF-MS analysis) lead to the tentative

identification of possible biomarkers specific of

GM vs. wild organisms.

NON-TARGET METABOLOMICS: FT-ICR-MS & CE-TOF-MS

TRANSGENIC vs. CONVENTIONAL MAIZE



Population study

Biological sample:

Cebrospinal fluid

(CSF)

Human cell lines

Biological samples:

DNA, RNA,

proteins and

metabolites

Human cell lines

Animal models

Biological samples:

DNA, RNA,

proteins and

metabolites



(Stockholm, Sweden)




Transgenic foods

GM corn, GM soya,

GM yeasts…

DNA, proteins and

metabolites


GSF

(Munich, Germany)

Safety, quality and


Colon cancer and diet

The most diagnosed cancer in Spain: 25000 new cases every year

The 2nd cause of death by cancer in Europe and 4th in the world

According to several studies, 80% of the cases are related to diet

Can we reduce the proliferation speed of colon cancer through

diet? This would be a great help since this cancer has a high

percentage of recovery if intervention can commence before the

period of tumor proliferation preventing the series of events

leading to metastasis

SFE Polyphenols

enriched extracts

characterized by

LC-UV-MS

HT29 colon cancer cells

Treated

Control

Selection of the

polyphenols

enriched extract

with the highest

anti-proliferative

activity at 10 µM

RNAs. proteins

and metabolites

fractions obtained

from control and

treated HT29 cells

(minimum x 3)

Data processing




RNAs analysis by

Human Gene 1.0 ST

microarrays. Genes

expressed differentially

confirmed by RT-qPCR

Transcriptomics

Proteins analysis by

2-D electrophoresis and

identification of

differential proteins by

MALDI-TOF-TOF

Proteomics

Metabolites analysis by

CE-MS. RP/UPLC-MS

and HILIC/UPLC-MS.

Identity confirmation

using standards

Metabolomics

Foodomics evaluation of dietary ingredients

vs. Human colon cancer cells proliferation

Natural source

Data

analysis

2DE MALDI-TOF-TOF

LC-MS

CE-MS

Control vs. treated human colon cancer cells with dietary ingredients

CGE-LIF

Microarrays (Serv)

RT-qPCR

CE-MS

LC-MS

FT-MS (Munich)

GC-MS

NMR (Bologna) Proved effects

and/or

Biomarkers discovery Health benefits

known and

scientifically

based

Legal issues:

Claims on new

functional foods

approval

SYSTEMS

BIOLOGY

BIOINFORMATICS

Protein

expression

GENOMICS/

TRANSCRIPTOMICS

Gene

expression

Nucleic acids Metabolites Proteins

METABOLOMICS

Metabolite

expression

FOODOMICS PLATFORM

PROTEOMICS

Data

analysis

Data

integration



Population study

Biological sample:

Cebrospinal fluid

(CSF)

Human cell lines

Biological samples:

DNA, RNA,

proteins and

metabolites

Human cell lines

Animal models

Biological samples:

DNA, RNA,

proteins and

metabolites



(Stockholm, Sweden)




Transgenic foods

GM corn, GM soya,

GM yeasts…

DNA, proteins and

metabolites


GSF

(Munich, Germany)

Safety, quality and


Chronic Myeloid Leukemia (CML)

-CML is linked to a hematopoietic stem cell disorder, characterized by increased production of granulocytes at all stages of differentiation. -Up to 95 % of CML patients carry the Philadelphia chromosome, product of a cytogenetic translocation. -This translocation is responsible for the expression of a 210 kDa chimeric fusion protein, BCR-ABL. -BCR-ABL triggers several downstream survival pathways, that collectively provide proliferative advantages and resistance to apoptosis.

Imatinib

Dasatinib

Nilotinib

Chronic Myeloid Leukemia (CML) Tyrosine kinase inhibitors (TKIs)

Typical drugs for CML treatment stop the activity of BCR-ABL enzyme by binding to the

site of ATP, this blocks BCR-ABL activity and with that CML proliferation. Other drugs like daunomycin (DNM) acts intercalating and inhibiting DNA replication.

However, drug-resistance may occur what is a major factor in the failure of chemotherapy. Several mechanisms of resistance have been reported for CML, e.g.,

mutations in the BCR/ABL gene, increased BCR/ABL protein expression, overexpression of ATP-binding cassette (ABC) transporters that increases drug removal.

