Compositional Analysis of New Microbial Feed Supplements Produced from Meat Processing Co-Products

COMPOSITIONAL ANALYSIS OF NEW MICROBIAL FEED SUPPLEMENTS PRODUCED FROM MEAT PROCESSING CO-PRODUCTS

TIAN TIAN CHEN MASTER OF ENGINEERING

Submitted in fulfilment of the requirements for the degree of Master of Philosophy (Research)

School of Biology and Environmental Science Faculty of Science and Engineering Queensland University of Technology 2020 Keywords

Microbial feed supplement, meat processing wastes, nutrient profiling, analytical methods, shotgun lipidomics.

Abstract

The conversion of wastes to enhanced microbe-derived feed supplements has great potential for reducing costs in the livestock industry, decreasing greenhouse gas emissions, and generating additional income for meat processors. The choice of a microbe with enhanced nutritional characteristics means it can be used as a feed supplement for increasing livestock productivity. The identification and selection of appropriate microbes requires analytical methods that are able to provide quantitative nutrient profiles and allow rapid nutrient screening. Moreover, accurate nutritional composition analysis is a valuable source of information that can be used to ensure the new microbe-derived supplement products are effective and optimised for animal production. Although the quantification of bulk microbial protein, fat and carbohydrate components has been studied in detail, methods for the streamlined and comprehensive quantification of specific nutritionally beneficial species, such as particular amino acids, fatty acids, and lipid species, are still needed.

A rapid, sensitive and quantitative nutritional analytical method has been developed and evaluated. The method employs Gas Chromatography Mass Spectrometry and Liquid Chromatography Mass Spectrometry coupled with Shotgun Mass Spectrometry systems. The protocols achieve the simultaneous identification and detection of nutrient profiles for amino acids, fatty acids and lipid species for a selection of potential feed supplement microbial strains. The method development and evaluation involved establishing protocols, determining model compound profiles, determining limits of nutrient detection and quantification, and generation of standard operating procedures that incorporate optimised metabolite extraction and mass spectrometry detection parameters. Method validation was carried out to evaluate system suitability, repeatability, reproducibility, specificity, linearity, accuracy, method sensitivity, and robustness. The validity and limitations of the techniques used in this study for nutritional composition detection are included in this thesis.

This study demonstrates the use of Hierarchical Clustering Analysis and Principal Component Analysis to screen nine different strains grown using two different cultivation mediums. Results of amino acid and lipidomics analysis reveal the similarity of target nutrient profiles between the tested strains. A correlation between nutrient profiles and growth conditions was developed to assist in strain characterisation, enhancing the fundamental understanding of the relationship between chemical composition, biological functions, and nutritional benefits of different microorganisms. Cluster analysis based on mass spectra of polar and non-polar extracts from eighteen different sample sets was used for correlation analysis, resulting in distinct cluster separation between filamentous fungi and yeast. Strains that were cultivated in the same growth medium were clustered together, which indicates that the growth medium may account for the differentiation in nutritional profiles. Similar weight percentages of target nutrients were found for strains that grouped in the same cluster. A rendering stickwater medium from a red meat processing facility with a lipid content of 9.3% induced metabolic changes in microorganisms, including enhanced production of sphingolipids and sterols. According to the nutrient profile of tested strains, yeast such as Saccharomyces cerevisiae and Rhodotorula dairenensis produced a relatively higher abundance of amino acids and lipids than filamentous fungi when grown on stickwater medium.

Some nutrients are only produced and required at trace levels, so highly sensitive analytical methods are required with very low limits of detection and quantification. This study showed that the optimised analytical method allowed identification of all target analytes within the detection range, and achieved quantification of metabolites covering 20 amino acids, 37 fatty acids, and 13 major lipid classes, including at trace levels. Amino acid analytical standards, 37 food industry fatty acid methyl ester standards, and phospholipid standards, together with the reference strain Saccharomyces cerevisiae, were used for method optimisation and validation. The proposed method provided remarkable sensitivity and linearity, and precision with coefficient of variation (CV) < 20% for all analytes. Limits of detection (LOD) and limits of quantification (LOQ) for amino acids were obtained in the ranges of 0.002-2.794 ng/mL and 0.005-8.468 ng/mL respectively, while LOD and LOQ for iii fatty acids were obtained in the range of 1.70-17.45 µg/mL and 5.16-52.88 µg/mL respectively. The detection limit of lipidomics quantification was in the low nano molar range. This method can now be used for detailed, sensitive and high-throughput nutritional analysis of feed supplement microbes.

Table of Contents

Keywords ...... i Abstract ...... ii List of Figures ...... viii List of Tables ...... xi List of Abbreviations ...... xii Statement of Original Authorship ...... xiii Acknowledgements ...... xiv Chapter 1: Introduction ...... 1 1.1 Background ...... 1 1.2 Context ...... 2 1.3 Purpose ...... 3 1.4 Significance, Scope and Definitions ...... 4 1.5 Thesis Outline ...... 5 Bibliography ...... 7 Chapter 2: Literature Review ...... 8 2.1 Historical Background ...... 8 2.2 General Waste Composition from Red Meat Industries ...... 9 2.3 Application of Red Meat Production Co-products to Cultivated Livestock Feed Supplements ...... 11 2.4 Essential and Important Nutrients Used in Feed Formulation...... 14 2.5 Methods for Nutrient Analysis ...... 20 2.6 Summary and Implications ...... 27 Bibliography ...... 29 Chapter 3: Theoretical Basis for New Analytical Method Design ...... 34 3.1 Methodology and Research Design ...... 34 3.1.1 Methodology Overview ...... 34 3.1.2 Research Design ...... 37 3.2 Samples and Extraction Reagent Selection ...... 42 3.2.1 Strains and Culture Conditions for Method Evaluation ...... 42 3.2.2 Strains and Culture Conditions for Nutrient Profile Screening and Characterisation ...... 43 3.2.3 Strains and Culture Conditions for Method Development and Validation ...... 43 v

3.2.4 Solvent and Reagent Selection ...... 44 3.3 Instruments ...... 44 3.3.1 Polar Metabolite Analysis ...... 44 3.3.2 Non-polar Metabolite Analysis ...... 46 3.4 Resources and Funding ...... 54 3.5 Research Procedure ...... 54 3.6 Participants ...... 55 3.7 Data Analysis Strategy ...... 56 3.8 Limitations ...... 59 3.9 Summary ...... 60 Bibliography ...... 61 Chapter 4: Evaluation and Use of an Analytical Method for Rapid Nutrient Profiling of Yeast and Filamentous Fungi ...... 63 4.1 Introduction ...... 63 4.2 Materials and Methods ...... 64 4.3 Results and Discussion ...... 70 4.4 Conclusion ...... 107 Bibliography ...... 108 Chapter 5: Development and Evaluation of a Rapid Analytical Method for the Simultaneous Determination of Polar and Non-Polar Metabolites from Yeasts .... 110 5.1 Introduction ...... 110 5.2 Materials and Methods ...... 111 5.3 Results and Discussion ...... 119 5.4 Conclusion ...... 145 Bibliography ...... 146 Chapter 6: Conclusions ...... 148 6.1 Main Conclusions ...... 148 6.2 Limitations of the Study ...... 149 6.3 Future Research Work ...... 150 Appendices ...... 151 Appendix A: Summary of Essential Nutrients for Animal Feeding ...... 151 Appendix B: Standard Operating Procedure for Polar and Non-polar Metabolite Extraction and Detection ...... 155 Appendix C: Internal Standards Preparation ...... 181 Appendix D: Standard Mixture Preparation ...... 182

Appendix E: Preliminary analytes profiling (Supporting Materials) ...... 183 Appendix F: Required Statements ...... 190 Coursework ...... 190 Research Ethics ...... 190 Intellectual Property Statements ...... 190 Data Storage ...... 190 Health and Safety Statements ...... 191 iThenticate Report ...... 191

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List of Figures

Figure 2. 1: Process flow diagram of the red meat processing industry...... 11

Figure 2. 2. Recommended percentage of fat in meat for optimal health [34] ...... 17 Figure 2. 3. The structure of phospholipids and the emulsification process of lipid droplets in the small intestine with phospholipids or lysophospholipids...... 19 Figure 2. 4 Procedure sequence of proximate analysis...... 21

Figure 3. 1. Overview of the methodology flowchart...... 36 Figure 3. 2. Shimadzu triple quadrupole Liquid Chromatograph Mass Spectrometer 8050 technology...... 45

Figure 3. 3. Shimadzu Gas Chromatograph Mass Spectrometer 8040 technology. .... 47 Figure 3. 4. The mass spectrum of methyl docosanoate (C22:0) and the formation of McLafferty rearrangement ion...... 48 Figure 3. 5. Gerstel Maestro auto-sampler. This equipment enables auto sample derivatisation and injection for batch analysis...... 49

Figure 3. 6. SCIEX Q-trap 6500 technology...... 50

Figure 3. 7. High-resolution MS technology Orbitrap Elite...... 52

Figure 3. 8. Specific structural characterisation of PC 16:0/18:1 (n-9)...... 53 Figure 4. 1. Overview of the proposed analytical method for the nutrient profiling of microorganism biomass……………………………………………………………………………………….. 66 Figure 4. 2. Overall LCMS chromatograms showing analysis of free amino acid from PBQC polar extracts of R.glutinis and R.toruloides...... 74

Figure 4. 3. Total ion transition graphs of 20 amino acids...... 77 Figure 4. 4. Superposition GC chromatograms for standards and derivatised non- polar extracts of R.glutinis and R.toruloides...... 80 Figure 4. 5. Specific detection of phosphatidylinositol (PI) in lipid extract of R.glutinis and R.toruloides by negative ion scan...... 85 Figure 4. 6. Specific detection of phospholipid classes in an unprocessed total lipid extract of oleaginous yeast R.glutinis and R.toruloides by positive ion ESI/MS...... 87

Figure 4. 7. Normalised intensity abundance of individual lipid class to 100%...... 88 Figure 4. 8. OzID neutral loss chromatogram of PC 34:1 and PC 36:3 from R.glutinis and R.toruloides lipid extracts (zoom in 20x to display)...... 90

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Figure 4. 9. CID/OzID chromatogram of PC 34:1 and PC 36:3 from R.glutinis and R.toruloides lipid extracts...... 91 Figure 4. 10. Repeatability/reproducibility (CV %) of peak area and retention time of the amino acid analysis using the PBQC sample of all tested strains...... 93 Figure 4. 11. The superposition of mass spectra showing the analysis of the most abundant lipid classes obtained from PBQC sample of all tested strains in positive ion scan...... 95 Figure 4. 12. AA profile and reproducibility of 9 strains based on two independent technical replicates with three measurements (n=1)...... 98 Figure 4. 13. Exploratory analysis of normalised intensity data collected for polar extracts of nine strains based on two independent bio-replicates with three measurements...... 99 Figure 4. 14. Lipid profile and reproducibility of PC, PE, PS, PG, Cer and SM of nine strains based on two independent technical replicates with three measurements (n=1) in positive ion mode...... 103 Figure 4. 15. Principal component analysis (PCA) based on MS data of lipid extracts of nine strains based on two independent bio-replicates with three measurements (n=3) in positive ion mode...... 105 Figure 5. 1. Methodology flowchart. The development and validation cycle of the proposed analytical method for nutrient profiling……………………………………………….. 113

Figure 5. 2. Precipitated cell proteins from extraction process...... 121 Figure 5. 3. Metabolite extraction flowchart for: (a) AA profiling and (b) lipid profiling...... 123 Figure 5. 4. Specific detection of phosphatidic acid (PA) standard PA 17:0/17:0 and phosphatidylglycerol (PG) standard PG 17:0/17:0...... 125

Figure 5. 5. Specific detection of PI using lipid extract of S.cerevisiae...... 127 Figure 5. 6. Schematic representation of derivatisation and fragment transition of ergosterol and cholesterol...... 129

Figure 5. 7. Specific detection of cholesterol and ergosterol...... 131 Figure 5. 8. Instrument repeatability results based on triplicate injection of system suitability sample...... 133 Figure 5. 9. Instrument specificity and selectivity results based on triplicate measurement...... 135 Figure 5. 10. The total amino acid profile and intermediate test of benchmark strain S.cerevisiae...... 141

Figure 5. 11. The fatty acid profile, target lipid class composition, and intermediate test of benchmark strain S.cerevisiae...... 143 Figure 5. 12. Composition overview of target nutrients of S.cerevisiae...... 144

List of Tables

Table 2. 1. Composition of amino acid of BSF feeds and other protein feeds (Unit % w/w), data presented as mean ± standard deviation [15-17] ...... 12 Table 2. 2. Composition of derived SCP products (Unit %w/w) from the representative type of species [18] ...... 13

Table 2. 3. Essential and important nutrients required by animals [15, 19, 21]...... 14 Table 2. 4. Comparison of the commonly used acid hydrolysis agents for various hydrolysis techniques [53, 54] ...... 23 Table 3. 1. A summary of the sample size of nine microorganisms for nutrient profiling……………………………………………………………………………………………………………….. 43

Table 3. 2. Key Tasks ...... 55 Table 4. 1. MS identification parameters for free amino acid detection………………..72

Table 4. 2. MS identification parameters for fatty acid detection...... 79 Table 4. 3. MS identification parameters for lipid detection and quantification using negative ion mode...... 82 Table 4. 4. MS identification parameters for lipid detection and quantification using positive ion mode...... 83 Table 4. 5. Lipid profiling of most abundant lipid species in the ESI mass spectra of non-polar extract of oleaginous yeast R.glutinis and R.toruloides...... 86 Table 4. 6. Lipid structure of selected lipid species from PBQC lipid extracts of R.glutinis and R.toruloides...... 92 Table 5. 1. Statistical analysis of biomass weight. Weight measurement of R.glutinis (RP2) and R.toruloides (RP15) biomass………………………………………………………………… 120

Table 5. 2. Acid vs alkaline hydrolysis protein test using the Bio-Rad protein Assay 122

Table 5. 3. Fragmentation efficiency of ergosterol and cholesterol...... 130 Table 5. 4. Calibration curve parameters and sensitivity of the optimised analytical method for amino acid detection...... 137 Table 5. 5. Calibration parameters and sensitivity of the optimised analytical method for fatty acid detection...... 138 Table 5. 6. The signal intensity and m/Z value of 1.0 µM phospholipid standard mixture in positive and negative ion mode...... 139 Table 5. 7. The fatty acid profile of benchmark strain S.cerevisiae...... 141

List of Abbreviations

AMPC Australian Meat Processor Corporation GCMS Gas Chromatography-Mass Spectrometry LCMS Liquid Chromatography-Mass Spectrometry HPLC High Performance Liquid Chromatography FAME Fatty Acid Methyl Easter BOD Biological Oxygen Demand TOC Total Organic Carbon SCP Single Cell Protein AA Amino Acid SFA Saturated Fatty Acid PUFA Polyunsaturated Fatty Acid PC Phosphatidylcholine PE Phosphatidylethanolamine PS Phosphatidylserine PG Phosphatidylglycerol PA Phosphatic Acid PI Phosphatidylinositol CL Cardiolipin Cer Ceramide SM Sphingomyelin IPC Inositolphosphoceramide MIPC Mannosyl-inositolphosphoceramide

M(IP)2C Mannosyl-diinositolphosphoceramide TAG Triacylglycerol

xii Statement of Original Authorship

The work contained in this thesis has not been previously submitted to meet requirements for an award at this or any other higher education institution. To the best of my knowledge and belief, the thesis contains no material previously published or written by another person except where due reference is made.

Signature: QUT Verified Signature

Date: ______

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Acknowledgements

Upon completion of this research project I would like to take the opportunity to express my sincere appreciation to all those who have taken interest in this research.

First and foremost, I would like to express my gratitude and appreciation to my principle supervisor Professor Robert Speight, for introducing me to the field of research. Thank you for your guidance, your extensive professional knowledge and experience in industrial biotechnology research, and your advice over the year, which has made the submission of this research paper possible.

I would like to show my appreciation for my associate supervisor Dr. Pawel Sadowski and Professor Stephen Blanksby, who helped me through their expertise and experience in regard to the methodologies for metabolite extraction and analysis. Dr. Sadowski supported this research with his supervisory experience in areas of proteomics and the utilisation of Liquid Chromatography Mass Spectrometry (LCMS) and Gas Chromatography Mass Spectrometry (GCMS) instrumentation. Prof. Blanksby supported this research with his exceptional expertise in the development of advanced analytical technologies for lipidomics research.

I would like to acknowledge the technical and personal support of Rajesh Gupta and Dr. Berwyck Poad. I wish to express my deepest gratitude for their assistance and support in mastering the techniques involved in lipidomics analysis.

I would like to thanks Australian Meat Processing Corporation (AMPC) for offering me the scholarship that making my educational pursuits possible. I also wish to thank the involvement and support from other members of the AMPC research team, namely Remya Purushothaman Nair, Ignacio Javier Gonzalez Aravena and Stephanie Angela. And thanks to Dr. Leigh Gebbie, Reuben Young, Joel Herring, and Lan Chen for their interest in this research, as well as offering their expertise and advice.

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Thanks to Dr Christina Houen of Perfect Words Editing for editing this thesis according to the guidelines of the university and of the Institute of Professional Editors (IPEd).

Finally, I would like to thank my family and friends for the love, support and encouragement that they have given me.

Chapter 1: Introduction

This chapter outlines the background of waste treatment strategies in the red meat processing industry (Section 1.1) and the problem statement for the research (Section 1.2). The aim, research questions, and study objectives are then outlined in Section 1.3. Section 1.4 describes the significance and scope of this research and provides definitions of terms used. Finally, Section 1.5 includes an outline of the remaining chapters of the study.

1.1 Background

The red meat processing industry is a key contributor to the Australia economy. A report developed by Meat & Livestock Australia (MLA) revealed that 98.3 million head of cattle were produced, generating $60.6 billion gross value in 2017-2018, which achieved an approximate 1.9-fold growth in meat production compared with previous years [1, 2].

Increases in livestock farming and meat production leads to rapid growth in industrial waste production. Treatment of wastes from the red meat processing industry is becoming increasingly important, as the meat processing industry generates large amounts of waste daily and aims to be waste neutral within the Australian red meat supply chain. The main wastes from meat processing operations contain considerable high Biological Oxygen Demand (BOD) and Total Organic Carbon (TOC) values, which leads to expensive physical or chemical treatment, as high capacity facilities or energy-intensive processes are necessary in order to remove those substances [3]. Therefore, reusing or recycling waste materials from the meat processing industry will not only minimise solid and liquid waste volumes and costs, but also reduce negative impacts on the environment.

In recent years, a few environmental innovation programs have emerged. The Australian Meat Processor Corporation (AMPC) is one of the Australia’s rural research

and development corporations, and supports a range of red meat industry improvement programs, including for sustainability and efficiency. One of the major objectives of the AMPC is to optimise the viability and efficiency of the red meat processing sector by supporting the development of environmental innovation, as the economy and technologies today are largely focused on sustainable development [4].

The conversion of wastes into microbial supplements that are enhanced livestock feed protein is a potential value-added product that can be generated from waste streams, which will improve red meat processing by generating additional income for processors [5, 6]. The production of microbial feed supplements will also provide a significant opportunity for reducing the cost of waste disposal, and will generate products for a variety of livestock that help improve animal health and feed conversion efficiencies.

1.2 Context

The nutritional value of a microorganism is affected by different factors, including the specific strains, growth medium, cultivation methods, and stage of harvesting. It is essential to establish analytical methods that are able to detect and quantify the composition of the new microbial feed supplements to ensure they are effective and optimised in terms of high value nutrients for livestock production. However, very limited nutrient detection methods have been developed, especially in terms of evaluating a variety of specific nutrient contents.

The most commonly used industrial analyses for nutritional content are for quality control; for example, the proximate analysis for the determination of the major components of feed, which partitioned nutrients into water, ash, crude protein, crude fibre, ether extract, and Nitrogen-Free Extract (NFE) [5, 7]. Consequently, it may be difficult to determine the important nutritional benefits within the microbe-derived supplements using such analyses, due to the inability of analyses to provide any indication of specific nutritional components of feed (such as specific amino acids and lipid species). Detailed analyses of the chemical profiles of selected species will be

necessary to evaluate appropriate microbes with new and important nutrient contents, which can be used as microbial supplements for animal feeding.

