METABOLIC FINGERPRINTING & FOOTPRINTING

1. METABOLIC FINGERPRINTING & FOOTPRINTING Ruthie Angelovici

2. The metabolom is very sensitive to perturbation. The metabolom is found to be more sensitive to perturbation than the transcriptome or the proteome . This because the activities of metabolic pathways are reflected more accurately in concentration of pools of metabolites then in concentration of the relevant enzymes (or indeed the mRNAs encoding them).

3. What is a metabolic fingerprinting?The official version • Metabolic fingerprinting: • A strategy of classification of samples on the basis of their biological statues or origin,using high throughput methods, usually spectroscopy. • Metabolic fingerprinting is involved in sorting datasets into categories so that conclusions can be drawn about classification of individual samples. Kell et al., Nature Reviews/Microbiology. 2005

4. How it is done X extraction Y Spectroscopy: Usually by direct injection without any chromatography step PCA: Principle Components analysis DFA: Discrimination Functional analysis

5. What is it good for? • Screening mutant collection to identify major alterations in biochemical pathways . • Assay of mode of action of drugs or assessment of cytoxicity. • Evaluation of unintended secondary changes in transgenic food crop. • Disease diagnostic Identification of biomarkers for any relevant classification.

6. The advantages: • It is rapid and can be used for high throughput analysis. • Avoiding process of signal assignments. • Attention is focused on those parts of the spectrum that are most relevant to the question addressed. • It is unbiased in detecting metabolites that happened to be present in the sample.

7. Disadvantages: • Substantial variability in metabolic composition of • samples • Quenching of the data set • Detection of the metabolite responsible for the • variability in the sample is often not possible.

8. Metabolic fingerprinting of WT and transgenic tobacco plants by H1 NMR and multivariate analysis techniqueChoi et al., phytochemistry 2004. Case Study no.1 The goal: To understand metabolic pathways connected with the defense response against TMV-infection The mean: fingerprint of WT and CSA tobacco metabolomic response to TMV was preformed. Major components contributing the discrimination are revealed.

9. step 1:1H NMR spectra for aqueous fractions of transgenic and WT plants. WT leaves 1H NMR spectra for aqueous fractions of samples (a) leaves of wild type plants, (b) leaves of CSA plants, (c) veins of wild type plants, (d) veins of CSA plants. CSA leaves WT veins CSA veins

10. PCA: principle component analysis PCA: is an unsupervised clustering method requiring no knowledge of the data set structure and acts to reduce dimensionality of multivariate data whilst preserving most of variance within it. Hence it terms as data compression method. D1= ax1 +bx2+cx3+dx4

11. Step 2: PCA analysis of the aqueous fractions can separate the different samples WNL: wild type non-inoculated leaf, WIL: wild type inoculated leaf, WSL: wild type systemic leaf, CNL: CSA non-inoculated leaf, CIL: CSA inoculated leaf, CSL: CSA systemic leaf, WNV: wild type non-inoculated vein, WIV: wild type inoculated vein, WSV: wild type systemic vein, CNV: CSA non-inoculated vein, CIV: CSA inoculated vein, CSV: CSA systemic vein

12. Step 3: Identification of the discriminatory variables: Chlorogenic acid- discriminatory variable of WNL and WSL leaves from the rest. Glucose- discriminate WNL and WSL veins from the rest Alanine and malic acid -discriminate CSA plants from the rest. Chlorogenic acid Malic acid Effect of chlorogenic acid (δ 7.66) and malic acid (δ 2.72) on the differentiation of aqueous fraction of tobacco plants on the plot of PC1 and PC2 scores. WNL: wild type non-inoculated leaf, WIL: wild type inoculated leaf, WSL: wild type systemic leaf, CNL: CSA non-inoculated leaf, CIL: CSA inoculated leaf, CSL: CSA systemic leaf, WNV: wild type non-inoculated vein, WIV: wild type inoculated vein, WSV: wild type systemic vein, CNV: CSA non-inoculated vein, CIV: CSA inoculated vein, CSV: CSA systemic vein.

