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Foodomics: Food Science in the Post-Genomic Era Alejandro Cifuentes

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Foodomics: Food Science in the Post-Genomic Era Alejandro Cifuentes Foodomics: food science in the post-genomic era Alejandro Cifuentes Laboratory of Foodomics, CIAL National Research Council of Spain (CSIC) Madrid, Spain CEICS Forum 2011, Tarragona CURRENT CHALLENGES IN FOOD SCIENCE Production of new functional foods (with scientifically proved claims) Food safety, quality and traceability (ideally as a whole) using omics approaches Develop, produce and monitor new transgenics foods Understand the effects of gene-food interaction on human health (Nutrigenomics) Explain the different answers from individuals to food: personalized diet (Nutrigenetics) CURRENT CHALLENGES IN FOOD SCIENCE Production of new functional foods Food safety,(scientifically quality andtraceability proved claims) (ideally as a whole) using omics Understand the effects of gene-food interaction on Develop, produce and monitor new Explain different answers from individuals to food: approaches human health (Nutrigenomics) transgenics foods Newpersonalizedneed diets, (Nutrigenetics) new answers:Foodomics Foodomics We have defined Foodomics for the first time in a SCI journal as: A discipline that studies the Food and Nutrition domains through the application of advanced omics technologies to improve consumer’s well-being, health, and confidence. (Cifuentes et al.; J. Chromatogr. A 1216 (2009) 7109; Electrophoresis 31 (2010) 205; Mass Spectrom. Rev. 2011, DOI 10.1002/mas). The interest in Foodomics coincides with a clear shift in medicine and biosciences toward prevention of future diseases. Foodomics: A new omics for a new food era FOODOMICS Goal To improve consumers well-being and confidence Food quality fulfilling legislation Epidemiological Food safety studies Nutrigenomics Nutrigenetics Development of: Nutraceuticals Functional foods GM foods New foods Bioinformatics GM foods monitoring Toxicity assays In-vitro assays In-vivo assays Clinical trials Foodomics papers from our group -A. Cifuentes “Food Analysis and Foodomics” J. Chromatogr. A 1216 (2009) 7109-7110. -M. Herrero, V. García-Cañas, C. Simo, A. Cifuentes “Recent advances in the application of CE methods for food analysis and foodomics” Electrophoresis 31 (2010) 205-228 -C. Simó, E. Domínguez-Vega, M.L. Marina, M.C. García, G. Dinelli, A. Cifuentes “CE-TOF MS analysis of complex protein hydrolyzates from genetically modified soybeans. A tool for Foodomics” Electrophoresis 31 (2010) 1175–1183 -M. Herrero, C. Simó, V. García-Cañas, E. Ibáñez, A. Cifuentes “Foodomics: MS-based strategies in modern Food Science and Nutrition” Mass Spectrom. Rev. 2011, DOI 10.1002/mas (impact factor: 3.569) Special issue on: “Advanced Food Analysis and Foodomics” Editor: Alejandro Cifuentes [email protected] (to be published in summer 2012) A book is now under preparation on: “FOODOMICS: ADVANCED MASS SPECTROMETRY IN MODERN FOOD SCIENCE AND NUTRITION” Editor: Alejandro Cifuentes [email protected] (to be published in autumn 2012) Foodomics Genomics & Epigenomics Metabolomics Transcriptomics Proteomics FOODOMICS: CURRENT CHALLENGES Production of new functional foods (scientifically proved claims) Food safety, quality and traceability (ideally as