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1 METABOLOMICS FOR RAPID IDENTIFICATION OF HUANGLONGBING IN ORANGE TREES AND RAPID DETECTION OF ESCHERICHIA COLI O 157:H7, SALMONELLA TYPHIMURIUM, SALMONELLA HARTFORD, AND SALMONELLA MUENCHEN IN GROUND BEEF AND CHICKEN By JUAN MANUEL CEVALLOS CEVALLOS A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHYLOSOPHY UNIVERSITY OF FLORIDA 2010
2 2010 Juan Manuel Cevallos Cevallos
3 To my M om, D ad and Family in Ecuador
4 ACKNOWLEDGMENTS I thank my committee chairman and major advisor Dr. Jos I. Reyes De Corcuera Without his guidance and support this work would not have been possible. I would also like to thank my supervisor y committee members, Dr. Michelle Danyluk, Dr. Gary Rodrick, and Dr. Edgardo Etxeberria for their continuous guidance and help. Also I want to thank lab managers Shelley Jones and Loretta Friedrich as well as my labmates Rosalia Mike, Narsi, and David fo r their constant and unconditional help. Extensive thanks are due to the Fulbright commission in Ecuador for supporting my initial graduate studies and enriching my cultural experience in U nited S tates I also thank the Univers f F ood Science and Human Nutrition, the College of Agricultural and Life S cience and the Citrus Research and Education Center for the economical support provided through awards and scholarships. More importantly I would like to thank my parents Esperanza Cevallos and Eddie Cevallos, as well as my siblings Maria Susana, Eddie Andres, Maria Del Pilar, Maria Esperanza, Maria Isabel, and Maria Ines for all their unconditional love and support. Finally I want to thank God. It is right t o say that with no doubt none of my achievements could have been possible consent
5 TABLE OF CONTENT S p age ACKNOWLEDGMENTS ................................ ................................ ................................ ........ 4 LIST OF TABLES ................................ ................................ ................................ .................. 8 LIST OF FIGURES ................................ ................................ ................................ ................. 9 LIST OF ABBREVIATIONS ................................ ................................ ................................ 11 ABSTRACT ................................ ................................ ................................ .......................... 12 CHAPTER 1 INTRODUCTION ................................ ................................ ................................ ........ 14 Metabolomics ................................ ................................ ................................ ............... 14 The Process of Metabolomics Analysis ................................ ................................ 16 Metabolomics in Food Quality ................................ ................................ .............. 21 Metab olomics in Food Safety ................................ ................................ ................ 23 Metabolomics for Compliance of Food Regulations ................................ ............... 24 Metabolomics in Food Microbiology ................................ ................................ ..... 24 Metabolomics in Food Processing ................................ ................................ ......... 26 Citrus Huanglongbing ................................ ................................ ................................ ... 27 Characteristics and Significance ................................ ................................ ............ 27 Current Research on Detection Methods ................................ ................................ 28 Escherichia coli and Salmonella spp. ................................ ................................ ............. 29 Objectives ................................ ................................ ................................ ..................... 30 2 HLB FINGERPRINTING BY HPLC MS ................................ ................................ ...... 39 Overview ................................ ................................ ................................ ...................... 39 Materials and Methods ................................ ................................ ................................ .. 39 Equipment, Software, and Reagents ................................ ................................ ...... 39 Sampling and Experimental Des ign ................................ ................................ ....... 40 Infection Value and Color Analysis ................................ ................................ ....... 41 HPLC MS Analysis ................................ ................................ .............................. 41 Results and Discussion ................................ ................................ ................................ .. 42 Infection Scale ................................ ................................ ................................ ...... 42 HPLC MS Analysis ................................ ................................ ........................... 43
6 3 HLB FINGERPRINTING BY CAPILLARY ELECTROPHORESIS .............................. 50 Overview ................................ ................................ ................................ ...................... 50 Materials and M ethods ................................ ................................ ................................ .. 51 Reagents ................................ ................................ ................................ .............. 51 Equipment and Software ................................ ................................ ....................... 51 Sampling and Experimental Design ................................ ................................ ....... 51 Extraction, Sample Preparation, and CE Conditions Tested. ................................ ... 52 Selected Extraction and CE Analysis Conditions. ................................ .................. 53 Compound Identification ................................ ................................ ...................... 54 Results and Discussion ................................ ................................ ................................ .. 54 Extraction ................................ ................................ ................................ ............. 54 Capillary Zone Electrophoresis (CZE) ................................ ................................ ... 55 Non Aqueous Capillary Electrophoresis (NACE) ................................ ................... 58 Micr o Emulsion Electro Kinetic Chromatography (MEEKC) ................................ 58 Selection of UV Wavelength ................................ ................................ ................. 59 Compound Identification and Statistical Analy sis ................................ .................. 59 4 HLB FINGERPRINTING BY GC MS AND ZN DEFICIENCY ................................ ... 70 Overview ................................ ................................ ................................ ...................... 70 Materials and Methods ................................ ................................ ................................ .. 71 Reagents ................................ ................................ ................................ .............. 71 Equipment and Software ................................ ................................ ....................... 7 1 Sampling and Experimental Design ................................ ................................ ....... 72 Extraction Conditions ................................ ................................ ........................... 73 Headspace Analysis ................................ ................................ .............................. 74 Liquid Extract Analyses ................................ ................................ ........................ 74 Compound Identification ................................ ................................ ...................... 75 Preliminary Validation of Possible HLB Bio markers ................................ ............. 75 Results and Discussion ................................ ................................ ................................ .. 76 Extraction and Derivatization Conditions ................................ .............................. 76 GC MS Analyses of Derivatized Samples ................................ ............................. 77 SPME Analyses ................................ ................................ ................................ .... 79 Preliminary Validation of HLB Biomarkers ................................ ........................... 81 5 METABOLOMICS OF FODBORNE PATHOGES ................................ ..................... 101 Overview ................................ ................................ ................................ .................... 101 Materials and Methods ................................ ................................ ................................ 103 Reagents and Bacterial Strains ................................ ................................ ............ 103 Equipment and Software ................................ ................................ ..................... 104 Experimental Design ................................ ................................ .......................... 105 HPLC MS Analysis ................................ ................................ ............................ 105 CE Analysis ................................ ................................ ................................ ....... 106
7 Sample Preparation for GC MS Analysis ................................ ............................ 106 Headspace Analysis ................................ ................................ ............................ 107 Liquid Extract Analyses ................................ ................................ ...................... 107 Compound Identification ................................ ................................ .................... 108 Results and Discussion ................................ ................................ ................................ 108 Detection and Culture Media Analysis ................................ ............................... 108 GC MS Analyses of Derivatized Samples ................................ ........................... 109 SPME Analyses ................................ ................................ ................................ .. 111 Prediction Model and Validation in Food Samples ................................ ............... 112 6 SUMMARY AND FUTURE WORK ................................ ................................ ......... 128 Metabolomics of HLB ................................ ................................ ................................ 128 Metabolomics of Food Pathogens ................................ ................................ ............... 130 Future Directions in Metabolomics in Food Science ................................ .................... 131 LIST OF REFERENCES ................................ ................................ ................................ ..... 132 BIOGRAPHICAL SKETCH ................................ ................................ ................................ 148
8 LIST OF TABLES Table p age 1 1 Most common metabolomics processes in food analysis ................................ ............... 32 1 2 Common number of peaks reported in food metabolomics ................................ ............ 35 2 1 Color values of leaves at different perceived infection scale values ............................... 45 3 1 List of the 24 peaks and IS detected by optimum CZE ................................ .................. 62 4 1 Effect of amount of MSTFA added on the compounds ................................ ................ 83 4 2 Main compounds detected in derivatized samples ................................ ........................ 84 4 3 Main compounds detected by headspace SPME ................................ ........................... 86 4 4 Ratios infected/healthy and infected/zinc deficient ................................ ....................... 88 5 1 Recent studies on rapid methods for detection of E. coli and Salmonella ..................... 115 5 2 Differences in peaks detected in samples run in N B and TSB ................................ ..... 117 5 3 Average peak areas +/ standard deviation of metabolites ................................ .......... 118 5 4 Average peak areas +/ standard de viation of headspace SPME samples ..................... 120
9 LIST OF FIGURES Figure p age 1 1 General metabolomics classification according to purpose of analysis .......................... 36 1 2 Metabolomics analysis process. ................................ ................................ ................... 37 1 3 Metabolomics potential for understanding consumer preference s. ................................ 38 2 1 Infection evaluation values for citrus Huanglongbing samples ................................ ...... 46 2 2 Chromatograms of orange leaves. ................................ ................................ ................ 47 2 3 Cumulative peak a reas per zone of interest versus infection scale. ................................ 48 2 4 Full MS scan for a leaf with infection evalua tion ( IE ) =0 (A) and IE=4 (B). .................. 49 3 1 Electropherogram at 190 nm of different extracts ................................ ........................ 63 3 2 Effect of the pH in sep aration. ................................ ................................ ..................... 64 3 3 Effect of acetonitrile in separation ................................ ................................ ............... 65 3 4 Ef fect of 1 butanol in separation ................................ ................................ ................. 66 3 5 Effect of sodium acetate in separation ................................ ................................ .......... 67 3 6 Typical electropherograms of healthy and infected leaves extracts ................................ 68 3 7 Mean concentration of the 6 potential biomarkers ................................ ........................ 69 4 1 Healthy (A), HLB infected (B), and zinc deficient leaves (C) ................................ ....... 89 4 ................................ ............ 90 4 3 Effect of the amount of reagent ................................ ................................ ................... 91 4 4 Effect of the reaction time. ................................ ................................ .......................... 92 4 5 Typical chromatograms of derivatized liquid extracts ................................ ................... 93 4 6 Principal components analysis of derivatized liquid extracts. ................................ ........ 94 4 7 Compounds showing significant differences in derivatized liquid extracts ..................... 95
10 4 8 Typical chromatogram of headspace analyses ................................ .............................. 96 4 9 Principal components analysis of headspace metabolites ................................ ............. 97 4 10 Significantly different headspace metabolites ................................ .............................. 98 4 11 Significantly different metabolites of HLB infected ................................ .................... 99 4 12 Behavior of potential biomarkers in mild or asymptomatic HLB ................................ 100 5 1 Metabolite differences between E. coli O157:H7 and cocktail A O. ........................... 121 5 2 Scores plot (A) and loadings plot (B) of liquid extracts ................................ .............. 122 5 3 Metabolites responsible for classification in deriv atized samples. .............................. 123 5 4 Scores plot (A) and loadings plot (B) of headspace samples ................................ ....... 124 5 5 Metabolites responsible f or classification in headspace samples ................................ 125 5 6 Prediction models for E. coli O157:H7 ................................ ................................ ..... 126 5 7 Prediction models for Salmonella ................................ ................................ ............ 127
11 LIST OF ABBREVIATION S BGE Background electrolyte CE Capillary Electrophoresis CFU Colony forming unit CW Cold water CZE Capillary zone electrophoresis EOF Electro osmotic flow ESI Electrospray ioniza tion GC Gas chromatography HLB Huanglongbing HPLC High Performance Liquid Chromatography HW Hot water IS Internal Standard MEECK Micro emulsion electrokinetic chromatography MS Mass Spectrometry MWC Methanol water chloroform NACE Non aqueous capi llary electrophoresis PCA Principal components analysis PCR Polymerase chain reaction PDA Photo diode array detector PLS Partial least squares SPME Solid Phase Micro Extraction TSB Tryptic soy broth
12 Abst ract o f Dissertation Pr esented t o t he Graduate School o f t he University o f Florida i n Partial Fulfillment o f t he Requirements f or t he Degree o f Doctor o f Philosophy METABOLOMICS FOR RAPID IDENTI FICATION OF HUANGLONGBING IN ORANGE TREES AND RAPID DETECTION OF ESCHERICHIA COLI O157 :H7, SALMONELLA TYPHIMURIUM, SALMONELLA HARTFORD, AND SALMONELLA MUENCHEN IN GROUND BEEF AND CHICKEN By Juan Manuel Cevallos Cevallos August 20 10 Chair: Jos I. Reyes De Corcuera Major: Food Science and Human Nutrition Rapid and reliable detection methods are of critical importance in preventing the spread of both plant diseases and foodborne pathogens. Citrus Huanglongbing (HLB) is the most destructive disease of citrus worldwide. Metabolomic techniques based on extraction, separation, and quantification methods were developed to find potential HLB biomarkers in leaves from naringeni n, hesperidin, and quercetin, as well as the amino acid L proline were significantly ( P < 0.05) up regulated in HLB infected trees. Conversel elemene, ( )trans humulene were significantly down regulated in HLB samples when compared to healthy and zinc deficient trees. Foodborne pathogens were also studied using metabolomic techniques. Escherichia coli O 157:H7, Salmonella Hartford, Salmonella Typhimurium, and Salmonella Muenchen were grown in tryptic soy broth (TSB) at 37 C followed by metabolite quantification at two hour intervals for 24 h. Results were compared to the metabolite profile similarly obta ined with E. coli K12, Pseudomonas aeruginosa, Staphylococcus aureus, Saccharomyces cerevisiae, and
13 Aspergillus o ryzae grown individually and as a cocktail under the same conditions described. P rincipal component analysis (PCA) achieved sample discriminati on of the microorganisms grown in TSB. Metabolites responsible for PCA classification were dextrose, cadaverine, the aminoacids L histidine, glycine, and L tyrosine, as well as the volatiles 1 octanol, 1 propanol, 1butanol, 2 ethyl 1 hexanol, and 2,5 dimet hyl pyrazine. Partial least square (PLS) models based on the overall metabolite profile of each bacteria group were created to predict the presence of E. coli O157:H7 and Salmonella spp. in food samples. The models were tested in ground beef and chicken an d were able to detect the presence of the pathogens at levels as low as 1 CFU/ g within 18 h.
14 CHAPTER 1 INTRODUCTION Metabolomics Metabolomics, the study of as many small metabolites as possible in a system, has become an important tool in many areas. Re cent reviews and perspectives in the areas of human diseases (Kaddurah Daouk and Krishnan 2009) drug discovery (Wishart 2008a) plant an alysis (Hall and others 2008) nutrition (Wishart 2008b) and others has shown the broad impact and rapid growth of metabolomics. Metab olomics analyses have been generally classified as targeted or untargeted ( Figure 1 1). Targeted analyses focus on a specific group of metabolites whit most cases requiring compound identification and quantification (Ramauta r and others 2006) Targeted analyses are important for assessing the behavior of a specific group of compounds in the sample under determined conditions. Targeted analyses typically require higher level of purification and a selective extraction of metab olites. In contrast, untargeted (aka comprehensive) metabolomics focuses on the detection of as many groups of metabolites as possible to obtain patterns or fingerprints without necessarily identifying nor quantifying compounds (Monton and Soga 2007) Untargeted analyses have been important for discovery of possible fingerprints of biological phenomena such as plant diseases (Cevallos Cevallos and others 2009a) A secondary classification can be done by the criteria used for data treatment. Most metabolomics analyses have been discriminative, predictive, and/or informative ( Figure 1 1). Discriminative analyses have been aimed to find difference s between sample populations without necessarily creating statistical models or evaluating possible pathways that may elucidate such differences. This is achieved by the use of multivariate data analysis tools (MVDA) described in other reviews (van
15 der Werf and others 2005; Kemsley and others 2007) In contrast, informative metabolomics is aimed to produce sample intrinsic inf ormation such as possible pathways, discovery of novel bioactive compounds, biomarker discovery, creation of specialized metabolite databases, and metabolites functionality. Informative metabolomics research has been successfully applied for the developme nt of metabolite databases such as the human metabolome database (Wishart and others 2007) Compound identification and quantification are usually important for this class of metabolomics. Finally, metabolomics reports have also been predictive. In this case, statistical models based on meta bolite profile and abundances are usually created to predict a variable that was not directly measured. Models in predictive metabolomics are usually produced by partial least square (PLS) regression as discussed in the data treatment section. In food sci ence, metabolomics has shown to have the potential for solving major problems worldwide and is considered as a tool for addressing future needs in agriculture (Green and others 2007) and nutrition (Green and others 2007; Hall and others 2008) Moreover, metabolomics has been considered for many food research programs such as the Metabolomics for Plants, Health and OutReach (METHA PHOR) initiative (Hall 2007) Discriminative, informative, and predictive metabolomics have been recently used for quality, nutrition, and food components analy sis (Wishart 2008b) with a significant expansion to other food applications in the last two years. This section critically reviews recent metabolomics s tudies in food from the perspective of the extraction, separation, detection, and data treatment as well as the application of discriminative, informative, and predictive metabolomics in the areas of food quality, safety, regulations, and microbiology, and processing. Main findings of this
16 section were published in Trends in Food Science and Technology (Cevallos Cevallos and others 2009b) The Pr o cess of Metabolomics Analysis Metabolomics analyses consist of a sequence of methods including sample preparatio n, metabolite extraction, derivatization, metabolite separation, detection, and data treatment ( Figure 1 2). However, not always the complete set of steps is needed. Only detection and data analysis have been mandatory steps for the reported metabolomics studies. The selection of the steps depends on the type of study (untargeted versus targeted), kind of sample (e.g. solids vs. liquids), instrumentation to be used for separation (e.g. GC vs. LC) and detection method (e.g. MS vs. NMR). Table 1 1 summarizes recent metabolomics studies used for food analysis. The first step in food metabolomics analysis is usually sample preparation. Solid samples such as apple peel (Rudell and others 2008) and potatoes (Dobson a nd others 2008) are typically ground under liquid nitrogen or after freeze drying. Proper grinding may enhance the release of metabolites during extraction. Freeze drying acts as a concentration step and minimizes possible differences in metabolites due t o dissimilarities in moisture content between groups of sample. Other concentrated liquid samples such as honey can be diluted as a preliminary step (Donarski and others 2008) However, to maximize the amount of information to be collected, concentration steps are more appealing. Metabolites in wine (Son and others 2008) and volatiles in olive oil (Cavaliere and others 2007) have been concentrated by lyoph ilization and solid phase microextraction (SPME) respectively. The following step is extraction. This step is aimed to maximize the amount and concentration of the compounds of interest. For this reason, extraction is probably the most critical step in met abolomics. In untargeted metabolomics, the nature of compounds of interest is
17 mostly unknown. Hence, several solvents and extraction methods should be tested and compared between the groups of samples. Most reports on untargeted food analysis do not descri be preliminary comparisons among extraction solvents tested. However, the extraction methods used in foods have been similar to those found optimal in comparable research fields such as non food plant metabolomics. For instance, the combination methanol wa ter chloroform (MeOH H 2 O CHCl 3 ) was shown to be superior to other solvents for untargeted studies in plants such as Arabidopsis thaliana (Gullberg and others 2004) because of its capacity of extracting both hydrophilic and hydrophobic compound s. Therefore, the effectiveness of MeOH H 2 O CHCl 3 in green tea (Pongsuwan and others 2008) potatoes (Dobson and others 2008) and other foods was anticipated. For untargeted analysis is the use of sequential a nd selective extractions followed by metabolite analysis of each extract has been recommended (Dixon and others 2006) Usually, an initia l hydrophilic extraction (typically with MeOH H 2 O) followed by centrifugation and hydrophobic extraction (typically with CHCl 3 ) of the pellet are done. Sequential extraction maximized the amount of metabolites from tomato paste (Capanoglu and others 2008) finding discriminating compounds in both hydrophilic and hydrophobic fractions. Conversely, ana lysis of other food stuff such as potato (Dobson and others 2008) and mushrooms (Cho and others 2007) has shown low or no sample discrimination in the hydrophobic fractions. Similar observations made in other areas such as non food plant analysis (Cevallos Cevallos and others 2009a) suggests a higher suitability of hydrophilic extracts for discriminative metabolomics analyses. Hydrophilic extractions in untargeted food analysis such as apple (Rudell and others 2008) and broccoli (Luthria and others 2008) have usually been made by MeOH or MeOH H 2 O. Other extractions based on deuterium oxide (D 2 O) for NMR analysis are also common. Novel methods for extraction of metabolites from frozen meat where a desorptio n gas hit the meat
18 surface extracting metabolites further carried to the ionization and detection chambers have been reported (Chen and others 2007) Extraction for targeted analysis relies on the knowledge of the analytes nature. Polyphenols have been extracted from berries by a water acet ic acid combination (McDougall and others 2008) and hot water was used for targeted analysis of glucosinolates in broccoli and mustard seeds (Rochfort and others 2008) To maximize the number and amo unt of metabolites to be obtained and reduce extraction time, disruption methods such as ultrasonic treatments are usually part of both untargeted and targeted extractions. After extraction, derivatization can be done. In food metabolomics, derivatization has mainly been used previous GC analysis to increase volatility of analytes. Derivatization is usually a two steps process starting with oximation of the sample to reduce tautomerism (especially from monosaccharide), followed by silylation to increase vo latility by reducing hidrophilicity of functional groups OH, SH or NH (Gullberg and others 2004) Several oximation and silylation reagents have been tested in the past. Gullberg et al. (2004) reviewed previous rea gents comparisons and reported that methoxiamine hydrochloride in pyridine and N methyl N trimethylsilyltrifluoroacetamide were the most appropriate reagents for oximation and silylation respectively. In food analyses, these reagents have shown to improve GC separation of metabolites in potato (Beckmann and others 2007) and other products. Derivatization times and temperatures affect each metabolite independently with major changes at the beginning of the reaction (Ma and others 2008) Therefore, preliminary experiments should be done to determine optimum derivatization times and temperatures that maximize the detection of compounds of interest. In food metabolomics analysis, several silylation reactions have been done for 90 min at (Beckmann and others 2007; Dobson and others 2008) with good results.