1.3 Purpose

This research aims to develop and validate new, effective analytical methods for microbe-derived feed supplements. These microbial supplements will be based on novel microbes with new and important nutrient profiles that can be produced from meat processing co-products. The co-product/waste substrate profiles were also analysed. These analyses enhance the fundamental understanding of the biology of the new microorganisms and the relationship between chemical composition, biological function and nutritional benefit. The project analysed specific lipids, amino acids, and other metabolites that are nutritionally important for livestock feed. The manufacture of these feed supplements will add value to red meat industry co- product streams, improve the farming and health of various livestock species, and enable production of higher quality meat products.

The identified gaps in nutritional analytical methods have generated the following applied research questions:

i. What are the optimum analytical methods for the quantitation of polar and non-polar compounds and metabolites in waste streams and microbial supplements that can be generated from meat processing co- products? ii. Can novel, recently developed lipidomic and metabolite analyses be applied to new microbial systems for the first time? iii. Which novel microbes provide enhanced value-added nutrients for monogastric and/or ruminant production?

Based on these research questions, the key objectives of this research are therefore:

i. To identify essential ingredients and nutritional requirements in feed formulations for monogastric and ruminant species.

ii. To achieve high-efficiency screening of different microorganisms for nutrient profiling. iii. To develop and validate analytical methods for value-added nutritional benefits, including: a. Industry standard nutritional analysis b. Advanced lipid analysis c. Advanced metabolite analysis iv. To use analytical methods on new microbial supplements that can be generated from meat processing co-products.

1.4 Significance, Scope and Definitions

Qualitative as well as quantitative investigations of important nutrient profiles in the microbial products, such as lipids and polar metabolites, have been included in this study. The experimental data have been interpreted using amino acid and lipidomics analysis in order to obtain detailed nutrient profiles of different microorganisms grown in low-cost mediums derived from livestock waste. The knowledge and results gathered from this study have contributed to the development and evaluation of a rapid analytical method for nutrient profiling of new microbe- derived feed supplements.

The research project has achieved the following outcomes. These outcomes will enhance the ability to analyse and understand the new supplement products for livestock feed applications.

i. Validated analytical assays for waste streams and value-added nutrients such as amino acid, fatty acid and lipid species.

ii. Chemical composition profiles of stickwater waste streams from the meat industry to assist the selection of appropriate microbes. The composition data on waste streams will also assist decision makers to select appropriate solutions/ treatments regarding waste management.

iii. Quantified composition profiles of selected chemical species to evaluate appropriate microbes with new and important nutrient contents, which can be used as microbial supplements for animal feeding.

iv. Conversion of waste into products: microbial feed supplements for monogastric and/or ruminant production.

v. Composition data on specific nutrient components of the generated supplement products could enhance the ability to ensure that they are effective and optimised for livestock feed applications.

vi. The nutritional analyses could also be extended to meat quality assessments.

1.5 Thesis Outline

The remaining content of this thesis is presented in five chapters that address the research questions.

Chapter 2 provides a literature review to supplement the focus of this study. It briefly describes the literature on the meat processing industry and related waste generation. The chapter explores the potential of microbe-derived feed supplements derived from livestock waste streams, and highlights the issues associated with current nutrient profiling approaches, as well as defining the need for detailed nutrient identification and quantification.

Chapter 3 presents method design, experimental approaches and instrument selection, data collection methods, and analysis techniques. This chapter also provides a description of the conceptual framework for development of the analytical method for the study.

Chapter 4 presents the evaluation of the analytical method for polar and non- polar metabolite profiling of microorganisms. It describes the design, detection, and identification process that was undertaken in this study to provide a detailed nutrient analytical method for complex microbe-derived feed supplements. The chapter shows the designed method for the rapid molecular characterisation of yeasts and filamentous fungi based on the profiling of amino acids and lipids. This chapter also reports the application of ozone-induced dissociation analysis to define the detailed

structure of selected lipid species of Rhodotorula glutinis and Rhodosporidium toruloides. Using a combination of Hierarchical Clustering Analysis (HCA) and Principal Component Analysis (PCA), a rapid and detailed nutrient profile for strains and cultivation conditions screening is provided.

Chapter 5 builds upon the work undertaken in Chapter 4 through the development and validation of the nutrient analytical method, using analytical standards and Saccharomyces cerevisiae as a reference microorganism. Individual analysis of important nutrients is again presented through amino acid and lipidomics analysis, focusing specifically on essential amino acids, omega-3 and omega-6 fatty acids, and phospholipid and sphingolipid species. The analyses also extend to complex lipid analysis through to lipid structural isomers analysis.

Chapter 6 provides the conclusion of the research, which contains summaries of the key findings, the limitations, and possible development of the current analytical method, as well as some recommendations for further investigation.

Bibliography

[1] AMPC. (2018). Introduction to the Red Meat Processing Industry. Available: https://www.ampc.com.au/2016/12/Introduction-to-the-Red-Meat-Processing- Industry [2] Ernst and Young. (2018). State of the Industry Report. Available: http://rmac.com.au/wp-content/uploads/2018/09/SOTI18.pdf [3] P. Jensen and D. Batstone, " A.ENV.0131 Energy and nutrient analysis on individual waste streams," Meat & Livestock Australia Limited, 2012. [4] AMPC. (2017, 26th Oct). About AMPC. Available: http://www.ampc.com.au/about [5] J. Rooke, "Basic Animal Nutrition and Feeding, 4th Edition, by W. G. Pond, D. C. Church and K. R. Pond. viii + 615 pp. Chichester: John Wiley and Sons (1995). ISBN 0 471 30864 1," The Journal of Agricultural Science, vol. 126, pp. 375-376, 1996. [6] Anupama and P. Ravindra, "Value-added food: Single cell protein," Biotechnology Advances, vol. 18, pp. 459-479, 2000. [7] P. Van Soest, "Development of a comprehensive system of feed analyses and its application to forages," Journal of animal Science, vol. 26, pp. 119-128, 1967.

Chapter 2: Literature Review

Chapter 2 provides a comprehensive literature review of the research area to support the study focus. The aim of this chapter is to delineate various theoretical aspects that are required for the study, and from these, to develop a conceptual framework for the research questions.

The literature review covers the extant literature on the following topics: historical background (Section 2.1); general waste characteristics from red meat industries and their treatment options (Section 2.2); recent application of red meat processing wastes to generate feed supplements (Section 2.3); important nutritive and non-nutritive feed supplements that are used in feed formulation (Section 2.4); proximate and quantitative analytical method for nutrient analysis (Section 2.5); and the potential benefits in meat quality evaluation (Section 2.6). Finally, Section 2.6 highlights the implications from the literature and develops the conceptual framework for the study.

2.1 Historical Background

Meat production of all major types, such as poultry, pig and beef, has increased rapidly due to the increase in the global population and their demand for meat. Per capita meat consumption has increased approximately 2.2 fold since 1961 [1]. The annual number of livestock slaughtered worldwide for red meat production has increased around three-fold, from 983 million to around 2.8 billion in 50 years. This large increase in red meat production results in increasing volumes of waste streams from meat processing [1, 2].

Widespread attention has been focused on the livestock industry, due to the possibility that the significant amount of waste associated with meat production can cause significant environmental impacts through water, land and air pollution [3]. In

view of the fact that the earth is now reaching its limits for utilisation of natural resources, more and more emphasis in livestock production has been put on the requirement for increasing production efficiency while simultaneously reducing emissions [3]. As such, there is a need for environmental innovation within the red meat supply chain to achieve waste treatment in a sustainable manner.

The red meat processing industry is the largest rural industry in Australia, and is aware of the importance of environment protection. The major focus of industry development strategies is now gradually shifting towards improving management of energy, water, waste streams, and greenhouse gas emissions more efficiently. Many programs have been developed in the past seven years for the goal of reducing negative environmental impacts while increasing profitability within the meat supply chain. A report from MLA pointed out a potential strategy proposition for environmental innovation in terms of converting processing wastewater to value added feed supplements [4]. It has been proven that the contents within waste streams can be identified as a significant source of phosphorus, nitrogen and carbon, which makes it an ideal source of the composition of the growth medium to sustain the microbial fermentation process [2, 4].

2.2 General Waste Composition from Red Meat Industries

By-products from the red meat industry come in a wide variety of forms, including manure, edible offal, non-edible by-products such as blood, hide, rendered products and gut paunch, and organics in wastewater [5]. The industry has sought to generate maximum value from by-products but there are opportunities for more value from streams such as paunch and wastewater.

Wastes or very low value by-products from the abattoir mainly include non- edible by-products, especially the gut paunch, which is one of the major waste streams from meat processing, with potential nutrient sources for microbial cultivation. In particular, the rumen paunch contains several potentially useful substances including undigested feeds, microorganisms, and nutrients [4, 5]. Wastewater is another major

type of waste from the abattoir and comes in various forms from various processing streams. Stickwater from rendering is rich in organic contaminants and nutrients; it contains high concentrations of fat, oil, and grease (FOG), and it is also relatively high in nutrients such as phosphorus and nitrogen [6, 7].

The disposal and treatment of wastes from the meat industry are challenging, due to the high content of insoluble and hard-to-degrade animal by-products in industrial wastes, including hair, hooves, proteins, fat, and grease [8]. The considerable biogenic and organic loading in wastewater stream can also be difficult and costly to treat [4]. A report from MLA indicated that an estimated $100-200 million is spent on disposal and waste treatment annually [4].

Figure 2.1 shows the major waste streams and products from the red meat processing industry. There are four potential waste streams that can be considered for the production of new microbial feed supplements, including slaughter floor wastewater streams [Option 1], streams after the dissolved air floatation (DAF) treatment [Option 2], streams after the rendering treatment [Option 3], and the paunch waste stream [Option 4]. Options 2 and 3 are the waste streams that have high a content of bulk inedible lipids and suspended solids that can be utilised as the optimised nutrient sources for single cell microorganism production [4].

Waste Streams Process Flow Products & By-products Solid Liquid Product flow By-product flow

Hide & dirt Feedlot Screenings required washing

Manure Microbial Stunning/Killing Blood processing Dried blood cultivation streams

Hide Removal Hide processing Hide Brain Heart Eviscerating & Viscera Handling Edible offal Liver Trimming Tongue Option 3 Kidney

Inedible by-products Paunch Option 4 Tallows (Edible & Inedible) Rendering process Proteins and Fats Inedible offal (Lungs,rumen,intestines) Protein residues Protein residues

Ovaries: Oestrogens&progesterone Value adding process Value added products Pancreas: Insulin&trypsin Pituitary: Adrenocorticotropic hormone Testes: Hyaluronidase FOG SCP: Microbial feed supplements Chilling Carcasses Biosurfactant/ Emulsifier: Lysolecithin

Deboning & Cutting Cut meat products for consumption Flesh; fat, oil & grease (FOG) Meat processing & Processed meat products Packaging Flesh; FOG

Bones; blood & Meat & Bone meal flesh Defrost water Defrost Freezing

Option 1 Plant cleaning Option 2 Solid waste DAF composting Protein residues; FOG Starch, fibres, lignin, Wastewater Lipid Extraction nitrogen, phosphate & treatment %Nutrients estimation trace elements Nitrogen, phosphate & trace elements Figure 2.1. Process flow diagram of the red meat processing industry. Inedible by-products such as meat residue, fat, bone, head, and unwanted offal parts were generated at various steps during the process. These waste materials were sent to the rendering process for tallow production. Stickwater (option 3) from low-temperature rendering process contains high lipid or protein levels that are a potential stream for microbial feed supplement production [9, 10]. Waste streams from floor cleaning (option 1), dissolved air floatation treatment (option 2) and paunch wastes (option 4) can also be considered for the production of microbial feed supplements.

2.3 Application of Red Meat Production Co-products to Cultivated Livestock Feed Supplements

MLA’s report showed the production of enhanced feed supplements offers a high revenue potential from waste treatment [4]. Work performed by Farshad et al. [11] reported that livestock wastes can be utilised as potential nutrient sources to enhance heterotrophic or mixotrophic microalgae cultivation. Microalgae have been

recognised as a promising nutritional supplement, including nutrients like proteins, lipids, and valuable ingredients such as carotenoids, antioxidants and vitamins, which can be efficiently applied to animal feeds [6, 12].

Recent studies have shown that the production of Black Soldier Fly Larvae (BSFL) on wastes is a strategy for efficient waste management, as well as lipid and protein production. Reports showed an estimated 42% lipid production or 41% protein production can be obtained from BSFL biomass [13, 14]. A comparison of the amino acids (AA) profile between BSFL feeds and reference protein feeds is summarised in Table 2.1. It indicates the BSFL products can be used as an alternative protein source in animal feedstock. In addition, BSFL products provide lower production cost as compared to soybean meal or fish meal.

Table 2.1. Composition of amino acid of BSF feeds and other protein feeds (Unit % w/w), data presented as mean ± standard deviation [15-17]. Category BSF prepupae meal Soybean meal Fishmeal

Arginine 3.25±0.03 3.17±0.19 3.84±0.48 Histidine 2.13±0.08 1.26±0.14 1.44±0.29 Isoleucine 2.37±0.02 1.96±0.19 2.56±0.31 Leucine 3.68±0.03 3.43±0.26 4.47±0.50 Lysine 3.23±0.10 2.76±0.24 4.56±0.90 Amino acid Methionine 1.04±0.03 0.60±0.06 1.73±0.45 Phenylalanine 2.27±0.03 2.26±0.16 2.47±0.22 Threonine 2.15±0.02 1.76±0.13 2.58±0.33 Tryptophan N/A 0.59±0.26 0.63±0.10 Valine 3.68±0.11 1.93±0.35 3.06±0.45 Protein yield 63.90±0.20 43.90±2.0 63.30±4.7

Palmitic 13.50±0.70 17.19±0.05 20.30±0.07 Linoleic 9.90±0.50 39.25±0.05 1.71±0.01 Linolenic 0.10±0.00 5.33±0.05 1.05±0.01 Fatty acid SFA 69.90±1.40 25.60±0.05 33.25±0.22 PUFA 12.50±0.40 45.75±0.05 31.98±0.10 Lipid yield 13.50±0.12 1.20±0.30 9.70±1.30

* N/A indicates the information is not provided.

Single cell proteins (SCP) refers to protein from cultivated microbial biomass such as bacteria, yeasts, fungi or algae, which can be considered as an enhanced feed protein for animal feeds [18]. The literature indicates that the nutritive value of SCP depends on different microbial protein sources [18]. A detailed comparison of the nutritive value of different derived SCP products is cited in Table 2.2. It is crucial to analyse the chemical composition of SCP before applying them to animals. Anupama and Ravindra have indicated that two commercially available SCP technologies are algae-based photosynthetic technologies and solid-state fermentation technologies; these two technologies appear highly suitable for water wastes and solid wastes respectively [18]. Both technologies have been useful in producing value-added products such as enzymes, vitamins, micronutrients or specific amino acids, and platform chemicals [18].

Table 2.2. Composition of derived SCP products (Unit %w/w) from the representative types of species [18]

Component Algae Fungi Bacteria

Spirulina; Pyrenoidosa Candida Cellulomonas spp. Chlorella; Rhodymenia Pichia Alcaligenes Representative species Scenedesmus Saccharomyces Pseudomonas fluorescens Ascophyllum; Fucus; Laminaria Breweries/Bakeries yeast Rhodopseudomonas gelatinosus

Sulfite waste liquor Agricultural wastes Light, carbon dioxide or Methanol Variety of substrates Substrate inoranic compounds Molasses, stillage Manure, animal wastes Plant origin liquid waste Wheat bran

Toxin N/A Mycotoxins Endotoxins Total proteins 40.0-60.0 30.0-70.0 50.0-83.0 Total nitrogen 45.0-65.0 35.0-50.0 60.0-80.0 Lysine 4.6-7.0 6.5-7.8 4.3-5.8 Methionine 1.4-2.6 1.5-1.8 2.2-3.0 Amino acids N/A 54.0 65.0 Lipids 5.0-10.0 5.0-13.0 8.0-10.0 Carbonhydrate 9.0 N/a N/a Mineral salts 7.0 6.6 8.6 Ash 3.0 N/a N/a Moisture 6.0 4.5-6.0 2.8 Fiber 3.0 N/a N/a

* N/A indicates the information is not provided.

2.4 Essential and Important Nutrients Used in Feed Formulation

Studies have shown that essential nutrients in the diet of both monogastric and ruminant animals can be divided into six classes, including macro nutrients such as carbohydrates, lipids, proteins, water, and energy, as well as micro nutrients such as vitamins and minerals [19, 20]. The list of essential nutrients required by animals for a dietary requirement can be summarised as shown in Table 2.3.

Table 2.3. Essential and important nutrients required by animals [15, 19, 21]. The five classes of nutrients are required in the diet of all animals to support their normal growth and reproduction; most of these nutrients cannot be synthesised by animals in sufficient amounts to satisfy their best growth performance.

Essential Nutrients Forms of Nutrients Chemical Species Essential Nutrients Forms of Nutrients Chemical Species

Arginine Boron Histidine Calcium Isoleucine Chlorine Leucine Magnesium Lysine Phosphorus Methionine Potassium Protein Amino Acids Phenylalanine Metal Ions/Trace Sodium Minerals Proline Elements Copper Threonine Iodine Tyrosine Manganese Tryptophan Iron Valine Selenium Linoleic Acid Cobalt α-Linolenic acid Zinc Arachidonic acid A Lipid Fatty Acid Eicosapentaenoic acid B12 (EPA) Fat-soluble/ Water- Vitamins Docosahexaenoic acid soluble Vitamins D3 (DHA) E Energy Carbohydrate Starches or sugars K

Most dietary requirements for animals can be supplied by a wide range of feedstuffs. However, specific ingredients may also need to be included in order to supplement some of the nutritional or functional deficiencies in feeds, including protein concentrates, energy concentrates, vitamin supplements, mineral supplements and non-nutritive feed additives [19].

Numerous studies show a rising interest in the role of nutrient supplements in animal nutrition [21-23]. Although animals usually consume sufficient nutrients from

bulk feeds to meet basic daily demand, specific nutrition is added to animal diets using feed supplements in order to improve diet nutrient digestibility, as well as growth performance and health [19]. Using grain meal as an example, grain mix is a well- known feed that provides a high content of protein, yet farmers add lysine to animal diets since imbalances of essential amino acids were found in grain meal, and they were particularly low in lysine. Lysine, L-methionine, threonine and tryptophan are the typical amino acid supplementations used in chicken and pig nutrition, as they are limiting nutrients in most commonly used feeds and cannot be synthesised by the animal [19, 24]. Many single cell microorganisms have been shown to be a promising feed supplement in protein enhancement, and they can be used as a substitute for protein-rich sources such as soybean meal and fish meal, due to their ability to synthesie all 20 amino acids [18]. In addition, dietary element supplementation, such as selenium, plays an important role in protein utilisation; it shows a positive relationship with protein digestibility, and it has been demonstrated in fish, sheep, and pigs [25].

Lipids, also known as fat and oil, are another important supplement that famers generally add to animal diets, especially for young, growing pigs, not only as a concentrated energy source, but also for their functional enhancement of the feed efficiency and animal’s growth performance, as well as to improve feed palatability and storage life [21]. Lipids are complex compounds incorporating a wide array of structurally diverse hydrophobic molecules, with correspondingly diverse nutritional values in feed [26-28].

Some research has been carried out during the last few years indicating that the content of essential free fatty acid is an important factor for measuring the nutritional value of feed [29-31]. There are three types of fatty acids considered to be essential that contain relatively high nutrient value to animal reproduction: omega 3, omega 6, and omega 9 (can also be expressed as n-3, n-6 and n-9 respectively) unsaturated fatty acids [21]. They are differentiated by the position of a carbon double bond within the carbon chain. Research carried out by Siriwardhana et al. (2012) [32] showed that omega 3 fatty acids such as linolenic acid (C18:3, n-3), eicosapentaenoic acid (C20:5,

n-3), docosahexaenoic acid (C22:6, n-3) and their derivatives have beneficial effects in reducing the risk of cardiovascular disease and inflammation, and assisting the development of eyes, nerves and the brain. Omega 6 fatty acids such as linoleic acid (C18:2, n-6) and arachidonic acid (20:4, n-6) are of particular nutritional interest to feed formulation, because they are the principal substrates for the conversion of omega 6 fatty acids to omega 6 eicosanoid [28]. Oleic acid (C18:1, n-9) and erucic acid (C22:1, n-9) are the two unsaturated acids that received great attention in terms of omega 9 fatty acid. Although omega 9 fatty acids are often not classified as essential fatty acids for the reason that many animals can generate omega 9 from other unsaturated fatty acids, the nutritional value of omega 9 in a balanced diet has been studied and proven to be important for the cardiovascular system [33].