K562wt cell line

Daunomycin (DNM)

Drug-sensitive phenotype

(K562wt or wild type)

IC50 DNM = 1.1 µM

Drug-resistant phenotype

(K562/R)

IC50 DNM = 398.7 µM

Chronic myeloid leukemia (CML): TWO cell lines


(K562wt)


(K562/R)

Control

Treated Rom1

Rom2

Rom3

Rom4

Rom5

Rosemary

DIETARY POLYPHENOLS EXTRACTS

Extract name

Gallic acid (µg/mg extract)

Chologenic acid (µg/mg extract)

Caffeic acid (µg/mg extract)

p-Coumaric acid (µg/mg

extract)

Rosmarinic acid (µg/mg

extract)

Carnosol (µg/mg extract)

Carnosic acid (µg/mg extract)

Rom1 <LOQ 0.063±0.001 0.314±0.001 0.024±0.002 16.781±0.643 104.269±0.651 66.233±0.736

Rom2 n.d. 0.141±0.006 0.525±0.027 0.020±0.003 14.195±0.852 45.821±2.842 0.014±0.003

Rom3 0.017±0.001 0.100±0.001 0.379±0.007 0.033±0.002 8.597±1.161 46.113±0.685 5.780±0.149

Rom4 n.d. n.d. 0.074±0.001 0.088±0.001 <LOQ 226.392±1.399 151.554±0.341

Rom5 n.d. n.d. 0.008±0.001 <LOQ <LOQ 224.658±21.260 106.467±9.499

Polyphenols

enriched extracts

characterized by

LC-UV-MS

PLE

SFE

PLE

PLE

PLE

SFE

SFE

Extract Rom4: SFE extract obtained using supercritical CO2 and 7% ethanol at 150 bar and 40ºC for 30 min

Extract

Rom4

After 48 h, 10 µM Rom4 extract decreased cell viability to 42% in K562wt and to 38% in K562/R

ADAM Cell Counter – Propidium iodide (25·10-3 µg/mL) – 617 nm

CELL VIABILITY ASSAY


(K562wt)


(K562/R)

Control

Treated

Rom4

Data processing


RNAs analysis by

Human Gene 1.0 ST

microarrays. Genes



Transcriptomics


CE-MS. RP/UPLC-MS.


using standards

Metabolomics

EFFECT OF DIETARY POLYPHENOLS ON K562 LEUKEMIA CELLS: A FOODOMICS APPROACH

Pathway analysis and

biological process understanding

MA plot Volcano plot MA plot Volcano plot

STATISTICAL ANALYSIS …. with limma (linear models for microarray data) package

LEUKEMIA CANCER CELLS

709 genes were identified as differentially expressed

K562wt K562/R

Setting FDR at 5% (adjusted p-value <0.05)

289

genes

Setting FDR at 5% (adjusted p-value <0.05)

387

genes

0.6 ≥ FC ≥1.5

Treated Control

Top 200 DEGs

884 genes were identified as differentially expressed

0.6 ≥ FC ≥1.5

Treated Control

Top 200 DEGs

TRANSCRIPTOMIC APPROACH

ANNOTATION …. with pd.hugene.1.0.st.v1 (Human Gene 1.0 ST Array Affymetrix) and hugene10sttranscriptcluster packages

TRANSCRIPTOMIC APPROACH

Annotate DEGs in toptables (removing replicates, Affymetrix controls, non annotated probesets).

As expected, strong induction of ABC was detected in K562/R cells (M-value of 7.5, corresponding to 181-fold change)


(K562wt)


(K562/R)

Control

Treated

Rom4

Data processing


RNAs analysis by

Human Gene 1.0 ST

microarrays. Genes



Transcriptomics


CE-MS. RP/UPLC-MS.


using standards

Metabolomics




CE-TOF-MS

K562wt and K562/R leukemia cell lines: treated vs. control

washing with PBS centrifugation

PELLET

Polytron disruption

SUPERNATANT

Cell culture

(Treated and Control)

centrifugation

< 3 kDa fraction

> 3 kDa fraction

3-kDa-cutoff membrane centrifugation

RP-UPLC-QTOF-MS

SAMPLE PREPARATION

METABOLOMIC APPROACH

Bare silica capillary: 87 cm, 50 µm ID BGE: 0.5 M HCOOH Voltage: +25 kV Sample injection: 80 s (0.5 psi)

Positive ion mode Seath liquid: IspOH-H2O (1:1, v/v) Seath liquid flow: 0.24 mL/h Nebulizar gas pressure: 0.4 bar Drying gas flow: 4L/min Drying gas temperature: 200º C Mass scan: 50-500 m/z