19 Other key steps in metabolomics are separation and detection. Separation and detection of the metabolites have been considered the key steps in metabolic profiling. In the m etabolomics literature, particular attention has been given to separation techniques such as liquid chromatography (LC) and its high performance (HPLC) or ultra performance (UPLC) types; gas chromatography (GC), capillary electrophoresis (CE); coupled to d etection techniques such as mass spectrometry (MS), nuclear magnetic resonance (NMR), and near infrared spectrometry (NIR). Working principle as well as individual and hyphenated suitability of these techniques in metabolomics have been broadly discussed (Rochfort 2005; Bedair and Sumner 2008; Toyo'oka 2008; Wishart 2008b) In food metabolomics most analysis have been done by GC, CE, and LC as seen in Table 1 1. Comparison and suitability of these techniques in food analysis have been discussed in other reviews (Wishart 2008b) Among non conventional techniques, ion mobility spectrometry (IMS) where food metabolites are carried in a flow of inert gas, ionized, and separated by a drift gas flowing in the opposite direction has been applied to metabolomics analysis of cheese, beer, and food packaging material (Vautz and others 2006) Detection methods are mostly based on UV, NIR, MS, or NMR techniques. In food metabolomics MS and NMR have been used the most ( Table 1 1). A greater amount of data is generally obtaine d by using MS accompanied by high throughput separation techniques such as HPLC or UPLC as shown in Table 1 2. For instance, green tea quality has been evaluated by NMR (Tarachiwin and others 2007) and UPLC MS (Pongsuwan and others 2008) Partial least square (PLS) models from UPLC MS yielded a higher prediction coefficient than models from NMR, probably due to the higher number of peaks detected by UPLC MS. However, other factors such as sample variability should also be considered. Although not as sensitive as the
20 other detection techniques, NIR has provided a fast non destructive fingerprint i n several metabolomics analysis such as strain differentiation of wine yeast (Cozzolino and others 2006) Another technique, direct infusion mass spectrometry (DIMS) methods do not require a previous separation step achieving faster results as applied for broccoli (Luthria and others 2008) Compound identification has been mainly achieved by database matching (mostly in GC MS methods) or by comparison with pure standards run under same conditions. Data analysis in food metabolomics is done by several chemometrics tools. Typically, metabolomics data has been aligned before comparison. Alignment has been shown to drastically improve data classification and discrimination analysis (Son and others 2008) Discriminative metabolomics usually relies on mult ivariate methods such as principal components analysis (PCA) for sample classification. PCA creates new variables (principal components) by linear combinations of the metabolites detected while maximizing sample variation. Classification occurs when compar ing the values of two or more principal components of each sample as used for discrimination of broccoli varieties (Luthria and others 2008) On the other hand, PLS is a MVDA technique that allows sample discrimination by reduction of dimensionality while maximizing correlation between variab les. PLS has been the main technique used for predictive metabolomics studies such as the creation of a metabolite based model for sensory evaluation of watermelon (Tarachiwin and others 2008) Similarly, linear discriminant analysis (LDA) with a priori classification hypothesis has been used for discrimination of olive oil according to origin (Cavalier e and others 2007) PCA PLS, and LD have been widely reviewed (van der Werf and others 2005; Kemsley and others 2007) Al so correlation techniques such as correlation network (CN) analysis have been used to determine the link between metabolites and establish possible reactions or pathways during a determined phenomenon. This has been an important tool in
21 informative metabo lomics. CN creates nodes representing the metabolites detected, connected by edges that indicate the correlation as used in tomatoes (Ursem and others 2008) Genetic programming (GP) is another cla ssificatory tool that has been used to improve the sensitivity and selectivity of the PLS models for honey origin determination (Donarski and others 2008) Most of the MVDA tools such as PCA and PLS reduce dimensionality of the data by linear combination of the original variables. In contrast, random forest (RF) analysis permits multivariate data comparison without dimensionality reduction RF has allowed classification of potato varieties by pairwise comparisons with accuracy values greater than 92%. Also creation of a Mastermix potato model allowed discrimination of a larger number of potato varieties through RF (Beckmann and others 2007) Other common statistical tools have been used in food metabolomics and are summarized in Table 1 1. Metabolomics in Food Q uality Targeted metabolomics focused on volatiles has shown to have the potential to assess pre harvest issues that may affect quality. Pre harvest fungal diseases in mango (Moalemiyan and others 2007) post harvest bacterial contamination of onions (Vikram and others 2005) and McIntosh apples (Vikram and others 2004) as well as diseases of stored carrots (Vikram and others 2006) have been assessed by sampling headspace metabolites and analyzed by GC MS. In each case, the volatile profile was found to disease specific, and several compounds were tentatively identified by GC MS databases. Changes in polyphenolic compounds during berries breeding (Stewart and others 2007) have been detected by informative metabolomics. In addition, post harvest metabolomics analysis have the potential for detection and understanding food spo ilage as reviewed by Kushalappa and others (2008)
22 The development of novel metab olomics techniques such as IMS has allowed monitoring of quality attributes during process. Because IMS allows in situ automatic sampling, it can be used for determining the completion of certain processes assuring standard quality based in a group of meta bolites. This type of analysis fits the needs of biotechnological food processes in which metabolites are changing with time. Targeted informative (concentration aimed) IMS has been applied for the detection of diacetyl and 2,3 pentadione compounds in beer to determine the endpoint of the fermentation (Vautz and others 2006) Quality of health supplements has also been evaluated using metabolomics tec hniques (Kooy and others 2008; Liu and others 2008) Future trends will involve the use of discriminative and predictive metabolomics as the ultimate tool for quality control analysis. Metabolite baseline of products meeting minimum quality standards can be developed. Individual samples obtained during processing can be analyzed and compared to the baseline trough MVDA techniques to det ermine acceptability of the batch produced. Moreover, accidental adulteration of food (e.g. allergenic inclusion or microbial contamination) can be detected by appearance of uncommon peaks in the sample metabolic profile. Informative metabolomics can eluci date the nature of the peaks of interest. In addition, combinations of predictive and informative metabolomics have the potential to become the single all parameter analysis tool. Quality parameters are usually measured individually in the industry. Many o f these parameters can be quantified in a single run of informative metabolomics. Additionally, the metabolite profile obtained can be put in a statistical model obtained by predictive analysis to estimate the parameters that are not detectable by the meta bolomics analysis process (e.g. sensory attributes). Predictive models have been created to estimate sensory attributes of green tea (Ikeda and others 2007; Tarachiwin and others 2007; Pongsuwan and others 2008) watermelon (Tarachiwin and others 2008) and mushrooms (Cho
23 and others 2007) Similarly, metabolomic s have the potential of identifying compounds that dictate consumer taste preferences. Products preferences can be obtained by taste panels whereas discriminating compounds can be identified and correlated by metabolomics techniques. Sensory evaluations wi th various concentrations of the chosen compounds will confirm their impact on consumer preferences ( Figure 1 3) Metabolomics in F ood S afety Untargeted discriminative metabolomics has been applied in food safety. Neutral desorption extractive electrospray ionization MS (EESI MS) was able to discriminate E. coli contaminated spinach trough the presence of unidentified high molecular weight peaks (Chen and others 2007) The same technique discriminated spoiled fish trough the presence of putrescine, cadaverine, and the toxic compound histamin e, showing a great potential of this type of analysis in food safety. Informative and predictive metabolomics in fresh raw fish has been suggested as tools to provide evidence of water contamination, temperature stress, and the fish health conditions at th e moment of the catch (Samuelsson and Larsson 2008) Metabolomics has the potential to assess the safety of novel pre and post harvest technologies. It has been proposed that unintended effects of genetic modificatio n of foods can been assessed by untargeted discriminative analyses (Zdunczyk 2006; Chao and Krewski 2008) Catchpole et al. (2005) utilized untargeted discriminative metabolomics to differentiate genetically modified (GM) potatoes. After removing the i ntended effect variable (fructans derivatives) no discrimination was observed, suggesting that GM potatoes are similar in composition to the original ones. Similarly, intended increase in flavonoid concentration in GM tomatoes have been reported trough tar geted informative metabolomics (Le Gall and others 2003b) whereas small non intended variations were detected by untargeted analysis (Le Gall and
24 others 2003a) concluding that no major unintended changes occurred after genetic modification. Future trends would involve the use of informative metabolomics to assess the safety of new or controversial processing technologies such as irradiation. Metabolomics for C ompliance of Food R egulations Compliance with country of origin regulations can be verified by discriminative and predictive metabolomics. Origins of honey (Donarski and others 2008) olive oil (Cavaliere and others 2007) and wine (Son and others 2008) have been determined by discriminative and pred ictive metabolomics. Regulations in many countries do not allow the use of GM foods. Compliance with this regulation can be verified by metabolomics. Discriminative and predictive analysis have been used to differentiate genetic modification in maize (Levandi and others 2008) soybean (Garcia Villalba and others 2008) potatoes (Le Gall and others 20 03a; Catchpole and others 2005) and wheat (Shewry and others 2007) Metabolomi cs can be used for compliance verification of labeled ingredients. These analyses have relied in the use of discriminative metabolomics to differentiate among varieties of several fruits and vegetables. For instance, cherry tomato has been separated by MV DA techniques from other varieties such as beef and round tomatoes by SPME GC MS (Tikunov and others 2005) LC MS and NMR (Moco and others 2008) Variety differentiation has also been applied to broccoli (Luthria and others 2008) wines (Pereira and others 2007; Son and others 2008) ginseng vari ety (Kang and others 2008) and age (Shin and others 2007) differentiation and potatoes (Parr and others 2005; Dobson and others 2008) Metabolomics in Food M icrobiology Several metabolomics application to microbial research have been suggested (van der Werf and others 2005) Bacteria identification and confirmation has been traditionally done b y
25 complex numerous biochemical tests. In contrast, discriminative and predictive analyses have the potential for rapid and accurate bacteria identification and confirmation. This analysis are mostly MS based (Ecker and others 2008) Microorgani sms are grown in culture media then concentrated (typically by centrifugation) and internal metabolites are extracted through cell disruption processes such as ultrasound or bead beating processes before separation or detection occurs. By following this me thod and the use of a matrix assisted laser desorption/ionization time of flight mass spectrometry MALDI TOF MS to detect high molecular weight compounds, 12 species of Aspergillus and 5 strains of Aspergillus flavus have been classified with 95 to 100% ac curacy (Hettick and others 2008) Similar methods have been used to classify Escherichia coli and Yersinia according to growing culture media, species, and strain (Parisi and others 2008) Metabolomics can also be use d for understanding microbial metabolism. Dynamics of glycolisis in E. coli have been assessed under systemic variation of growth rate and different glucose availability (Schaub and Reuss 2008) generating information on how glycolisis is affected under these conditi ons. Wine and baking yeasts have been differentiated from medical strains by using DIMS and GC TOF MS (MacKenzie and others 2008) In addition, exo metabolites of several wine yeast strains were anal yzed by HPLC and GC FID to compare aroma relevant compounds to gene expression (Rossouw and others 2008) showing the potential of metabolomics for assessing gene expressions. Current methods for quantification of bacteria in food stil l rely on lengthy plate counts and most probable number procedures. Metabolomics analysis coupled to sensor development can have a big impact in the detection and quantification of bacteria. Metabolomics has been successfully used for biomarker discovery i n other areas such as plant physiology (Glauser and others 2008) The discovery of bacteria biomarkers and their monitoring throughout growth phases has the potential to be related to the final pathogen
26 colony forming units ( CFU ). Sensors may be developed to monitor the for mation of the biomarker in the culture broth and incorporate the rate of biomarker production to an algorithm that predicts the expected CFU Metabolomics studies during E. coli growth have shown the time related progression of several metabolites (Koek and others 2006) Moreover, metabolomics has the potential to find new antimicrobial compounds and to determine the analytes responsible of the antimicrobial characteristics of certain plants and food. Zhi, Yu, and Yi (2008) utilized discriminative metabolomics based on HPLC to identified dihydrocucurbitacin F 25 O acetate as the major antimicrobial component of the herb Hemsleya pengxian ensis PCA data showed that Staphylococcus aureus treated with dihydrocucurbitacin F 25 O acetate grouped with those treated with the herb extract. Metabolomics in F o od P rocessing Food processing involves the combination of physical and chemical events tha t may cause important changes in food components that can be detected by metabolomics. The production of c heonggukjang (a soybean and rice straw fermented drink) has been monitored by informative and discriminative untargeted analysis using NMR (Choi and others 2007) The method showed the expected time related reduction of sugars (e.g. sucrose and fructose) and increase of acetic acid, tyrosine, phenylalanine and others. Final products were differentiated as a function of fermentation time by PCA. In addition, Capanoglu et al. (2008) utilized both targeted and untargeted informative metabolomics analyses to show that several flavonoids such as rutin, naringenin and derivates, as well as some alkaloids increased significantly after the breaking step ( fruit chopping). This was explained by the possible activation of pertinent enzymes after wounding. In addition, reduction of these compounds after the pulping step was observed because of the high presence of these analytes in the removed skin and seeds. Metabolomics can
27 also be used to understand the suitability of certain varieties for processing. For instance, several potatoes varieties are preferred for frying whereas other for baking. To assess differences, potato varieties have been analyzed by flow infusion electrospray ionization MS (FI ESI MS) and compound identification was aided by GC MS (Beckmann and others 2007) Cultivars Salara and Agria were low in tyrosine (major substrate for polyphenol oxidases) making them suitable for slicing and frying. Tyrosine is also a precursor of aroma and flavor compounds in boiled potat oes by Strecker degradation. Cultivars found to be high in tyrosine (Dsire and Granola) are more suitable for baking (Beckmann and others 2007) This type of analysis has shown the potential for providing valuable information to food product and process development industry. Informative metabolomics has the potential to assess unintended effects during processing and pre processing such as changes in nutrient composition, degradation of health related compounds, and formation of new compounds like toxins. In addition informative and discriminative metabolomics have the potentia l to study other pre Citrus Huanglongbing Characteristics and S ignificance Citrus Huanglongbing (HLB) is one of the most important plant disease s affecti ng citrus. The causal agent of HLB is Candidatus Liberibacter, a gram negative bacteria not yet cultured to Symptoms include marked yellow regions on leaves and poor quality, small, inedible, and misshapen fruits (Halbert and Manjunath 2004) HLB has had serious damaging impact in the citrus industry in many Asian countri es, wiped out a great number of trees in Brazil, and as of 2009 has seriously affected groves in 33 Florida counties making Candidatus
28 Liberibacter the most dangerous citrus pathogen in the world (Callaway 2008) and the most significant threat for t he citrus industry worldwide (Bove 2006) HLB originated in Asia and was first detected in the Americas in 200 4 (Brazil), reaching the United States in 2005 (Chung and Brlansky 2005) The devastating effect of HLB on citrus production in Africa, Asia, and the Americas has been well documented (Chung and Brlansky 2005) and the importance of this disease has triggered several reviews of HLB in countries such as Pakistan (Batool and others 2007) India (Das 2008) Malaysia (Hajivand and others 2009) as well as Brazil and US (Gottwald and others 2007) ely cultivated citrus variety in the world (Papadakis et al., 2008). Therefore HLB effects in this variety have the potential of causing serious economical loses. Since the appearance of HLB, many efforts have been directed towards controlling this diseas e including frequent pesticide spray to reduce psyllid population, the use of pheromone traps and psyllid repellents. Early detection of HLB spread by reducing inoculums through tree elimination Present methods for its quantification based on real time polymerase chain reaction are currently being optimized (Wen and others 2008) The disease is transmitted through a psyllid ( Diaphorina citri ) vector that feeds on citrus and other tropical and sub tropical plants making vector eradication impossible. Current Research on Detection Methods Currently, PCR is the only approved method for diagnosis of HLB; however, this is an expensive, laborious and time consuming alternative tha t does not allow in fie l d analysis. Other limitations such as the low concentration and uneven distribution of the bacteria in the tree (McClean 1970) make PCR detection very difficult, especially at early stages. Research on new methods such as the isothermal and chimeric primer initiated amplification of nucleic acids
29 combined with cycling probe technology (Urasaki and others 2008) application of nested PCR (Kawai and others 2007) comparisons of primers for PCR and nested PCR (Ding and others 2007) along with the improvement of DNA isolation for conventional PCR (G opal and others 2007a) has been reported. However, PCR amplification of the bacteria is very weak during spring and summer seasons (Gopal and others 2007b) increasing the probability of ob taining false negatives during these periods. Therefore methods that do not rely on the presence of the bacteria in the sample are sought as more reliable throughout the year. To the best of our knowledge, only two non PCR methods have been comprehensivel y researched. One relies o n the presence of excessive amounts of starch (Takushi and others 2007a) whereas the other on overproduction of genti sic acid (Hooker and others 1993) in infected trees. The disadvantages of these methods are that they are not HLB spec ific, since excessive amounts of starch are noticed with other stress situations such as girdling (Li and others 2003) and gentisic acid overproduction is caused by other infections in several plants (Belles and others 2006) Taba et al., (2006) showed that the starch method has a 75% agreement with PCR in leaves and 95% agreement for other parts of the trees. Current research is focused on the quantification of the starch accumulation in HLB trees by using spe c trophotometric metho ds (Taba and others 2006) This new approach may help to distinguish the starch accumulation due to HLB as oppose d t o starch accumulation due to other factors such as stress, zinc deficiency, and girdling (Li et al., 2003) These factors are very common and make starch detection by itself a less useful method. Escherichia coli and Salmonella spp. Escherichia coli and Sa lmonella spp are G ram negative, facultative anaerobe bacteria. Some strains have flagella and are motile. Both are considered amongst the most important food borne pathogens worldwide. Escherichia coli O157:H7 is considered a serious hazard to
30 public healt h in North America and Europe Symptoms from E. coli O157:H7 infection range from mild, watery diarrhea to hemolytic uremic syndrome and hemorrhagic colitis. Several E. coli O157:H7 outbreaks in food have being reported over the years Salmonellosis is als o one of the most important food borne disease s and causes substantial medical and economic burdens worldwide. Eggs egg products, poultry meat, and pork are the most important sources of salmonellosis in humans Implementation of hazard analysis and criti cal control points ( HACCP ) programs and detection methods are crucial for controlling food outbreaks. In conventional detection method s the contaminated food sample is suspended in an enriched media for 6 18 h and a portion of the broth is then plated on a gar media and analy z ed by biochemical tests and serological reactions which take 1 3 days. Another conventional technique is t he most probable number (MPN) MPN is particularly useful for determination of low concentrations of bacteria Here, triplicate sa mples or five replicates are prepared from 10 fold serial dilutions. The ratio of positive results to negative results is in relation to the concentration results in a MPN/g value. The MPN method assumes that bacteria are distributed randomly within the sa mple and separated from each other The growth medium and incubation conditions have been chosen so that one viable cell multiplies and can be detected O bjectives The overall objective of this research was to determine the suitability of GC MS, CE, and HP LC MS based metabolomic techniques for the rapid diagnosis of HLB and detection of E. coli O157:H7, Salmonella Hartford, Salmonella Muenchen, and Salmonella Typhimurium. The central hypothesis was that HLB, E. coli O157:H7, Salmonella Hartford, Salmonella Muenchen, and Salmonella Typhimurium produce metabolites that can be used for their specific detection. The s pecific objectives are as follows:
31 1. To characterize the differences in the metabolite profile of HLB infected citrus leaves trough HPLC coupled to M S. The working hypothesis was that changes in the metabolite profile that take place during HLB infection can be quantified to determine the main response or during bacteri al metabolism. 2. T o use CE PDA for untargeted analysis of plant metabolites and develop a CE method for characterization of possible citrus HLB biomarkers. The working hypothesis was that biomarkers that can be measured by CE are produced during HLB infectio n. 3. T o find metabolic differences between leaves f rom HLB infected, zinc deficient, and trees from commercial groves as a first step to identify potential HLB biomarkers by combined GC MS analysis o f headspace and derivatized liqui d extracts. The working hypothesis was that specific changes in metabolite profile of HLB infected trees can be quantified by GC MS. 4. To determine the suitability of CE, HPLC MS, and GC MS based metabolomic techniques for rapid and simultaneous detection of E coli O157:H7, Salmonella Typhimurium, Salmonella Muenchen, and Salmonella Hartford in beef and chicken The working hypothesis was that each pathogen produces a specific metabolite profile that can be used for rapid detection trough multivariate analys is.