13. Metabolic fingerprinting of Salt stressed tomatoesJohnson et al., phytochemistry. 2003 Case study No.2 • The aim of this study was to study the effect of salinity on tomatoes fruits: • Two varieties were studied: • Salt tolerant tomatoes -Edkawy (growth wise) • Regular tomatoes -Sigme F1 (growth wise)

14. Representative FT-IR spectra from whole tomato fruit flesh of Edkawy and Simge F1 In both tomato varieties, salt treatment significantly reduced: mean fruit fresh weight size class marketable yield due to BER in response to salinity no effect on total fruit number.

15. PCA analysis could not separate the two samples Principal component analysis (PCA) models for (a) Edkawy and (b) Simge F1 showing no discrimation between the control fruit (0) and the fruit from salt-treated plants (1) in either variety. (a) Edkawy (b) Simge F1

16. DFA: Discrimination Function Analysis DFA is a supervised clustering method that requires a priory knowledge method of replicate structure within the data set and seeks to minimize the within group variance and to maximize the between group variance. The number of principal components used by the DFA is optimized by cross validation, which involves forming the model on a training data set and then projecting a previously unseen set of data , the test set onto a model. This is a cyclical process where the numbers of PC’s are gradually reduced to find the optimum model.

17. DFA analysis could discriminate between the two samples Fig 3 and 4 Fig. 3. Discriminant function analysis (DFA) model using 20 principal components (PCs) accounting for 99.99% total variance derived from the raw FT-IR spectral data for Edkawy tomatoes. Training data set contained samples 1 to 149 and test data set contained samples 150–200. The number of PCs to be used for DFA was optimised using the training data set and then the test data were projected onto the DFA model. The model shows discrimination between control and salt-treated Edkawy tomato fruit although there are misclassified samples in both the training and test data sets. 0=control fruit and 1=salt treated fruit. Fig. 4. Discriminant function analysis (DFA) model using 20 principal components (PCs) accounting for 99.99% total variance derived from the raw FT-IR spectral data for Simge F1 tomatoes. Training data set contained samples 1–90 and test data set contained samples 91–120. The number of PCs to be used for DFA was optimised using the training data set and then the test data were projected onto the DFA model. The model shows discrimination between control and salt-treated Simge F1 tomato fruit. 0=control and 1=salt treated.

18. A genetic algorithm: A genetic algorithm is an optimization method based on principles of Darwinian selection where over a series of generation, a population of parameters sets evolve until an optimal, or near optimal , solution to a given problem.

19. On average total error of classification for the GA models was below 10% fig. 6. Variable selection percentage by 50 independent genetic algorithm (GA) models, with each model using only 5 variables, for the discrimination between control and salt-treated Simge F1 tomato fruit samples based on FT-IR spectral data. vig. 5. Variable selection percentage by 50 independent genetic algorithm (GA) models, with each model using only 5 variables, for the discrimination between control and salt-treated Edkawy tomato fruit samples based on FT-IR spectral data.

20. Conclusions: The spectral regions selected by the GA for discrimination between control and salt treated tomatoes are indicating a shift in biochemistry of nitrile containing compound although further mass spectrometric studies is required.

21. A functional genomic strategy that uses metablome data to reveal the phenotype of silent mutations. Raamsdonk et al., nature biotechnology 2001 • Mutants with identical phenotype should cluster in this plot. • Mutant with qualitatively different phenotype should be clearly displaced from each other. (1) FY23.cox5a; (2) FY23.ho; (3) FY23.0; (4) FY23.pet191; (5) FY23.pfk26; (6) FY23.pfk27.

22. Metabolic footprinting: • Metabolic footprinting: • A strategy for analyzing properties of cells or tissues by looking in a high throughput manner at the metabolites that exert or fail to be taken up from their surrounding. • The metabolic foot printing approach recognizes the significance of overflow metabolism in appropriate media.

23. Advantages over fingerprinting • Rapid-Direct injection of the media • Measuring intracellular metabolites is time consuming and subject to technical difficulties caused by rapid turnover of intracellular metabolites • no need to quench metabolism and separate metabolites from the intercellular space.