In addition, many nutritionists and researchers have shifted their interest in regard to the level and ratio of unsaturated fatty acid (UFA) and saturated fatty acid (SFA) in animal nutrition [30, 34, 35]. Nutritional guidelines indicated that the UFA/SFA and n-6/n-3 polyunsaturated fatty acid (PUFA) ratios are important indicators to evaluate the nutritional quality of meat [34]. Certain dietary FA ratios can be advantageous for obtaining higher quality and healthier meat products, and the recommended value for UFA/SFA ratio should be 0.4 or higher, whereas the n-6/n-3 PUFA ratio should be 4.0 or lower [35]. Figure 2.2 illustrates the recommended percentages of SFA, PUFA and monounsaturated (MUFA) fats in diet to reduce the risk of obtaining cardiovascular diseases. The content of essential FA in meat can be modulated through the composition of diet in animals [29]. The use of feed supplements with n-3 FA enriched components enhances the content of essential FA in meat, and lowering the dietary n-6/n-3 ratio can enhance the meat quality traits [30]. Therefore, detailed lipid profiles and FA analysis of microbial supplements are capable of predicting meat yield and quality.

Figure 2.2. Recommended percentage of fat in meat for optimal health. Linoleic acid and arachidonic acid are n-6 PUFA, α-Linolenic Acid, DPA and EPA are n-3 PUFA. The n-6/n-3 PUFA ratio from the recommended nutritional guideline is 1.33, and the recommended value for UFA/SFA ratio is 2.03 [34].

Traditionally, lipids in livestock feed are primarily composed of triglycerides (TAG), which provides high content of energy, but is considered to be less digestible to animals, especially for young animals, whose lipid digestion mechanisms are not fully developed [21]. In recent years, the supplementation of lipid mixtures with a high content of phospholipids and sphingolipids has been proposed and received a lot of attention because of their nutritional and functional benefits [26].

Phospholipids have a positive effect on several metabolic processes. Another effect of phospholipids is that they work as an emulsifier. Figure 2.3 (a) shows the structure of phospholipids. Phospholipids are composed of one hydrophilic phosphate head group and two hydrophobic fatty acid tails that combine to function as bio- emulsifiers, to mix oil and water together. In other words, phospholipids play an important role in the splitting and absorption of dietary fats [36]. Lysophospholipids can be obtained by an enzymatic treatment of phospholipids. The formation of the lysophosphatidylcholine (LPC) from phosphatidylcholine (PC) is displayed in Figure 2.3

(a). The structure of LPC shows that lyso-lipid groups have higher hydrophilic-lipophilic balance than do native lipid groups, which indicates that lyso-groups generally have better emulsification of fats. Figure 2.3 (b) demonstrates the process of lipid digestion with the utilisation of phospholipid /lysophospholipid. Lipids in livestock feed, in the form of TAGs, are hydrolysed by lipase and then bound with bile salts to form micelles [21]. The supplementation of phospholipid/lysophospholipid has the capacity to emulsify large TAG globules to smaller lipid droplets, which gives a larger surface area to interact with lipases and bile salts. The formation of mixed micelles with bile phospholipid and bile Lysophospholipids promotes better hydrophilic interactions, thereby improving overall lipid digestibility [37].

The supplementation of phospholipid/lysophospholipid in feeds can enhance lipid splitting, digestion and absorption, and eventually leads to more energy efficiency, thus improving the growth performance of animals such as broilers, turkeys and piglets [36, 37]. The positioning of saturated fatty acid on the TAG or phospholipid is another major factor that affects the digestibility of lipids. The literature indicates that once lipase adheres to TAG or phospholipid droplets, enzymatic activity occurs and cleaves the bond from the sn-2 position. It is believed that saturated fatty acids at the sn-2 position provide higher absorption efficiency than those on the sn-1 or sn-3 positions [21]. Dansen et al. [37] reported that SFA such as palmitic acid (C16:0) and stearic acid (C18:0) on the sn-2 position had a positive effect on the lipid digestibility in broilers. Furthermore, studies indicate that feed with higher concentrations of total phospholipids are less likely to be peroxidised as compared to TAG, which would provide higher anti-oxidative potential and longer feed storage life [21, 26].

Figure 2.3. The structure of phospholipids and the emulsification process of lipid droplets in the small intestine with phospholipids or lysophospholipids. (a) Phospholipids undergo hydrolysis with phospholipase; the enzymatic activity occurs and yields lysophospholipid and free fatty acid. (b) Lipids emulsified with bile salts to form mixed micelles; phospholipids or lysophospholipids act as emulsifiers to reduce the size of micelles in order to improve lipid absorption [36, 37].

In recent years, sphingolipids have been proposed as a new mycotoxin binder that have the capacity for improving mycotoxin elimination. The contamination of animal feed with mycotoxins can lead to severe health problems in livestock, which is a worldwide issue for farmers [38]. Although feed mills have taken precautions to reduce feed contamination, livestock and poultry can still be exposed to mycotoxins. Through electrostatic and hydrophobic interactions between molecules, the detoxifying sphingolipids are able to bind mycotoxins and prevent adsorption into the animal [38-40]. Therefore, sphingolipids help to reduce or eliminate the effects of

mycotoxins in the intestine by limiting the metabolic processes of mycotoxins during digestion.

An investigation carried out by Merrill et al. [40] demonstrated that sphingolipids are not considered to be essential for animal growth or survival, for the reason that they do not contribute sufficient daily energy, and they can be found in most diets. However, sphingolipids are often classified as “functional ingredients” because they play an important role in regulating various cellular functions. Experimental studies with livestock have shown that long-term supplementation with high concentration dietary sphingolipids in feed (about 1% of the diet) inhibit colon cancer or tumor formation, and there is no evidence of any deleterious effects, which is an important prerequisite for feed supplement studies [40]. Research carried out by Eva and Hubert et al. [39, 40] shows that sphingolipids may facilitate mycotoxin elimination; the hypothesis is that sphingolipids are released after emulsification; they may compete for cellular binding sites, therefore facilitating the elimination of pathological organisms from the intestine [26, 39-41].

Non-nutritive feed additives are also commonly used in animal diets, as they can be used to improve animal growth performance and health by improving the digestibility as well as increasing the palatability of the feeds [19, 42]. Despite the potential to generate Antibiotics resistances, antibiotics have been widely used to promote animal growth as well as improve efficiency of feed utilisation [42]. Animal feed enzymes are an established feed supplement, and the major function of enzymes is to improve the utilisation of nutrients and energy [42]. Other feed additives like probiotics can also be added to aquaculture, monogastric and ruminant feed to improve growth performance, nutrient utilisation, gut health and feed conversion efficiency [43, 44].

2.5 Methods for Nutrient Analysis

The official analytical methods from the Association of Official Analytical Chemists (AOAC) are conventionally applied to feed manufactures or nutritionists for

nutrient quantification, including physical, chemical and biological evaluations. Physical evaluation tests the qualitative criteria of feeds, such as smell, colour and taste. Chemical evaluation allows accurate estimation of the nutrient contents of feeds. Biological evaluation provides the most precise measures of the nutrient value of feeds; however, it is expensive and time consuming. Therefore, chemical evaluation is generally used for nutritional analysis. Methods can be summarised into two major platforms - analyses for general constituents, and analyses for specific nutrients such as protein, fat, sugar, and feed additives [45].

Proximate analysis, which is accredited by ISO 20588, is one of the oldest well- known feed analytical methods to determine the major constituents of feeds. This method is currently used to fulfil labelling requirements as per Australian legislation. It provides information on the various components in each product, including moisture content, ether extract (crude fat), crude protein, crude fibre, ash and NFE [19, 46]. The procedure sequence of proximate analysis is illustrated as shown in Figure 2.4.

Air Dry sample

Dry at 105 °C Moisture-free sample

Kjeldahl Ether extraction Ether extract Crude protein Fat-free residue

Boil in acid Boil in alkali

Crude fiber + ash

Burn in furnace

Ash Crude fiber

Figure 2.4 Procedure sequence of proximate analysis. The Kjeldahl method is applied for crude protein estimation. This method does not measure the protein content directly, hence a conversion factor is required to convert the measured nitrogen contents to a protein contents.

The major limitation of these general analyses is the inability to provide any indication of specific chemical composition and nutritional value of feed. The general extraction method typically cannot completely extract protein or lipid, especially for products like microorganism biomass, because the lipid compounds are linked together with proteins or carbohydrates. More detailed and biochemically oriented analytical methods for feed and nutrition analysis are required for nutritional studies in order to ensure they are effective and optimised in terms of high value nutrients for livestock production.

Amino acid (AA) supplementation is an important topic in animal feeding, as AAs play a necessary role in biological processes, providing essential AA that cannot be manufactured or synthesised by the animal [19]. AA analyses have been developed for animal feed based on AA profiling, using semi-automated equipment, including High Performance Liquid Chromatography with ultraviolet detection (HPLC-UV) and Liquid Chromatography Mass Spectroscopy (LCMS). Both types of equipment are capable of identifying and quantifying data on AA composition, as well as on other metabolites [47-50]. However, LCMS analysis has been developed for the specific assay of selected chemical components; it offers higher sensitivity and precision than HPLC-UV analysis [51].

A complete AA profile includes data from free AA and protein-bound AA. The protein bound AA must first be hydrolysed to individual AA residues prior to analysis. Acidic, alkaline and enzymatic hydrolysis are the three classical methods for protein hydrolysis. Alkaline and enzymatic hydrolysis are rarely used for AA profiling since they are designed for the determination of tryptophan and for glutamine and asparagine respectively, whereas other amino acids are degraded [47, 52, 53]. Although acidic hydrolysis covers most amino acids, tryptophan, methionine, cysteine and cysteine undergo different levels of degradation because of oxidation. The use of hydrolysis agents such as phenol or dithiothreitol can slow down the oxidation reaction to a certain extent [53].

The comparison of the benefits of a list of commonly used acid hydrolysis agents for various hydrolysis methods is summarised in Table 2.4. Compared to other

hydrolysis reagents, methanesulfonic acid (MSA) allows the determination of all AA residues; however, protein that was hydrolysed with MSA becomes non-volatile because MSA is not volatile and it cannot be evaporated after hydrolysis, so this method cannot be used for Gas Chromatography Mass Spectrometry (GCMS) analysis. MSA is not widely used for LCMS analysis either. Because it may result in contamination of small amounts of substrates, and it has problem with replicating ion pair quality with volatile acids such as methanol, which may lead to difficulty in analyte quantification [53, 54].

Table 2.4. Comparison of the commonly used acid hydrolysis agents for various hydrolysis techniques [53, 54]

Method of Method Specific for Hydrolysis Agent Hydrolysis Conditions Additives Advantages Disadvantages Hydrolysis Determination of

All residues except of 110 °C, 24h 0.02% Phenol Cys and Trp 5% Thioglycolic acid and 110 °C, 18h Cys 0.1% Phenol 6M HCl 110 °C, 24h 20% Dithiothreitol Trp and Met Long duration time for hydrolysis, most analysers Oxidised with Perfomic acid Conditions not as have relatively short Vapour phase 145 °C, 4h Cys, Met and Lys before hydrolysis process extreme. Samples can be derivatisation and analysis under vacuum Oxidised with Perfomic acid processed in batches times and the seals of the 4M MSA or 5.7M HCl 150 °C, 90 min at 50 °C for 10 min before All residues reaction veal require hydrolysis process regular inspection as above 12M HCl-propionic 150 °C, 90 min N/a Resin-bound peptides acid (1:1) TFA-HCL (1:2) 166 °C, 25-50 min 5% Thioglycolic acid Trp and Met

12M HCl-propionic Potential contamination 840W, 1-7 min Resin-bound peptides acid (1:1) from the reusable, Samples can be Microwave ρ-Toluenesulfonic expensive tubes and more 840W, 15 min N/a Sensitive residues processed in batches and irradiation acid extreme conditions, rapid hydrolysis time Deuterium Chloride Medium power, 30 therefore, more dangers Sensitive residues (DCL) min from exploding vials

Samples can be analysed in batches of 18–21 every 24 h, conditions Automatic 3M ρ-Toluenesulfonic N/a N/a Methionine sulfoxide can be mixed during a Expensive equipment hydrolysis acid run and temperature and time carefully controlled and accurately replicated

Metabolites such as carotenoids, flavonoids, or vitamins are widely used as dietary antioxidants in a variety of feed supplements to protect cells against the negative effects of free radicals, as well as to prevent lipid peroxidation. Schierle et al. [55] reported that the determination of carotenoids, particularly β-carotene, can be conducted with the Liquid Chromatography (LC) method. In other study, Magiera et al. [56] reported that with the support of an ultraviolet detector, the chemical composition of flavonoids and their derivatives can also be identified using an LC method.

In recent years, lipidomics has gained high attention and has become an emerging field of research because of the high nutritional value of lipids; it has been recognised that lipids have the core function of controlling cellular function and disease [57]. However, lipidomics remains challenging and the lipidome is relatively uncharacterised, due to the enormous diversity of lipid structures. Lipid structures range from simple short chain fatty acids to more complex ester compound such as triglycerides. Lipids can be divided into eight categories: fatty acyls (FA), glycerolipids (GL), glycerophospholipids (GP), sphinglipids (SP), sterol lipids (ST), prenol lipids (PR), saccharolipids (SL), and polyketides (PK) [58]. Up to 1.68 million lipid species and 40,825 unique lipid structures have been recorded in the Lipid Maps Structure Database [59]. Lipid isomers are lipid molecules with identical molecular formula that are structurally different, and can be classified as structural isomers and stereoisomers. The specific arrangement of atoms and bonds in a particular lipid will have significant effects on its structure and function [59]. Hence the major difficulty for lipid analysis is the explicit identification of individual molecular lipid species/isomers.

Several analytical methods have been developed and evaluated for lipid profiling and quantification, including enzyme-linked immunosorbent assays (ELISA), thin-layer chromatography (TLC), nuclear magnetic resonance (NMR), and mass spectrometry (MS) [57]. Among the analytical methods that have been developed for lipidome analysis, numerous studies indicate that MS remains the most widely used and powerful analytical technology for targeted lipid characterisation, due to its capacity

to identify lipid categories and structures, as well as to quantify chemical substrates with high accuracy [57, 60-62]. Various analytical methods have been developed using the MS technique, in particular the electron ionisation method (EI) for GCMS analysis, together with the electrospray ionisation method (ESI) for LCMS analysis. Lipid species can be identified in accordance with retention time and accurate mass in GCMS and LCMS experiments; yet uncertainty still remains over the detection of lipid structure, because lipid isomers share similar mass-to-charge ratios [57].

Fatty acid profiling is generally obtained by GCMS. The GCMS methods for fatty acids require esterification/transesterification or methylation/transmethylation for sample preparation in order to generate non-polar fatty acid methyl esters (FAMEs) for detection [63, 64]. Research from Kurata et al. [63] indicated that GCMS has the ability to separate FAMEs from short-chain fatty acid C4 to long-chain fatty acid C24 according to the length of carbon chain and the degree of unsaturation. However, the analysis of FAMEs is not able to define the position of carbon double bonds, and no classification of lipids can be obtained using this method [17, 21]. Furthermore, the esterification process and analyte detection are time-consuming, especially for complex mixtures such as biological materials or the rendering stickwater used in this study. Longer measurement times are required due to the complexities of the fatty acids [63, 64].

More recently, electrospray ionisation mass spectrometry (ESI/MS) has been extensively applied to the analysis of lipids, with the detection limit in a low pico-molar range [65]. The ESI/MS technique allows direct lipid analysis without pre-separation by LC, which also has been called “Shotgun Lipidomics” [57, 66]. In other words, lipids are separated to ions by LC; lipid extracts can be directly infused for MS detection without any derivatisation reactions. The introduction of shotgun lipidomics minimised the ion suppression effect, and maximised the ionisation efficiency, as well as reducing the sample preparation time.

In recent years, lipidomes have been characterised by Ejsing et al. [65, 67], Guan et al. [68], and Li et al. [57]. These researchers investigated the influence of different extraction methods (e.g. solvent, extraction reagent, etc.) and detection parameters

(e.g. collision energy, temperature, fragments, etc.) on the profiling of different lipid classes. One of the most comprehensive lipid profiling studies is the one investigated by Ejsing et al. [65], which covered the detection of 21 major lipid classes and achieved the absolute quantification of 250 lipid species. The work presented by Ejsing et al. also improved the lipid extraction method; the introduction of two-step lipid extraction improved the analytical specificity, sensitivity, and recovery efficiency by separating extracts to relatively polar and apolar lipids. Relatively polar lipids include: phospholipids such as phosphatidic acid (PA), phosphatidylserine (PS), phosphatidyinositol (PI), cardiolipin (CL); or sphingolipids such as long-chain base phosphate (LCBP), inositolphosphoceramide (IPC), mannosyl-inositolphosphoceramid

(MIPC), and mannosyl-diinositolphosphoceramide (M(IP)2C); whereas apolar lipids include: phospholipids such as phosphatidylcholine (PC), phosphatidylethanolamine (PE), phosphatidylglycerol (PG); sphingolipids such as long-chain sphingoid bases (LCB) and ceramide (Cer); glycerolipids such as TAG and DAG; and ergosterol. The results show that the transitions of lipid species obtained from MS spectra were varied, with different configurations or detection parameters under similar experimental conditions. Additionally, advanced lipidomics analysis has not been applied to most of the microorganisms except for Saccharomyces cerevisiae; therefore, further investigation of specific details is needed.

The critical limitation in regard to precise lipid structure detection is the inability of analytical methods to identify molecular features such as the position of double bonds and/or functional groups in molecules contained within complex biological extracts [60]. Work performed by S. H. J. Brown et al. [69] reported that ozone-induced dissociation (OzID) is an advanced technology that is capable of identifying the positions of carbon double bonds in unsaturated lipids. This technology can also be utilised in polyunsaturated lipid double bond detection, particularly the identification of omega 3 (n-3) and omega 6 (n-6) PUFA in a complex biological mixture. For example, C18:1 n-6 fatty acid and C18:1 n-3 fatty acid are two different types of PUFA that have the same amount of double bonds within their carbon chain, but the location of the double bonds differentiates the two different types of fatty acid. This refers to the last double bond on the 3rd carbon for n-3 fatty acid and 6th carbon for the n-6 fatty acid 26

[70]. The techniques may also be applied to analyse meat quality based on PUFA to SFA and n-6 to n-3 PUFA ratio [35].

2.6 Summary and Implications

Despite microbial co-product treatments being successfully applied for a range of applications, there is still a need to understand both the nutrient composition of industry wastes or co-products, and the chemicals that are nutritionally important as supplements for livestock feed, in order to develop technologies for the production of new feed supplements.

According to previous research, nutritional analytical methods used for animal feed remain challenging, because the most commonly used methods are general analyses that focus on basic nutrient composition. Therefore, significant attention will be given in this study to the advancement of analytical methods for specific nutrient components, as well as chemical substances of new microbial feed supplements. Nutritional analyses can be improved by quantifying the amount of a particular nutrient chemical (such as a specific lipid species), as well as acquiring meaningful chemical and biological information about novel microorganisms that could be used as feed supplements.

Shotgun lipidomics still remains at an early stage; the analytical method still needs to be developed and tailored for detailed lipidomic profiling and characterisation. Specifically, the sample pretreatment procedures, including sample collection, extraction and storage, need to be standardised. There is also a need to find out the appropriate internal standards with suitable concentrations for each individual lipid class. The utilisation of accurate internal standards is essential for accurate quantification of different lipid species. Furthermore, this study focuses on identifying and defining the analytical method’s operating parameters for specific lipid classes, especially phosphatidylinositol, sphingolipids and sterol lipids, because accurate detection and quantification of these types of lipids is still lacking.

The analytical methods employed in this project for the detailed analysis of waste streams and the generated feed supplements could therefore also be extended to meat quality assessments. Overall, significant knowledge gaps exist in the detailed chemical analysis of red meat processor products, co-products, and wastes, as well as the products that could be made from waste streams. This project aims to address these gaps whilst supporting work towards new processing technologies.