32 Table 1 1. Most common metabolomics processes in food analysis Sample: Purpose of analysis Type Extraction and preparation Separation Detection Data treatment Reference Apples: light induced changes in peel Untargeted/ discriminative MeOH Deriva tization for GC MS GC MS LC MS PCA (Rudell and others 2008) Berries: polyphenol composition Targeted/ informative Acetic acid + water C18 and Sephadex LH 20 columns LC MS DIMS Compound identification (McDougall and others 2008) Broccoli, mustard, and brassica: glucosinolates co mposition Targeted/ informative Hot water (90 sonication LC MS n Compound identification (Rochfort and others 2008) Broccoli: variety differen tiation Untargeted/ discriminative Freeze dried MeOH + H 2 O LC UV MS DIMS PCA, ANOVA (Luthria and others 2008) Cheese: Production control Untargeted/ informative IMS Compound identification (Vautz and others 2006) E. coli : glycolisis metabolites Targeted/ informative Indirect thermal treatment LC MS Compound identification (Schaub and Reuss 2008) Ginseng: variety Differentiation Untargeted/ discriminative Deuterated MeOH + buffered water NMR PCA (Kang and others 2008) Green tea quality Untargeted/ predictive Freeze dried MeOH +H 2 O + CHCl 3 UPLC TOF MS PCA, PLS (Pongsuwan and others 2008) Honey: Origen verification Untargeted/ discriminative / predictive Buffered water NMR PLS GP (Donarski and others 2008) Maize: GMO identification Untargeted/ discriminative MeOH + Water + Ultrasonic CE TOF MS (Levandi and others 2008) Meat: Quality/safety Untargeted/ discriminative Neutral desorption EESI MS PCA (Chen and others 2007)
33 Table 1 1. Continued Sample: Purpose of analysis Type Extraction and prepa ration Separation Detection Data treatment Reference Olive oil: Origen differentiation Targeted/ discriminative SPME GC CI MS LDA Kruskal Wallis and Wald Wolfowitz tests (Cavaliere and others 2007) Pine m ushrooms Untargeted/ discriminative MeOH +H 2 O + CHCl 3 NMR PCA (Cho and others 2007) Potato: GM different iation Untargeted/ discriminative MeOH +H 2 O + CHCl 3 Derivatization for GC MS GC MS DIMS PCA (Catchpole and others 2005) Potato: Identification of cultivars Untargeted/ discriminative / informative Freeze dried + MeOH + Water + Chloroform + Derivatization GC TOF MS ANOVA, PCA (Dobson and others 2008) Potato: Variety differ entiation Untargeted/ discriminative / informative MeOH +H 2 O + CHCl 3 Derivatization for GC MS GC MS DIMS RF (Beckmann and others 2007) Soybean: GMO differentiation Untargeted/ informative MeOH EtOH H2O CE TOF MS Compound identification (Garcia Villalba and others 2008) Spinach: E coli contamination Untargeted/ discriminative Neutral desorption EESI MS PCA (Chen and others 2007) Tomato paste: changes during production Targeted to antioxidants/ info rmative Untargeted/ informative Targeted: H 2 O MeOH and MeOH CHCl 3 Untargeted: Formic acid MeOH H 2 O LC Antioxida nt detector LC TOF MS ANOVA, PCA (Capanoglu and others 2008) Tomato: metabolite correlations Untargeted/ predictive Volatiles: EDTA NaOH H 2 O + SPME Sugars and organic acids: MeOH + Derivatization GC MS PCA, LDA, CN (Ursem and others 2008)
34 Table 1 1. Continued Sample: Purpose of analysis Type Extraction and preparation Separation Detection Data treatment Reference Tomato: variety Differentiation Untargeted/ discriminative Liophy lizatio + MeOH + Sonication LC TOF MS NMR PCA (Moco and others 2008) Tomato: volatiles analysis Targeted/ discriminative EDTA NaOH H 2 O + SPME GC MS PCA, HCA (Tikunov and others 2005) Watermelon: Quality evaluation Untargeted/ predictive Buffered D 2 O NMR PLS LDA (Tarachiwin and others 2008) Wine: metabolite characterizat Untargeted/ discriminative Liophilized + buffered D 2 O NMR PCA, PLS (Son and others 2008) Yeast: Aroma compounds production Targeted/ discriminative Diethyl ether GC FID PCA, PLS (Rossouw and others 2008) Yeast: strain differentiation Untargeted/ discriminative Liophylizati + derivatization GC TOF MS PCA, HCA (MacKenzie and others 2008) Yeast: strain differentiation Untargeted/ discriminative NIR PCA LDA (Cozzolino and others 2006)
35 Table 1 2. Common number of peaks reported in food metabolomics 1 Technique Peaks reported Main references HPLC UV 4 0 detected (Defernez and others 2004) UPLC MS 1560 detected (Pongsuwan and others 2008) GC MS 91 142 detected (Beckmann and others 2007; MacKenzie and others 2008) CE MS 27 45 detected (Garcia Villalba and others 2008; Levandi and others 2008) NMR 16 20 identified (Jahangir and others 2008; Son and others 2008) 1 Table intended to provide general idea, since food matr ix and extraction methods greatly influence the number of detected peaks.
36 Figure 1 1. General metabolomics classification according to purpose of analysis METABOLOMICS Targeted Untargeted Discriminative Predictive Informative Extraction & sample preparation Data treatment
37 Figure 1 2. Metabolomics analysis process Data treatment Peak identification, Alignment, Statistical anal ysis: ANOVA, PCA, PLS, LDA, CN, RF, HCA SAMPLE Preparation Grinding, freeze drying, dilution, etc Extraction Targeted or untargeted Derivatization mainly for GC analysis Separation LC, GC, CE Detection UV, MS ( require ionization), NMR, NIR
38 Figure 1 3. Metabolomics potential for understanding consumer preferences Foo d samples Taste panel Metabolomics Discriminative and informative Preferences Discriminating compounds New formulations Sensory confirmation
39 CHAPTER 2 HLB FINGERPRINTING B Y HPLC MS Overview Metabolite profiling is becoming a good alternative to consider when dealing with specific biomarkers in plants Kell and other (2005) summarized this technique by stating the importance of their use for a better understanding of plant metabolites and disease diagnosis. Abu N ada and others (2007) used GC MS as a Metabolomics tool for the assessment of the effect of the Phytophthora infestans infection on the metabolites normally present in potato leaves finding tha t the concentration of approximately 42 metabolites significantly changed after bacterial inoculation. Glauser and others (2008) successfully used HPLC coupled to Nuclear Magnetic Resonance (NMR) as a Metabolomics tool for the detection of stress biomarkers in plant extracts; however, no research has been done regarding effects of HLB on citrus metabolites. This part of the research characterize d differences in the metabolit e profile of HLB infected leaves trough HPLC Mass Spectroscopy (MS) based on the hypo thesis that changes in the metabolite profile occurring during HLB infection can be quantified to determine the main during bacterial metabolism. The identifica tion of these key compounds will serve as a baseline for the development of a diagnosis technique. Main findings of this section were published in Proceedings of the Florida State Horticultural Society (Cevallos Cevallos and others 2008). Materials and Met hods Equipment, Software, and Reagents Methanol, chloroform, acetonitrile, and acetic acid were from Fisher ( Fisher Scientific Inc., Miami, OK ). The water bath was an Isotemp 3016s also from Fisher Sci (Miami, OK); the
40 HPLC system was composed of a Survey or HPLC, autosampler, and PDA detector; the MS was a LCQ Advantage Ion Trap with ESI (electrospray ionization) as ion source, and the data was processed by using the Xcalibur 2.0 software. The whole HPLC MS software system was from Thermo Scientific Inc. (Waltham, Ma). Color characterization was done by a Chroma Meter CR 331 from Minolta Co. (Osaka, Japan). PCR analysis was performed by the plant pathology group at the IFAS Citrus Research and Education Center (Lake Alfred, FL). Tandem MS was done at the D epartment of Chemistry at the University of Florida Gainesville, FL. Sampling and Experimental D esign Healthy (PCR negative) and diseased leaves (PCR positive ) were sampled from ring shoots from 10 year old trees) in a grove at Plant City, FL. Only three PCR positive trees were kept in the grove for analysis during that period of time. At least 3 leaves from different symptom intensities (infection value) per each infected tree we re sampled each month from December 2007 to April 2008 and analyzed individually by HPLC MS. The same sampling amount and frequency was applied for three healthy (PCR negative) trees. For PCR analysis at least 3 extra leaves from various parts of the tr ee were sampled each month to make sure that all the healthy trees remained healthy during the sampling period. Samples were stored on dry ice during transportation (45 minutes), analyzed for color characterization, and then stored at 80 C until processe d (approximately 1 month). PCR analysis was also performed on individual leaves. Statistical analysis were performed by using SAS 9.0 from SAS Institute Inc. ( Cary, North Carolina ) to compare chromatograms from each infection value and significance w as rep orted at levels of P lower than 0.05
41 Infection Value and Color Analysis An infection scale was created based on the appearance of diseased leaves. Large yellow areas and roughness are the usual sensory parameters that characterize the intensity of the s ymptoms in a leaf. The degree of infection was assigned by giving each leaf a number from zero to four in which zero represents a healthy looking leaf. The scale goes up if the yellowness and roughness of the leaves become more noticeable. Color characteri zation was performed by setting the colorimeter to perform averages of three different measurements in different parts of a leaf. The runs were consistently done in the middle, left, and right front parts of each individual leaf. HPLC MS Analysis The solv ent chosen for extraction was methanol due to its great extraction power. Chloroform was also used in small amounts to increase the extraction of more lipophillic compounds. Individual leaves removed from the storage were immediately weighed and ground und er liquid nitrogen and an equal weight of chloroform was added and stirred in the water bath at 0 C for one hour. Then an 80% methanol solution was added to achieve a 5:1 methanol: chloroform mixture. The solution was continuously stirred at 0 C for 12 h ours, filtered in a 0.45 m nylon filter, and injected in the HPLC MS system operating with a stationary phase C 18 column and a mobile phase consisting of 80% of an acetic acid solution (0.05% in water) and a 20% of acetonitrile (with 0.05% acetic acid al so) during the first 12 minutes, then a gradient phase during the next 47 minutes was applied to reach a final concentration of 90% acetonitrile and 10% acetic acid solution which was held during the final 11 minutes. The MS worked with an electrospray ion ization source and was operated in the 80 1000 m/z range.
42 Results and Discussion Individual healthy leaves were analyzed monthly from December 2007 till April 2008 to determine seasonal variations in their metabolic profile. Disease suspected leaves wer e confirmed by PCR and positive samples were analyzed under the HPLC MS method. Control (PCR negative) samples were also run under described conditions. Infection Scale An infection evaluation (IE) scale was created to determine the intensity of the symp toms and correlate it with the metabolites detected during the HPLC MS analysis. The scale consisted of an integer number from 0 (healthy looking leaf PCR negative) to 4 (highest symptomatic level of a PCR positive leaf) and was assigned by agreement of t he observers after a visual inspection. The scale goes from 0 to 4 and all the samples were PCR positive except for IE= 0. Figure 2 1 shows the IE values created for this experiment. PCR positive leaves with symptoms more intense than an IE=4 (tentative IE =5) were also found; however these were not considered in this research due to their high similarity to characteristic zinc deficient leaves and the rarity of this type of sample. This may be due to the fact that some times HLB induces zinc deficiency, thu s producing symptoms indistinguishable from genuine zinc deficiency (Bove 2006) HLB induced zinc deficiency should be further investigated. As we can see in Figure 2 1 the scale is mainly dictated by the amount of yellow and green tonalities, so the use of a color measurement technique might be helpful in order to more precisely quantify the scale. Table 2 1 shows the L* (lightness), a*(greenness), and b*(yellowness) v alues from the colorimeter. As we can see in Table 2 1, no trend was found between the IE value and the color values. This may be due to the fact that different yellow and green tonalities can be seen in
43 leaves from the same infection scale value. Area val ues instead of L*, a*, and b* values may give a better correlation (we are currently working on this hypothesis by using machine vision techniques). For now the L*, a*, and b* values can only be used as a rough reference of the degree of infection of a cer tain leaf. HPLC MS Analysis Approximately 900 compounds were detected in both, healthy and diseased leaves. Figure 2 2 shows typical chromatograms of healthy ( Figure 2 2 A) and diseased leaves from different infection scale values ( Figure 2 2 B, C, and D ). We can also distinguish three zones of interest: The zone marked as 1 goes from the 5 th to the 14 th minutes, the zone marked as 2 goes from 20 to 30. Both zones show a significant increase (P < 0.05) in their peak areas with increasing IE value. Finally the zone marked as 3 goes from 50 to 70 minutes and shows a significant (P < 0.05) decrease in the peak area with increasing IE. Samples of IE greater than 0 were PCR positive, whereas IE = 0 was PCR negative. No samples from IE = 1 were reported at this time, since these extreme value of IE is the most difficult to find during that time of the year. The variation of the total area of each of the zones of interest strongly correlates with the IE value. The correlation coefficients obtained were 0.92, 0.96, and 0.91 for the zones of interest 1, 2, and 3 respectively. Figure 2 3 shows these relationships. Future research will include the identification of the compounds shown in the zones of interest one and two and the comparison of the diseased metabolic pr ofile with the ones from stressed PCR negative trees. Only some compounds from the zone of interest number three have been identified so far through library matching. These compounds are Chlorophyll (eluting at 55 minutes, especially noticed at IE=0) with molecular weight of 892.3 and its derivates such as chlorophyll a, b, and c with molecular weights of 892.3, 906.3, and 610.3 respectively eluting
44 at 55, 57, and 58 minutes. M ass spectrometry data is shown in Figure 2 4. The full MS scan is presented for s amples of IE= 0 ( Figure 2 4 A) and IE=4 ( Figure 2 4 B). As we can see in Figure 2 4 most of the compounds show an increase in intensity when increasing the IE value whereas some others are being reduced. This correlates with the findings observed in Figure 2 2. Another important issue is that HPLC MS is not suitable for the detection of big molecules such as proteins and non ionizable molecules such as sugars. Future research will include p rotein determination through 2D electrophoresis and sugar characteri zation through derivatization followed by GC MS or HPLC Also mineral analysis performed by capillary electrophoresis is needed to determine if HLB causes deficiencies and in which stage. In conclusion several metabolites showed a very strong correlation b etween their peak area and the infection scale. This shows a strong potential of the use of metabolomics for the development of a technique for detecting citrus Huanglongbing. More work is needed to establish the sugars and proteins profile of diseased tre es and to compare these profiles with the ones obtained under other stress conditions. Also more work is needed to match the spectra profile of each compound for identification.