24. High throughput classification of yeast mutants for functional genomics using metabolic footprinting. Allen et al., nature biotechnology 2003 Insert fig 1

25. Metabolic footprinting may be used to classify strains on the basis of the deletion they carry. An experiment was set up in which 24-h microtiter plate footprints of 19 different deletant strains with a broad range of metabolic defects were compared. (a,b) Footprint data were used to train a DFA model (20 PCs, 99.6% of the variance). Footprint data from strains harboring the nit3 and pfk27 deletions clustered closely together with strains carrying deletions in the respective isoenzymes nit2 and pfk26. Box in a indicates region enlarged in b. DF, discriminant function. (c) Hierarchical cluster analysis of the data using all 18 DFs. The scale represents the Euclidean distance in DF space.

26. Discrimination of mode of action of antifungal substances by use of metabolic foot printingAllen et al., Applied and environmental biotechnology. 2004 What this is ? The DFA scores (1 to 3) from the analysis illustrated in Fig. 3 were averaged according to compound (i.e., scores for the members of each class were averaged) and subjected to HCA. A separation of the respiratory and nonrespiratory inhibitors was observed in the resulting dendrogram. Fluazinam (marked with an asterisk) is cited as an uncoupler of oxidative phosphorylation (17), and although it might conceivably be regarded as a respiratory inhibitor, it is not, of course, a respiratory chain inhibitor, and the level of inhibition it induces in cells growing on a fermentable carbon source is too great to arise from the inhibition of respiration-coupled processes alone; and therefore, this compound must inhibit other reactions within the cell, most likely on some proton-coupled uptake process necessary for fermentative growth.

27. 6- rules can discriminate the respiratory and non respiratory inhibitors GA models applied on the data set exert 6 rules that use the combination of just three m/z ratios to discriminate the classes. Chemical interpretation of these peaks may lead to identification of useful marker for understanding the bilogycal basis of these discriminations.

28. Diseasediagnostic Hart et al., Neurological sciences. 2003 Hart et al., 2003 Developing of biomarker to identify the extent of white matter destruction in the urine.70 % prediction for the model. fig. 2. Multivariate analysis of 1H-NMR spectra of urines from patients with MS (MS) or other neurological diseases (OND) or healthy controls (H). (A) Spectra of urines from patients with MS (MS) or other neurological diseases (OND) or from asymptoomatic control individuals were subjected to discrimant analysis. The geographical representation shows three distinct clusters with some overlap between the MS and control clusters. Individual samples are represented by an asterisk. By a geometric rotation of the D2 axis in the indicated direction, a new field to achieve distribution of the three clusters is over different quadrants. Using the newly defined D2 axis, a factor plot is calculated (B) displaying the peaks that are more prominent in MS urines in negative direction and the predominating peaks in OND+control urines in positive direction. (C) This representation is based on male and female MS patients and healthy persons, showing a significant difference between urine profiles of males and females in MS, but not between male and female healthy individuals.

29. Developing of biomarker to identify the extent of white matter destruction in the urine F- contain more N-acetyleaspartate (neural damage marker|) A-B- contain marker for demyelination, namly choline, inositol, and inflamation like neoterpin Fig. 3. 1H-NMR spectroscopy of myelin-immunised monkey urines: Urines were collected twice before and four times after EAE induction, each with 1-week interval. Of all collected urine samples, 1H-NMR spectra were recorded in triplicate and the data were subjected to multivariate analysis. The score plot in A shows localisation of the pre-disease clusters in one quadrant, clearly separated from the post-disease samples. The variation between samples collected at one time point is remarkably low. The factor plot corresponding to the D2 axis of score plot A is depicted in B. In this pseudo spectrum, the peaks in positive direction correspond to compounds that predominate in samples A to E; peaks in negative direction correspond to compounds predominating in samples F. It can be seen that the spectra from samples in cluster F contain relatively more (than average for the whole set) of compounds in the 0.5–3.5 part of the spectrum. Peaks of known urine biomarkers of MS have been tentatively identified, namely choline (3.19 and 3.94 ppm), inositol (3.28 and 4.10 ppm), neopterine (4.34, 4.44, 4.60, 4.70 and 5.20 ppm), NAA (2.05 and 2.51 ppm). Panel C depicts two representative spectra from the same animal. Some differences can indeed be seen in the suspected region.Fig 3

30. Conclusions : • Metabolic footprinting is a convenient , reproducible and high throughput way for genome -wide, physiological-level characterization of microorganisms. • It can have applications on metabolic engineering. • Footprinting can also contribute strongly to testing mathematical models of cell behavior. • Construction of metabolic models.