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Chapter 3: Theoretical Basis for New Analytical Method Design

This chapter covers the design and methodology of this research in order to achieve the aims and objectives stated in Section 1.3 of Chapter 1. Section 3.1 provides a detailed overview of the methodology, study approach and design used in this research; Section 3.2 details the material and reagents used in this experimental research; Section 3.3 lists all the instruments which have been used in this research and justifies their use; the resources and funding of this study are detailed in Section 3.4; Section 3.5 outlines the research procedure, and its corresponding timeline and participants are listed in Section 3.6; Section 3.7 demonstrates how the experimental data was processed and analysed. Experimental data were collected through both qualitative and quantitative methods, and analysed using different analytical software programmes. Specifically, the mass spectrometer (MS) data of polar metabolites were analysed using Skyline, whereas the MS data of non-polar metabolites were analysed using Lipidviewer and Peak View. This chapter also provides the potential limitations of this research in Section 3.8; finally, section 3.9 summarises this chapter.

3.1 Methodology and Research Design

3.1.1 Methodology Overview The overall purpose of this study was to develop and validate an analytical method that can be used in conjunction with specific waste streams and novel microbial strains to optimise growth of microorganisms through media development. The research methodology flow chart is illustrated below in Figure 3.1.

In order to achieve the objectives of this research, the essential and important nutrients for each specific target animal were defined (refer to Appendix A for details). Both nutritional and functional supplements for monogastric and ruminant animals

were defined through extensive literature searches and discussions with nutritionists from Bioproton Pty Ltd.

The analytical experiments started with the evaluation of analytical methods for amino acids, fatty acids and lipid species profiling, through the use of the standards and two oleaginous yeasts. The evaluation involved establishing methods and model compounds, determining limits of nutrient detection and quantification, and generation of standard operating procedures (SOP).

Step two was the composition analysis of waste streams from the red meat processing industry using the proposed analytical method. Waste streams that contain high lipid levels were prioritised for testing, as lipids were a major focus of the analytical research. Lipids were extracted from waste stream samples and identified by using MS techniques. Amino acids and metabolite analyses in waste streams were also an initial priority.

The next step was the culturing of different microorganisms that can grow on and transform waste streams to biomass, and characterising these strains to generate their nutrient profiles. The purpose of this step was to ensure that the proposed analytical method can be applied to various strains, and allow rapid screening of nutrient profiles in order to define the appropriate strain for microbial feed supplement production. The cultivation of different microorganisms was completed by Remya Purushothaman Nair. Hierarchical cluster analysis (HCA) and principal component analysis (PCA) were performed using the algorithm of R studio to identify the difference between the obtained MS spectra of nutrient profiles of strains.

The development and validation of the proposed analytical methods for polar and non-polar metabolite profiling were conducted using an established feed supplement yeast, Saccharomyces cerevisiae. Quantitative analysis of S.cerevisiae was performed to define the microbial amino acids, fatty acids, and lipid species profiles. The nutrient profile of S.cerevisiae was then compared to the literature to demonstrate that the proposed method provided rapid, accurate, reproducible, and detailed composition analysis of the microorganisms. Parameters that affect the completeness of the analytical method were evaluated and their influence on limited

of detection of chemical residues were also recorded and analysed; an understanding of how such analytical method can affect the throughput of microorganisms was achieved.

Define animal nutritional requirements

Evaluate analytical method: polar and non-polar metabolite contents profiling and identification

Analyse waste streams from red meat processing industry

Grow microbes on waste streams and identify microbes that grows well in media

Rapid nutritent profile screening and characterisation of different microbes

Results can be used Develop and validate analytical method : AA/FAME/Lipid species as positive controls profiling and quantification

Define whether the obtained nutrient profiles are similar to the No Manipulate operating literature or not parameters

Yes Microbe-derived Apply analytical method to batch analysis, optimise biomass yield feed supplement through media development based on the analytical method.

Figure 3.1. Overview of the methodology flowchart. Methodology can be summarised in three major sections. The essential nutrients for monogastric and ruminant animals were defined first, follow by analytical method evaluation using waste streams from red meat processing industry and various filamentous fungi and yeasts. Methods development and validation were then conducted in order to provide robust and reproducible analysis of each selected microorganism.

3.1.2 Research Design The study was carried out with both qualitative and quantitative analysis on the red meat processing wastewater and microbial biomass to identify/define the detailed nutrient composition within it, as well as to gain an understanding of how such growth media with high lipid contents effect on the nutrient profile of microorganisms.

Analytical Method Evaluation: Polar and Non-polar Profiling

The Universal Metabolite and Lipid Extraction Protocol was developed by Dr. Sadowski (personal communication) and had not been tested before. The reason that this protocol was selected for amino acid and lipid analysis is because it is designed for rapid simultaneous extraction of both polar and nonpolar analytes from cells of microorganisms.

Briefly, the method involves adding 200 μL of ice cold MS grade methanol with all required internal standards to approximately 5 mg of freeze dried biomass. Biomass was quickly homogenised for 90 seconds using a TissueLyser, followed by 10 minutes sonication using an Ultrasonic Bath Sonicator. A hot methanol extraction was conducted in order to enhance metabolite extraction. 350 μL Water and 400 μL methyl tert-butyl ether (MTBE) were added to the cell lysates, followed by centrifugation to show a phase separation of the organic phase, polar phase, and a solid protein pellet for lipid, free amino acid and protein-bound amino acid analysis respectively. The amino acid and lipid profiles of prepared samples were analysed by using Mass Spectrometry techniques, including both Gas Chromatography-Mass Spectrometry (GCMS) and Liquid Chromatography-Mass Spectrometry (LCMS) (refer Appendix B for detailed extraction and detection SOP, and Appendix C for internal standards preparation).

Samples were made ready for analysis together with positive control, negative control, and a pooled biological quality control (PBQC), which is applied to ensure the analysis was performed appropriately and meets the predefined chemical substances. In this study, the positive control is the standard mix that is used for instrument calibration, including amino acid analytical standards (Sigma-Aldrich), 37 food industry

FAMEs standards (Restek), and phospholipid standards (Avanti). The negative control is 20 µL distilled water which contained the reagent, solvents, and internal standards used for extraction and detection. Analysis of the negative control aids in ensuring that no artefact or contaminant peaks are misidentified as metabolites in samples, as well as being used as a control for the extraction procedure. All the control tests mentioned above were conducted before and after sample testing for normalisation and validation purposes. At least three injections of blank solvent were performed at the beginning of the batch test to remove external contamination. Sample randomisation and running a blank solvent every 10 samples were conducted so as to ensure the instrument performed properly during data analysis. A loadings test exercise was always conducted before a large-scale analysis to optimise the amount of extract injected into the instrument, so as to ensure the signal of interest is free from interference and below the detector saturation level, as well as within the linear range of the instrument (refer Appendix D for standard mix preparation).

The microorganisms for method evaluation were supplied by QUT. Two strains of oleaginous yeast isolated from Australian sugarcane bagasse piles were available for testing. The sugarcane bagasse was obtained from the Rocky Point Sugar Mill, Woongoolba, Queensland. These microorganisms are identified as Rhodosporidium toruloides and Rhodotorula glutinis. Studies have shown that these two pink coloured strains are the potential source of lipids and metabolites. They are capable of synthesising valuable compounds such as linoleic acid and β-carotene. Furthermore, R.glutinis has also been found to be safe and non-toxic for use as feed supplement in animal feeding. Its use in the nutrition of poultry has also been documented [1]. Polar and non-polar metabolite profiles of R.glutinis and R.toruloides can be used as a reference nutrient profile for new microbial supplements analysis.

Nutrient Profile Screening and Characterisation of Different Microorganisms

Low temperature rendering stickwater from the red meat processing industry that contains high lipid levels was tested and used as a test culture medium. The tested stickwater based medium was composed of 60 g.L-1 solid residue, 8.3 g.L-1 fatty acids.

In particular, it was composed of six different types of fatty acids, including saturated fatty acid C14:0, C16:0, C18:0, C20:0 and C22:0; mono-unsaturated fatty acids C18:1; and polyunsaturated fatty acid C18:2 (refer to Appendix D for detail). Growth medium

-1 -1 -1 -1 that contained 1 g.L glucose, 1 g.L yeast extract, 0.5 g.L MgSO4, and 1 g.L K2HPO4 was used as reference culture medium. A group of microorganisms were defined by selecting the strains with good growth rate in the media with low or no additional supplemented nutrients. The biomass of nine different strains that were cultivated from the mentioned two media were provided by Mrs Remya Purushothaman Nair (QUT). A relative quantification was conducted for rapid nutrient profile screening and characterisation. Results of important nutrients from lipid and amino acid analysis were obtained and compared in order to develop an understanding of the individual nutrient profile between strains and the contents and features of growth medium that influence their nutrient profiles.

Proposed Development for Polar Metabolite Detection

Amino acid analysis includes analysing both free amino acids and amino acid incorporated as proteins that were measured after protein hydrolysis. Issues encountered for protein hydrolysis for MS protocols in current use can be summarised as: Carboxylated amino acids, i.e. glutamic acid and aspartic acid, are de-carboxylated during acidic hydrolysis processes [2]. Asparagine and glutamine are also completely hydrolysed to aspartic acid and glutamic acid, respectively. Tryptophan is completely destroyed after the acid hydrolysis process [2]. Cysteine cannot be directly determined, however, some authors suggest cysteines can be preserved by an alkylation reaction, and iodoacetamide and chloroacetamide are the proposed alkylation reagents [3]. Detection of other amino acids may also be negatively affected by acidic hydrolysis. Salt was contained within the samples due to the acidic hydrolysis process, and it needs to be removed prior to MS detection by using a valve switching technique to transfer salt to a second analytical column [4]. In addition, the internal standard for MS analysis of protein-bound amino acids has not yet been clarified.

Finally, factors such as temperature, time, hydrolysis agent, and additives may affect the completeness of the hydrolysis process [2].

The proposed improvements over the existing literature methods for acid hydrolysis of protein that were investigated in this project included: a) run a total nitrogen test before metabolite extraction; b) the ratio between protein and acid during the hydrolysis process should be 1 mg protein to 400 µL acid; c) tryptophan and methionine in peptides and proteins can be determined by use of Dithiothreitol (DTT) reducing agent. Phenol can be used to avoid decarboxylation of glutamic acid and aspartic acid.

Therefore, the experimental design employed in this process was to conduct acid hydrolysis using 5 mL hydrochloric acid (20%) with 0.02% phenol and 20 mM DTT at 110°C for 24 hours under vacuum conditions. Then to resuspend the hydrolysed sample to balance pH value to approximately pH 2-3, to remove the solid residue using 0.22 µm syringe filter for both hydrolysed protein pellet and polar extracts, and then perform a 1:100 dilution using 0.1% formic acid solution before LCMS injection.

Proposed Development for Non-polar Metabolite Detection

Lipid analysis includes analysing both fatty acids and lipid species. The fatty acid methyl esters (FAMEs) analysis provides the composition of saturated fatty acids (SFAs) and unsaturated fatty acids (UFAs) of testing samples. Shotgun MS allows quantitative analysis of molecular lipid species of testing samples. The OzID detection not only identifies the stereoisomers of lipid species, but also defines the position of double bonds within it, enhancing the identification of individual molecular species of complex lipids within the testing sample. Therefore, more functional specific lipid species can be identified as target nutrients for animal feeding. The main issue encountered for the lipid study is the detection of complex lipid extracts from microorganisms, as the ion suppression and mutual conversion among different lipids can lead to a systemic error by MS analysis [5]. Another issue encountered is that some lipids such as pregnenolone, phosphoinositide and ergosterol cannot be directly

detected by the proposed MS method [5]. Furthermore, the linear dynamic range and standard calibration curve are usually pre-determined for quantitative analysis using the MS technique which diminishes the impact on chromatographic separation conditions that cause insignificant changes in retention. The pre-determined linear dynamic range may introduce alterations of the detected absolute ion counts of a particular chemical species, affecting the accuracy and variability of sample analysis [6].

The proposed development for lipid analysis was focused on the extensive evaluation of the precision and robustness of analytical methods for lipid detection, which includes: a) define the appropriate operating parameters for individual lipid class, including scan mode, detection range, collision energy, de-clustering potential, etc.; b) develop a calibration curve for ergosterol detection. The calibration curve was generated using the ratio of cholesterol standard to ergosterol standard against their baseline corrected peak area ratio. c) Define the appropriate concentration of internal standards of each individual lipid class for extraction and detection. d) In order to obtain more precise lipid species profile, compare the MS spectra results between lipid extracted with internal standards and the one extracted without internal standards. e) Perform derivatisation with auto-sampler for FAMEs analysis.

The study performed a second lipid extraction for protein purification that combined 300 µL lipid extracts with 125 µL ammonium acetate solution (150 mM) for additional phase separation. For each sample, a 40 µL aliquot of the lipid extracts was derivatised with 20 µL trimethylsulfonium hydroxide (TMSH) for fatty acids analysis, and a 10 µL aliquot of the lipid extracts was re-suspended with 990 µL chloroform/methanol (1:2 with 0.01% butylated hydroxytoluene) and 5 mM ammonium acetate for lipid species analysis.

Method validation and stability test

The proposed analytical method was validated with S. cerevisiae Fali strain, which was obtained from AB Mauri, to check whether it is able to provide efficient,

accurate, reproducible and robust analysis of nutrient detection for microorganisms. Validation includes tests of system suitability, specificity, repeatability, reproducibility, linearity, accuracy, method sensitivity, and robustness. This validation was followed by the identification of potential parameters that may affect the throughput of nutritional analysis. Results of important nutrients from lipid and amino acid analysis were obtained and compared with the required level documented in literatures (see Chapter 2, Section 2.3).

3.2 Samples and Extraction Reagent Selection

3.2.1 Strains and Culture Conditions for Method Evaluation Rhodosporidium toruloides and Rhodotorula glutinis were stored in glycerol stocks frozen at -80°C, streaked onto agar plates and incubated at 28°C to obtain single colonies. Single colonies of R.glutinis and R.toruloides were used to inoculate a liquid starter culture.

Two types of sample sets which were cultivated with different liquid culture media were prepared for testing. According to the reconstitution procedure provided by Sigma-Aldrich, the first sample set was formulated with 500 mL Yeast Nitrogen Base (YNB) without amino acids and ammonium sulfate and 1% glucose as carbon source (0.85 g YNB w/o amino acid and ammonium sulfate medium powder; 2.5 g ammonium sulfate powder; 5 g glucose) at pH 6.0. The pH was adjusted by adding 10 M NaOH dropwise. The other sample set was formulated with 500 mL YNB with amino acids and 1% glucose as carbon source (3.34 g YNB with amino acid medium powder; 5 g glucose) at pH 6.0. The use of ammonium salt as the sole nitrogen source is preferred to identify what amino acids the microorganisms produce rather than assimilate from the media.

Precultures was prepared in a 5 mL medium in 50 mL tubes; this step was conducted to provide consistent culture density and prevent overgrowth of culture. The liquid culture was prepared from the preculture broth in 50 mL culture medium in 500 mL shake flasks with an initial optical density of approximately 0.1 to 0.2 at 600

nm. Both preculture and liquid cultures were incubated at 28°C for 24 hours with shaking at 200 rpm.

3.2.2 Strains and Culture Conditions for Nutrient Profile Screening and

Characterisation

The samples for this step were provided by Remya Purushothaman Nair. Two types of sample sets which were cultivated with different growth media were obtained for testing. The first sample set contained growth media that were prepared by mixing 60 g.L-1 low temperature rendering stickwater residue, which was supplied

-1 -1 by Australian Country Choice in Queensland, with 0.5 g.L MgSO4, 1 g.L K2HPO4. The other sample set contained growth media that were prepared by mixing 1 g.L-1

-1 -1 -1 glucose, 1 g.L yeast extract, 0.5 g.L MgSO4, and g.L K2HPO4, which was used as a reference medium. The biomass of all testing samples is detailed in Table 3.1.

Table 3.1. A summary of the sample size of nine microorganisms for nutrient profiling. Wet cell weight of biomass used for extraction (mg) Sample Name Stickwater based medium Reference medium

Aspergillus oryzae 35 45 Thielavia terricola 30 30 Mortierella isabellina 20 32 Rhodotorula glutinis 27 33 Rhodotorula dairenensis 21 32 Rhodosporidium toruloides 30 30 Rhodotorula mucilaginosa 30 24 Geotrichum candidum 30 25 Saccharomyces cerevisiae 30 29

3.2.3 Strains and Culture Conditions for Method Development and Validation

Saccharomyces cerevisiae provided by Remya Purushothaman Nair was used for method development and validation. Yeast was cultured in media with 15 g.L-1 glucose and 15 g.L-1 yeast extract. The obtained biomass was washed with phosphate-buffered saline solution, freeze dried, and stored at -20 °C.

3.2.4 Solvent and Reagent Selection

The literature emphasises that the selection of solvent used for lipid extraction has a strong influence on the analytical specificity and sensitivity of analytes [5, 7]. Solvent Methyl tert-butyl ether (MTBE) instead of chloroform or hexane has been used in this study as the optimised method for lipid extraction. MTBE-based phase separation allows cleaner and faster lipid recovery of most of the major lipid classes, which is a prerequisite for lipid profiling of complex biological samples.

A great proportion of lipid extract is composed of nonvolatile compounds that are difficult to separate by GC, hence derivatisation is required for fatty acid analysis using GCMS. Methylation with derivatisation reagent TMSH generates methyl derivatives that convert lipids to corresponding FAMEs in one simple transesterification reaction. In contrast to trimethylsilyl (TMS) derivatisation, TMSH derivatisation allows complete derivatisation at room temperature due to the high reactivity of this reagent [8]. In addition, TMSH derivatisation is fully water compatible. Water removal is required for GC analysis, mainly because water produces approximately 7 times higher vapour volume than that of the same amount of non- polar solvent in MS. The large amount of vapour decreases the vacuum in the ion source of the mass selective detector, which leads to poor sensitivity and ionisation efficiency of analytes [9]. Removal of excess water is not required for TMSH derivatisation, which makes the process very convenient and efficient.

3.3 Instruments

3.3.1 Polar Metabolite Analysis The amino acid analysis was examined through a chromatographic separation technique using triple quadrupole LCMS. Multiple data tests were recorded for analysis of each sample set.

Triple quadrupole LCMS, as shown in Figure 3.2, is capable of acquiring multiple reaction monitoring (MRM) transitions for high molecular specificity and detection sensitivity analysis. In other words, LCMS enables detection of all required amino acids

in one injection. This instrument consists of three quadrupoles, which are Q1, Q2 and Q3. Q1 and Q3 act as mass analysers, while Q2 is used as a collision cell. There are three basic elements for operation: ionisation unit, mass analyser and detector. The components of the sample mixture are separated depending on their chemical affinity with the mobile and stationary phases. The ionisation of the sample mixture occurs after repeated sorption and desorption activity that occurs when the liquid extract interacts with column material at the stationary bed. By adjusting the voltages, ions that are stable with a certain mass will pass through to the collision cell, and hit the detector at the end of process, whereas unstable ions such as carrier solvent will hit the sides of the rods and never reach the detector. Collision is an important process for ion separation in Q2; it is filled up with adjusted energy of helium gas, and the ions with collision gas will lead to fragmentation of analytes. The detector is a secondary electron multiplier; all quantitation ions, reference ions, and internal standard ions will be detected and collected at this step [10].

Figure 3.2. Shimadzu triple quadrupole Liquid Chromatograph Mass Spectrometer 8050 technology. This technology has high capability for the trace-level free amino acids identification, profiling, and quantitation [10].

Therefore, ten essential and non-essential amino acids were analysed with electrospray ionisation and detected in positive MRM transitions. Normalisation of MRM results must be performed for quantification, because the dwell time, collision energy, and ionisation efficiency of each amino acid are different. The spectra results were firstly normalised to individual amino acids in a standard with known concentration. That is, the injection concentration of both free amino acid and protein-bound amino acid were calculated in regard to the six-point linear calibration curve of Amino Acid Analytical Standards. The actual mass of amino acids from biomass were then calculated by normalising the results of injection concentration to

13 the recovery rate measured from surrogate standard Cvaline, in conjunction with the total nitrogen result that was obtained before extraction.