a whole) using omics approaches Develop, produce and monitor new transgenics foods Understand the effects of gene-food interaction on human health (Nutrigenomics) Explain the different answers from individuals to food: personalized diet (Nutrigenetics) Transgenic maize (Bt corn) A new CryIA(b) gene (encodes for a Bacillus thuringiensis protoxin) is inserted by recombinant DNA techniques into the maize genome. The new protoxin acts as insecticide against lepidopters. Transgenic soybean (RR soybean) A new CP4 EPSPS gene from Agrobacterium (that encodes for a CP4 5-enolpyruvylshikimate-3-phosphate sintase, CP4-EPSPS) is inserted by recombinant DNA techniques into the soy genome. The new CP4-EPSPS enzyme allows to the GM plant to resist the effect of the herbicide glyphosate. Can the new inserted genes give rise to other unintended effects? The European Food Safety Agency (EFSA) recommends the development of profiling techniques to study these unexpected effects. Second Generation GMOs THEIR SUCCESS WILL DEPEND (AMONG OTHER FACTORS) ON PROVIDING STRONG SCIENTIFIC EVIDENCES ON: -THEIR (HEALTH) BENEFITS FOR CONSUMERS Macronutrients: -THE EQUIVALENCE WITH THEIR NATURAL COUNTERPARTS Proteins -NO ENVIRONMENTAL RISKS Amino acid composition Functionality, e.g. bread dough Drought tolerance Carbohydrates Starch composition, inulin, monosaccharides Vegetable oils High-PUFA (e.g., oleic acid) Micronutrients: Vitamins, anti-oxidants (Golden rice) Minerals (iron-fortified rice) Miscellaneous: Hypoallergenic foods Drought tolerance Prolonged ripening Ideal Foodomics platform for GMO analysis GMO detection GENOMICS/ Traceability GMO labeling TRANSCRIPTOMICS PROTEOMICS METABOLOMICS SYSTEMS BIOLOGY Gene Protein Metabolite Data GMO biomarkers expression expression expression integration Unintended effects STATISTICS GMO METABOLITES PROTEINS DNA/mRNAs GMO safety Legal issues GMO removed or improved DNA analysis by CGE-LIF Development of a novel analytical methodology, based on MLGA-CGE- LIF, to simultaneously detect multiple GMOs in a single reaction Multiplex ligation dependent genome amplification (MLGA) Vector (universal sequence) III. Circular DNA enrichment Long oligonucleotide (exonuclease treatment) probe (Selector) Target 3 II. Ligation of targets I. Digestion of gDNA with (Circularization) specific endonucleases Target 1 Target 2 Targets 5’ 5’ Fwd primer 5’ Target 3 Rev primer V. Detection of PCR products Target 2 Target 1 1 3 Target 1 IV. Linearization of targets and PCR 2 5’ 3’ Target 2 5’ 3’ Target 3 CGE-LIF 5’ 3’ Simultaneous detection of multiple GMOs with MLGA-CGE-LIF mon863 selector CGE-LIF ga21 selector Fwd primer adh V2 selector 3.5 1% GA21, 1% MON863 & 1% MON810 maize mixture mon810 selector Rev primer Adh V2 selector (2.5 nM) vector MON863 selector (5 nM) 3.0 Mon810 selector (2.5 nM) GA21 selector (5 nM) 2.5 Ligation Linearization 2.0 & & Enrichment PCR RFU 1.5 Mon810 (172 bp) mon863 (155 bp) Digested 1.0 Ga21 (131 bp) Adh (199 bp) genomic Circular DNA DNA targets 0.5 0.0 MLGA is very flexible since the incorporation 0 2 4 6 8 10 12 14 16 18 20 22 24 of new additional selector probes to the ligation Minutes step requires minor adjustments of the selector Calculated LODs (S/N=3) concentration to detect all the DNA target with 0.2% GA21 maize LODs below 1% 0.3% MON863 maize 0.1% MON810 maize SHOTGUN PROTEOMICS by CE-TOF-MS: GM vs. wild soybean SOYBEAN Protein content 40 % Well-known difficulties in protein separation: ¾ Different physico-chemical properties (size, isoelectric point, hydrophobicity) within a wide range of concentrations. ¾ Difficult to separate complex mixtures of proteins. ¾ Challenging identification of (large) proteins. SHOTGUN PROTEOMICS by CE-TOF-MS Analysis of peptides obtained after hydrolysis of complex protein mixtures GM soybean PEPTIDE PROTEIN ENZYMATIC DATA ANALYSIS BY EXTRACTION HYDROLYSIS ANALYSIS Wild soybean CE-TOF-MS Optimization required Intens. x105 Base peak electropherogram 4 3 2 1 0 0 5 1015202530(min) Some (63) extracted ion electropherograms Relative intensity Relative 0 5 1015202530Time (min) CE-TOF MS ANALYSIS UNDER SELECTED CONDITIONS F BPE Soy Protein Isolate 5.0 B C E D 2.5 G H A 0.0 0 5 10 15 20 25 30 Time (min) Intens. M+H Intens. x104 5 641.3511 Mass spectra x10 M+3H Mass spectra M+H 919.4486 5 (9.5 min, peak A) (25.7 min, peak G) 417.2570 0.8 4 Intens. x105 M+4H Mass spectra M+H 734.1155 0.6 M+2H 3 631.3779 2.5 (15.2 min, peak C) 1378.6605 M+H 0.4 2 685.3153 2.0 0.2 1 1.5 M+1H 0 1.0 658.3186 0.0 200 300 400 500 600 700 800 900 m/z 500 1000 1500m/z M+5H M+3H 0.5 587.4964 978.4777 0.0 400 600 800 1000 1200 1400 1600 m/z Complexity of the Peptidic Map Automated interpretation OPTIMIZATION OF AUTOMATED INTERPRETATION Use of deconvolution tool → Study of the peptides obtained in 5 consecutive injections The same peptides were not ↑ cutoff in order to eliminate 5 % Cutoff found in all the injections unstable signals The same peptides were found in all injections 15 % Cutoff (simultaneous analysis of more than 150 peptides) Intens. Intens. M+H Mr (3) x104 641.3511 Mass spectra x105 Deconvoluted (9.5 min, peak A) 640.3 mass spectra M+H (9.5 min, peak A) 5 417.2570 1.0 Mr (1) 4 416.2 M+H 3 631.3779 Mr (2) 630.4 M+H 2 685.3153 0.5 Mr (4) 684.3 1 0 0.0 200 300 400 500 600 700 800 900 m/z 200 300 400 500 600 700 800 900 m/z SHOTGUN PROTEOMICS by CE-TOF-MS: GM vs. wild soybean Intens. x105 BPE 7.5 6.0 Conventional 4.5 3.0 Transgenic 1.5 0.0 0 5 10 15 20 25 30 Time (min) SIMILAR PEPTIDE PROFILE No differences were observed between the GM and wild soybean when a 15% abundance cutoff was used for the automatic deconvolution of the detected ions METABOLOMICS by FT-ICR-MS, PLE and CE-TOF-MS: GM vs. wild corn Intens. 175.06122 257.11426 x108 301.03528 6 515.12475 339.09313 380.99850 276.98773 4 577.13423 477.06693 2 605.19248 12 tesla FT-ICR-MS at GSF/Munich Germany P. Schmitt Kopplin 0 150 200 250 300 350 400 450 500 550 600 m/z Intens.9 -MS x10 438.23902 4 Details on m/z 438.239 GMO 3 2 439.24247 1 440.24610 0 Mass resolution: >600.000 in full scan 2500 438.23873 Theoretical compound Mass accuracy: <0.1 ppm 2000 C H N O "Lunarine 1500 25 32 3 4 >10.000 signals/mass spectra 1000 439.24213 >300 elementary composition assignements 500 440.24498 0 (depending on the extraction conditions) 438.23899 2000 Theoretical compound 1500 C4H50N6O6S5 1000 440.23478 500 439.24044 0 438.0 438.5 439.0 439.5 440.0 440.5 m/z Although the high resolution and sensitivity provided by FT-MS allows the detection and identification of an impressive number of compounds, PLE and CE-TOF-MS can provide additional information useful to corroborate (or not) the metabolites identification.
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