45 Table 2 1. Color values of leaves at different perceived infection scale val ues. Each value represents the average and standard error of four different leaves IE value L* a* b* 0 34.82 5.2 6.81 1.8 11.78 1.2 1 53.19 4.3 19.35 7.2 38.92 6.2 2 37.9 0 2.8 7.76 2.8 13.16 3.8 3 47.11 4.3 14.14 5 .2 35.08 5.9 4 45.78 5.5 10.12 3.8 35.12 5.2
46 Figure 2 1. Infection evaluation values for citrus Huanglongbing samples
47 Figure 2 2. Chromatograms of orange leaves. A) Healthy leaf with IE=0, B) symptomatic leaf with IE = 2, C) Symptomatic leaf with IE=3, and D) symptomatic leaf with IE= 4. Numbers 1, 2, and 3 represent the zones of interest A B C D
48 Figure 2 3. Cumulative peak areas per zone of interest (ZI) versus infection scale. The area values were obtained by adding all the peak areas within a zone of interest ZI 1 ZI 2 ZI 3 Infection Evaluation (IE) Total peak area
49 Figure 2 4. Full MS scan for a leaf with IE=0 (A) and IE=4 (B) B A
50 CHAPTER 3 HLB FINGERPRINTING B Y CAPILLARY ELECTROP HORESIS Overview Capillary electrophoresis (CE) is one of the most used techniques for metabolomics based fingerprinting. Several forms of capillary electrophoresis have been successfully used in targeted analysis (J. W. Jorgens on and Lukacs 1981; Negro and others 2003; Chen Wen Chiu and others 2007; Erny and others 2007; Herrero and others 2007; Ravelo Perez and others 2007; Garca Villalba and others 2008) Untargeted metabolomics studies are very limited (Monton and Soga 2007) and in most cases involve the us e of CE coupled to electrospray ionization mass spectrometry ( ESI MS ) system (Ramautar and others 2006) However, background electrolyte ( BGE ) formulations capable of being used in an ESI system are limited to low salts, and volatile components which do not necessarily provide the best separation efficiency (Monton and Soga 2007) Another limitation is the extra cost of the ESI MS system. Capillary electrophoresis coupled to photodiode array detector ( CE PDA ) is a powerful and inexpensive separation tool. It h as been successfully applied in untargeted analysis in mice ( Vallejo and others 2008) For instance, Garcia Perez and colleagues (2008) demonstrated the effectiveness of CE PDA for non targeted metabolic differentiation of Schistos oma mansoni infection in mice. Recently, untargeted CE PDA analysis was successfully used for fingerprinting Siraitia grosvenorii for quality m onitoring purposes (Hu and others 2008) However, to the best of our knowledge, there is no report on untargeted metabolomi cs analysis for fingerprinting of plant diseases using CE PDA The objective of this research was to use CE PDA for untargeted analysis of plant metabolites and develop a CE PDA method for
51 characterization of possible citrus Huanglongbing ( HLB ) biomarkers. Main findings of this section were published in Electrophoresis (Cevallos Cevallos and others 2009a). Materials and M ethods Reagents HPLC grade reagents (methanol, chloroform, and acetonitrile), sodium tetraborate decahydrate, 1 butanol, ethanolamide, so dium acetate, sodium phosphate, hexane, sodium dodecyl sulfate, naringenin, narirutin, hesperidin, tangeritin, synephrine, quercetin, gentisic acid, and ferulic acid were purchased from Fisher Scientific Inc. (Pittsburg, PA). Deionized, ultrafiltered water was used for all experiments. Pure reagents were run to determine their UV Vis spectra in CE analysis. Equipment and S oftware The water bath used for all the experiments was an Isotemp 3016s from Fisher Scientific (Pittsburg, PA). The capillary electropho resis system model P/ACE MDQ with PDA the data acquisition and analysis software Karat 32 version 5.0 was from Beckman Coulter (Fullerton, CA). The capillary was bare fused silica from Polymicro Technologies (Phoenix, AZ) 50 m ID, 56 cm total length (48 cm to the detector). Analysis of variance was carried out using SAS 9.0 from SAS Institute Inc. (Cary, NC) and significant differences were reported at 95% confidence level. Sampling and Experimental D esign and type of shoot (summer and spring shoots from 10 year old trees) in a commercial grove in Plant City, FL. Sampling started 4 weeks after symptoms were discovered. However, as of 2008 there was no way to estimate whe n the trees were initially infected by HLB. Polymerase chain
52 reaction ( PCR ) analyses were outsourced to the Plant Pathology Laboratory at the University of three di fferent PCR positive and PCR negative trees were sampled monthly from November 2007 to April 2008 to assess changes in metabolite profiles caused by uncontrolled changes in climate and other seasonal stress. After using some of the samples for PCR and CE o ptimization experiments, 36 HLB infected and 18 healthy leaves were individually run under CE PDA optimized conditions and statistically compared. Samples were stored on dry ice during transportation (45 min), and then stored at onth). Extraction, Sample Preparation, and CE Conditions T ested Solvents with different polarities were tested in both healthy and diseased samples to maximize extraction of the compounds that showed significant differences between control and infected sa nitrogen. Solvent was added to reach a final concentration of 4% w/v of ground tissue. Pure water, methanol, and chloroform were tested as solvents for extraction. The combinatio n methanol/water/chloroform (MWC) used by Gullberg and others (Gullberg and others 2004) was also tested in an 8:1:1 ratio since a variety of compounds with different polarities was expected in this type of sample. The mixture was sonicated on ice for 30 min in a sonicator model FS20H from Fisher Scientific (Pittsburg, PA). Three extraction time temperature combinations were tested: 12 h at 4C, 12 h at 0C, and 2 h at 60C in the temperature controlled water bath. After extraction samples were filtered using 0.45 m nylon syringe filters and ferulic acid was added to a final concentration of 100 mg/L as internal standard (IS). Endogenous ferulic acid was not detected in either healthy or infected samples under tested conditions, and did not int erfered with any peaks in the electropherograms. The effect of pH was tested by adjusting the aqueous borate
53 solution with 1 N NaOH or 1 N HCl as needed to reach pH values of 10.91, 9.30, 8.08, 6.51, 5.2, and 3.81. Sodium phosphate adjusted to pH 6.51 wit h 1 N HCl and sodium acetate adjusted to pH values of 5.2 and 3.81 with 1 N HCl were also tested as BGE to evaluate the influence of a buffering system at those low pH in which borate does not have buffering capacity. Phosphate and acetate buffers were pre pared at half and three times the borate concentration, respectively, to keep ionic strength constant. Adjustment of pH was done before the addition of the organic BGE components. Organic modifier tested were acetonitrile (ACN) in the 0 30% range, and 1 bu tanol in the 0 15% range. Selected Extraction and CE Analysis C onditions Of the extraction and CE analysis conditions tested the following provided the best results. Extraction conditions required the use of MWC in an 8:1:1 ratio as solvent, followed by 3 0 min sonication on ice, and 12 h extraction at 0C. Optimum BGE for capillary zone electrophoresis ( CZE ) consisted of 76% 11.2 mM sodium borate solution at pH 9.3, 15% ACN, and 9% 1 butanol (after sample addition sodium borate concentration was 8.5 mM). F or non aqueous capillary electrophoresis ( NACE ) BGE was composed of 15% ACN and 20 mM sodium acetate in methanol with no pH adjustment. For micro emulsion electrokinetic chromatography ( MEEKC ) BGE contained 15% ACN, 0.8% hexane, 15% SDS, and 20% 1 butanol in a 10 mM Sodium borate solution pH 9.3 at 20 kV and 25C. In all cases, separation was run at 20 kV and 25C. All solutions were prepared fresh weekly and kept at 4C. Before the first use the capillary was conditioned with methanol for 10 min, 0.1 M HC l for 5 min, 1 M NaOH for 5 min, and BGE solution for 20 min with 2 min water rinsing between each solution. For CZE analysis the capillary was rinsed between runs with 0.1M NaOH for 2 min and BGE for 3min. For NACE
54 and MEEKC analysis rinsing was with deio nized, ultrafiltered water ( > 18 M .cm) for 2 min followed by BGE for 3 min. Compound I dentification Compound identification was done by comparing UV spectra and migration time of each peak to those of pure standards. UV spectra of each peak were obtaine d by PDA analysis from from the 32 Karat software. This tool uses an algorithm that compares the UV spectra at the apex of each detected peak with the spectra of pure standards run under same conditions and stored in our internal library. A similarity value in the scale from 0 to 1 was obtained with each library match. Compound identity was obtained and reported in Table 3 1 only when the similarity value of the s pectra comparison was 0.90 or higher and an increase in the size of the peak was observed when spiking the sample with the corresponding pure standard. Mobility of each peak was calculated by using synephrine and ferulic acid (IS) as reference analytes as described by Erny and Cifuentes (2007) because mobility values obtained by this approach are independent of the voltage and capillary dimensions (Erny and Cifuentes 2007) Results and D iscussion Extraction Extraction with water at 4C and 60C for 12 and 2 h, respectively, were compared to methanol and chloroform extracts at 0C. Cold water (CW), hot water (HW), methanol, and MWC extracts were run under CZE conditions. HW and CW showed the lowest extraction efficiency since they each were only able to yield 10 peaks. Based on migration time and UV spectra comparis on, the same compounds and 14 others were extracted with pure methanol.
55 However, methanol alone was not effective at extracting non polar compounds. Typical electropherograms are shown in Figure 3 1. Chloroform extracts were run under MEEKC conditions. E xtraction with chloroform yielded nine peaks of which five were not detected with water and methanol based extractions according to UV spectra matching. No significant difference (P > 0.05) was found when comparing healthy and infected chloroform extracts. Therefore, pure chloroform as extraction solvent was not further considered. MWC extraction was selected for the remaining experiments because it showed the best extraction efficiency since the analytes detected included those obtained from water, methano l, and chloroform extracts alone based on UV spectra comparison. C apillary Zone Electrophoresis (CZE) Several CZE separation parameters were tested. Increasing pH values from 3.61 to 8.08 increased the analyte mobility as shown in Figure 3 2. This is due t o the increase in the EOF caused by greater ionization of silanol groups in the capillary wall at high pH (Rabanal and others 2001) In contrast pH values of 9.3 and above decreased the mobility of most of analytes. This may be explained by the high flavonoids content in plant extracts, which usually have pKa values above 8.5 (Herrero Martnez and others 2005) Therefore, the net negative charge at those pH values may cause attraction to the opposite pole. The use of phosphate as BGE was also tested at pH 6.51. Phosphate conce ntrations tested were half borate concentration in order to test under same ionic strength. In the same manner acetate was also tested as BGE at pH values of 5.2 and 3.8. Acetate concentration was three times greater than borate concentration but equal ion ic strength. Both buffers yielded separation and peak intensity very similar to that obtained by borate BGE at the same pH. As shown in Figure 3 2, sodium borate pH 9.3 resulted in the best
56 resolution and therefore, was chosen for further experiments. A si milar effect was observed when changing the BGE concentration. Sodium borate concentrations were tested from 5 to 75 mM. The best compromise between peak resolution and analysis time was with 8.5 mM sodium borate. High concentrations of sodium borate impro ved separation at the expense of the electro osmotic flow ( EOF ) which decreased with increasing ionic strength as previously reported (Edwards and others 2006) However borate concentrations greater than 8.5 mM caused peak broadening and e ven disappearance of the late migrating peaks, possibly due to higher longitudinal diffusion and stronger interaction of analytes with the capillary at low EOF (Bohuslav Gas and others 1997) The use of ACN as an organic modifier in CZE and NACE has been widely studied (Loranelle L. Lockyear and others 2002; Belin and Glaarla 2007) However, optimal ACN concentration depends on the type of analyte and BGE used. Increase in ACN concentrations in the range of 0 to 30% caused increments in the migration time as shown in Figure 3 3 EOF reduction by ACN in aqueous BGE has been reported before (Loranelle L. Lockyear and others 2002; Belin and Glaarla 2007) According to equation, a decrease in permittivity and zeta potential will decrease EOF. eo (3 1 ) where eo zeta potential has been observed when increasing ACN. This is due to ACN absorption to the capillary wall which changes the electrical properties of the Stern layer and reduces the capillary surface charge (Schwer and Kenndler 1991) ACN has a low permittivity value of 35.94 (Marja Liisa 2002) that lowered EOF. Also the lower permittivity constant of ACN may have caused changes in the dissociation equilibrium of the
57 dissolved compounds, thus modifying their electr ophoretic properties. In contrast, ACN has a low viscosity of 0.341 MPas (Marja Liisa 2002) accounting for a theoretical increase of EOF. However, the effects of permittivity and zeta potential changes are greater as in dicated by the decrease in EOF shown in Figure 3 3. ACN also allowed a better separation between compounds and detection of a higher number of baseline resolved peaks, likely due to the increase in solubility of the organic analytes. The most efficient ACN concentration was 15%, as higher concentrations did not improve separation but increased total analysis time and baseline instability. To further improve separation, MEKC was also evaluated by adding SDS from 5 to 120 mM (data not shown). SDS micelles di d not improve separation at concentrations below 20 mM. Above 20 mM resolution, baseline stability, and repeatability were compromised. This same effect was also reported in previous MEEKC studies (Yin and others 2008) Because the addition butanol as second organic modifier was evaluated due to its lower polarity that may dissolve low polar compounds. Increasing 1 butanol increased analyte migration time but improved resolution and increased the number of detected peaks by four compared to BGE with no 1 butanol as shown in Figure 3 4. Decreased mobility was probably due to a decrease in EOF related to the higher viscosity o f 1 butanol (Marja Liisa 2002) Increase in the number of peaks may be due to additional partitioning of the analytes in the different zones of the BGE. 1 butanol has also been used in NACE and MEEKC modes (Yin and others 2008) However, its use in CZE is not very common. Up to 15% 1 butanol was tested but 9% was chosen because higher concentrations increased peak broadening probably due to longitudinal diffusio n at low EOF (Herrero Martnez and others 2005)
58 Non Aqueous Capillary Electrophoresis ( NA CE ) NACE was also tested because the nature of our analytes of interest was unknown. In addition, NACE is particularly suitable for analytes that are not rea dily soluble in aqueous BGE (Cunhong Li 2005) Concentrations of ACN ranging from 5 to 50 % in methanol were tested along with ethanol amide to increase the pH of the organic solution. Fifteen percent ACN and 20 mM ethano l amide provided the best results in NACE mode. Higher amounts of these compounds did not improve separation. EOF was reduced by increasing ionic strength with sodium acetate 20 to 50 mM to provide better separation. Sodium acetate resolved the peaks at 20 mM or higher as shown in Figure 3 5. However, fewer peaks were detected by NACE in a 90 min run compared to CZE and none of the compounds detected in NACE were absent in CZE (compared by UV spectra of standard compounds run under both conditions). This su ggests that presence of water in the BGE improves the solubility of some important analytes. Also, water increases the permittivity constant of the BGE. Higher relative permittivity facilitates ionic dis sociation and increases EOF (Marja Liisa 2002) In NACE systems ion ion interactions are stronger and, in the cases where the relative permittivity of the BGE is below 10, ions tend to pair to neutral species makin g separation more difficult (Marja Liisa 2002) Micro Emulsion Electro Kinetic Chromatography ( MEEKC ) A common MEEKC BGE consists of a micro emulsion of hexane in a sodium borate aqueous solution aided by high conce ntrations of SDS and 1 butanol (Yin and others 2008) Optimum proportion of these reagents varies with the type of analyte. In this case no separation was observed with SDS concentrations below 15%. Higher concentrations increased analysis time to over 90 min Concentrations of 1 butanol below 20% yielded poorer emulsion stability and lower repeatability. Therefore, 15% SDS and 20% 1 butanol were chosen to form the
59 microemulsion with a 10 ppm sodium borate solution. Higher concentrations of sodium borate incre ased migration time and did not provide additional resolution. Hexane concentration was 0.8%, and as previously reported (Yin and others 2008) its variation did not cause significant changes (dat a not shown). No significant differences (P > 0.05) were found in chloroform extracts. Therefore, all further analyses were run using MWC extracts for which CZE gave the largest number of peaks and highest resolution. Selection of UV W avelength For compari son purposes 190 nm was chosen because most analytes of interest showed one maximum absorbance peak at this wavelength. Higher wavelengt hs are usually more specific (Monton and Soga 2007) and thus, very useful for targeted metabolomics analysis. However, assessing untargeted differences in biological systems require the detection of as many metabolites as possible. For NACE experiments the wavelength used was 205 nm because this is the UV cutoff of methanol in the BGE. Compound Identification and Statistical A nalysis A series of standards c ompounds were dissolved in the MCW mixture to simulate the sample preparation and run under the selected CZE conditions. A total of 36 HLB infected and based o n UV spectra and confirmed with migration time and UV spectra of pure standards run individually and by spiking the sample. Electropherograms were aligned before performing statistical analysis as recomm ended by Garcia Perez (Garca Prez and others 2008) Alignment were selected based on peak identity (migration time and UV spectra). We found this tool to be particularly suitable when a low number (e.g. 35 or lower) of peaks are detected with no major
60 deviations in migration times. Normalization was done to the IS since other normalization methods usua lly give comparable results (Garca Prez and others 2008) ANOVA of 36 PCR confirmed infected and 18 PCR confirmed healthy samples revealed six compounds present in significantly higher (P < 0.05) concentrations in HLB infected samples Three of these compounds were identified by mobility and UV spectra as hesperidin, naringenin, and quercetin by our internal database and using pure standards. However, unlike in orange juice, quercetin has not been previously detected in orange leaves b y other methods. These six compounds were always in significantly (P < 0.05) higher concentrations (154% or higher increase) in the infected samples as shown in Figure 3 7. Similar results have been reported for other plants si nce Abu Nada and others (2007) found a much higher proportion of compounds being up regulated than those down regulated in potato infected with Phytophtera infestans In this study, we provide evidence that HLB induces pr oduction of hesperidin, quercetin, and naringenin. Hesperidin has been reported to increase in orange leaf during blight induced zinc deficiency (Manthey and others 2000) suggesting its participation in the plant response to stress mechanism. Also HLB may possibly be causing mineral deficiency trough phloem obstruction in infected trees. However, this type of HLB secondary effects need to be further researched. Naringenin (Aziz and others 1962) and quercetin (Mamani Matsuda and others 2004) have been re ported to have microbial inhibition properties. Therefore, the plant probably synthesizes these compounds as a defense mechanism against pathogens. Gentisic acid (free form) concentration was not significantly (P > 0.05) different as shown in Table 3 1 sug gesting that it may not be an effective biomarker. However, the glycoside form of gentisic acid previously reported to increase in HLB infected trees (Hooker and others 1993) was not detected in this analysis probably because of its low UV Vis absorbance. Table 3 1 summarizes the migration time, mobility, wavelengths of
61 maximum absorbance, and the potential identity of eac h detected metabolite. While spectra allowed tentative identification of hesperidin, naringenin and quercetin, three other potential biomarkers remain unknown. Other analytical tools are required for their positive identification.