As described in Section 3.1.2, the polar filtrates were vacuum evaporated and re-suspended with 0.1% formic acid in MS-grade water. 1 µL of resuspended polar extracts were picked up by auto-sampler and injected onto F5 column (Sigma-Aldrich) for interaction as well as ionisation. Ions were scanned using the method of “scheduled MRM analysis” (refer to Appendix B for detail). This method analyses analytes in a positive MRM scan with scheduled retention time, which provides selective and sensitive quantification for target analytes.

3.3.2 Non-polar Metabolite Analysis The fatty acid analysis was examined through an electron ionisation (EI) technique using GCMS. Multiple tests were recorded for analysis of each sample set.

GCMS that shown in Figure 2.3, is the instrument that vaporises the non-polar molecules and separates and analyses the various fatty acids within sample extracts. The gas chromatography portion separates the non-polar mixtures into individual pulses of pure FAME molecules by using a temperature-controlled capillary column with a highly polar biscyanoprophyl stationary phase, and then the mass spectrometer identifies and quantifies the compounds [11]. The separation of organic compounds is based on the different boiling points, as well as different strengths of interaction of high energetic electrons with gas phase atoms at the stationary phase [11]. In other

words, compounds with a lower boiling point and weaker interaction strength in the stationary phase can be detected by GCMS first. In this case, SFAs or fatty acids with shorter carbon chain appear first, followed by UFA or fatty acids with longer carbon chain. Ideally, the retention time will aid in differentiating between similar compounds within the sample solution; each compound will produce its corresponding spectral peak, which can be recorded in the chromatogram, and the size of the peak is proportional to the quantity of it.

Figure 3.3. Shimadzu Gas Chromatograph Mass Spectrometer 8040 technology. This technology provides identification, profiling, confirmation and quantitation of low abundance free fatty acids [12].

The fatty acid profile of a sample can be defined by comparing the peaks observed from MS spectra results to the peaks of Food Industry 37 FAME Mix; only those peaks which give the same retention time and fragmentation were defined for quantification. Peaks that were not included in the FAME Mix spectra result can be defined by comparing the observed spectra to the spectra registered in the GC mass spectral library; the one with the highest similarity and its corresponding concentration will be present in the qualitative spectrum table. In addition, since EI causes much fragmentation of a molecule, its spectral pattern is useful for the identification of SFA, monounsaturated fatty acid (MUFA) and polyunsaturated fatty

acid (PUFA). Take SFA C 22:0 as an example (see Figure 3.4): the resulting spectra with + the highest amount of ion at m/Z = 74 [CH3OCO(CH2)] has a special significance, as it is the McLafferty rearrangement fragment ion, which indicates that it is unlikely to have double bonds in the carbon chain of this molecule [13]. The ion at m/Z = 323 [푀 − 31]+ represents loss of a methoxyl group, which confirms that product ion m/Z = 354 [푀]+ is indeed an ester derivative of SFA C22:0. Therefore, the GCMS technology is capable of identifying and quantitating the fatty acid composition and total lipid contents in samples.

Figure 3.4. The mass spectrum of methyl docosanoate (C22:0) and the formation of McLafferty rearrangement ion. The molecular ion at m/Z 354 represents methyl docosanoate, which is also known as docosanoic acid methyl ester. The ions at m/Z 323 and 311 represent loss of a methoxyl group and C3 unit, respectively. The ion at m/Z 74 is important for identification purpose as it is the evidence of McLafferty rearrangement [13].

As described in Section 3.2.4, derivatisation was performed in order to transform lipids to the methyl ester conformation. Non-polar extract samples were transferred to glass vials and placed into auto-sampler trays, as shown in Figure 3.5, for auto derivatisation procedures. The auto-sampler in combination with the Gerstel Maestro software provide efficient and identical treatment of all samples, with automation for sample derivatisation. Multiple analyte derivatisations were

performed in advance during GC analysis of the preceding sample. Hence, approximately 10% of the overall run-time can be reduced for sample analysis, which is ideally suited for routine/batch analysis [14].

40 µL of non-polar extracts were picked up by auto-sampler and injected with 20 µL TMSH reagent. Samples were then transferred to the agitator and agitated for 10 minutes at 60 °C, 500 rpm to enhance derivatisation. A syringe needle was rinsed thoroughly with 50% ethyl acetate and 50% acetone after each use. 1 µL of each derivatised sample was injected and ions were scanned using the method of “FAME_37mix_comprehensive” (refer Appendix B for details). This method was scheduled for a total 30 minutes run-time; analytes were injected for analysis after 4 minutes to ensure the injector of the GC reached 250 °C; hence complete derivatisation can be obtained and can detect fatty acid from C4:0 to C24:0. At least one injection of 37 FAME Mix was performed before sample testing for retention time locking purpose.

Figure 3.5. Gerstel Maestro auto-sampler. This equipment enables auto sample derivatisation and injection for batch analysis.

Q-trap triple quadrupole LCMS has been used for compound screening and selective lipid detection and quantification from lipid extracts of biomass. It is hybrid quadrupole technology powered by a linear ion trap (LIT) analyser. This type of high 49

resolution mass analyser facilitates direct infusion electrospray ionisation (ESI) MS for the simultaneous analysis of multiple lipid classes without prior ion separation [5, 15]. Multiple tests were recorded for analysis of each sample set.

Q-trap triple quadrupole LCMS, as shown in Figure 3.6, is the instrument that provides tandem mass spectrometric techniques such as precursor ion scan (PIS) and neutral loss scan (NLS), which enable simultaneous acquisition of multiple lipid classes in one analytical run [16]. All individual lipid species that are within the detected lipid classes can be identified. Similar to triple quadrupole LCMS, the quadrupole Q1 and Q3 of Q-trap LCMS are operated as mass analysers, while Q2 is operated as a collision cell. This instrument provides higher sensitivity and receives a higher response for heavy molecules like lipids, and is capable of covering a molecular mass range from 0 to 1200.

Figure 3.6. SCIEX Q-trap 6500 technology. This technology has high capability for the trace- level lipid species identification, profiling, and quantitation [17].

The detection of specific lipid classes can be achieved by varying ESI parameters such as scanning mode and collision energy. Both PIS and NLS are screening scans; PIS is used when the head group of lipid molecules is lost as a charged fragment, whereas

NLS is used when the head group of lipid molecules is lost as a neutral fragment. In PIS, all ions are scanned in Q1, then the precursor ions collide with collision gas and a certain collision energy in Q2 to break down analytes to specific fragment ions; only those compounds that give the specific fragment ion are allowed to pass Q3 and are detected by the detector. In NLS, the precursor ions are collided in Q2 to create fragments; both Q1 and Q3 mass analysers are scanned simultaneously, but Q3 is offset by the neutral loss under detection; only those compounds with specific loss of fragment ions are detected [16, 18]. Spectra results of PIS and NLS provide the molecule information of lipids, such as the number of carbon double bonds within the carbon chain; however, the limitation is that they are not capable of defining lipid isomers. Production ion scan is a structural scan, which is used for obtaining isomer structural information for lipid species. Q1 is set to select the transmission of one m/Z value which is equivalent to the product ion to be detected; the ions are then collided in Q2 to create product ions and their corresponding fragments, and finally ions are scanned through Q3 and detected by detector [18]. The fatty acid fragments observed from product ion spectra are able to define the possible formation of lipid isomers; however, the limitation of this method is that it is not capable of defining the position of double bonds.

As described in Section 3.1.2, a 1:100 dilution was performed; 10 µL lipid extracts were re-suspended with 990 µL 1:2 chloroform/methanol 5 mM ammonium acetate (with 0.01% BHT) solution for MS analysis. Hence, neutral lipid molecules can be analysed as adducts with hydrogen or ammonia cations in a positive ion scan, or hydrogen or acetate anions in a negative ion scan, to confine their m/Z value on spectra. 100 µL of resuspended polar extracts were picked up by the auto-sampler and injected onto the LC column for ionisation. Ions were scanned using either the positive ion scan method or negative ion scan method (refer Appendix B for details). Both methods analyse analytes with scheduled ESI parameters, which provide selective and sensitive quantification for target analytes. The obtained ion signal of each m/Z value in the spectra can be accurately assigned to individual lipid species. The size of the peak is proportional to the quantity of analytes; they can be quantified after normalisation with internal standards. 51

The selective detection of carbon double bond positions within lipids requires an instrument called Orbitrap LCMS (see Figure 3.7). This instrument has a similar operating mechanism to Q-trap, but with additional ozone generator and Orbitrap mass analyser. The analytical method relies on the gas phase ion-molecule reaction between ozone vapour and a mass of selected unsaturated lipid ions in the quadrupole linear ion trap mass spectrometer. Two primary product ions are formed from individual carbon double bonds based on this reaction [19, 20]. Collision-induced dissociation (CID) has been applied together with ozone-induced dissociation (OzID) in this analytical method. The method is able to identify the isomeric lipids, as well as the double bond position to be assigned to a specific fatty acid at a specific sn-position. In addition, Orbitrap has the capability to catch more ions in one scan, which increases the resolution for analytes detection. The high resolution MS analyser is able to reduce the tolerance of the mass to charge value to 2 ppm, which enhances the precision of lipid species quantification and characterisation.

Figure 3.7. High-resolution MS technology Orbitrap Elite. This technology is capable of screening, quantifying and defining structural information of lipid species in one analytical run [21].

In this study, the chemically selective reaction is observed with sodium adducts [M + Na]+. As an example for structural characterisation of lipid using OzID, Figure 3.8(a) shows the mechanism of PC 16:0/18:1 (n-9) under OzID transitions.

According to Criegee’s mechanism of ozonolysis [22], the reaction between ozone and sodium adduct ion [PC 16: 0/18: 1 (n − 9) + Na]+ yields two fragment ions at m/Z 672 and 688, which are an aldehyde ion and criegee ion respectively. The formation of these two ions represents an overall neutral loss of 110 Da and 94 Da from the precursor ion at m/Z 782, which confirms the presence of an n-9 double bond position [22]. Figure 3.8(b) demonstrates the mechanism of CID/OzID for regioisomer (sn- isomer) determination. Sodium adduct ion [PC 16: 0/18: 1 (n − 9) + Na]+ loses the headgroup and forms a fragment at m/Z 599 after CID. Subsequent OzID yields fragment ions at m/Z 379, representing that the acyl chain C16:0 is located at sn-1 position from the precursor ion at m/Z 782 [22, 23].

Figure 3.8. Specific structural characterisation of PC 16:0/18:1 (n-9). (a) Proposed reaction mechanism of PC 16:0/18:1 (n-9) with OzID neutral loss scan. (b) CID/OzID charged ion fragment mechanism of PC 16:0/18:1 (n-9).

The sample solution was prepared in chloroform/methanol with sodium acetate to assist adduct formation under ESI conditions. 10 µL lipid extracts were resuspended with 990 µL 1:2 chloroform/methanol 100 µM sodium acetate (with 0.01% BHT) solution for MS analysis.

3.4 Resources and Funding

This research project is situated within Prof. Speight’s biotechnology research group at QUT, funded by The Australian Meat Processor Corporation (AMPC) as part of the AMPC-QUT Integrated Scholarship Scheme in Process Engineering. The researcher is an AMPC scholarship holder that has support from QUT with a QUT Higher Degree Research Tuition Fee Sponsorship.

In this research project, laboratory facilities such as fume hoods, incubator, centrifuges, pipettes, biologicals, chemicals etc. were required for microbial culturing, as well as for metabolites and lipid extraction. LCMS and GCMS are the required chemical substance identification facilities for specific metabolite and lipid chemical species detection, and Skyline, Lipidviewer, PeakView, RStudio and GCMS simulators are the software for result analysis. All equipment is readily available at QUT; however, nutritional analyses such as proximate analysis, neutral detergent extraction, and acid detergent extraction were required to use external services for composition analysis. Training on LCMS and GCMS facilities, as well as microbial culturing, was necessary for the researcher to obtain full access.

3.5 Research Procedure

Table 3.2 below represents the key tasks undertaken for the study, outlining the procedure across and within the techniques used in the study for collecting and analysing data.

Table 3.2. Key Tasks

Phase Task Description

Identify the chemical composition of wastewater streams and Waste streams analyses streams with high content of bulk fat and suspended solids.

Liquid culture microbes on waste streams and reference medium. Microbial culturing Analytical Method Evaluation Identify strains with high growth rate or biomass yield. and Analytes Profiling Phase Polar and non-polar Amino acid, fatty acid and lipid species profiling of different strains. metabolite profiling and Stability study will be also conducted in order to validate the qualification analytical method is sensitive, reproducible and accurate.

Define the appropriate internal standards, positive and negative Optimise extraction control for nutrient quantification. Optimise homogenisation method procedures.

Identify factors that affect the completeness of the hydrolysis Amino acid analysis process, and develop analytical method for chemical substances that development affected by the protocol MS method. Method Development Phase Develop analytical method to identify all lipid classes of testing Advanced lipid analysis samples, as well as the structral information of selected lipid species, development in order to enhance the identification of individual molecular species of lipid.

Optimise operating parameters in order to enhance analyte Optimise analytical method detection and quantification.

Stability study of proposed analytical methods was conducted in Validate proposed Method Validation Phase order to provide efficient, accurate, reproducible and robust analysis analytical method for complex biological sample characterisation.

Selection of value added Define the nutritional valuable metabolite in the selected microbes Evaluation Phase nutrients that is required for specific animals.

3.6 Participants

The investigation of this research project involved Remya Purushothaman Nair from the AMPC-QUT research scholarship team. The biomass of different tested strains was provided by Remya. Advanced metabolite analysis and the utilisation of LCMS and GCMS instrumentation benefitted from a strong supervisory contribution by Dr. Sadowski. Advanced lipid analysis was supported by Prof. Blanksby and Dr. Poad.

3.7 Data Analysis Strategy

This section provides an overview of the intended strategy for analysing data which were obtained through various techniques. Data analysed helped to profile nutrients of each tested microorganism.

Spectra data analysis is time consuming. It is crucial to use analyst software (e.g. software program/package included in instrument) for general result checking prior to detailed composition analysis. The analyst must check if all samples are tested under consistent operating condition. Poorly shaped peaks which contain a steep front or a drawn-out tailing should be removed from the database, since this kind of peak indicates traces of contaminant within the sample extract, or the concentration of analyte may be too low to be quantified. Peaks at a non-horizontal background noise level should be also removed from the database, because high level background noise may lead to invalid detection of analytes.

Spectra results observed from the amino acid analysis are analysed using a software package called Skyline. This software tool offers direct import of raw MS data, and performs peak integration for quantitative analysis of small molecules such as amino acids. Skyline defines productions and precursor ions of individual amino acid based on their mass to charge value, and defines the collision energy and expected retention time by exporting setting parameters from the analytical method. All amino acids would produce at least two transitions for identification. However, some amino acids need to be identified by analysing the transition value along with retention time. For example, lysine and glutamine are identified mainly by their retention time, since they produce the same transition ion of m/Z 130.1 and m/Z 84.1. The product ion peak can be considered as a valid result for content analysis when both transition peaks are aligned nicely together, and shows CV<20% on a minimum of three technical replicates.

GCMS Solution is the software used for fatty acid data analysis. This software is included with the Shimadzu GCMS instrument package, which offers real-time data acquisition, post-run data processing, and statistical analysis. The integration area has

been changed to a small value for auto peak selection, so that all possible peaks were marked and recorded to the spectrum table. As described in Section 3.3.2, the corresponding chemical compounds can be identified by using the method of similarity comparison between the observed spectrum result and the spectrum from the library; the one with the highest similarity and its corresponding concentration was presented in the qualitative spectrum table for further analysis. The quantifications of fatty acid content present in the original samples can be conducted by using the response factor of internal standard C19:0 to normalise the peak area of analytes, followed by the equation as shown below [12]:

푃퐴푎푛푎푙푦푡푒 퐶퐼푆 푅푒푠푝표푛푠푒 푓푎푐푡표푟 = × 푃퐴퐼푆 퐶푎푛푎푙푦푡푒

Hence,

푃퐴푎푛푎푙푦푡푒 [ 푃퐴 × (퐶퐼푆 × 푉퐼푆)] × 푀푎푛푎푙푦푡푒 푚 = 퐼푆 푎푛푎푙푦푡푒 퐴푚표푢푛푡 표푓 푏푖표푚푎푠푠 푓표푟 푒푥푡푟푎푐푡푖표푛

Where:

푃퐴푎푛푎푙푦푡푒 = Baseline corrected peak areas of observed individual analyte, ranging from C4:0 to C26:0

푃퐴퐼푆 = Baseline corrected peak areas of internal standard C19:0

퐶퐼푆 = Concentration (nM/mL) of internal standard C19:0

푉퐼푆 = Injection volume (µL) of internal standard C19:0

푀푎푛푎푙푦푡푒 = molecular weight (g/mol) of observed analyte, ranging from C4:0 to C26:0

푚푎푛푎푙푦푡푒 = mass (mg/g) of observed analyte from biomass, ranging from C4:0 to C26:0

In order to process lipid content analysis and characterisation, the software tool Lipid View was utilised. Accordingly, lipid species analysis was carried out on the spectra results collected from Q-trap LCMS detection. Lipid View has the capacity to

identify and quantify lipid species from electrospray mass spectrometry data, and it enables lipid profiling by matching the ion mass of fragments with reference data stored in the lipid fragment database, which contains over 25,000 lipid molecular species. The software is performed with the following parameters: (1) ESI LSMS is set as default instrument type; (2) the mass tolerance of mass to charge value is ±0.3; (3) the threshold of S/N is set to 3 for identification and 10 for quantification; (4) the threshold of relative intensity percentage is 0.2. That is, Lipid View only identifies peaks with intensity greater than 0.2% of the total intensity counts. (5) T-test is the statistical test applied in Lipid View for data validation. (6) The de-isotope algorithm was implemented in data analysis in order to consider natural isotopic masses in sample results.

Metabolite profile of biological samples can result in up to hundreds of analytes and there are numerous variables available. Multivariable regression modelling is necessary for compositional analysis of different microorganisms. Hierarchical Cluster Analysis (HCA) together with Principle Component Analysis (PCA) were employed to statistically evaluate mass spectra results of both polar and non-polar metabolites. These two techniques were performed using R Studio software. All spectra results were normalised before HCA and PCA statistical analysis. HCA is a two-way display technique for visually displaying the similarity of input data using a clustered heat map and dendrogram. HCA attempts to group variables that have similar features into clusters. Input variables would form a data matrix with colour, and the displayed colour is proportional to the scale of the colour gradient [24]. In other words, HCA is capable of classifying heterogeneous data into relatively homogeneous clusters based on input features. For example, strain A and B would form a cluster if the composition ratio of their amino acid content was found to be similar to each other. PCA is the most commonly used technique which is able to preserve the majority of the variance while reducing the dimensionality of multivariate input data [25, 26]. PCA illustrates the similarities and dissimilarities between samples based on input features. For example, samples that cluster closely together would have more similar nutrient profiles than samples that are further apart.

3.8 Limitations

In order to quantify analytes from complex biological samples, MS technologies were primarily used. However, limitations were found with regard to repeatability and reproducibility due to the high diversity of chemical composition of different microorganisms, instrumental drifts (e.g. retention time drifts), as well as variations in ionisation efficiency for different analytical runs [27]. These limitations may skew the validity of analyte profiling. The potential limitations of the proposed analytical methods can be summarised as follows:

1) MS technologies provide analyte quantification with high molecular specificity and sensitivity. The detection limits such as lower limit of detection and upper limit of detection for LCMS and GCMS have relatively lower values compared to other technologies. Hence, samples require severe dilution before testing, leading to interference of matrix effects, and increased error tolerance of the final result. In addition, analytes from biological samples may have wide concentration ranges; spectrum intensity of analytes with low concentration may fall outside the analytic range when dilution is utilised to obtain quantitative measurement. 2) The contamination of the MS ion source or deterioration of the analytical column can cause MS detection instability. Any remaining residual substance inside the column or quadrupole may result in increased level of background noise, which leads to difficulty in analyte quantification. Regular instrument maintenance is required. Analysis results will not be reliable if the instrument is not maintained properly. 3) The LCMS spectra results of analysed compounds are variable due to different ionisation efficiency and unpredictable adduct formation in ESI. It is hard to reproduce the fragmentation even using the same type of instrument. In order to monitor chemical and physical losses and variations during analytical runs, an internal standard with a known concentration is required to be added prior to metabolite analysis by LCMS.