+ +

ZORBAX C18, Rapid Resolution HT (2.1 × 50 mm, 1.8 µm) Gradient: 0-6 min: 2-20% B; 6-10 min: 20-100% B; 10-12 min: 100% B A: water 0.1% formic acid B: ACN 0.1% formic acid Sample injection: 80 s (0.5 psi)

Positive ion mode Nebulizar gas pressure: 40 psi Drying gas flow: 10L/min Drying gas temperature: 300º C Mass scan: 50-1000 m/z

High Resolution Separation Techniques Coupled to mass spectrometry

K562wt

CE-TOF-MS RP-UPLC-QTOF-MS Measured m/z Tentative identification FC Measured m/z Tentative identification FC

203.2215 Spermine 1.5 ↑

189.0883 N-Acetylglutamine 2.7 ↓

188.1745 N1-Acetylspermidine/N8-Acetylspermidine 5.5 ↓

153.0668 N1-Methyl-4-pyridone-3-carboxamide 1.5 ↑

147.1138 Lysine 1.2 ↓

133.0981 Ornithine 9.9 ↓

132.0775 Creatine 1.1 ↑

146.0915 4-Guanidinobutanoic acid 7.4 ↓

162.1136 Carnitine 7.1 ↓

308.0930 Reduced glutathione 1.1 ↑

146.1661 Spermidine 1.5 ↑ 146.1650 Spermidine 1.8 ↑

148.0614 Glutamate 1.5 ↑ 148.0601 Glutamate 1.8 ↑

166.0870 Phenylalanine 2.1 ↓ 166.0863 Phenylalanine 1.8 ↓

307.0850 Oxidized glutathione 2.8 ↑ 307.0833 Glutathione, oxidized 2.2 ↑

371.2401 Pro Val Arg 1.1 ↑

358.2082 Asn Pro Lys 1.0 ↓

341.1081 3-Ketolactose 1.1 ↑

241.1263 Trp Tyr Leu d) ↓

310.1845 Leu Arg 1.1 ↑

280.0907 5-Methylcytidine 1.5 ↓

251.1407 Caffeoylputrescine c) ↑

189.1230 Acetyllysine 2.5 ↑

253.1182 Tyr Ala c) ↑

118.0870 Valine c) ↑

182.0810 Tyrosine 4.2 ↑

122.0959 Phenylethylamine 1.1 ↑

205.0968 Tryptophan c) ↑

132.1021 Leucine 4.3 ↑

150.0578 Methionine 1.6 ↑

33 (4)

68

Id 14

121

Id 19

Tentatively identified

STATISTICALLY ALTERED METABOLITES: TOTAL 189


c) Metabolites only detected in polyphenol-treated cells d) Metabolites only detected in control cells

CE-TOF-MS RP-UPLC-QTOF-MS Measured m/z Tentative identification FC Measured m/z Tentative identification FC

189.0882 N-Acetylglutamine 1.7 ↓

147.1135 Lysine 1.4 ↓

166.0858 Phenylalanine 1.1 ↓

308.0890 Reduced glutathione 3.5 ↑

255.1469 Homoanserine 1.6 ↑

136.0625 Adenine 1.9 ↓

122.0956 Phenylethylamine 2.7 ↑

246.1690 Valerylcarnitine 2.4 ↓

137.0466 Hypoxanthine 1.2 ↓

188.1751 N1-Acetylspermidine/N8-Acetylspermidine 2.4 ↓ 188.1763 N-acetylspermidine 4.7 ↓

146.0928 4-Guanidinobutanoic acid 4.5 ↓ 146.0930 4-Guanidylbutanoate 6.1 ↓

148.0614 Glutamate 2.4 ↑ 148.0607 Glutamate 2.2 ↑

307.0851 Oxidized glutathione 2.9 ↑ 307.0849 Glutathione, oxidized 2.8 ↑

146.1660 Spermidine 1.1 ↑

280.0920 5-Methylcytidine 1.5 ↓

150.0588 Methionine 1.2 ↑

182.0816 Tyrosine 2.1 ↑

132.1025 Leucine 1.8 ↑

385.1304 S-adenosylhomocysteine d) ↓

220.1190 Pantothenic acid 8.2 ↓

332.2302 Lys, Lys, Gly d) ↓

218.0653 2-amino-4-hydroxy-6-(hydroxymethyl)-7,8-

dihydropteridine 1.8 ↓

388.2554 Lys, Gln, Leu 7.5 ↑

407.1899 Gln, Tyr, Pro 29.7 ↑

162.5860 Ala, Pro, His 1.2 ↓

474.2065 Arg, Tyr, Asn 1.8 ↓

K562/R


c) Metabolites only detected in polyphenol-treated cells d) Metabolites only detected in control cells