62 Table 3 1 List of the 24 peaks and IS detected by optimum CZE conditions as described in Figure 3 6. Compounds marked with a showed significant difference (P < 0.05) between healthy and diseased samples Compound No. Migration time (min) Mobility (cm 2 V 1 min 1 )10 3 max (nm) Possible compound 1 7.01 19.17 227 Unknown 2 7.31 18.38 190, 234 low Synephrine 3 7.51 17.89 190, 210, 330 Tangeritin 4* 8.43 15.94 204, 280 Hesperidin 5 8.63 15.57 205, 286 Narirutin 6* 9.41 14.28 190, 250, 350 Unkn own 7 10.13 13.26 190, 210 Unknown 8* 10.48 12.82 196, 310 Naringenin 9 10.73 12.52 190, 210, 230 Oxalic acid 10 11.06 12.15 230, 275 Unknown 11 11.71 11.47 212, 278 Unknown 12 11.98 11.21 224, 280 Unknown 13* 12.34 10.89 220, 278, 320 Quercet in IS 12.65 10.62 210, 300 Ferulic Acid 14 13.39 10.03 200, 310 Gentisic Acid 15 16.44 8.17 190, 220 Unknown 16* 16.62 8.08 227 Unknown 17 17.25 7.79 190, 220 Unknown 18 17.52 7.67 229 Unknown 19 18.88 7.11 228 Unknown 20 18.75 7.16 230 Unknown 21 19.54 6.87 225 Unknown 22* 20.24 6.64 231 Unknown 23 22.07 6.09 227 Unknown 24 24.75 5.43 234 Unknown
63 Figure 3 1. Electropherogram at 190 nm of CW, HW, methanol, and MWC extracts of BGE: 8.5 mM sodium borate, 15% ACN, and 9% 1 butanol pH 9.3 at 20 kV, 10 s sample injection. 10 12 14 16 18 20 22 Time (min) MWC Methanol CW HW Absorbance 0.01 AU MWC M ethanol CW HW Absorbance
64 Figure 3 2. Effect of the pH in separation. Electropherogram at 190 nm of MWC extracts of BGE: 8.5 mM sodium borate, 5% ACN, 10 s sample Time ( min) 6 8 10 12 14 16 18 20 22 24 26 pH 3.6 pH 5.13 pH 6.6 pH 8.08 pH 9.31 pH 10.91 Absorbance 0.01 AU
65 Figure 3 3. Effect of ACN in separation by CZE. Electropherogram at 190 nm of MWC extracts and 10 s sample injection. 30% ACN 20% ACN 15% ACN 10% ACN 0% ACN Time (min) 4 6 8 10 12 14 16 Absorbance 0.01 AU
66 Figure 3 4. Effect of 1 butanol in separation by CZE. Electropherogram at 190 nm of MWC ate, 15% ACN, at s sample injection. 6 8 10 12 14 16 18 20 22 24 Time (min) 12% Butanol 9% Butanol 6% Butanol 3% Butanol 0% Butanol Absorbance 0.01 AU
67 Figure 3 5. Effect of sodium acetate in separation by NACE. Electropherogram at 206 nm of ethanol amide 50mM CH 3 COONa 35mM CH 3 COONa 20mM CH 3 COONa 0mM CH 3 COONa 5 10 15 20 25 30 35 40 Time (min) Absorbance 0.01 AU
68 Figure 3 6. Typical electropherograms of healthy and infected leaves extracts run with BGE: 8.5mM sodium borate (pH 9.3), 15% ACN, 9% butan s sample injection. Significant difference was found in the circled compounds. Peak numbers correspond to compounds in T able 3 1. Infected Healthy 6 8 10 12 14 16 18 20 22 Time (min) Absorbance 0.01 AU
69 Figure 3 7. Mean concentration (in area units) of the 6 compounds found to be sig nificantly different in healthy ( ) and infected ( ) samples. Error bars represent standard errors of 36 infected and 18 healthy samples extracted and run under the optimized CZE conditions. Percentages represent the mean increment of e ach compound in infected samples. Area 154% 669% 555% 467% 612% 1360% Hesperidin Unk no wn 1 Naringenin Quercetin Unknown 2 Unknown 3 100000 90000 80000 70000 60000 50000 40000 30000 20000 10000
70 CHAPTER 4 HLB FINGERPRINTING B Y GC MS AND COMPARISON WI TH ZINC DEFICIENCY Overview Previous m etabolite profiling reports of healthy and Huanglongbing ( HLB ) infected leaves of sweet orange by techniques based on HPLC MS (Cevallos Cevallos and others 2008; Manthey 2008) and capillary electrophoresis photo diode array detector (Cevallos Cevallos and others 2009c) reported several flavonoid type compounds and hydroxycinnamates as possible biomarkers. However, sampl es with zinc deficiency ( ZD ) were not analyzed and specificity of suggested biomarkers was not reported. Besides HPLC and capillary electrophoresis, metabolomic analyses by GC MS provide an alternative for detecting less polar and low molecular weight comp ounds (Fancy and Rumpel 2008) and have been shown to be a powerful tool for metabolite profiling in plants. This kind of studies are considered as targeted metabolomics because the y are aimed at those compounds detectable by GC MS (Cevallos Cevallos and others 2009b; Sawada and others 2009) Additionally, metabolomic discrimination in plants can be achieved without the need of compound identification (Hall and others 2002) increasing differentiation power by including unknowns compounds. Abu Nada (2007) suggested specific metabolite variations during several stages of Ph ytophthora infestans infection in potato leaf extracts using GC MS. Additionally, metabolite changes due to stress conditions such as drought (Semel and others 2007) and wounding (Yang and Bernards 2007) have been quantified by GC MS analyses of tomato and potato plants, respectively. In most of the GC MS based metabolomics reports, liquid extracts have been dried and derivatized prior to analysis in order to increase detection of more pol ar compounds (Fancy and Rumpel 2008) However, drying may cause substantial losses of the highly volatile compounds usually found in
71 ompounds have been shown to play an important role in metabolite profiling of several plant stresses and diseases (Tikunov and others 2007) As an example, GC MS profiling of headspace metabolit es allowed differentiation of two fungal diseases in mangoes (Moalemiyan and others 2007) Similarly, headspace analysis of tomato plants permitted determination of changes in metabolit es occurring after infection with tomato mosaic virus (Deng and others 2004) In spite of the importance of these two types of analysis, no combined headspace and liq uid extracts analyses of plant diseases have been reported. Additionally, no GC MS based metabolomic studies of HLB as well as no comparison between HLB and zinc deficien cy in sweet orange leaves have been reported. The objective of this study was to find metabolic differences between leaves f rom HLB infected, zinc deficient, and healthy trees from commercial groves as a first step to identify potential HLB biomarkers by combined GC MS analysis o f headspace and derivatized liquid extracts Main findings were published in Phytochemical Analysis (Cevallos Cevallos and others 2010). Materials and Methods Reagents HPLC grade reagents L proline, L threonine, L alanine, arabitol, inositol, butanedioic acid, methoxyamine hydrochloride (20mg mL 1 ) in pyridine (Styczynski and others) and N methyl N trimethylsilyl tri f luoroac etamide (MSTFA) were purchased from Fisher Scientific Inc. (Pittsburg, PA). Trans humulene were from Sigma Aldrich (Saint Louis, MO). All other reagents were from Fisher as well. Equipment and S oftware The GC model HP 5890 coupled to an HP 5971 series quadrupole mass spectrometer with ChemStation B.02.02 data acquisition software and the Wiley 138K mass spectral database
72 were from Hewlett Packard (Palo Alto, CA). Sensitivity and reproducibility of liquid extracts and h eadspace volatile analyses was maintained by regular cleaning of the ion source (approx once a month) and verified by daily running known concentrations of pure standards in the GC MS The chromatographic column used for both headspace and liquid extract a nalyses was a DB5 MS 60 m x 0.25 mm x 0.25 m (length x I.D. x film thickness ) from J & W Scientific (Folsom, CA). The water bath (model Isotemp 3016s) and sonicator (model FS20H) were from Fisher Scientific (Pittsburg, PA). Data was aligned to correct devi ations in retention time by using MetAlign software ( www.metalign.nl ) and normalized to the total area prior to principal components analysis (PCA). Compounds were tentatively identified as described in the materials and methods section prior to analysis of variance (ANOVA). PCA was run to compare the overall metabolite profile of the samples and ANOVA was run to determine significance of individual compounds. ANOVA of peak areas was performed for each detected compoun d in all samples and for all analyses. PCA and ANOVA were carried out using MATLAB R2008a from The MathWorks (Natick, MA) and significant differences were reported at 95% confidence level. Both PCA and ANOVA were run on the peak intensities, similar to wh at was done in previous studies (Abu Nada and others 2007; Moalemiyan and others 2007; Semel and others 2007) Sampling and Experimental D esign cultivated citrus variety in the world. L eaves of h ealthy and symptomatic HLB infected (P CR positive) sweet orange trees were sampled in commercial groves located in Plant City (grove 1) and Lake Alfred (grove 2) FL and leaves from trees showing ZD were collected from research Education Center ( CREC ) grove
73 in Lake Alfred, FL HLB infected samples were taken approximately 3 months and 3 weeks after symptoms were first noticed for groves 1 and 2 respectively. All sampled leaves were from trees of the same age and similar shoots (s pring and summer shoots from 10 year old trees). Samples were kept on dry ice during collection and transport (45 min approximately), and then stored at 80C until analyzed (approximately 2 months). PCR analyses were run to confirm HLB infection and were outsourced to the Plant Pathology Laboratory at the CREC in Lake Alfred, FL. At least six leaves from three different PCR positive, PCR negative (healthy), and ZD affected trees were sampled monthly from November 2007 to October 2008 to assess seasonal var iability. After using some of the samples for PCR analyses and GC MS optimization experiments, 36 HLB infected, 28 ZD affected, and 18 healthy leaves were individually analyzed. Extraction C onditions Extraction conditions were similar to those previously reported for HLB metabolite profiling in capillary electrophoresis (Cevallos Cevallos and ot hers 2009c) with some modifications. Briefly, nitrogen. Approximately 15 mg of L threonine were added as a first internal standard (IS1) Solvent was to a final concentration of 4% w/v of ground tissue Solvent was a methanol/water /chloroform combination suggested by Gullberg et al. (2004) in a 8:1:1 ratio and was added within one minute of grinding to stop degradation reactions. The mixture was sonicated on ice for 10 min. Th e extraction was done overni ght at 0 C in the temperature controlled water bath. After extraction samples were filtered using 0.45 m nylon syringe filters and (E,E,) 2,4 nonadienal was adde d to a final concentration of 8 00 mg L 1 as second internal standard (IS 2 ). IS1 and IS2 were added as quality control of the derivatization and headspace
74 extraction respectively as well as to assure adequate GC MS analysis and library matching Endogenous IS1 and IS2 were not detected in either sample category under tested conditions, and did not interfere with any peaks in the chromatograms Extraction with pure chloroform and pure methanol overnight at 0 C were also tested but yielded significantly less peaks than the combination methanol/water/chloroform described above and therefore were not u sed for this study (see results and discussion section ). Headspace A nalysis A solid phase micro extraction ( SPME ) fiber 50/30 m DVB/Carboxen TM / PDMS StableFlex TM for manual holder 57328 U from Supelco (Bellefonte, PA) was conditioned prior to its first use at 270 C for 1 h, and at the start of every day at 240 C for 5 min. Fifteen milliliters of the samples extracted with methanol/water/chloroform 8:1:1 were transferred to a 50 m L vial and equilibrated at 40 C for 30 min while stirred The p re conditi oned SPME fiber was exposed to the headspace of the equilibrated samples for 40 min at 40 C and then splitlessly injected into the GC MS. The injector temperature was 240 C, th e oven was initially held at 55 C for 1 mi n, the temperature rate was 7 C mi n 1 and the final temperature was 260 C held for 5 min. Ultrapure hydrogen was used as the carrier gas at 1 mL min 1 The MS was tuned to maximum sensitivity in electron impact mode, positive polarity, and the total ion current was recorded for a mass ra nge of 25 650 amu The GC MS interface was set to 318 C. The scan was recorded after a solvent delay of 8 min with scan frequency of 4 s 1 Liquid Extract A nalyses For liquid extracts, 180 L were transferred into a 1 mL GC vial and dried under a nitrogen flow. MOX w as added (30 L) to the dried extract and allowed to react for 17 h at room temperature. Other combinations of time and temperature showed lower reproducibility as
75 reported in similar studies (Gullberg and others 2004) After meth oximation with MOX silylation reactions were induced by adding 80 L of MSTFA for 2 h at room temperature. Other times and amounts of MSTFA yielded lower number of detected peaks and poorer reproducibility (see results and discussion section) Volumes of 0.3 L of derivatized sample were splitlessly injected into t he GC MS. The injector was at 25 0 C, the initial oven temperature was 70 C held for 1 min, the temperature rate was 10 C min 1 and the final temperature was 315 C held for 10 min. After 8 mi n of solvent delay the total ion current of mass fragments in the range of 50 650 amu was recorded. Other MS conditions were identical to that used for headspace analysis. C ompound I dentification Mass spectra obtained were visually observed at the beginn ing, middle, and end width of each peak to detect coelution. No coelution was detected in any of the peaks. Compound identification was done by library matching of mass spectra using the Wiley library and our internal databases. Compound identity was obtai ned and reported in Tables 2 and 3 only when the matching value of the mass spectra comparison was 70 or higher and an increase in the size of the peak was observed when spiking the sample with the corresponding pure standard. Additionally, the linear rete ntion index (LRI) was reported for each compound detected. A series of alkanes from C8 (LRI = 800) to C20 (LRI = 2000) were run under the GC MS conditions set for both liquid extracts and headspace volatiles. The LRI were estimated by direct correlation with the retention times of the alkanes. Preliminary Validation of Possible HLB B iomarkers Preliminary validation was done using training and validation groups of samples from each of the previously described groves, as performed in other metabolomic studi es (Ikeda and
76 others 2007; Tarachiwin and others 2007; Pongsu wan and others 2008) The validation group consisted of t en samples of each treatment (HLB, ZD, and healthy) taken from each of the two groves and analyzed as described in previous sections ANOVA was run on each compound to determine significance of the potential biomarkers. Peak areas as well as infected healthy and infected zinc deficiency ratios were compared between groves. Additionally, non symptomatic leaves from HLB infected trees were analyzed following the methodology previously described and su itability of suggested biomarkers for pre symptomatic HLB differentiation was proposed. PCR analyses of both symptomatic and non symptomatic leaves were also performed. Results and Discussion Extraction and Derivatization C onditions Polar extracts obtained with pure methanol were compared to chloroform and methanol/water/chloroform (8:1:1) extracts. No significant differences were found when comparing chloroform extracts from healthy and HLB infected leaves confirming previous observations that non polar co mpounds are less likely candidates for fingerprinting in plant metabolomic analyses (Cevallos Cevallos and others 2009b) Samples from healthy and HLB infected trees showed significant differences when extracted with pure methanol and with the combination methanol/wate r/chloroform (8:1:1). However, the 8:1:1 mixture yielded a higher number and greater concentration of peaks (Figure 4 2) and therefore was used as the extraction media for this study. Results were in accordance to previously reported data for HLB fingerpri nting using capillary electrophoresis (Cevallos Cevallos and others 2009c) Amount of sily lation reagent and duration of the reaction are the two known concerns that need to be addressed prior application of a derivatization methodology (Kanani and others 2008) Several derivatization conditions were tested by adding MSTFA from 40 L to 120 L
77 (20 L intervals) dur ing 40, 80, 120, 160, and 200 min. The amount of the silylation agent MSTFA added caused a significant effect on the 11 compounds reported in Figure 4 3 and Table 4 1. The derivatized form of glucose, galactose oxime, and mannitol ( Figure 4 3 ) as well as L proline, unknown 6, and inositol ( Table 4 1) showed a maximum peak area after reacting with 80 L of MSTFA. Lower and higher amounts of MSTFA caused a reduction in peak areas probably due to incomplete derivatization and dilution effect respectively. Conv ersely, the derivatized form of glucitol ( Figure 4 3 ), unknown 5, glutamic acid, and glycine ( Table 4 1) showed a maximum peak area when reacting with 40 L of MSTFA. Higher levels of MSTFA caused a reduction in their peak areas, probably due to a dilutio n effect. All compounds were detectable at both 40 and 80 L of MSTFA but the latter showed the lowest standard error and improved reproducibility ( Figure 4 3 and Table 4 1) and was chosen for this study. Variation in MSTFA reaction time significantly aff ected six compounds only ( Figure 4 4). The lowest standard error and better reproducibility was observed between 80 and 150 min of reaction. Therefore, a reaction time of 120 minutes was selected for the metabolomics study. Adding 80 L of MSTFA followed b y a reaction time of 120 min yielded the best compromise between peak area and reproducibility. GC MS Analyses of Derivatized S amples Figure 4 5 shows typical chromatograms of samples from healthy, HLB infected, and ZD affected trees from groves 1 and 2. Data was aligned and normalized to the total area prior to statistical analysis. PCA was carried out to compare the overall metabolite profile of each sample group. Figure 4 6A shows that PCA was able to classify all the samples according to their initial physiological condition suggesting marked differences in the metabolite profile of each sample group. No classification was obser ved based on groves or sampling time suggesting that
78 differences between HLB, ZD, and healthy samples were greater than possi ble differences between groves or sampling season Principal components (PC) 1 and 2 accounted for 56.34% of the variation and sample classification occurred mostly in the PC1. Out of the 10 compounds with the highest absolute loading values in PC1 ( Figure 4 6B), only L proline, unknown 20, inositol, fructose, and arabitol showed significant differences among sample groups. ANOVA w as run in each detected compound to find other possible biomarkers among the metabolites with low loading values While a ll comp ounds detected were present in all of the samples, significant differences were found in the concentration of several compounds Table 4 2 shows the tentative identity of the most abundant compounds detected (peak to noise ratio of 4) as well as those comp ounds showing significant differences among sample groups L Proline and unknown 22 showed significantly higher concentrations in HLB infected leaves when compared to the healthy and ZD affected ones ( Figure 4 7A ). These results are in agreement with previ ous reports of increased proline in citrus under physiological or biological stresses. Proline accumulation in citrus (Rivas and others 2008; Gimeno and others 2009) and other plants such as sugar cane (Suriyan and Chalermpol 2009) has been reported with stress conditions such as girdling and drought stress Additionally, proline accumulation has also been linked to bacterial attack in other plants such as potatoes (Abu Nada and others 2007) Therefore, L proline alone cannot be considered as an HLB specific biomarker. Fructose and un known 18 were in significantly higher concentration in both HLB infected and ZD affected samples when compared to the healthy ones ( Figure 4 7B ). Fructose may be released by sucrose synthase mediated ADPglucose synthesis, which has been reported to be an i mportant step in starch biosynthesis in leaves (Munoz and others 2006) This observation is in agreement with previous reports on starch accumulation in HLB infected leaves (Etxeberria and others 2009; Achor and
79 others 2010) Starch concentration in HLB infected leaves has been reported to increase more than 10 folds (TAKUSHI and others 2007b) M ineral im balances, su ch as boron deficiency has also been shown to cause fructose accumulation in citrus (Han and others 2008) Although combined accumulation of f ructose and glucose in HLB infected plants has been reported (Dagraca 1991) no significant differences in glucose content were detected in this study. Unknown compound 20 and oxo butanedioic acid were in significantly higher concentrations in zinc deficient th an in healthy and HLB infected samples whereas arabitol, neo inositol and unknown 15 were in significantly lower concentrations (Figure 7C ). SPME A nalyses Figure 4 8 shows typical chromatograms of headspace analyses of leaves from healthy, HLB infected, a nd ZD affected trees Table 4 3 shows the tentative identity of the volatiles detected. None of the compounds detected by SPME analyses were detected in liquid extracts, probably due to the lost of volatiles during sample drying prior to derivatization. Da ta show the potential of using combined headspace and liquid extracts analysis for maximizing the number of tentative biomarker s in GC MS based metabolomic research. As with liquid extracts SPME data was aligned and normalized to the total area prior to statistical analysis. PCA ( Figure 4 9A) was not able to classify the analyzed samples, suggesting similarities in the headspace metabolite profile in the sample groups. A weak discrimination of the HLB infected samples can be seen in PC2, suggesting very s mall differences in the compounds with the highest PC2 loading values. Figure 4 9B shows the loading plot of PC1 and PC2 as well as the 10 compounds with the highest absolute PC2 loading values. Additionally, ANOVA was run for each compound to determine si gnificant differences and possible biomarkers Although PCA was not able to classify samples run by SPME, significant
80 differences were found in the co ncentration of several headspace volatiles after running ANOVA. This shows the importance of ANOVA in meta bolic differentiation studies. Table 4 3 shows the tentative identity of the most abundant compounds detected (peak to noise ratio of 4) as well as those compounds showing significant differences among sample groups Significantly lower concentrations of s everal sesquiterpenes were observed in the non E lemene, trans caryophyllene, h umulene were significantly lower in HLB infected samples only ( Figure 4 10A s s elinene were significantly lowe r in the HLB infected and zinc deficient samples ( Figure 4 10B ). Decrease d concentrations of sesquiterpenes has been reported in citrus with drought stress (Hansen and Seufert 1999) The increased amounts of sesquiterpenes such as e lemene and trans caryophyllene detected upon infection with several pathogens such as phytoplasmas in Hypericum perforatum (Bruni and Sacchetti 2005) and other plants, suggested a strong antimicrobial activity of these compounds However, the low level of sesquiterpenes found in diseased leaves in this study, suggests an important post infection inhibition of ses quiterpene Recently, eight different degrees of susceptibility to this infection were characterized (Folimonova and others 2009) Further research is needed to confirm sesquite rpene inhibition in susceptible and tolerant varietes after infection with HLB. Although the change in concentration of no single compound may be exclusively attributed to HLB, the combined use of L elemene, trans humulene increased specificity The combination of these biomarkers has the potential to be found with drought stress only. However, drought those of HLB. Hence, L elemene, trans humulene along with
81 visual observation of the sy mptoms have the potential to differentiate trees with HLB from those healthy or with zinc deficiency the most common and HLB similar symptomatic condition in citrus These results complement previous work on HPLC MS in which flavonoids and hydroxycinnamates were observed to change in HLB infected trees (Cevallos C evallos and others 2008; Manthey 2008) Additionally, proposed biomarkers can be targeted by traditional chemical assays, sensors or biosensors, reducing the analysis costs and improving portability as well as rapidity. Future research is need ed to determ ine physiological similarities between HLB and drought stress affected plants Preliminary Validation of HLB B iomarkers Ten samples from each treatment and each grove (total 30 samples per grove) were used to validate proposed HLB biomarkers among groves. Each of the two groves analyzed showed the same significant differences in the compounds reported in Tables 2 and 3. Figure 4 11 shows that the peak areas were similar between groves for each proposed biomarkers, except for Unknown 22, L proline, and Unkno wn 11. Same effect can be seen when comparing ratios of HLB infected with healthy or zinc deficient samples between groves ( Table 4 4). The differences in the concentrations and ratios of these potential biomarkers might be due to dissimilarities in the se verity of the infection To further test this hypothesis, s amples showing less severe or no symptoms from HLB infected trees were analyzed and compared to healthy ones. Figure 4 12 shows reduced differences in all proposed biomarkers. Only trans caryophyll ene and humulene showed significant differences when comparing mildly infected with healthy leaves. Therefore, the combination of all the proposed biomarkers cannot be used for HLB detection in non symptomatic leaves. These results are in agreement with previous reports on HPLC MS fingerprinting of HLB showing that metabolic differences are proportional
82 to the intensity of the visual symptoms (Cevallos Cevallos and others 2008) suggesting the need of sampling highly symptomatic leaves for metabolomic analysis All PCR analysis performed in non symptomatic l eaves were negative, showing agreement with metabolomic results. This suggests that the bacteria were probably not present in the sampled non symptomatic leaves but changes in trans caryophyllene and humulene were probably induced by the presence of the bacteria in the symptomatic leaves of the same tree. Further research is needed to better understand the response mechanism of citrus trees after HLB infection. Further validation studies involving different cultivars, groves, seasons, diseases, and stress es are needed to find HLB specific biomarkers and determine possible metabolic differences among biotic and abiotic stresses. Additional research in greenhouse plants is needed to determine if pre symptomatic and pre PCR positive changes in the metabolite profile occur in citrus.