3.9 Summary

In conclusion, this chapter provided a detailed description of the research design and analytical methods that were applied to this study. The proposed analytical methods were used to fulfil the objectives and research questions for this study. The chapter details the operating principle and techniques of each instrument used, as well as the methods for qualitative and quantitative data collection and data analysis.

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Chapter 4: Evaluation and Use of an Analytical Method for Rapid Nutrient Profiling of Yeast and Filamentous Fungi

A rapid analytical method is proposed and evaluated for nutrient profiling and screening of fungi and yeast in this chapter. It demonstrates the use of Mass Spectrometry for analyte detection at trace levels, including analysis of amino acids, fatty acids, and complex lipids. The correlation between nutrient profiles and growth conditions of different microbes was analysed by performing Hierarchical Cluster Analysis and Principal Component Analysis statistical analysis. Results showed that microbes cultivated in a medium with enhanced lipid levels may induce metabolic changes in microorganisms, including enhanced production of sphingolipids.

4.1 Introduction

A review of MLA reports showed that the red meat processing industry can generate up to 315 kL of rendering stickwater daily [1]. The conversion of meat processing stickwater to protein or lipid enhanced microbes is a novel strategy to reduce the costs associated with wastewater treatment [1]. Moreover, rapidly growing global livestock industries are particularly interested in high quality nutrients to supplement formulated feed. Microorganisms offer rapid growth and high nutrient content (e.g. proteins and lipids), and they can be grown with minimal demands on environmental resources if waste streams are used in the growth media [2-4]. Hence it is proposed to produce microbe-derived feed supplements from inexpensive waste materials of meat processing, using different species of microorganism, including filamentous fungi and yeast. Several microbes across different classes were cultivated on different media in order to find out the range of their nutrient profiles.

Compositional analysis of feed is primarily for quality control, but such analysis is also important with regards to particular nutritional properties of feed and feed supplements for animal growth performance. The determination of crude protein and crude fat in feed or feed supplements via the proximate analysis method from the Association of Official Analytical Chemists is a well-established method, and it provides precise results [5]. However, this method has disadvantages in regard to the trace level detection of some targeted nutrient species. The quantification of amino acids, fatty acids, and complex lipids may provide better values to describe feed supplements [6]. Microbe-derived feed supplements contain both polar and non-polar compounds, which is a challenging matrix for determination of target nutritional residues and contaminants. In addition, some of the mentioned nutrients are only produced and utilised at trace levels [7]. Therefore, a highly sensitive analytical methods are required. The universal metabolite and lipid extraction protocol, which was developed by Dr. Sadowski, was adapted for the quantification of amino acid and lipid contents from microbe-derived supplements. This protocol requires less sample volume and experimental time. It is designed for rapid simultaneous extraction of both polar and nonpolar analytes from cells of microorganisms. Such a method could be applicable for high-throughput nutrient profiling with a group of different strains. Furthermore, the influence of different growth conditions on the nutrient profiles could be rapidly determined.

4.2 Materials and Methods

4.2.1 Strains and Culture Conditions Filamentous fungi isolates Aspergillus oryzae, Mortierella isabellina, Thielavia terricola, and Geotrichum candidum were collected from Dr. James Strong, Queensland University and Technology (QUT), Brisbane, Australia. Oleaginous red yeasts Rhodotorula glutinis, Rhodotorula dairenensis, Rhodosporidium toruloides, and Rhodotorula mucilaginosa were obtained from Dr. Leigh Gebbie’s culture collections. The Saccharomyces cerevisiae Fali strain was obtained from AB Mauri, Sydney. All strains used in this study were stored as glycerol stocks at -80°C.

The proposed analytical method was evaluated using oleaginous yeast R. toruloides and R. glutinis. For proteomic and lipidomic experiments, both strains were cultured on Yeast Extract-Peptone-Dextrose (YPD) agar plates at 28°C for three days. Colonies of yeasts were isolated and pre-cultured in Yeast Nitrogen Base (YNB) with and without amino acid medium at 28°C, 200 rpm for 48 hours. Growth of sample strains over time was measured by optical density experiments at a wavelength of 600 nm (OD600) using a Beckman Coulter UV/Visible spectrophotometer. The pre-cultured sample broth was diluted to 0.7 OD600, followed by growth in liquid cultured to stationary phase (approximate 9.3 OD600). Growth medium (supernatants) was discarded by centrifugation at 15,000 rcf for 15 min. The obtained biomass of cultured samples was washed with phosphate-buffered saline (PBS) solution and weighted in a pre-weighed Eppendorf tube.

All strains were cultured in two different growth media to stationary phase for polar and non-polar metabolite profiling experiments. The stickwater based medium was composed of 60 g.L-1 low temperature rendering stickwater solid residue, which was collected from the meat processing industry Australian Country Choice, Brisbane,

-1 -1 Australia, with 0.5 g.L MgSO4, 1 g.L K2HPO4 also added. The received stickwater residues were autoclaved at 121°C for 20 minutes prior to cultivation. The reference

-1 -1 -1 medium was prepared by mixing 1 g.L glucose, 1 g.L yeast extract, 0.5 g.L MgSO4,

-1 and 1 g.L K2HPO4. Sample biomass was obtained from Remya Purushothaman Nair (QUT); the wet weight biomass of all cultured strains is summarised in Chapter 3 Table 3.1.

4.2.2 Universal Polar and Non-polar Metabolite Extraction Protocol As outlined in Figure 4.1, the proposed protocol is for simultaneous extraction of polar and non-polar analytes from a complex bio-sample, including microbe biomass or cell culture/waste stream media. The method includes mechanical homogenisation, solvent based polar and non-polar metabolite extraction, hydrolysis, and derivatisation. Identification and quantification of amino acids was performed by liquid chromatography tandem mass spectrometry (LC-MS/MS) using targeted multiple reaction monitoring (MRM). The analytical parameters for individual amino

acids were modified on the basis of the method package from Shimadzu (i.e. pentafluorophenylpropyl (PFPP) column for 99 primary metabolites). Whereas the identification and quantification of fatty acids in the form of fatty acid methyl esters was conducted by gas chromatography-mass spectrometry (GC-MS) employing electron ionisation at 70 eV. Complex lipid species were examined through shotgun lipidomics, in which diluted lipid extracts were directly infused into either a Q-trap triple quadrupole LC-MS/MS or an Orbitrap LC-MS/MS.

PC/PE/PS/PG/PA/PI/ Positive ion scan QTRAP LC-MS/MS & CID/Ozone-induced SM/Cer/Ergo/TAG Dissociation Negative ion scan CL/IPC/MIPC/M(IP)2C Non-Polar AmOAc Lipid PIS/NL Scan Mode Phase Extracts GC-MS: FAMEs SFA/PUFA Methylation Quadrupole

Free Amino Acid Tri-column LC-MS Amino Acid MeOH/H 2 O Cell Lysate/ MTBE Extracts Sonication Supernatant Polar Triple quadrupole [Q3] Phase Cell culture pellet Polar Organic GC-MS: Sugar/Sugar OR Derivatisation Metabolites Carbohydrates Alcohol Waste stream Quadrupole medium sample

HCl Protein-bound Protein/Starch Tri-column LC-MS Amino Acid Hydrolysis Amino Acid Extracts Triple quadrupole [Q3]

Figure 4.1. Overview of the proposed analytical method for the nutrient profiling of microorganism biomass.

4.2.3 Chemicals and sample preparation Methyl tert-butyl ether (MTBE), acetonitrile, formic acid, MS-grade methanol

(MeOH), MS-grade water, and chloroform (CHCl3) were obtained from Thermo Fisher

Scientific (QLD, Australia). Ammonium acetate (NH4OAc), Trimethylsulfonium hydroxide (TMSH), Butylated hydroxytoluene (BHT), Ergosterol standard, Cholesterol standard and Amino Acid standards were obtained from Sigma-Aldrich (NSW, Australia). Fatty acid methyl ester (FAME) standards were obtained from Restek (LECO, Australia). Phospholipid standards were obtained from Avanti (Alabama, USA).

All chemicals were obtained in the highest purity grade available for MS analytical purposes.

Sample biomass was analysed in biological triplicate for nutrient profiling, using the proposed analytical method. Each replicate was resuspended in 200 µL ice-

13 cold MeOH (containing internal standard Cval, 16.24 nmol), and homogenised by TissueLyser using tungsten carbide beads (3mm, Qiagen). A hot methanol extraction was conducted in order to enhance metabolite extraction. 350 μL Water and 400 μL MTBE was added to cell lysates for polar and non-polar metabolite extraction. Phase separation was done by centrifugation at 15,000 rcf for 20 min. Thereby, lipids were separated from proteins and cell debris.

300 μL of the upper non-polar phase was collected and re-extracted with 125

μL NH4OAc solution (150 mM, in MS-grade water). 40 µL aliquot of the lipid extracts was derivatised with 20 µL TMSH for fatty acids analysis. 10 µL aliquot of the lipid extracts was diluted with 990 µL chloroform/methanol (1:2 with 0.01% BHT) and 5 mM ammonium acetate for lipid species analysis. 400 µL of lower polar phase was collected and filtered with 0.22 µM syringe membrane (13mm diameter). 50 µL polar extracts were vacuum evaporated. The polar extracts were then diluted with 0.1% formic acid (v/v) approximately 100-fold for free amino acid analysis.

Metabolites extraction and detection was performed as described in Appendix B and Appendix C. A loading test was conducted to optimise the injection concentration. Each replicate was randomly tested in order to remove any systematic bias.

4.2.4 Instrumentation: Instrument Selection and Instrumental Setup

Liquid Chromatography Determination of Amino Acid. A triple quadrupole LC- MS/MS (8050 Shimadzu) was employed for identification and quantitation of amino acids from microbes. This instrument is equipped with Nexera ultra-high performance liquid chromatograph (UPLC) and ultrafast response MS detector [8]. Data acquisition, recording and chromatographic integration was performed by Labsolution Real-time

Analysis. The chromatographic separation of analytes was achieved using Discovery HS F5 HPLC column (15 cm x 2.1 mm, 3 µm).

The mobile phase solutions were prepared by dissolving 1 mL formic acid in 1000 mL MS-grade water for pump A and dissolving 1 mL formic acid in 1000 mL acetonitrile for pump B. Solutions were filtered through 0.22 µM membrane and degassed in a sonicator. The flow rate was set at 0.25 mL/min in a binary gradient mode and the injection volume was set at 10 µL for all sample testing.

MS data were processed and analysed by Skyline software version 4.20 [9]. The obtained MS spectra was smoothed using the Savitzky-Golay algorithm. Analyte quantification was performed using the most abundant/intense ion transition peak, whereas the second transition peak was used for analyte identification according to the area ratio of two ion peaks calculated from standards [10]. The concentration of each target analyte was calibrated based on the concentration of amino acid

13 standards, and re-calibrated based on the recovery of internal standard Cvaline.

Gas Chromatography Determination of Fatty Acid. For fatty acid analysis, mass spectra of fatty acid methyl ester (FAMEs) derivatives were detected and recorded using triple quadrupole GC-MS/MS (8040 Shimadzu) equipped with GERSTEL MPS Auto-sampler. The separation of FAMEs was performed using a Restek Rtx-2330 GC column (60m length, 0.25mm internal diameter, 0.2 µm 푑푓). The separation of cis- and trans- isomers of FAMEs can be resolved using this column.

The oven temperature was held at 100 Cͦ for 30 min before sample injection. The temperatures of injector and detector were 240 Cͦ and 260 C,ͦ respectively. The purge flow rate and pressure of carrier gas helium was set up as 3 mL/min and 145 kPa, respectively. Samples were injected in split mode, with a split ratio of 1:22 and injection volume of 1 µL. The mass spectrometer was operated using EI technique at 70 eV, with a scan range from m/z 50 to 500.

MS data were processed and analysed by GC Labsolution. The obtained MS data were smoothed using the Savitzky-Golay algorithm. FAMEs were identified by comparison to a library of m/z value, retention times, and MS/MS fragmentation spectra of authentic standards (37 food industry FAMEs standards from Restek). The

concentration of each observed fatty acid was calculated on the basis of the baseline corrected peak area ratio between fatty acid and internal standard C19:0.

Direct infusion analysis of complex lipids. Lipid analysis was carried out in the tandem MS mode (e.g., neutral loss, precursor ion, or product ion scan) on a triple quadrupole LC-MS/MS (QTRAP 6500 SCIEX). This type of instrument is equipped with prominent high performance liquid chromatograph (HPLC) and lonDrive high energy detector [11]. Lipid classes/lipid species were identified and quantified using the linear ion traps analyser system. Ozone-induced dissociation (OzID) and incorporated on an orbitrap mass spectrometer were employed for enhanced structural elucidation of lipid species [12].

For the LC-MS/MS system, the electrospray capillary was positioned at a distance of 0.5 mm to enhance ion production. The chromatographic separation of analytes was achieved using Kinetex C18 column (30 × 2.1 mm, 1.7 µm). Dual switchable mass range was set from 200 to 1200 Da. Data acquisition, recording and chromatographic integration were performed by LCMS Analyst software.

The mobile phase solutions for pump A and B were filled up with methanol with 5 mM ammonium acetate. The flow rate was set at 20 µL/min in a binary gradient mode, with a desired pressure to approximately 170 psi. Ion separation was performed by direct infusion of ESI with instrumental parameters as follows: the interface voltage was set to 5.5 kV for positive ion mode, and - 4.5 kV for negative ion mode; the default declustering potential (DP) was set to 40 V; and the default entrance potential was set to 10 V. Ion response of different lipid classes can be optimised by changing the value of DP or collision energy. The injection volume was set at 100 µL for all sample testing.

Detected lipid species were identified and quantified using Peak View and Lipid View software. The obtained MS spectra were smoothed using the Gaussian algorithm. Lipid profiling was done by comparing product ion and fragment ion masses to the lipid fragment database. For quantitative analysis, results were normalised to phospholipid standards (Avanti) for concentration calculation (Refer Appendix C for details). For preliminary nutrients profiling, the baseline corrected peak area of each

sub class lipids were normalised to their corresponding lipid classes to 100% for concentration estimation.

4.2.5 Preliminary Analytes Identification The proposed analytical method was evaluated using various microbes, which have significant differences in their physical or chemical properties. A wide range of nutrient species identifications were initially trailed in order to cover as much as possible of all nutrient substances that may be present in the microorganism. The nutrient profiling of microbes was determined based on their nutrient content, such as amino acids, fatty acids, and lipid species. The effect of operational factors on nutrient profiling was investigated. Operational factors include selection of extraction method, and instrument operating parameters such as targeted fragment ions, retention time, scan mode, scan range, collision energy, and declustering potential.

The mass spectral data were analysed using software as described in Section 4.2.4. Concentration of each target analyte was estimated after peak alignment, baseline peak area correction, blank removal, and normalisation. (Further details appear in Appendix E). All resulting data were imported and processed using RStudio algorithm for statistical analysis. The similarity in nutrient profile between the tested strains was evaluated using principal component analysis (PCA) with the mean of bio- triplicate as the variable. Two PCA were performed separately on the amino acids and lipid composition. The outcome was plotted with PC1 and PC2 dimension. The score loading was analysed using the bi-plot of PC1 versus PC2.

4.3 Results and Discussion

4.3.1 Analyte identification MS spectra were collected from triplicate cultures of oleaginous strains R.glutinis and R.toruloides under two growth conditions for analyte identification. In the early phase of method evaluation, data from the pooled biological quality control (PBQC) sample were used in order to define the instrument drift, as well as to obtain the operating conditions (e.g. retention time, scan mode, collision energy etc.) for

each targeted analyte. Data were signal corrected to remove peaks that passed the threshold with CV greater than 20%.

Amino acid profiling

Polar extracts of R.glutinis and R.toruloides cultivated under two media, YPD media with and without amino acids, were analysed using the LCMS/MS platform. According to the observed MS/MS spectra of amino acids, specific precursor ions and two specific fragmentations for that precursor, the quantifying ion and confirmatory ion, were selected for LC-MRM analysis [13].

The selected fragmentation properties for MRM are presented in Table 4.1 for detection, confirmation and quantification of ten essential and non-essential amino acids in the sample. Also included in the table are the observed values of average retention time and coefficient of variation (CV %) of baseline corrected peak area of all detected amino acids. Results show that the signal of amino acids was recorded as a function of time (i.e. retention time) from liquid chromatographic elution behaviour. Amino acids such as phenylalanine and tryptophan showed distinct retention times; however, the other amino acids have similar retention times. Amino acids such as histidine and lysine, or serine and aspartic acid, have minor time differences. In this case, the analyte detection is highly dependent on the targeted fragmentations.

Table 4.1. MS identification parameters for free amino acid detection. Identification of the essential and non-essential amino acids of polar extracts of PBQC sample (R.glutinis and R.toruloides). The analysis was performed by LCMS/MS using targeted MRM scan. Results show the average retention time and baseline corrected peak area CV (%) for free amino acid detection. The data are presented as average of three technical replicates from each of the bio- replicates (n=3).

Precursor Ion Quantifying Ion Confimatory Ion Average RT Peak Area Amino acid Abbreviation (M/z) (M/z) (M/z) (min) CV (%)

Arginine Arg 174.1 70.1 60.1 3.44 4.14 Histidine His 154.9 110.1 56.0 2.95 1.80 Threonine Thr 119.1 74.2 56.1 2.10 0.53 Lysine Lys 146.1 84.1 130.1 2.92 1.66 Valine Val 117.1 72.2 55.1 5.07 4.12 IS 13C_Valine IS 13C 122.1 76.0 59.0 5.07 6.02 Methionine Met 148.9 56.1 104.1 2.19 4.78 Isoleucine Ile 131.1 86.2 69.2 7.81 6.69 Leucine Leu 131.1 86.2 69.2 8.19 7.47 Phenylalanine Phe 165.1 120.1 103.1 8.67 7.89 Tryptophan Trp 204.1 188.2 146.1 10.86 1.59 Tyrosine Tyr 181.1 136.0 165.0 7.19 4.08 Proline Pro 115.1 70.2 28.1 2.63 1.85 Serine Ser 104.9 60.1 42.0 1.90 1.89 Aspartic acid Asp 133.0 74.1 88.1 1.90 4.93 Glutamic acid Glu 146.9 84.1 56.1 2.22 0.50 Glutamine Gln 146.1 84.2 130.0 2.02 2.47 Cysteine Cys 240.0 151.9 73.9 1.80 13.19 Glycine Gly 74.9 30.2 48.0 1.95 5.56 Asparagine Asn 132.1 87.2 28.1 1.88 2.53 Alanine Ala 88.9 44.1 n/a 2.23 0.30

The overall LC-MRM chromatogram of the amino acid profile obtained from polar extracts of R.glutinis and R.toruloides is presented in Figure 4.2. Results demonstrate that all analytes were well-resolved chromatographically. The chromatogram of amino acid was well separated and did not interfere with the others, which indicates that the injection concentration of target analytes was below the detector saturation level. The analyte detection is valid because the peak of internal

13 standard Cvaline is aligned closely to the peak of valine from the sample (at 푡푅 = 5.07 min). The results also show the PBQC of these two oleaginous yeasts contain all twenty amino acids. The proposed analytical method enabled identification of all amino acids

by MRM, using 1 µL of sample solution (sample extracts were resuspended in 0.1% Formic acid) in a single injection. The peak intensity of analytes presented in the Figure 4.2 only reflects the ionisation signal of individual analytes from the sample solution. It does not reflect the overall injection concentration of analytes because the efficiencies of compound ionisation of amino acids are different. According to the results, arginine appears to have a higher MRM peak than that of lysine, which does not correlate with the fact that there is more arginine than lysine in sample. Quantification of the analytes required standard calibration for each amino acid to work out the correct proportions from the calibration curve.

Figure 4.2. Overall LCMS chromatograms showing analysis of free amino acid from PBQC polar extracts of R.glutinis and R.toruloides. Simultaneous detection of 20 amino acids in a single injection.