30 (4)

70

Id 13

105

Id 17

Tentatively identified

STATISTICALLY ALTERED METABOLITES: TOTAL 175


(K562wt)


(K562/R)

Control

Treated

Rom4

Data processing




RNAs analysis by

Human Gene 1.0 ST

microarrays. Genes



Transcriptomics


CE-MS. RP/UPLC-MS.


using standards

Metabolomics


Transcriptional induction of genes encoding phase II detoxifying: - NQO1 (NADPH quinone oxidoreductase) - GST (glutathione S-transferase) - SULT (sulfotransferase)

- OSGIN1 (reponse to the oxidative stress)

DATA INTERPRETATION-transcriptomics

FUNCTIONAL ENRICHMENT ANALYSIS

The polyphenols effect on the modulation of these genes was less evident in K562wt cells than in the K562/R cells

K562/R

Cell line Transcription

Regulator

Predicted Activation

State z-score Target molecules in dataseta

K562/R MYC Inhibited -3,230 ↑ANXA4, ↑CASP8, ↓CCNE1, ↑CLU, ↑CTSB, ↑CTSD, ↑ERAP1, ↑GCSH, ↑GM2A,

↑ICAM1, ↑IFI35, ↑ITGAX, ↓KIAA0664, ↓PKLR, ↑PLAUR, ↓PPID, ↓RPL13

PPARGC1A Inhibited -2,707 ↓CAT, ↓CD36, ↑CD68, ↓SREBF1, ↓TFAM

PPARG Inhibited -2,108 ↓CA2, ↓CAT, ↓CD36, ↑EPHX1, ↓SREBF1, ↑TBXA2R, ↑TBXAS1

RB1 Activated 2,001 ↑CASP8, ↓CCNE1, ↓HIST1H2AB, ↓MYC


TRANSCRIPTION FACTOR ANALYSIS

K562/R

MYC Transcription Factor


TRANSCRIPTION FACTOR ANALYSIS

Downregulated

Upregulated

K562/R

Hibridization Microarray Affymetrix: Human Gene 1.0 ST microarray

RNA extraction RNeasy Mini Kit

Total RNA

NanoDrop

Bioanalyzer

RT-qPCR

Reverse transcription

mRNA cDNA

Gene Symbol (Accession N.) Sequence (5’-3’) LNA probe number

MYC (AY214166) For: GCTGCTTAGACGCTGGATTT

Rev:CGAGGTCATAGTTCCTGTTGG 66

CCNE1 (AF518727) For: GAAGGAGCGGGACACCAT

Rev:CGTCCTGTCGATTTTGGC 1

OSGIN1 (AY258066) For: CTTCTACGCCCAGACACAGAC

Rev: GGATCACCATGGAGCCTTC 12

NQO1 (BC007659) For: CTTTGAAGAAGAAAGGATGGGA

Rev: ACAGACTCGGCAGGATACTGA 22

ABL1 (DQ145721) For: TGCCCAGAGAAGGTCTATGAA

Rev:GGATTTCAGCAAAGGAGGG 86

GUSB (BC014142) For: AACGCCCTGCCTATCTGTATT

Rev: GATGAGGAACTGGCTCTTGG 57

B2M (AB021288) For: CTATCCAGCGTACTCCAAAGATT

Rev:TGGATGAAACCCAGACACATAG 42

RT-qPCR SAMPLE PREPARATION

Quality and quantity

Amplification

LNA probes

Ref

DATA CONFIRMATION-transcriptomics

Gene symbol K562/R K562wt

Microarray RT-qPCR Microarray RT-qPCR

FCa p-valueb FCa p-valuec FCa p-valueb FCa p-valuec

MYC 0.56 0.006 0.30 <0.001 0.72 0.049 0.32 0.037

CCNE1 0.64 0.013 0.42 <0.001 0.84 0.271 0.53 0.037

OSGIN1 2.39 0.004 4.80 0.036 2.09 0.009 3.85 0.033

NQO1 1.67 0.024 2.57 <0.001 2.42 0.002 3.66 0.033

a) M-values (log ratio) obtained from microarray analysis were converted to Fold Change (FC) values. b) Adjusted p-value (FDR). c) Statistical significance calculated by REST.