83 Table 4 1. Effect of amount of MSTFA added on the compounds expressing the highest variation. Amount of MSTFA (L) Compound 60 80 100 Unknown 5 3356 +/ 553.311 2878.33 +/ 237.1 44 1418.33 +/ 249.843 Unknown 6 3925 +/ 488.257 3849 +/ 260.407 2845 +/ 186.551 L proline 2TMS 13662 +/ 738.219 12120.3 +/ 153.576 10189.3 +/ 340.36 L glutamic acid 10649 +/ 472.347 9604 +/ 344.252 7437 +/ 472.168 L glycine,TMS 7188 +/ 1610 .79 6340.33 +/ 861.635 4478.33 +/ 228.803 Inositol 6TMS 8785 +/ 2989.65 8897 +/ 2099.09 6402.33 +/ 1940.34
84 Table 4 2. Main compounds detected in derivatized samples* Lin Ret Index (LRI) Tentative identity Relative abundance (%) ZD HLB Heal thy 967 UN1 0,177 +/ 0,005 0,192 +/ 0,004 0,121 +/ 0,003 979 Benzene, 1,2,5 trimethyl 1,400 +/ 0,026 1,198 +/ 0,022 0,593 +/ 0,016 994 Tetrasiloxane, decamethyl 0,048 +/ 0,001 0,076 +/ 0,001 0,218 +/ 0,005 1004 UN2 0,037 +/ 0,001 0,074 +/ 0, 001 0,017 +/ 0,000 1020 Butanoic acid, 2 [(trimethylsilyl)oxy 0,153 +/ 0,004 0,201 +/ 0,004 0,130 +/ 0,003 1024 UN3 0,030 +/ 0,001 0,017 +/ 0,001 0,057 +/ 0,001 1030 UN4 0,006 +/ 0,000 0,002 +/ 0,000 0,000 +/ 0,000 1062 L Alanine, N (trimethy lsilyl) trime 0,055 +/ 0,001 0,038 +/ 0,001 0,004 +/ 0,000 1134 UN5 0,034 +/ 0,001 0,316 +/ 0,007 0,057 +/ 0,001 1206 3,7 Dioxa 2,8 disilanonane, 2,2,8,8 t 0,025 +/ 0,001 0,038 +/ 0,001 0,100 +/ 0,001 1212 UN6 0,374 +/ 0,006 0,462 +/ 0,005 0,421 +/ 0,003 1248 L Proline 2TMS 1,257 +/ 0,014 4,258 +/ 0,038 a 0,928 +/ 0,008 1280 UN7 0,141 +/ 0,003 0,078 +/ 0,001 0,271 +/ 0,003 1294 L Serine TMS 0,136 +/ 0,002 0,220 +/ 0,002 0,191 +/ 0,001 1406 Butanedioic acid, [(trimethylsilyl)ox 3 ,009 +/ 0,043 1,802 +/ 0,025 3,028 +/ 0,020 1451 L glutamic acid, N (trimethylsilyl) 0,272 +/ 0,003 0,226 +/ 0,003 0,315 +/ 0,003 1462 Glycine, N,N bis(trimethylsilyl) 0,135 +/ 0,002 0,402 +/ 0,007 0,841 +/ 0,009 1468 Tetronic acid TMS 0,071 +/ 0,002 0,000 +/ 0,000 0,000 +/ 0,000 1486 UN8 0,123 +/ 0,001 0,391 +/ 0,006 0,176 +/ 0,001 1519 UN9 0,001 +/ 0,000 0,007 +/ 0,000 0,016 +/ 0,000 1599 Arabitol TMS ND a 0,166 +/ 0,002 0,209 +/ 0,002 1609 UN10 0,395 +/ 0,004 1,020 +/ 0,011 0,473 +/ 0,006 1617 UN11 0,103 +/ 0,002 a 0,397 +/ 0,005 ,b 0,675 +/ 0,007 b 1623 UN12 0,149 +/ 0,002 a 0,065 +/ 0,001 ,b 0,027 +/ 0,001 b 1670 Citric acid 4TMS 0,096 +/ 0,002 0,015 +/ 0,000 0,008 +/ 0,000 1675 1 (4' Trimethylsilyloxypheny l) 1 Trime 0,194 +/ 0,003 0,472 +/ 0,007 0,228 +/ 0,002 1701 UN13 18,930 +/ 0,229 5,513 +/ 0,054 3,381 +/ 0,091 1707 Fructose 5TMS 5,141 +/ 0,058 a 7,429 +/ 0,140 a 0,608 +/ 0,007
85 Table 4 2. Continued Lin Ret Index (LRI) Tentative identity Relative abundance (%) ZD HLB Healthy 1716 D gluco hexodialdose, 4TMS 0,130 +/ 0,001 0,203 +/ 0,005 0,029 +/ 0,001 1721 D Mannitol 6TMS 20,334 +/ 0,151 11,780 +/ 0,184 5,365 +/ 0,050 1740 Galactose oxime 6TMS 0,600 +/ 0,010 0,397 +/ 0,010 0,0 06 +/ 0,000 1764 Inositol 6TMS 0,190 +/ 0,002 a 1,299 +/ 0,013 1,844 +/ 0,015 1789 Galactonic acid 6TMS 0,027 +/ 0,000 a 0,088 +/ 0,0 2 a,b 0,175 +/ 0,002 b 1811 UN14 0,118 +/ 0,004 0,173 +/ 0,002 0,282 +/ 0,002 1855 Myo Inositol 6TMS 0,570 +/ 0 ,006 1,019 +/ 0,008 0,907 +/ 0,005 1864 D Glucitol 6TMS 0,349 +/ 0,004 0,564 +/ 0,007 0,300 +/ 0,002 1894 UN15 0,012 +/ 0,000 a 0,099 +/ 0,001 0,062 +/ 0,000 1905 Glucose 5TMS 0,260 +/ 0,003 0,734 +/ 0,011 0,544 +/ 0,009 2043 UN16 0,127 +/ 0 ,002 0,140 +/ 0,002 0,088 +/ 0,001 2047 UN17 0,127 +/ 0,002 0,045 +/ 0,001 0,117 +/ 0,001 2063 UN18 0,182 +/ 0, 03 a 0,128 +/ 0,002 a 0,009 +/ 0,000 2081 Butanedioic acid, oxo (TMS) 0,176 +/ 0, 03 a 0,000 +/ 0,000 0,000 +/ 0,000 2091 UN19 0,125 +/ 0,002 0,067 +/ 0,001 0,170 +/ 0,002 2096 Butanedioic acid, oxo (TMS) 0,182 +/ 0,004 0,052 +/ 0,001 0,026 +/ 0,001 2147 UN20 0,181 +/ 0, 03 a 0,051 +/ 0,001 0,006 +/ 0,000 2175 Sucrose TMS 26,443 +/ 0,254 46,378 +/ 0,354 55,111 +/ 0,262 2 188 UN 21 0,002 +/ 0,000 0,034 +/ 0,001 0,000 +/ 0,000 2240 UN22 0,023 +/ 0,001 0,186 +/ 0,003 a 0,032 +/ 0,001 Values are in percentage +/ standard deviation. Values in the same raw with the same superscript are not significantly different.
86 Table 4 3. Main compounds detected by headspace SPME* Linear Ret Index (LRI) Tentative identity Relative abundance (%) ZD HLB Healthy 900 4 Octene, 2,6 dimethyl ,[S (E)] 0,070 +/ 0,001 0,230 +/ 0,003 0,213 +/ 0,003 908 3 Octene, 2,6 dimethyl 0, 053 +/ 0,001 0,147 +/ 0,002 0,305 +/ 0,003 919 Thujene 0,777 +/ 0,010 1,067 +/ 0,011 1,248 +/ 0,014 922 Octane, 2,6 dimethyl 0,307 +/ 0,005 0,759 +/ 0,010 0,373 +/ 0,005 934 UN1 0,513 +/ 0,004 0,835 +/ 0,007 0,860 +/ 0,005 937 UN2 0,075 + / 0,001 0,186 +/ 0,003 0,216 +/ 0,003 946 UN3 0,138 +/ 0,003 0,424 +/ 0,004 0,518 +/ 0,007 958 2 Octene, 2,6 dimethyl 0,546 +/ 0,005 1,081 +/ 0,007 1,250 +/ 0,009 970 Sabinene 8,429 +/ 0,080 9,042 +/ 0,086 8,715 +/ 0,082 976 1,5 Heptadiene, 2,3,6 trimethyl 0,249 +/ 0,004 0,671 +/ 0,007 0,532 +/ 0,006 980 Pinene 1,407 +/ 0,010 1,692 +/ 0,008 1,276 +/ 0,011 985 Trans carane 0,000 +/ 0,000 0,247 +/ 0,003 0,190 +/ 0,003 990 2,6 Octadiene, 2,6 dimethyl 0,585 +/ 0,005 1,098 +/ 0,006 1,147 +/ 0,007 996 1,3 Hexadiene, 3 ethyl 2,5 dimethyl 0,445 +/ 0,004 0,550 +/ 0,005 0,732 +/ 0,004 1008 3 Carene 4,659 +/ 0,044 5,360 +/ 0,046 5,159 +/ 0,050 1016 Terpinene 0,283 +/ 0,004 0,504 +/ 0,003 0,478 +/ 0,004 1025 Benzene, 1 methyl 2 (1 methylethyl) 3,041 +/ 0,024 8,168 +/ 0,067 3,717 +/ 0 ,030 1031 d Limonene 8,130 +/ 0,048 19,208 +/ 0,137 5,980 +/ 0,061 1041 1,3,7 Octatriene, 3,7 dimethyl 5,229 +/ 0,023 3,679 +/ 0,033 5,010 +/ 0,023 1059 Terpinene 0,336 +/ 0,004 0,765 +/ 0,003 0,549 +/ 0,003 1088 Terpinolene 1,060 +/ 0,00 6 1,069 +/ 0,004 1,084 +/ 0,005 1096 Undecane 1,290 +/ 0,007 2,453 +/ 0,017 0,890 +/ 0,003 1152 UN4 0,508 +/ 0,004 0,771 +/ 0,006 0,465 +/ 0,002 1274 (E,E) 2,4 nonadienal 3,287 +/ 0,002 3,287 +/ 0,002 3,287 +/ 0,002 1350 Cubebene 0,191 +/ 0,006 0,289 +/ 0,004 0,440 +/ 0,003 1354 UN5 0,000 +/ 0,000 0,025 +/ 0,001 0,074 +/ 0,002 1365 UN6 0,000 +/ 0,000 0,030 +/ 0,001 0,221 +/ 0,002 1372 Dihydro neoclovene (II) 0,336 +/ 0,004 0,903 +/ 0,007 0,821 +/ 0,010 1383 UN7 2,602 +/ 0 ,009 a, b 2,029 +/ 0,007 a 2,939 +/ 0,011 b
87 Table 4 3. Continued Linear Ret Index (LRI) Tentative identity Relative abundance (%) ZD HLB Healthy 1391 Elemene 26,983 +/ 0,152 14,053 +/ 0,082 a 21,582 +/ 0,153 1396 UN8 0,185 +/ 0,003 0,497 +/ 0,005 0,655 +/ 0,006 1404 UN9 0,400 +/ 0,005 0,978 +/ 0,009 0,900 +/ 0,010 1407 UN10 0,126 +/ 0,003 0,067 +/ 0,001 0,384 +/ 0,004 1413 Ledane 1,564 +/ 0,010 2,744 +/ 0,016 3,034 +/ 0,020 1425 Trans caryophyllene 10,471 +/ 0,028 5,408 +/ 0,02 a 8,996 +/ 0,032 1431 U nknown 11 1,561 +/ 0,07 1,286 +/ 0,0 4 a 1,678 +/ 0,007 1434 Trans Farnesene 0,692 +/ 0,0 8 a ,b 0,382 +/ 0, 04 a 1,573 +/ 0,0 19 b 1443 UN12 0,400 +/ 0,007 0,278 +/ 0, 07 0,719 +/ 0,004 1448 Isocaryophyllene 0,548 +/ 0,008 a 0,927 +/ 0, 09 a 1,442 +/ 0,008 1456 Humulene 3,607 +/ 0,016 1,700 +/ 0,01 a 2,687 +/ 0,012 1467 UN13 0,072 +/ 0,002 0,123 +/ 0, 04 0,390 +/ 0 ,004 1481 UN14 0,354 +/ 0,006 0,289 +/ 0, 05 0,527 +/ 0,007 1485 Selinene 0,475 +/ 0,004 a 0,416 +/ 0,0 6 a 0,796 +/ 0,003 1489 Selinene 0,765 +/ 0,005 a 0,692 +/ 0,0 8 a 1,095 +/ 0,003 1502 Cadinene 0,373 +/ 0,004 0,100 +/ 0, 0 2 0,432 +/ 0,003 Values are in percentage +/ standard deviation. Values in the same raw with the same superscript are not significantly different.
88 Table 4 4. Ratios infected/healthy (i/h) and infected/zinc deficient (i/z) of the proposed HLB biomarker s from two different groves* Grove 1 Grove 2 i/h i/z i/h i/z L proline 6.5 +/ 1.35 3.84 +/ 1.01 4.67 +/ 0.47 5.43 +/ 1.65 Unknown 22 255.64 +/ 62.41 291.40 +/ 63.02 23.20 +/ 6.46 47.82 +/ 14.35 Elemene 0.52 +/ 0.19 0.74 +/ 0.21 0.56 +/ 0.43 0.68 +/ 0.38 Trans caryophylene 0.48 +/ 0.14 0.63 +/ 0.15 0.28 +/ 0.04 0.48 +/ 0.20 Unknown 11 0.28 +/ 0.03 0.65 +/ 0.35 0.58 +/ 0.20 0.64 +/ 0.11 Humulene 0.44 +/ 0.14 0.39 +/ 0.10 0.52 + / 0.34 0.59 +/ 0.27 *Grove 1 = grove in Plant City, Grove 2 = grove in Lake Alfred.
89 Figure 4 1. Healthy (A), HLB infected (B), and zinc deficient leaves (C)
90 Figure 4 wa ter chloroform 8:1:1, methanol alone, and chloroform MeOH Water Chloroform MeOH Chloroform Time (min) 10 15 20 25 30 10 7 Abundance
91 Figure 4 3. Effect of the amount of MSTFA added in the compounds showing the highest variation. Galactose oxime 6TMS D glucitol 6TMS Glucose 5TMS 1 trimethylsilyloxyphenyl) 1 trime D mannitol 6TMS MSTFA added (L) 0 50 100 150 3300 2800 2300 1800 1300 Peak area
92 Figure 4 4 Effect of the MSTFA reaction time in the co mpounds showing the highest variation. 20000 14000 8000 2000 0 0 50 100 150 200 1 trimethylsilyloxyphenyl) 1 trime Glycine, N,N bis(trimethylsilyl) L proline 2TMS L Alanine, N (trimethyl silyl) trime Butanoic acid, 2 [(trimethylsilyl)oxy] Reaction time (min) Peak area
93 Figure 4 5. Typical chromatograms of derivatized liquid extracts of healthy, HLB infected, and zinc deficient leaves. Circled compounds represent visible significant differences. Peak number s correspond to Table 4 1. Healthy HLB ZD Time (min) 10 15 20 25 30 10 7 Abundance
94 Figure 4 6. Principal components analysis of derivatized liquid extracts. Figure A: score plot of HLB infected (i), healthy (h), and zinc deficient (z) samples illustrated in PC1 and PC2. Fi g ure B: Loading plot of PC1 and PC2. The 10 compounds with the highest loadings on PC1 are marked A B
95 Figure 4 7. Compounds showing significant differences in derivatized liquid extracts of zinc deficient ( ), HLB infected ( ), and healthy ( ) leaves. Figure A: Significantly differen t compounds in HLB infected leaves only. Figure B: Significantly different compounds in HLB infected and zinc deficient leaves. Figure C: Significantly different compounds in zinc deficient leaves only. Insets show the mass spectra of each unknown compound
96 Figure 4 8 Typical chromatogram of SPME analyses of healthy, HLB infected, and zinc deficient leaves. Circled compounds represent visible significant differences. Peak numbers correspond to Table 4 2.
97 Figure 4 9. Principal comp onents analysis of headspace metabolites. Figure A: score plot of HLB infected (i), healthy (h), and zinc deficient (z) samples illustrated in PC1 and PC2. Figure B: Loading plot of PC1 and PC2. The 10 compounds with the hig hest loadings on PC2 are marked. A B
98 Figure 4 10. Significantly different headspace metabolites of zinc deficient ( ), HLB infected ( ), and healthy ( ) leaves. Figure A: Metabolites showing significant differences in HLB infected samples only. Figure B: Metabolites showing significant differences in both HLB infected and zinc deficient samples when compared to healthy ones. The inset shows the mass spectra of the unknown compound.
99 Figure 4 11. Significantly different metabolites of HLB infected ( ) when compared to zinc deficient ( ) and healthy ( ) leaves from grove 1 (Plant City, FL) and grove 2 (Lake Alfred, FL).