Confirmation of amino acids was performed using criteria of two fragment ion transitions. Individual amino acids were identified, as the quantifying ion and confirmatory ion were aligned in the same region. A peak with higher intensity are the quantifying ion peak, also known as the target peak. Whereas a peak with lower intensity are the confirmatory ion peaks, also known as the reference peak. The relative intensity ratio between these two product ions of the amino acids was unique, and the ratio did not change with concentration, so can be used for analyte confirmation purposes [13]. Transition ion chromatograms of amino acids are shown in Figure 4.3. According to the observation from the product ion chromatogram, most of the amino acids demonstrated clear ion transition without interference from other target analytes. However, leucine and isoleucine are different amino acid compounds with the same molecular mass, both with an [M + H]+ ion at m/z 131.1, and they produce the same product ions at m/z 86.2 and m/z 69.2. Hence, they are identified through their retention time; isoleucine (at 푡푅 = 7.81 min) appeared before leucine (at

푡푅 = 8.19 min). Similarly, glutamine and lysine also have the same precursor ion [M + H]+of m/z 146.1 and produce the same fragments at m/z 84.1 and m/z 130.1.

They are differentiated by their retention time: 푡푅 is at 2.02 min and 2.92 min, respectively. Methionine had produced fragments at m/z 104.1 that were very weak and it was hard to distinguish them from the noise baseline; therefore, the confirmatory ion peak of methionine was removed from the analysis.

Overall, the observations indicate that the identification of amino acids was achieved through both targeted ion transitions (i.e. precursor-fragment pair) and corresponding LC retention time (i.e. chromatographic elution) of the individual analytes. Only the baseline corrected peak area of the quantifying ion was used for quantification. The peak area of analyte is proportional to its injection concentration. Standard calibration together with internal standard normalisation were required in the quantitative analysis in order to calculate the actual concentration of each analyte from sample biomass.

Figure 4.3. Total ion transition graphs of 20 amino acids. (a) Quantifying and confirmatory ion transition graphs of essential amino acid from PBQC polar extracts of R.glutinis and R.toruloides; (b) Quantifying and confirmatory ion transition graphs of non-essential amino acid from PBQC polar extracts of R.glutinis and R.toruloides.

Total fatty acid profiling

The GC-MS identification of fatty acids from hydrolysed and derivatised lipid extracts of yeast biomass are presented in Table 4.2. According to the fatty acid composition, it shows that these two strains were composed of six major fatty acids. The peak number represents the order of fatty acids detected by GCMS. Saturated fatty acids (SFA) and unsaturated fatty acids (UFA) can be detected and identified by specific fragmentation, in which the numbers of carbon double bonds within the carbon chain can be determined by the highest fragment intensity [14]. For example, a saturated fatty acid such as C16:0 would have highest abundance of fragment ions at m/z 74. In addition, the results show that unsaturated fatty acids can be distinguished from their unique fragmentation. Monounsaturated fatty acid C18:1 can be distinguished from polyunsaturated fatty acid C18:2 and C18:3 by their highest fragment intensity at m/z 55, 67, and 79 respectively.

It is shown that SFAs with shorter carbon chains appear in the earlier detection section, as compared to SFAs with longer carbon chain. And UFAs with more carbon- carbon double bonds appear in the later detection section. Results indicate that fatty acids with the same numbers of carbon-carbon double bonds were differentiated by their corresponding retention time. The recorded retention time was determined from the interaction strength of derivatised fatty acid compounds with the stationary phase. For SFA, retention time increases with the number of carbons within the fatty acid chain. Hence the observed SFAs from the sample were identified in this order: palmitic acid (C16:0), stearic acid (C18:0) and eicosanoid acid (C20:0). Whereas for UFAs that have the same carbon chain length, retention time increases with the number of double carbon bonds. The observed UFAs from the sample were identified in order of: oleic acid (C18:1), linoleic acid (C18:2), and α-Linolenic acid (C18:3). Results show that the proposed analytical method provided the required precision with relative standard deviation (%) less than 12% for all analytes.

Table 4.2. MS identification parameters for fatty acid detection. Total fatty acid profile of non- polar extracts of PBQC sample (R.glutinis and R.toruloides). The analysis was performed by GCMS/MS using targeted MRM scan. Results show the average retention time and baseline corrected peak area CV (%) for free amino acid detection. The data are presented as average of bio-triplicates.

Fragment RT Peak # Name %RSD M/z (min)

1 Palmitic acid C 16:0 74.00 14.22 2.00 2 Stearic acid C 18:0 74.00 16.25 0.80 3 Oleic acid C 18:1 cis n-9 55.00 16.85 3.00 5 Linoleic acid C 18:2 cis (n-6,9) 67.00 17.76 1.90 6 Eicosanoic acid C 20:0 74.00 18.26 11.50 7 α-Linolenic acid C 18:3 cis (n-3,6,9) 79.00 18.81 1.40

The total ion chromatogram from Figure 4.4 shows that the most abundant fatty acid species were detected from derivatised lipid extracts of the mentioned oleaginous yeasts. The ion chromatogram is data obtained from MS, which represent the changes of signal intensity of ions at specific m/z values upon time [15]. Confirmation of fatty acids was performed using criteria of ion fragmentation, as well as the GC retention time of each FAME standard. In other words, the observed peaks from the sample were compared to the peaks of the FAME standard, in order to check if they were aligned in the same retention time [16]. Results demonstrated that the retention time of derivatised fatty acids from the sample were similar to those obtained from the standards.

According to the observed total ion chromatogram, narrow peaks (i.e. width less than 2 s) and flat baselines were presented, which indicate good chromatographic separation was achieved for analyte quantification. The peak areas were presented corresponding to the signal response generated by the analytes from samples. In this case, peak area is proportional to the concentration of targeted fatty acid compounds. The fatty acid content was calculated on the basis of the peak area ratio in the total ion chromatogram, as analytes were ionised and detected with similar efficiencies [17]. The most abundant fatty acids of R.glutinis and R.toruloides detected in the total ion chromatogram were UFA C18:1 n-9, C18:2 (n-6, 9), and SFA C16:0 and C18:0. Minor

amounts of UFA C18:3 (n-3, 6, 9) and SFA C20:0 were also detected by the GC-MS analysis.

Figure 4.4. Superposition GC chromatograms for standards and derivatised non-polar extracts of R.glutinis and R.toruloides. RP2_W and RP2_W/o represent R.glutinis cultivated under YPD medium with and without amino acid, respectively. Whereas RP15_W and RP15_W/o represent R.toruloides cultivated under YPD medium with and without amino acid, respectively.

Lipid class/lipid species profiling

Shotgun tandem MS analysis was used in this study to rapidly profile the lipid content per wet weight biomass. It provides structural information and thus identification of complex lipids. For the preliminary profiling test, lipids were extracted from sample biomass without spiking any internal standards. The reason was that the method of internal standards was designed based on testing samples. The type and concentration of internal standards should be selected similar to the analytes that have close ionisation efficiency [18]. Spiking non-isotope labelled internal standards (i.e. the phospholipid standards, refer Appendix C for detail) to unknown biological samples may cause interfere with the lipid profile, because these complex lipids may present in microorganism.

Operating parameters for comprehensive lipid class identification are presented in Table 4.3 and Table 4.4. The choice of scan mode was determined according to the

possible charge polarity of lipids in the samples solution [19]. Phospholipids including phosphatidylcholine (PC), phosphatidylserine (PS), phosphatidylethanolamine (PE), phosphatidylinositol (PI), phosphatidic acid (PA), and phosphatidylglycerol (PG) were tested in either the positive or negative ion modes. Diacylglycerol (DAG), triacylglycerol (TAG), sphingomyelin (SM), and ceramide (Cer) were tested in positive mode with [M + H]+ ions. The glycosphingolipids, inositolphophoceramide (IPC), mannosylinositolphosphoceramide (MIPC), and mannosyldiinositolphosphoceramide − (M(IP)2C), were tested in negative mode with [M − H] ions. For multiple lipid class profiling, a series of 41 and 25 specific scan experiments were performed in positive and negative ionisation modes respectively. The use of precursor-product and neutral loss-product relationships enabled a better compound identification confidence [20]. Scanning range was set from m/z 400 to m/z 1200 in both scan modes in order to cover all expected lipid classes. The demonstrated lipid analytical method enabled detection of lipid species covering 14 major lipid classes.

Table 4.3. MS identification parameters for lipid detection and quantification using negative ion mode. Overview of the operating parameters for quantitative detection of lipid classes PS, PI, PA, PG, IPC, MIPC, M(IP)2C and phospholipid fatty acids using shotgun lipidomics analytical method in negative ion mode.

Negative Ion Scan Mode Lipid Species Scan Mass [Precursor Ion] Range CE (eV)

Phosphatidylglycerol PG Prec 153.0 580 - 1040 55 Phosphatidic Acid PA Prec 153.0 Lyso-Phosphatidic Acid LPA Prec 153.0 300 - 650 30 Phosphatidylserine PS NL 87.0 720 - 860 30 Lyso-Phosphatidylserine LPS NL 87.0 380 - 730 22 Phosphatidylinositol PI Prec 241.0 580 - 1040 65 Lyso-Phosphatidylinositol LPI Prec 241.0 450 - 730 45

Inositolphosphoceramide IPC Prec 241 650 - 1000 55/65 Mannosyl-inositolphosphoceramide MIPC Prec 403.1 690 - 1200 80 Mannosyl-diinositolphosphoceramide M(IP)2C Prec 241.0 400 - 730 50

253.2 [16:1] 600 - 900 255.2 [16:0] 640 - 930 55 269.3 [17:0] 277.2 [18:3] 660 - 950 279.2 [18:2] 40 281.3 [18:1] 670 - 960 283.2 [18:0] Phospholipid fatty acids Plipid FA Prec 297.3 [19:0] 301.2 [20:5] 55 303.2 [20:4] 305.2 [20:3] 690 - 980 40 307.2 [20:2] 309.2 [20:1] 311.2 [20:0] 55 327.3 [22:6] 710 - 1010 40

Table 4.4. MS identification parameters for lipid detection and quantification using positive ion mode. Overview of the operating parameters for quantitative detection of lipid classes PC, PE, PS, PI, PA, PG, SM, Cer, Ergosterol, DAG and TAG using shotgun lipidomics analytical

method in positive ion mode.

Positive Ion Scan Mode

Lipid Species Scan Mass [Precursor Ion] Range CE (eV)

Phosphatidylcholine PC Prec 184.1 640-850 40 Lyso-Phosphatidylcholine LPC Prec 184.1 490 - 590 35 Lyso-Phosphatidylethanolamine LPE NL 141.0 420 - 540 30 Phosphatidylethanolamine PE NL 141.0 680-830 30 Phosphatidylserine PS NL 185.0 720 - 860 25 Phosphatidylinositol PI NL 277.0 500-1000 30 Phosphatidic Acid PA NL 98.0 600 - 800 30 Phosphatidylglycerol PG NL 172.0 700 - 860 25

Sphingomyelin SM Prec 184.1 600-1000 40 Ceramide Cer Prec 264.3 530 - 730 20

Sterol Lipids Erg NL 77.0 350 - 500 15

311.2 [16:1] 570 - 670 313.2 [16:0] 327.3 [17:0] 600 - 630 337.3 [18:2] 339.3 [18:1] 600 - 700 Diacylglycerol DAG Prec 32 341.3 [18:0] 359.3 [20:5] 620 - 720 361.3 [20:4] 385.3 [22:6] 650 - 740 387.3 [22:5]

243.2 [14:1] 730 - 950 245.2 [14:0] 760 - 980 271.2 [16:1] 273.2 [16:0] 790 - 1000 295.3 [18:3] 297.3 [18:2] 790 - 1005 299.3 [18:1] 301.3 [18:0] 810 - 1020 319.3 [20:5] 321.3 [20:4] Triacylglycerol TAG NL 35 323.3 [20:3] 840 - 1050 325.3 [20:2] 327.3 [20:1] 345.3 [22:6] 347.3 [22:5] 860 - 1080 349.3 [22:4] 351.3 [22:3] 353.3 [22:2] 860 - 1090 355.3 [22:1] 357.3 [22:0] 880 - 1105

Lipid species were named by their corresponding molecular composition. Phospholipids such as PC, PE, or PI species were annotated as: [lipid class] [no. of total fatty acid carbons]: [no. of carbon-carbon double bonds]. Sphingolipids such as SM

and IPC species were annotated as [lipid class] [no. of total fatty acid and sphingoid base carbon]: [no. of double bonds]; [no. of hydroxyl groups] [21]. The mass to charge value (m/z) shown in this study refers to the monoisotopic mass of detected lipid ions [19]. Based on their monoisotopic values, the major ion signals in the spectrum can be correctly assigned to each lipid molecular species and provide structural information such as length of carbon chain and number of carbon double bonds.

Using PI as an example, Figure 4.5 (a) demonstrates the spectra resulting from the precursor ion scanning of m/z 241.0 in negative ion mode for the specific detection of PI and IPC. Both PI and IPC show formation of the polar head group fragment at m/z 241.0 as their common fragment ion, hence [M − H]− ions of PI and IPC from sample extracts can be specifically detected. Based on the precursor scan spectra, the following lipids were assigned: PI 34:1 (m/z 835.5), PI 34:2 (m/z 833.5), PI 36:2 (m/z 861.5), PI 36:3 (m/z 859.5), PI 36:4 (m/z 857.5). The ion signals observed in the precursor scan were verified using a product ion scan. Based on the product ion scan spectra, signal at m/z 940.7 and m/z 952.7 show formation of the polar head group fragment, hence they may assign as IPC 42:0; 5 and IPC 44:0; 4. However, the signal intensity of these two IPC lipids were present below the limit of detection level (i.e. the S/N ratio is less than 3.0), it requires the use of IPC internal standards for peak identification and quantification. Figure 4.5 (b) shows the product ion scan of the most abundant signal at m/z 835.5, resulting in the formation of fragments at m/z 241.0, 255.2 and 281.1, which represents the polar head group fragments (anion of inositol phosphate minus water), and fatty acid fragments of palmitic (C16:0) and oleic acid (C18:1), respectively. In addition, the observed fragment m/z 553.2 represents the neutral loss of 282 (C18:1) from PI 34:1, and fragment m/z 391.3 indicates an additional neutral loss of 162 (anion of inositol minus water) from lyso-PI fragment at m/z 553.2. The obtained fragment ions characterised the structural information of lipid PI 34:1, which PI (16:0_18:1) is the dominant PI presented in the sample extract.

Figure 4.5. Specific detection of phosphatidylinositol (PI) in lipid extract of R.glutinis and R.toruloides by negative ion scan. (a) Detection of PI by precursor ion scan using m/Z 241.0, - 55eV. The most abundant signals observed at m/Z 835.5 and m/Z 861.5 are PI 34:1 and PI 36:2 respectively. (b) Product ion spectra of the [M-H]- ion of PI 34:1 at m/Z 835.5, -55eV, with fatty acid fragment of 16:0 and 18:1, and polar head group fragment of PI at m/Z 241.0.

The identification of the most abundant signals in the ESI-MS of the total lipid extract of R.glutinis and R.toruloides biomass are summarised in Table 4.5. Lipid species were identified based on comparing peaks between target fragment scans using QTRAP and high resolution full scan using Oribitrap. The obtained results show that oleaginous yeasts produce five different classes of phospholipids. Abundant hydrogen adducts of PC, PE, and PS, and acetate adducts of PA and PG lipids were identified in the positive ion scan. Accordingly, abundant PI and IPC lipids can be identified in the negative ion scan.

However, sphingolipids such as SM and ceramides were present at low levels or below the limit of detection level, therefore, the obtained spectra could not be used for analyte identification. Negligible signal intensity of MIPC and M(IP)2C were observed in negative ion ESI-MS. The molecular mass of MIPC and M(IP)2C lipids are in the region m/z 500 - 1450. Hence mass range may be a limitations for the identification of lipids with high molecular weight, as the quantitative detection range

of the tested instrument is from m/z 200 to 1200, which may lead to incomplete profiling of these two lipid classes. Therefore, instruments with higher mass range (e.g. Orbitrap) are required for comprehensive lipid profiling.

According to the observed full scan result (Appendix E) using the Orbitrap, the

signal intensity of MIPC and M(IP)2C was present below the limit of detection level, it requires the use of sphingolipid internal standards for peak identification and quantification. Furthermore, based on the spectral result of product ion scan using m/z 456.4, it shows that the signal intensity of ergosterol acetate is negligible, indicating that most of the ergosterol compounds are not ionised. Therefore, in the analysis of ergosterol compounds, chemical acetylation is required in order to achieve acetate adducts of ergosterol prior to the CID fragmentation.

Table 4.5. Lipid profiling of most abundant lipid species in the ESI mass spectra of non-polar extract of oleaginous yeast R.glutinis and R.toruloides. Lipid class PC, PE, PA, PG and PS were detected in positive ion mode, showed signals at even-numbered m/z values. Lipid class PI was

detected in negative ion mode, and the signals of PI lipids appear at odd-numbered m/z values. Lipid Species Lipid Class Ion 34:1 34:2 34:3 36:1 36:2 36:3 36:4 36:5 36:6 38:5 38:6

PC [M+H]+ 760.5 758.5 756.5 788.5 786.5 784.5 782.5 780.5 PE [M+H]+ 718.6 716.6 714.6 746.6 744.6 742.6 740.6 738.6 PA [M+NH4]+ 712.5 710.5 740.5 738.5 PG [M+NH4]+ 766.5 764.5 794.5 792.5 790.5 PS [M+H]+ 762.5 760.5 790.5 788.5 786.5 PI [M-H]- 835.5 833.5 831.5 863.5 861.5 859.5 857.5 855.5

Positive ion ESI spectra of the dominant lipid species from R.glutinis and R.toruloides lipid extract are shown in Figure 4.6. This figure presents the results of the specific scans for the detection of lipid classes. PC and SM lipids were detected by precursor ion scanning of m/z 184, and the most intense signals observed at m/z 760.5, 782.5, 784.5 and 786.5 are [M + H]+ ions of PC 34:1, PC 36:4, PC 36:3 and PC 36:2, respectively. Accordingly, PE lipids were detected by neutral loss scanning for 141 Da, with the most intense signals observed at m/z 718.5, 740.5, 742.5 and 744.5

are [M + H]+ ions of PE 34:1, PC 3E:4, PE 36:3 and PE 36:2, respectively. The lipid species composition was calculated by normalising individuals to the sum of all lipid species composing the same lipid class to 100% (Figure 4.7). Results show the most abundant lipid species from PC and PE were (36:2), (36:3) and (36:4). In addition, the spectral result of precursor ion scan using m/z 281.3 and m/z 283.3 demonstrated that C18:0, C18:1 and C18:2 are the fatty acid chains present in the PC lipids. However, because lipids from different lipid classes were ionised with different efficiencies in regard to experimental conditions, the normalised intensity of lipid molecular species can be used to represent their molar abundances only if they are from the same lipid classes. Results of lipid species from different lipid classes were not able to directly reflect their molar abundances without internal standards.

Figure 4.6. Specific detection of phospholipid classes in an unprocessed total lipid extract of oleaginous yeast R.glutinis and R.toruloides by positive ion ESI/MS. (a) Detection of [M + H]+ ions of lipid classes PC and SM by precursor ion scan of m/Z 184.1 with collision energy 40eV. (b) Detection of [M + H]+ ions of lipid classes PE by precursor ion scan of m/Z 141.0 with collision energy 30 eV.

Figure 4.7. Normalised intensity abundance of individual lipid class to 100%. (a) PC composition of R.glutinis and R.toruloides with significant CV less than 2.45%. (b) PE composition of R.glutinis and R.toruloides with significant CV less than 3.75%

The use of OzID as a method for characterisation of specific lipid species was tested on lipid extracts of R.glutinis and R.toruloides. This method provides accurate mass determination and structural information. All detected peaks show an absolute mass deviation of 0.5 ppm. Detailed OzID and CID/OzID spectra results of PC 34:1 and PC 36:3 are presented in Figure 4.8 and Figure 4.9. Carbon double bond positions can be determined by evaluating the abundance of aldehyde and Criegee ion loss [12]. Take lipid PC 34:1 as an example: the spectrum result illustrated that it produced abundant [M + Na]+ adducts at m/z 782, and yielded high abundant product ions at m/z 672 and 714 after ozone-induced dissociation. These two fragments show a neutral loss of 110 Da and 68 Da, representing the ozone-derived aldehyde ions, which indicate the double bonds are located at the n-9 and n-6 position, respectively. In addition, the formation of the m/z 688 and 730 ions represent a neutral loss of 94 Da and 52 Da from the precursor ion, and have 16 Da difference to aldehyde ions, representing Criegee ions involving n-9 and n-6 lipids respectively, which is identical to the analysis of aldehyde ions. Taken together with the FAMEs and lipid species analysis, the cited results demonstrate that PC 34:1 is present as a mixture of PC

(16:0_18:1, n-9) as the dominant component, accompanied by a small amount of PC (16:0_18:1, n-6). Accordingly, lipid PC 36:3 is likely to be presented as a mixture of PC (18:2_18:1) and PC (18:3_18:0), indicating double bonds in the n-6 or n-9 positon for C18:1, n-6, 9 position for C18:2 and n-3, 6, 9 position for C18:3.