RT-qPCR DATA ANALYSIS

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

MYC CCNE1 OSGIN1 NQO1

Co

py

nu

mb

er n

orm

aliz

ed

to

AB

L1,

GU

SB a

nd

B2

M

GENE EXPRESSION

K562 Control

K562 Treated

K562/R Control

K562/R Treated

K562wt Treated

K562wt Control

DATA CONFIRMATION-transcriptomics

DATA INTERPRETATION-metabolomics

FUNCTIONAL ENRICHMENT ANALYSIS

K562/R K562wt

Pathway p-valuea Metabolites p-valuea Metabolites

Aminoacyl-tRNA biosynthesis 2.3 x 10-6 ↑Met, ↑Leu, ↑Glu, ↑Tyr, ↓Lys, ↓Phe 1.4 x 10-8 ↑Met, ↑Leu, ↑Glu, ↑Tyr, ↓Lys, ↓Phe, ↑Val, ↑Trp

Glutathione metabolism 5.5 x 10-4 ↑GSSG, ↑GSH, ↑glutamate

Arginine and proline metabolism 2.0 x 10-3 ↑Glu, ↑spermidine, ↓adenine, ↓Gbn 3.3 x 10-5 ↑Glu, ↓Orn, ↑spermidine, ↑spermine, ↓Gbn, ↑creatine

Nitrogen metabolism 2.4 x 10-3 ↑Tyr, ↓Phe, ↑Glu 2.6 x 10-4 ↑Tyr, ↓Phe, ↑Glu, ↑Trp

Glutamate metabolism 3.0 x 10-3 ↑GSSG, ↑GSH, ↑Glu

Urea cycle and metabolism of

amino groups

1.6 x 10-5 ↓Orn, ↑spermine, ↑spermidine, ↑Glu, ↑creatine

a) Significance value calculated with the right-tailed Fisher’s exact test.

K562/R K562wt

OMICS DATA INTEGRATION

Overlapping genes in metabolic pathways

K562/R K562wt

Pathway p-valuea Metabolites Genes p-valuea Metabolites Genes

Aminoacyl-tRNA

biosynthesis

2.3 x 10-6

↑Met, ↑Leu, ↑Glu, ↑Tyr, ↓Lys,

↓Phe

↑VARS 1.4 x 10-8

↑Met, ↑Leu, ↑Glu, ↑Tyr, ↓Lys,

↓Phe, ↑Val, ↑Trp

Glutathione metabolism 5.5 x 10-4 ↑GSSG, ↑GSH, ↑glutamate ↑GSTM2

Arginine and proline

metabolism

2.0 x 10-3

↑Glu, ↑spermidine, ↓adenine,

↓Gbn

↑P4HA2

↓CKMT1A

↑ALDH1A1

3.3 x 10-5

↑Glu, ↓Orn, ↑spermidine,

↑spermine, ↓Gbn, ↑creatine

↑P4HA2

↓CKMT1A

↑CPS1

Nitrogen metabolism 2.4 x 10-3 ↑Tyr, ↓Phe, ↑Glu

↓GCSM

↓CA2

2.6 x 10-4 ↑Tyr, ↓Phe, ↑Glu, ↑Trp

↑CPS1

↓THM2

Glutamate metabolism 3.0 x 10-3 ↑GSSG, ↑GSH, ↑Glu ↑NAGK

Urea cycle and

metabolism of amino

groups

1.6 x 10-5

↓Orn, ↑spermine, ↑spermidine,

↑Glu, ↑creatine

↑CPS1

↓CKMT1

a) Significance value calculated with the right-tailed Fisher’s exact test.

CONCLUSIONS

Rosemary polyphenols activate genes that encode antioxidant and phase II detoxifying enzymes NQO1, GST, SULT, and OSGIN1 (especially in K562/R).

The antiproliferative effect of rosemary polyphenols could be linked with the inhibition of MYC transcription factor.

The integrative Foodomics strategy enabled the identification of various differentially expressed genes in the metabolic pathways modulated by rosemary polyphenols. However, direct associations between transcriptome and metabolome changes could only be established in few cases.

Foodomics is a suitable approach to solve current

and future challenges in Food Science and

Nutrition…

GENERAL CONCLUSION

Foodomics: Fundamentals and...

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