100 Figure 4 12. Behavior of potential biomarkers in mild or asymptomatic HLB infected ( ) and healthy ( ) leaves. Compounds marked with showed significant differences between the two types of samples.
101 CHAPTER 5 METABOLOMICS OF FODB ORNE PATHOGES Overview E scherichia c oli O157:H7 and Salmonella spp. have been related to several outbreaks in United States during the past years In particular, Salmonella spp. have been regarded as the leading cause of foodborne illnesses in the US (Jarquin and others 2009) an d one of the most predominant pathogens in Europe (Malorny and others 2009) Several foods such as peanut butter in 2008 2009, peppers in 2008, and poultry in 2007 have been the vehicle of numerous Salmonella outbreaks in the US as reported by the Center for Disease Control (CDC) In addition, several E. coli O157:H7 outbreaks such as those linked to romaine lettuce in 2010, ground beef and cooki e dough in 2009, and spinach in 2006 were reported by CDC Rapid methods for detection of these microorganisms are needed a s an aid to reduce the number of outbreaks Table 5 1 summarizes recent progress on the development of rapid methods for detection of E. coli and Salmonella Pathogen detection by polymerase chain reaction (PCR) is very specific but require s pre enrichment that may last up to 18 h (Malorny and others 2009) making the total analysis time, in many cases, longer than 24 h Other methods such as surface plasmon resonance (SPR) are very rapid (~1 h) but have higher limit of detection (LOD) (Table 5 1). M ost novel methods such as t hose based on immunosensors have yield ed best results when used in uncontaminated food samples or after enrichment in selective media (Tokarskyy and Marshall 2008) Furthermore, most methods are specific to one single pathogen, making it difficult to test severa l pathogens at the same time. Therefore, simultaneous detection of pathogens in real food sample s is desirable. Recent studies on multiplex detection of E. coli and Salmonella along with other pathogens have been carried out using immunoassays (Magliulo and
102 others 20 07) and PCR (Seidavi and others 2008; Wang 2008; Suo and others 2010) However, the required LOD of 1 CFU/25g of food along with the analysis time of 24 h or less were not met. Therefore methods that al low testing of food that mee t current regulations that require the absence of pathogenic E. coli and Salmonella per 25 g of food are still needed (Table 5 1). Metabolomics, the study of small metabolites present in a system, has been regarded as an alternat ive for biomarker identification M etabolomics, has been proposed for discovery of specific biomarkers of foodborne pathogens growing in culture media which in turn can be used to predict the presence of the pathogen (Cevallos Cevallos and others 2009b) Metabolomic strategies have been used to identify biomarkers formed during plant path ogen infections by capillary electrophoresis (CE) (Cevallos Cevallos and others 2009c) Foo dborne pathogens have also been analyzed by metabolomic techniques. Maddula and others (2009) used a multi capillary column coupled to ion mobility spectrometry to analyze volatiles produced by generic E. coli and confirmed with GC MS that ethanol, propanone (acetone), heptan 2 one, and nonan 2 one correlated with bacterial growth. Maharjan and Ferenci (2005) were able to differentiate E. coli strains from different origins by two dimensional high performance thin layer chromatography. Other studies on cellular metabolomics have been carried out on E. coli (Rabinowitz 2007) and recently, modified strains of E. coli were cla ssified from their wild type counterparts by using GC FID and GC MS finding that succinic acid, proline, and asparctic acid were the main metabolites responsible of the classification (Tian and others 2008) Metabolite based efforts for rapid detection of E. coli and Salmonella have been carried out using an electronic nose (Siripatrawan and others 2006) and neutral desorption sampling coupled to extractive electrospray ionization MS (Chen and others 2007) However, no specific biomarkers for each target pathogen were reported.
103 Even though these techniques are better suited when bacteria levels in food are above 10 5 CFU/g, they show the potential of metabolomics for rapid detection of foodborne pathogens. Therefore, there is still a need of metabolomics based methods capable of detecting pathogens at levels as low as 1 CFU/25 g of food in less than 24 h. The objective of this study was to determine the suitability of CE, HPLC MS, and GC MS based metabolomic technique s for rapid and simultaneous detection of E coli O157:H7, Salmonella Typhimurium, Salmonella Muen chen, and Salmonella Hartford in beef and chicken Materials and Methods Reagents and Bacterial Strains HPLC grade methanol putrescine L threonine, L alanin e, butanedioic acid, methoxyamine hydrochloride (20mg mL 1 ) in pyridine (MOX), and N methyl N tr imethylsilyl tri f luoroacetamide (MSTFA) were purchased from Fisher Scientific Inc. (Pittsburg, PA). The Difco tryptic soy broth (TSB) and nutrient broth (NB) as well as Sorbitol Mac Conkey were from Becton, Dickinson and Company (Sparks, MD) Chormagar EC C and Chromagar Salmonella Plus were from CHROMagar Microbiology (Paris, France). The bacteria cultures used in this experiment were from various sources: Escherichia coli O157:H7 associated with leafy greens (PVTS88) Salmonella Hartford associated with an orange juice outbreak ( HO778 ) Salmonella Typhimurium LT2 (ATCC 15277) Salmonella Muenchen associated from an orange juice outbreak ( IJH592 ) E. coli K12 ( LJH506 ) Pseudomonas aeruginosa, Staphylococcus from wound (ATCC 29213) Saccharomyces cerevisiae was from molasses distillery yeast ATCC4132 and ATCC 26785 and Aspergillus Oryzae from soy sauce ATCC 14895. ATCC refers to American Type Culture Collection (Manassas, VA).
104 Equipment and Software The GC model HP 5890 coupled to an HP 5971 series mass s pectrometer with ChemStation B.02.02 data acquisition software and the Wiley 138K mass spectral database were from Hewlett Packard (Palo Alto, CA). The chromatographic column used for both headspace and liquid extract analyses was a DB5 MS 60 m x 0.25 mm ( length x I.D.) from J & W Scientific (Folsom, CA). The water bath (model Isotemp 3016s) and sonicator (model FS20H) were from Fisher Scientific (Pittsburg, PA). The Class II Type A Biological safety cabinet model NU 425 600 was from Nuaire Inc. (Plymouth, MN). The HPLC system was composed of a Surveyor HPLC, autosampler, and PDA detector; the MS was a LCQ Advantage Ion Trap with ESI (electrospray ionization) as ion source, and the data was processed by using the Xcalibur 2.0 software. The whole HPLC MS soft ware system was from Thermo Scientific Inc. (Waltham, Ma). The CE system model P/ACE MDQ with photodiode array detector (PDA) the data acquisition and analysis software Karat 32 version 5.0 was from Beckman Coulter (Fullerton, CA). The capillary was bare fused silica from Polymicro Technologies (Phoenix, AZ) 50 m I.D. 56 cm total length (48 cm to the detector). Data was aligned to correct deviations in retention time by using an in house alignment program and compounds were tentatively identified prior to principal components analysis (PCA) and partial least square regression (PLS) as described in the compound identification section PCA and PLS were carried out to compare the overall metabolite profile of each bacteria by using MATLAB R2008a from The Mat hWorks (Natick, MA). PLS models were built by using the metabolite profile obtained after growth of microorganisms in TSB as the training data set. P ure cultures growth in TSB as well as inoculated raw chicken and ground beef samples were used as the test data set
105 Experimental Design All microorganisms were individually grown in TSB and NB at 37 C for 18 h Then, one hundred microliters of each microorganism were added to 10 mL of TSB to prepare bacteria cocktails. T hree cocktails containing all the ba cteria (cocktail A), all except E. coli O157:H7 (cocktail A O), or all except Salmonella spp. (cocktail A S) were prepared and grown in both TSB and NB as described before Samples were taken at 2 h intervals during 24 h and processed as in sample preparat ion section Ground beef and chicken samples obtained from two different supermarkets located in Winter Haven and Haines City, FL were surface inoculated with E coli O157:H7, Salmonella Hartford, Salmonella Typhimurium, and Salmonella Muenchen at levels of approximately 1 CFU/25 g of food Inoculation levels were achieved by serially diluting in phosphate buffer the microorganisms grown in TSB. Inoculated meats were allowed to dry for 20 min in the biological safety cabinet Twenty five gram samples were suspended in 225 mL of phosphate Butterfield water and hand shaken for 2 min to extract bacteria. One milliliter of the suspension was inoculated in 10 mL of TSB and incubated for 18 h at 37 C. HPLC MS Analysis Two milliliter aliquots of each sample taken after incubation were analyzed by HPLC MS. The HPLC MS system operated with a stationary phase C 18 column and a mobile phase consisting of 80% of an acetic acid solution (0.05% in water) and a 20% of acetonitrile (with 0.05% acetic acid also) during the first 12 minutes, then a gradient phase was applied during the following 47 min to reach a final concentration of 90% acetonitrile and 10% acetic acid solution. The final concentration was held for 11 min. The MS worked with an electrospray ionization source and was operate d in the 80 1000 m/z range.
106 CE Analysis Two mililiter aliquots of each sample were analyzed by CE. The CE analysis was done using a background electrolyte solution consisting of 76% 11.2mM sodium borate solution at pH 9.3 and 5% A CN. Separa tion was done at 20 k V and detection was done by scanning the total rang e of 190 to 600 nm. These conditions were selected because the y yielded no peak coelution. Sample Preparation for GC MS Analysis After incubation, samples were spiked with approximately 50 mg of internal standard (IS1) malic acid and with 20 L of (E,E,) 2 ,4 nonadienal as second internal standard (IS2). IS1 and IS2 were added as quality control of the derivatization and headspace extraction respectively as well as to assure adequate GC MS analysis and library matching. Endogenous IS1 and IS2 were not detec ted in either sample category under tested conditions, and did not interfere with any peaks in the chromatograms One milliliter of each spiked sample was mixed with 3 mL of methanol and metabolites were further extracted by sonication on ice for 10 min a nd stored at 20 C for 24 h. Quenching and metabolite extraction by cold methanol have been shown to yield the best results in E. coli studies (Prasad Maharjan and Ferenci 2003; Winder and others 2008) Extracts were derivatized prior to GC MS a nalysis as described in liquid extracts analysis section. The remaining of each sample (10 mL) was submitted to headspace analysis as described in the headspace analysis section Levels of E. coli O157:H7 and Salmonella spp were determined by serial diluti on in inoculation on Sorbitol Mac Conkey agar and Chromoagar ECC (for E. coli strains ) as well as Chromoagar Salmonella Plus (for Salmonella s trains ).
107 Headspace Analysis A solid phase micro extraction ( SPME ) fib er 50/30 m DVB/Carboxen TM / PDMS StableFlex TM for manual holder 57328 U from Supelco (Bellefonte, PA) was conditioned at 270 C for 1 h prior to its first use and daily at 240 C for 5 min Ten milli liters of the incubated samples were transferred to a 50 mL vial and equilibrated at 47 C for 30 min while stirring The p re conditioned SPME fiber was exposed to the headspace of the equil ibrated samples for 40 min at 47 C and then splitlessly injected into the GC MS. The injector temperature was 25 0 C, t h e oven was initially held at 55 C for 1 mi n, the temperature rate was 7 C min 1 and the final temperature of 300 C was held for 5 min. Ultrapure hydrogen was used as the carrier gas at 0.8 mL min 1 The MS was tuned to maximum sensitivity in electron impact mode, positive polarity, and the total ion current was recorded for a mass range of 25 650 amu The GC MS interface was set to 318 C Liquid Extract Analyses For liquid extracts, 540 L were transferred into a 2 mL GC vial and dried under a nitrog en flow. Thirty microliters of MOX w ere added to the dried extract and allowed to react for 17 h at room temperature as recommended in previous reports (Gullberg and others 2004) After methoximation with MOX silylation reactions were induce d by adding 80 L of MSTFA for 70 min at room temperature. Other amounts of MSTFA and reaction times yielded lower number of detected peaks and poorer reproducibility Volumes of 0.3 L of derivatized sample were splitlessly injected into t he GC MS. The in jector was at 25 0 C, the initial oven temperature was 70 C held for 1 min, the temperature rate was 10 C min 1 and the final temperature was 315 C held for 10 min. After 8 min of solvent delay the total ion current of mass fragments in
108 the range of 50 650 amu was recorded. Other MS conditions were identical to that used for headspace analysis Compound Identification Visual examination of m ass spectra obtained at the beginning, middle, and end width of each peak revealed n o coelution in any of the chromatogram peaks. Compound identification was done by library matching of mass spectra using the Wiley library and our internal databases. Compound identity was obtained and reported in Tables 5 3 and 5 4 when the matching value of the mass spectra compa rison was 70 or higher and retention time matched those of the pure compound run under identical conditions. Results and Discussion Detection and Culture Media Analysis Individual and cocktail samples were run by HPLC MS and CE PDA to determine potential biomarkers. Only one peak with potential difference was detected by using any of these two technologies by comparing any set of pathogens. Figure 5 1 A and 5 1 B show the results for E. coli O157:H7 and cocktail A O However, GC MS yielded the highest numb er of detected peaks and greatest significant differences (Figure 5 1C ). These results were expected, because GC MS is a better alternative for detecting compounds with low polar ity and molecular weight (Fancy and Rumpel 2008) which are the ones involved in bacterial metabolism in this study Therefore GC MS was used for the rest of the study. Metabolites were measured and compared during and after growth in both TSB and NB by GC MS A higher number of metabolites were detected in TSB ( Table 5 2 ) and all the compounds detected in NB were also present in TSB but in higher concentrations This may be
109 due to a greater nutrient availability in TSB. Therefore, TSB was selected for the res t of the study GC MS Analyses of Derivatized Samples Analysis of derivatized samples yielded 62 compounds. Table 5 3 shows the metabolites with potential ID detected in each type of sample Metabolites not detected (ND) in cocktails A, A S, and A O as w ell as in the pure culture of Salmonella Typhimurium and E. coli O157:H7 a nd reported in Table 5 3 were detected in control samples (TSB with no inoculation). All Salmonellae produced metabolite profile with no significant differences ; hence only T was rep orted in Table 5 3 Among all metabolites detected, only putrescine was identified as a potential biomarker for Salmonella spp. because of its presence in all the samples containing any of the Salmonella strains tested and its absence in any cocktail conta ining non Salmonella microorganisms. However, putrescine is known to be produce by other bacteria such as Citrobacter freundii Enterobacter spp. strains, Serratia grimesii Proteus alcalifaciens Morganella morganii and Proteus mirabilis (Durlu Ozkaya and others 2001) among other s that were not included in this study. Therefore, putrescine and all of the detected compounds were not regarded as biomarkers. Multivariate techniques such as PCA were tested to compar e overall metabolite profile of each pathogen and cocktail. Data w ere aligned to the total area prior to PCA. Figure 5 2 A shows that PCA of derivatized samples was not able to fully classify samples, suggesting similarities between the metabolite profiles of sample groups. Principal components (PC) 1 and 2 accounted for only 35% of the variation A weak discrimination of control staph, Saccharomyces cereviseae and cocktails A S and AO can be seen in PC1, suggesting small differences in the compounds with the highest PC1 loading values Out of the nine identified compounds with the
110 highest absolute loading values in PC1 and 2 (Figure 5 2 B), only cadaverine, glucose, and the amino acids glycine, histidine, and tyrosine showed significant differences among sa mple groups. Figure 5 3 shows the levels of these differences. Further research is needed to determine the ID of the unidentified compounds with the highest loading values. Cadaverine was produced by all microorganisms tested but in much lower amounts in s taph and Saccharomyces cereviseae Cadaverine formation by Saccharomyces cereviseae has been linked to ornithine decarboxylase activity (Walters and Cowley 1996) Theref ore, the lower levels of cadaverine in these microorganisms may be due to enzyme activity lower than in other micros Cadaverine formation has often been related to the presence of members of the Enterobacteriaceae family (Halasz and others 1994) such as E. coli and so me Salmonella and mostly due to decarboxylation of lysine by lysine decarboxylase. D extrose was significantly higher in C due to its consumption by all the microorganisms tested. Changes in the amino acids glycine, histidine, and tyrosine may be due to the different metabolic pathways of each microorganism tested. Further research is needed to determine the reasons of these differences. G lycine was significantly higher in E. coli K 12 E. coli O 157:H7 and cocktails. Glycine synthesis by E. coli has been sug gested as a result of the metabolism of L s erine as precursor (Pizer 1965) This is in agreement with L serine levels reported in Table 5 3 because L serine was initially present in TSB and not detectable in any sample containing any E. coli or Salmonella supporting the o bservation of L serine as glycine precursor in E. coli and suggesting the same for S almonella metabolism as well. L H istidine was significantly higher in staph and Saccharomyces cereviseae but consumed by Aspergillus oryzae all E. coli and Salmonella sug gesting possible synthesis of this amino acid by staph and Saccharomyces cereviseae Pathways for biosynthesis of histidine has been previously discussed for staph (Burke and Pattee
111 1972) and Saccharomyces cereviseae (Fink 1966) re porting clusters of genes involved in the biosynthesis process L tyros ine was significantly lower in control samples suggesting biosynthesis or release of the amino acid by specific proteases possibly present in each of the microorganisms tested Further research is needed to test this hypothesis and to better understand the reason s of the differences in metabolite production of the microorganisms tested SPME A nalyses Headspace metabolomic analysis has been shown to provide additional information that m ay be lost during derivatization of extracts (Cevallos Cevallos and others 2010) Analysis based on headspace SPME yielded 39 compounds. None of the compounds detected by SPME were detected after derivatiz ation of liquid extracts. Therefore, SPME analysis provided an additional list of metabolites. Tentative identity was obtained for the compounds listed in Table 5 4 All Salmonell a strains used in this study produced similar metabolite profile; hence only Salmonella T yphimurium was reported in Table 5 4 None of the compounds detected were exclusively related to any single pathogen Therefore, n o potential biomarker was reported for E. coli O 157:H7 and all S almonellae As with liquid extracts statistical analysis was carried out after data align m e nt and normaliz ation to the total area. Analysis by PCA (Figure 5 4 A) achieved a full classification of the samples tested. Only E. coli O 157:H7 samples were not fully classified from S almonella samples, suggestin g similarities between these two types of pathogens. Figure 5 4B shows the loading plot as well as the 8 compounds with known identity with the highest absolute loading values in PC1 and 2. Only 5 compounds showed significant differences among samples anal yzed (Figure 5 5). The alcohol 1 octanol was significantly higher in samples containing Aspergillus oryzae Significant production of higher alcohols including 1 octanol by Aspergillus oryzae has been previously reported (Kaminski and others 1974)
112 Similarly, significantly higher concentrat ions of 1 propanol and 1 butanol were observed in samples containing Saccharomyces cereviseae Reports describing a significant production of 1 propanol (Carrau and others 2008) and 1 butanol (Valero and others 2002) by Saccharomyces cereviseae are common in the literature. Higher content of 2,5 dimethylpyrazine was found in E. coli K 12 samples Production of 2,5 dimethylpyrazine by pathogenic and non pathogenic E. coli (Yu and others 2000) has been previously reported. Additionally, the significantly higher production of 2 ethyl 1 hexanol was also related to E. coli K 12 samples. Further research is needed to elucidate the pathways required for the prod uction of the reported compounds and determine the reason of differences in production levels by each microorganism Prediction Model and Validation in Food Samples Although the change in concentration of no single compound may be exclusively attributed to S almonella or E. coli O157:H7 the use of the overall metabolite profile may increase specificity To test this hypothesis, PLS prediction models were built based on the overall profile of volatile metabolites in the headspace for each sample group (train ing set). The microorganisms volatile profile was chosen as the training set because of its better classification by PCA (Figure 5 4A). Two PLS models were built, one for prediction of E. coli O 157:H7 and another for S almonella Three test sets were select ed for testing the model by inoculating each pathogen at the level of ~1 CFU/mL of sample into approximately 10 independent samples of TSB (set 1), raw ground beef (set 2), and raw chicken (set 3). Data from training sets was aligned and normalized prior to PLS regression. Poor prediction models were obtained when test sets were not normalized (data not shown). Models for prediction of E. coli O 157:H7 and S almonella were tested in the same samples. Figures 5 6 and 5 7 show the results of the models built for E. coli O157:H7 and
113 S almonella respectively. The model for E. coli O157:H7 consisted on 13 PLS components which accounted for the 86% of the mean square error (MSE) of the response variables and 99% MSE of the predictor variables with a coefficient R 2 of 0.86. The model was able to predict 100% (Figure 5 6A) of the blind samples tested in TSB at levels of 1 CFU/mL after 15 18 h incubation. However, when testing the model in ground beef and chicken samples less than 8 0% was predicted ( with 10% of false n egatives ). This may be due to the high number of bacteria present in these raw samples that were not included in the training set. To correct for these differences, two samples of raw ground beef and two samples of chicken inoculated with E. coli O157:H7 w ere added to the training set to create additional PLS models that will work in these two matrices. The model for ground beef consisted in 11 components whereas the model for chicken contained 8. Each model accounted for more than 70% of the response MSE a nd 97% of the predictors MSE with R 2 greater than 0.8. Prediction of ~ 1CFU/mL in 15 18 h was 100% for both ground beef (Figure 5 6B) and chicken (Figure 5 6C) models. Similarly, PLS model for detection of S almonella in TSB was accurate in all the samp les tested (Figure 5 7A) but poor prediction was achieved in ground beef and chicken samples. To account for this matrix effect two additional models were created by adding two ground beef and two chicken samples to the training set respectively. The resul ting models consisted of 34 and 33 components for ground beef and chicken samples respectively, accounting for 98% of the response MSE and 99% of the predictors MSE with a R 2 value greater than 0.98 each. As reported for E. coli O157:H7 models, ground beef (Figure 5 7B) and chicken (Figure 5 7C) models were able to predict 100% of the samples inoculated with Salmonella at levels of ~ 1 CFU/mL after 15 18 h after adjustment of the models Other studies reporting low LOD values have been based in expensive techniques such as immunomagnetic separation and PCR (Notzon
114 and others 2006) or take ~ 24 h to complete (Techathuvanan and others 2010) The LOD of 1 CFU/mL achieved in this study in less than 18 h shows the potential of the use of metabolomics for rapid bacterial detection. This research shows evidence of the potential use of GC MS based metabo lomics for rapid detection of Salmonella and E. coli O157:H7 at levels as low as 1 CFU/25 g of food. The potential use of this technique in food processing environments will allow reducing holding time of product before shipment while waiting for microbial results carried out by traditional methods. Additionally, the use of independent models for E. coli O157:H7 and Salmonella in the same culture media, reduces labor and saves the cost of buying additional microbiology reagents.
115 Table 5 1. Recent studies o n rapid methods for detection of E. coli and Salmonella Target bacteria Method Sample Time LOD Ref. E. coli O157:H7 Gold nanoparticle Inductively coupled plasma MS PBS < 1 h 500 CFU/mL (Li and others 2010) E. coli O157:H7 Optical biosensing PBS 2 h 10 100 CFU/mL (Li and Su 2006) Salmonella spp. Magnetic bead electrochemical detection Milk 8 h 2.7 CFU/25g (Liebana and others 2009) E. coli Signal enhanced SPR Spinach <5 h 10 4 CFU/mL (Linman and others 2010) E. coli O157:H7 and Salmonella Typhimurium Multiplex chemilu minescent immunoassay PBS ~10 h 10 4 10 5 CFU/mL for each (Magliulo and others 2007) Salmonella Typhimurium SPR Milk 1 h 10 5 cells/mL (Mazumdar and others 2007) Salmo nella Typhimurium and Heidelberg Multiplex PCR Cheddar cheese and turkey ( raw and cooked ) ~45 h 7, 10 3 and < 1 ( raw and cooked turkey) CFU/mL (McCarthy and others 2009) Salmonella spp. Real time PCR Meat c arcass 26 h 1 10 CFU/100 cm 2 (McGuinne ss and others 2009) E. coli Nanoparticles surface enhanced Raman scattering Milk and apple juice <1 h 10 3 cells/mL (both) (Naja and others 20 10) Salmonella Typhimurium Label free electrochemical impedance spectroscopy Lennox broth 6 min 500 CFU/mL (Nandakumar and others 2008) Salmonella spp. Immunomagnetic separation/RT PCR Meat 13 h 10 CFU/25 g (Notzon and oth ers 2006) E. coli O157:H7 Immunomagnetic separation and real time PCR Fresh produce 7 h 0.04 0.4 CFU/g (Prentice and others 2006) Salmonella spp. Nested PCR Fresh produce and poultry ~8 h 4 CFU/25g (Saroj and others 2008)
116 Table 5 1. Continued Target bacteria Method Sample Time LOD Ref. E. coli O157:H7 Array biosensor Ground beef, turkey sausage, apple juice ~30 mi n 10 4 CFU/mL (Shriver Lake and others 2007) Salmonella Enteritidis Immunomagnetic separation and PCR Milk 16 h 1 10 CFU/mL (Taban and others 2009) Salmonella Typhimuriu m Loop mediated isothermal amplification Pork 24 h 1 0 2 CFU/25g (Techathuvanan and others 2010) Salmonella Typhimuriu m Reverse transcriptasa PCR Pork 24 h 10 1 CFU/25g (Techatruvanan and others 2010) E. coli K12 Nanoimmunosepar ation and surface enhanced Raman Water 30 min 4 5 CFU/mL (Temur and others 2010)
117 Table 5 2. Differences in peaks detected in samples run in NB and TSB Total area Number of peaks Nutrient broth 523453543 62 Tryptic soy broth 23456781 25
118 Table 5 3 Average peak areas +/ standard deviation of the metabolites identified in derivatized samples Ret. Time (min) Possible ID Cocktail A Salmonella Typhimurium E. coli O157:H7 8.46 Butanoic acid, trimethylsilyl ester 12 ND ND 801 +/ 33 9.40 Propanoic acid, 2 [(trimethylsilyl)ox 8565 +/ 1255 8205 +/ 1078 6663 +/ 1615 10.06 L Alanine, N (trimethylsilyl) trime 2247 +/ 584 1741 +/ 1032 2446 +/ 782 11.22 Butanoi c acid, 2 [(trimethylsilyl)ami 1917 +/ 273 1334 +/ 1140 577 +/ 475 11.88 L valine 3992 +/ 729 3350 +/ 1794 4170 +/ 985 12.80 Silanol, trimethyl phosphate (3:1) 36993 +/ 5986 29992 +/ 22757 35874 +/ 10077 13.11 L Isoleucine, N (trimethylsilyl ) tr 3064 +/ 560 2564 +/ 1410 3295 +/ 725 13.34 Glycine, N,N bis(trimethylsilyl) tr 8451 +/ 445 9481 +/ 1186 8806 +/ 2086 14.14 L Serine, N,O bis(trimethylsilyl) ND ND ND 14.58 L Threonine, N,O bis(trimethylsilyl) ND 150 +/ 300 1556 +/ 567 16.58 L Methionine, N (trimethylsilyl) tr 1527 +/ 363 1374 +/ 839 1627 +/ 398 16.67 L Proline, 5 oxo 1 (trimethylsilyl) 6784 +/ 209 7124 +/ 2188 7017 +/ 1848 16.99 Phenylalanine 1tms 511 +/ 472 1397 +/ 1346 435 +/ 240 17.83 Unknown 1986 +/ 449 1776 +/ 1433 2158 +/ 726 18.02 Phenylalanine 2tms 5639 +/ 996 5438 +/ 2375 5748 +/ 1520 18.50 L Asparagine, N,N2 bis(trimethylsilyl ND 148 +/ 297 ND 19.38 Trimethylsilyl of putrescine 3253 +/ 2163 2746 +/ 754 ND 20.68 Cadaverine tetratm s 14894 +/ 3184 14137 +/ 6682 14807 +/ 3903 20.84 Xylitol 5tms 1069 +/ 121 350 +/ 701 840 +/ 471 21.06 1H Indole 3 carboxaldehyde, 1 (trimet 209 +/ 362 500 +/ 650 188 +/ 293 21.39 Glucose oxime 6TMS ND ND ND 21.47 L Histidine, N,1 bis(trimeth ylsilyl) ND ND ND 21.71 L Tyrosine, N,O bis(trimethylsilyl) 1306 +/ 186 1338 +/ 244 1094 +/ 268 21.90 Glucose 5tms 162 +/ 281 134 +/ 269 82 +/ 200
119 Table 5 3 Continued Ret. Time (min) Possible ID Cocktail A Salmonella Typhimurium E. coli O157: H7 22.67 Butanoic acid, 4 [bis(trimethylsilyl) 230 +/ 398 264 +/ 304 83 +/ 203 24.74 Tryptophan 2tms 983 +/ 418 1287 +/ 703 1116 +/ 351 29.12 Ribitol 1,2,3,4,5 pentatms 682 +/ 303 4680 +/ 536 3652 +/ 944 30.13 .Alpha. d glucopyranoside, me thyl 2,3 ND 456 +/ 537 ND
120 Table 5 4 Average peak areas +/ standard deviation of the metabolites identified in hea d space SPME samples Ret. Time (min) Compound Id Cocktail A Salmonella Typhimurium E. coli O157:H7 3.35 Ethanol 73054 +/ 10364 23617 + / 20430 27900 +/ 19986 3.71 1 Propanol 24351 +/ 3455 7872 +/ 6810 9300 +/ 6662 3.93 2 Butanone 26360 +/ 8848 14477 +/ 6687 16203 +/ 5761 4.35 1 Butanol 4255 +/ 3162 ND ND 5.34 1 Butanol, 2 methyl 53889 +/ 4522 32229 +/ 10845 36975 +/ 8748 5.4 1 Butanol, 3 methyl 192492 +/ 35750 127879 +/ 9782 147583 +/ 22789 8.92 Pyrazine, 2,5 dimethyl 14118 +/ 8110 33338 +/ 10343 17619 +/ 2759 11.68 1 Hexanol, 2 ethyl 43998 +/ 20844 103627 +/ 18299 58698 +/ 3524 12.71 1 Octanol 80446 +/ 71 878 10125 +/ 2927 12215 +/ 3174 13.58 Nonanal 227 +/ 393 ND 807 +/ 746 15.99 2 Nonen 1 ol, (E) 26580 +/ 14228 6972 +/ 1217 6726 +/ 2378 22.51 Phenol, 2,6 bis(1,1 dimethyl 10032 +/ 5296 2436 +/ 1303 1791 +/ 306 24.01 Propanoic acid, 2 methyl 1 (1,1 1023 +/ 1771 ND 528 +/ 1182
121 Figure 5 1. Metabolite differences between E. coli O157:H7 and cocktail A O. Analysis done by HPLC MS (A), CE PDA (B), and GC MS (C). Circled compounds show potential differences Time (min) Time ( min) Abundance Absorbance units Abundance Time (min) B C A
122 Figure 5 2. Scores plot (A) and loadings plot (B) of the principal components analysis of liquid extracts of Escherichia coli O157:H7 (O) Salmonella Hartford (H), Salmonella Typhimurium (T), Salmonella Muenchen (M) E. coli K12 (K) Staphylococcus aureus (ST) S accharomyces cerevisiae ( SC) and Aspergillus Oryzae (AO) S ignificant differences are in Figure 5 5. 2 nd principal component (14%) 2 nd principal component (14%) 1 st principal component (21%) 1 st principal component (21%) A B
123 Figure 5 3. Metabolites responsible for PCA classification in derivatized samples. Codes are as in Figure 5 2. Glycine Cadaverine L Histidine Dextrose Peak area C A NP M H T A S ST O K A O SC AO S amples L Tyrosine
124 Figure 5 4. Scores plot (A) and loadings plot (B) of the p rincipal components analysis of headspace SPME samples Codes are as in Figure 5 2 Significant differences are in 5 5. 2 nd principal component (15%) 2 nd principal component (15%) 1 st principal component (24%) 1 st principal component (24%) A B
125 Figure 5 5. Metabolites responsible for PCA classification in headspace SPME samples Codes are as in Figure 5 2. Peak area A A S A O NP H M T K ST O C SC AO S amples
126 Figure 5 6. Prediction models for E. coli O157:H7 tested in pure culture (A), and adjusted models for raw ground beef (B) and raw chicken (C). Codes are as in Figure 5 2
127 Figure 5 7. Prediction models for Salmonella tested in pure culture (A), raw ground beef (B) and raw chicken (C) Codes are as in figure 5 2
128 CHAPTER 6 SUMMARY AND FUTURE W ORK Metabolomics ha s shown to be an important tool for the progress of the main food science areas such as compliance of regulations, processing, quality, safety, and microbiology. This research suggest s that the potential of metabolomics can also be expanded to the rapid diagnosis of plant diseases and foodborne pathogens. Two forms of rapid detection can be achieved by metabolomics: by using biomarkers or multivariate analysis. This dissertation can be divided into three major areas: Metabolomics of Huanglongbing ( HLB ) metabolomics of foodborne pathogens, and general metabolomics in food science. Metabolomics of HLB Our research on metabolomics for rapid detection of HLB showed s everal metabolit es with a strong correlation between their peak area and the infection scale. These metabolites were detected by HPLC MS but no individual identification was reported due to lack of databases for this technique. Further research will involve a selecti ve extracts of compounds in the area of interest number two (flavonoids derivativ es) for better identification, o ther techniques such as capillary electrophoresis ( CE ) were also tested in this study Capillary electrophoresis coupled with photo diode array detection ( PDA ) was able to detect significant differences in metabolite profile between healthy and HLB infected citrus leaves. Results show CE PDA potential for untargeted metabolomic analysis for plant diseases as well as its use for monitoring HLB inf ection based on the concentration of hesperidin, naringenin, and quercetin in leaf extracts. A limitation of PDA is the difficulty in identifying compounds using their UV Vis spectrum. Identification of unknown compo unds from this research will require fur ther use of alternative methods such as mass spectrometry (MS) Additionally, further research will involve application
129 of new detection methods such as cyclic voltammetry to aid identification by UV Vis spectra. Testing the specificity of potential bioma rkers reported by CE needs to follow. Metabolomics by GC MS was also performed in zinc deficient trees and compared to HLB by GC MS. Metabolite profiling by combined headspace and liquid extracts GC MS analysis increased the number of candidate biomarker s for HLB detection. Compounds identified via headspace analysis were not detected in the derivatized liquid extracts, strengthening our hypothesis that headspace volatiles were lost during sample drying or they were present in a very low amount in the liq uid phase needing to be concentrated in the headspace through solid phase micro extraction ( SPME ) The clear principal components analysis ( PCA ) classification of the derivatized liquid extracts showed greater metabolite differences among sample groups whe n comparing with the headspace analysis. Even though PCA was not able to discriminate among SPME samples, ANOVA reported significant differences in the levels of sesquiterpenes of each treatment, sowing the importance of the use of ANOVA in metabolic diffe rentiation studies. The significant changes in content of L elemene, trans humulene in HLB infected samples were comparable to those reported with drought stress in citrus. Therefore, water transport in HLB infected plants may be compromised. Future research is needed to determine physiological similarities between HLB and drought stress affected plants. The low amounts of the sesquiterpenes known to have antimicrobial properties in HLB infected samples propose a post infection inhibitory mechanism HLB. Further research comparing the sesquiterpene profile of tolerant and susceptible orange varieties after HLB infection is recommended. Additional research in greenhouse plants is needed to determine if pre symptomatic and pre PCR positive changes in the metabolite profile
130 occur in citrus. Untargeted GC MS metabolite analyses were also able to find possible biomar kers for zinc deficiency, showing the potential of liquid and headspace based GC MS analysis for biomarker identification in both citrus disease and stress conditions. Proposed biomarkers have the potential of being targeted by traditional chemical assays or biosensors, reducing the analysis costs and improving portability as well as rapidity. Even though an initial validation involving two groves was done in this research, additional studies involving different groves, seasons, levels of infection, and str esses are needed More work is also needed to establish the sugars and proteins profile of diseased trees and to compare these profiles with the ones obtained under other stress conditions. needed becau se HLB is caused by phloem limited bacteria. Therefore, possible biomarkers may be present in the phloem in concentrated amounts. Metabolomics of Food Pathogens This study shows the potential of the use of metabolomics along with multivariate data analysis such as PLS for rapid and simultaneous detection of E. coli O 157:H7 and S almonella in food samples. This study showed that metabolomic research of microorganisms can be done by GC MS as opposed to CE and HPLC MS because of the nature of the bacteria metab olites which are usually small in size. GC MS based metabolomics was able to detect E. coli O157:H7 and S almonella at levels of ~1CFU/25g of food. The rapid nature of this method (less than 18 h) makes it possible to predict presence of E. coli O157:H7 and S almonella in food samples while waiting for confirmation by conventional methods. Due to the differences in microbial flora of each sa mple type, models built by partial least squares ( PLS ) we re matrix speci fic. Therefore, models were calibrated for each type of food. Even thought food samples analyzed in this study were from different supermarkets, further research is needed to validate models in samples
131 coming from different sources and seasons. Further research is needed to find potential biomarkers pr oduced during pathogen growth in selective culture media. Moreover, more work is needed to establish metabolic pathways for production of metabolites in bacteria. Future Directions of Metabolomics in Food Science Metabolomics has been mostly based on CE, H PLC, and GC. However, t he development of rapid technologies such as direct infusion MS ( DIMS ) ion mobility spectrometry ( IMS ) and extractive electrospray ionization ( EESI ) has helped the growth of metabolo mics in food science. F urther improvement on thes e techniques is necessary to overcome sensitivity and compound identification issues. Even though metabolomics analyses in food have been much diversified, most studies can be considered as discriminative (Table 1 1 ) with very few compounds identified. The refore, the development of a food metabolome database, as suggested by Wishtar (2008b) is needed in order to facilitate compound identi fication and the development of informative metabolomics. In addition, most reports have focused on fruit and vegetables (Table 1 1 ) leaving the meat, seafood, and related areas still underexplored. Because of some metabolic similarities, identification of many compounds in red meat can be carried out by using available human metabolome databases Wishtar (2008b)
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148 BIOGRAPHI CAL SKETCH Juan Manuel Cevallos was born and raised in Manta, Ecuador. He is the sixth of seven children born to Eddie Cevallos and Esperanza Cevallos. He attended Julio Pierregrosse elementary and high s chool and graduated with honors being the best student graduating in 1999. He was admitted to do an undergrad program in food engineering and received a full tuition waiver at the Escuela Superior Politecnica del Litoral in Guayaquil, Ecuador. He graduated with honors being the best student graduating with a food engineering degree in 2004. In early 2004 he started working in the quality assurance department for one of the biggest tuna processing companies in Ecuador: Sociedad Ecuatoriana de Alimentos y F rigorificos Manta C.A. (SEAFMAN C.A.) In 2005 he was awarded a Fulbright fellowship to attend University of F lorida to pursue a m he n 2007 and started his Ph.D. program in food science at the University of Florida immediately. He obtained his degree in summer 2010 and will continue his research as a postdoc toral associate at the University of