The relative regioisomeric composition of selected lipid species were identified by assessing the fatty acid group and sn-position using CID/OzID charged ion fragments. The spectra in Figure 4.9 reveal a set of product ions at m/z 379, 403 and 405, representing fatty acid chain C16:0, C18:2 and C18:1, respectively. C16:0 and C18:2 show the highest abundance of signals in the spectra, indicating these two fatty acid chains were located at the sn-1 position on the glycerol backbone. According to previous results, lipid PC 34:1 and PC 36:3 can now be assigned as PC (16:0/18:1n-9) and PC (18:2(n-6,9)/18:1(n-9)). The structure of each tested lipid species is summarised as shown in Table 4.6. Results show that SFA C 16:0, monounsaturated fatty acid (MUFA) C18:1, and polyunsaturated fatty acid (PUFA) C18:2, are dominant acyl chains presented in the tested species. The observed results can be used as a reference to evaluate the nutritional benefits of each sample. Lipid PC (16:0/18:1n-9), PS (16:0_18:1n-9), PI (16:0/18:2n-6,9) and PI (18:0_18:2n-6,9) are nutritionally important, not only because of their relatively smaller lipid droplet sizes compared to TAG, but also because the SFAs are located at the sn-1 position (Refer Appendix E for the sn-assignment of PI and PS lipids). Lipase from the digestive system would initially catalyse the fatty acids at sn-2 position of phospholipids, yielding lyso-phospholipids and free fatty acids [22]. Lyso-lipids, especially lyso-PC (i.e. Lysolecithin), have a positive impact on energy digestibility and/or growth performance in animals, whereas C18:2 is an omega 6 fatty acid that is essential in feed.

Figure 4.8. OzID neutral loss chromatogram of PC 34:1 and PC 36:3 from R.glutinis and R.toruloides lipid extracts (zoom in 20x to display). The product ion of aldehyde and Criegee ions were formed via ozonolysis of carbon double bonds.

Figure 4.9. CID/OzID chromatogram of PC 34:1 and PC 36:3 from R.glutinis and R.toruloides lipid extracts. The regiospecificity of PC 34:1 is C16:0 for sn-1 position, whereas PC 36:3 is C18:2 for sn-1 position.

Table 4.6. Lipid structure of selected lipid species from PBQC lipid extracts of R.glutinis and R.toruloides. The regiospecificity of SFA, MUFA and PUFA was analysed by Orbitrap LCMS.

Ion Mode Lipid Species M/z Structure Possible C=C position

PC 36:3 806.5 18:2*/18:1 n-3,6*,9 PC 34:1 782.5 16:0*/18:1 n-9 Positive PE 36:2 766.5 18:2*/18:0 n-6,9* PE 34:2 738.5 18:2*/16:0 n-6*,9 PS 34:1 784.5 16:0*/18:1 n-9 PI 34:2 833.5 16:0*/18:2 n-6*,9 Negative 18:1*/18:1 n-6,9* PI 36:2 861.5 18:0*/18:2 n-6,9*

* Refer to higher probability

4.3.2 Variety Test with Different Microbes and Growth Conditions Primary nutrient profiling was performed on all selected filamentous fungi and yeasts under two growth conditions in order to compare and evaluate the metabolic responses to lipid enhanced growth medium. MS spectra were collected from triplicate microorganism biomass samples for nutrient profiling and screening. Data from PBQC samples were used to align for instrument drift, as well as a quality control for each targeted analyte. Peaks that were observed in the blank or negative control runs were removed from nutrient profiling. Traces were signal corrected to remove peaks that passed the threshold with a CV greater than 20%. Data were normalised to 100% on the basis of individual nutrient profiles for statistical analysis.

Amino acids profiling

Twenty amino acids were identified and analysed using the Skyline software (refer Appendix E for details). The observed results demonstrated that both baseline- corrected peak areas and ionisation retention times show significant reproducibility with CV in the range of 2.1-17.8% and 1.5-12.5% for analyte comparison and replicate comparison, respectively (Figure 4.10). Therefore, the amino acid analysis is reliable, and the influence of the instrument drift on results is low. According to the results,

leucine and asparagine showed high CV% for their reference ions. The inconsistent signals indicate low fragment formation of the reference ion; however, it doesn’t affect the accuracy of sample analysing, since only the results of the quantitative ion were used for analyte quantification. Tyrosine showed high CV% for both ions, reflecting that a low concentration of tyrosine was presented in sample extracts. Dilution is the major factor that affects the accuracy of tyrosine analysing, as it increases error tolerance. Therefore, a lower dilution ratio is needed for tyrosine testing in order to ensure there are sufficient ions for analyte identification, but this may also cause other analytes to exceed saturation. The obtained peak area for individual amino acids was normalised to their corresponding standards for quantification and statistical analysis.

Figure 4.10. Repeatability/reproducibility (CV %) of peak area and retention time of the amino acid analysis using the PBQC sample of all tested strains. Regarding the peak area, 20 amino acids were identified correctly with maximum CV of 17.8%. Regarding the retention time, maximum CV of 10.2%. Replicates were reproducible with median CV of 1.8%. Blue, purple and brown colors correspond to different ions monitored for each compound.

Lipid profiling

The lipid classes detected in QTRAP LC-MS were mostly phospholipids, including PC, PE, PS, PG, PA, and PI, and their lyso forms. Figure 4.11 displays the lipid mass spectra of PBQC of nine different strains recorded within the detection mass range of m/z 650-850 in the positive ion mode. It clearly illustrates that PC lipids have the strongest signal response, whereas PG lipids give negligible response. The spectra result can be used to identify the complex lipids presented in sample extracts, and the obtained lipid classes are identical to the literature [21, 23]. Based on the detailed lipid identification of all tested strains using Lipid View software, there are about thirty major lipid species presented in the sample, involving twenty-three phospholipids and seven sphingolipids (refer Appendix E for details). Since lipids were extracted without internal standards, the obtained peak area of individual lipid species was normalised to its corresponding lipid class to 100% for statistical analysis.

Figure 4.11. The superposition of mass spectra showing the analysis of the most abundant lipid classes obtained from PBQC sample of all tested strains in positive ion scan.

Statistical analysis of different strains for their amino acid and lipid contents

The method of the dendrogram was used in order to explore the similarity of the nutrient profiles out of various strains. From the dendrogram of amino acid profile (Figure 4.12), a good inter-correlation between bio-replicates was presented. It is evident that bio-replicates were clustered together, confirming the reproducibility of the sample preparation. Additionally, the dendrogram also displays the similarity of the targeted nutrients between strains. Strains that have similar amino acid profiles were gathered in one cluster, resulting in detailed cluster separation between fungi and yeast. Seven clusters were formed in order to show the grouping of individual strains. For example, filamentous fungi such as A.oryzae and G.candidum formed a group distinct from yeasts, and therefore their amino acids profiles are the most similar. Based on the obtained amino acid profile, repeatability was found to be in the range of 0.1-17.89% CV for three measurements. S.cerevisiae produced a relatively higher abundance of amino acid content than other strains, which is consistent with the hypothesis that S.cerevisiae is a protein-enhanced strain.

A clustered heat map, also known as a double dendrogram, is a two-way display of the amino acid content of different strains in individual coloured cells. In the clustered heat map, the colour of a cell is proportional to its position along a colour gradient, and the order of the rows is determined by performing hierarchical cluster analysis (HCA) of the rows [24]. The HCA output is shown in the form of a dendrogram above the rows and columns. From the clustered heat map of amino acid profiles (Figure 4.13d), it is clearly visible on a heat map that the targeted amino acids are light yellow (i.e. positive gradient code) in the case of S.cerevisiae and R.mucilaginosa, but dark orange (i.e. negative gradient code) in the case of M.isabellina and G.candidum. In particular, lysine, arginine, alanine, and glutamic acid show a very similar pattern for yeast strains and are distinct from the pattern of fungi strains. Overall, M.isabellina has the lowest similarity to S.cerevisiae, reflecting M.isabellina produced a relatively lower abundance of amino acids than other strains.

A PCA plot is another way to explore the similarity of amino acid profiles between strains. The dataset from twenty amino acid variables was reduced to ten principal components (PCs) for statistical analysis, and those ten PCs represent 99.1%

of the variance (Figure 4.13a). The first two PCs, PC1 and PC2, which cover 76.2% of the variance, were applied on the PCA scores plot and the loading plot. The PCA scores plot (Figure 4.13b) clearly illustrated distinct cluster separation between fungi and yeast. In this case, filamentous fungi strains were displayed in the negative PC2 region, whereas yeast strains were shown in the positive PC2 region, which indicates that fungi and yeast were mainly distinguished by the variables on the PC2 axis. The PCA loading plot was used in order to define those amino acids that are mainly responsible for cluster separation (Figure 4.13c). A high cos2 indicates a good representation of the variables on the principal component. In this case the variable is positioned close to the circumference of the correlation circle. Whereas a low cos2 indicates that the variable is not perfectly represented by the principle components. In this case most of the variable is close to the center of the circle [25]. Aspartic acid, histidine and glycine presented with low cos2 value, indicating they are the components that contribute a relatively smaller portion to the total distance; therefore, they are less important for cluster separation. Take S.cerevisiae and A.oryzae as an example: on the PCA scores plot, they are most far away from each other along PC2 axis, and glutamine and glutamic acid arrows are aligned closely with that PC2 axis in the PCA loading plot, which indicates that these two amino acids are the main variables responsible for this separation.

As stated above, both HCA and PCA results enabled evaluation and comparison of the amino acid profile of different strains, and the profile reflected the metabolic responses to two cultivation media, which are protein with glucose (i.e. reference medium) and protein with lipid (i.e. stickwater based medium). The obtained results show that the enhanced carbon source did not cause any significant change in the amino acid profile. In addition, the results can be used as an initial nutrient screening of the tested strains. Results demonstrated that the selected yeast strains produced a relatively higher abundance of amino acids than did filamentous fungi strains. Lysine, proline, arginine, alanine and glutamic acid are the most abundant amino acids presented in the sample extracts (Input data were presented in Appendix E).

Figure 4.12. AA profile and reproducibility of 9 strains based on two independent technical replicates (i.e. duplicate extraction of the same sample) with three measurements (n=1). The mean and +/- standard deviation (SD) of the measurements are indicated. Hierarchical cluster analysis of the AA profiles of those nine investigated strains showed a good intra-strain reproducibility.

Figure 4.13. Exploratory analysis of normalised intensity data collected for polar extracts of nine strains based on two independent bio-replicates with three measurements. (a) Scree plot shows the amount of explained variance for each of the first ten principal components. As can be seen, the first two principal components explain most of the variance in the data (76.2% variance explained). (b) PCA scores plot generated by plotting the first two principal components explores the relationship between the samples (strains) and shows six putative groups of observations when data is analysed visually. However, no additional statistical analysis has been performed to determine the actual number of clusters. (c) PCA loadings plot explores the relationship between the variables (amino acids) and shows which variables account for most of the separation of the strains. (d) Heatmap is another way to visualise the same data and explore the relationship between the observations (strains). The colour scale indicates the scaled distance, where the colour red shows the lowest distance of -3 and the colour white shows the highest distance of 3 in the Euclidean space.

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Accordingly, from the dendrogram of the lipid profile (Figure 4.14), the obtained clusters also showed a good inter-correlation between bio-replicates, hence the sample preparation provides great reproducibility. The dendrogram also displays the similarity of content of targeted lipid species between strains. Nine clusters were formed in order to show the grouping of individual strains. Strains that were cultivated in the same growth medium were clustered together. That is, strains that were cultivated in the stickwater based medium formed a group that was distinct from strains that were cultivated in the reference medium, which indicates that the growth medium may account for the differentiation in lipid profiles. Based on the obtained lipid species profile (the baseline corrected peak area of each sub class lipids were normalised to their corresponding lipid classes to 100%), repeatability was found to be in the range of 0.1-20.0% CV for target lipid species in regard to three measurements. All tested strains show the presence of phospholipids, especially for PC and PE lipids, which is consistent with the hypothesis, as they are the dominant lipids present in the cell membrane.

The clustered heat map output (Figure 4.15d) clearly shows the stickwater- based medium induced metabolic changes in microorganisms, with respect to enhanced production of sphingolipids such as ceramides and sphingomyelins. Take S.cerevisiae as an example: sphingolipid SM 42:2;2 is dark orange (i.e. negative gradient code) in the case when this sample strain was cultivated with the reference medium, but light yellow (i.e. positive gradient code) in the case where it was cultivated with the stickwater based medium. Significant increases of sphingolipid production were observed. In contrast, a decreased concentration of phospholipid PC 36:3 occurred in response to the stickwater-based medium. Overall, strains that were cultivated in the same conditions would result in a very similar lipid profile. The results also indicate that other metabolic changes may also occur when using different nutrient treatments in addition to lipid and protein.

From the PCA output, the dataset from thirty variables were reduced to ten principal components (PCs) for statistical analysis, and those ten PCs represent 96.5% of the variance (Figure 4.15a). The first two PCs, PC1 and PC2, that cover 51.6% of the variance, were applied on a PCA scores plot and a loading plot. The PCA scores plot

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(Figure 4.15b) clearly illustrated distinct cluster separation for strains with different nutrient cultivations. In this case, strains cultivated with protein and glucose were displayed in the negative PC1 region, whereas strains cultivated with protein and lipid were shown in the positive PC1 region, which indicates that lipid profiles were mainly distinguished by the variables on the PC1 axis. The PCA loading plot identified the lipid species that were mainly responsible for cluster separation (Figure 4.15c). Lipids such as PC 34:1, PC 34:2, PE 36:4 and SM 42:2; 2 are important for cluster separation. Whereas most of the PG lipids contribute a relatively smaller portion to the total distance; therefore, they are less important for cluster separation. SM 42:2; 2 and SM 34:1; 2 arrows are aligned closely with the PC1 axis in the PCA loading plot, which indicates that these two lipid species are the main variables responsible for cluster separation. Figure 4.15e and 4.15f show the PCA scores plot and loading plot results of PC lipid profiles of tested strains. The obtained results show that S.cerevisiae has different lipid profiles to other tested strains, and PC 34:2 is the component that is mainly responsible for the separation.

As stated above, both HCA and PCA of lipidomics analysis enabled evaluation and revealed the correlation between growth mediums and nutrient profiles. The obtained results show that the enhanced carbon source caused a significant change in the lipid profile. Results also demonstrated that most of the selected yeast strains have similar lipid profiles (Input data were presented in Appendix E).

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Figure 4.14. Lipid profile and reproducibility of PC, PE, PS, PG, Cer and SM of nine strains based on two independent technical replicates (i.e. duplicate extraction of the same sample) with three measurements (n=1) in positive ion mode. The mean and +/- standard deviation (SD) of the measurements is indicated. Hierarchical cluster analysis of the lipid profiles of those nine investigated strains showed a good intra-strain reproducibility.

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Figure 4.15. Principal component analysis (PCA) based on MS data of lipid extracts of nine strains based on two independent bio-replicates with three measurements (n=3) in positive ion mode. (a) Scree plot for six lipid classes, illustrating nine principal components out of 36 samples represent more than 97% of the variance. (b) Factor map for six lipid classes, showing the results of hierarchical clustering based on the nine principal components, resulting in a grouping of the individual strains within eight distinct clusters. (c) Variable factor map for six lipid classes, displaying the result of variable test and p value analysis to identify the factor of normalised intensity values, which are responsible for cluster formation. (d) Heatmap for six lipid classes, displaying the Euclidean distance from each individual strain to the centroid of each one of the eight clusters. The colour scale indicates the scaled distance, where the colour red shows the lowest distance of zero and colour and white shows the highest distance of one. (e) Variable factor map for PC lipid class. (f) Factor map for PC lipid class.

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4.4 Conclusion

Overall, the proposed method described in this chapter was successfully applied to analysis of a group of different fungi and yeasts for nutrient profiling and screening. This method offers a rapid and sensitive detection for quantitative determination of both amino acids and lipids at the level of their individual molecular species. The results showed that the tested strains contain important nutrients including essential amino acids and different classes of complex lipids. The major amino acid of tested strains was lysine, followed by proline and arginine. Phospholipids such as phosphatidylcholine and phosphatidylethanolamine were presented in all tested strains. The growth medium plays an important role in the nutrient profile, therefore it will be useful for further research.

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[1] P. Jensen and D. Batstone, "A.ENV.0131 Energy and nutrient analysis on individual waste streams," Meat & Livestock Australia Limited, 2012. [2] Anupama and P. Ravindra, "Value-added food: Single cell protein," Biotechnology Advances, vol. 18, pp. 459-479, 2000. [3] F. Khademi, İ. Yıldız, A. C. Yıldız, and S. Abachi, "An assessment of microalgae cultivation potential using liquid waste streams: opportunities and challenges." In Proceeding of 13th International Conference on Clean Energy ICCE [Presentation], pp. 2084-2097. 2014. [4] R. L. Olsen and J. Toppe, "Fish silage hydrolysates: Not only a feed nutrient, but also a useful feed additive," Trends in Food Science & Technology, vol. 66, pp. 93-97, 2017. [5] W. Horwitz, P. Chichilo, and H. Reynolds, "Official methods of analysis of the Association of Official Analytical Chemists," Official methods of analysis of the Association of Official Analytical Chemists., 1970. [6] J. Rooke, "Basic Animal Nutrition and Feeding, 4th Edition, by W. G. Pond, D. C. Church and K. R. Pond. viii + 615 pp, " The Journal of Agricultural Science, vol. 126, pp. 375-376, 1996. [7] K. C. Surendra, R. Olivier, J. K. Tomberlin, R. Jha, and S. K. Khanal, "Bioconversion of organic wastes into biodiesel and animal feed via insect farming," Renewable Energy, vol. 98, pp. 197-202, 2016. [8] Shimadzu. (2013). Shimadzu LCMS 8050, Avaliable: https://shimadzu.com.au/sites/default/files/sites/files/products/lcms/8050/LCM S8050_Catalog_EN.pdf. [9] J. D. Egertson, B. MacLean, R. Johnson, Y. Xuan, and M. J. MacCoss, "Multiplexed peptide analysis using data-independent acquisition and Skyline," Nature protocols, vol. 10, p. 887, 2015. [10] R. Mohamed, J. Richoz-Payot, E. Gremaud, P. Mottier, E. Yilmaz, J.-C. Tabet, et al., "Advantages of molecularly imprinted polymers LC-ESI-MS/MS for the selective extraction and quantification of chloramphenicol in milk-based matrixes. Comparison with a classical sample preparation," Analytical chemistry, vol. 79, pp. 9557-9565, 2007. [11] Sciex. (2016). QTRAP Technology, Avaliable: https://sciex.com/Documents/brochures/Compendium_LowRes.pdf. [12] H. T. Pham, A. T. Maccarone, J. L. Campbell, T. W. Mitchell, and S. J. Blanksby, "Ozone-induced dissociation of conjugated lipids reveals significant reaction rate enhancements and characteristic odd-electron product ions," Journal of the American Society for Mass Spectrometry, vol. 24, pp. 286-296, 2013. [13] F. Guan, C. E. Uboh, L. R. Soma, Y. Luo, J. Rudy, and T. Tobin, "Detection, quantification and confirmation of anabolic steroids in equine plasma by liquid chromatography and tandem mass spectrometry," Journal of Chromatography B, vol. 829, pp. 56-68, 2005. [14] R. R. Ran-Ressler, P. Lawrence, and J. T. Brenna, "Structural characterization of saturated branched chain fatty acid methyl esters by collisional dissociation

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of molecular ions generated by electron ionization," Journal of lipid research, vol. 53, pp. 195-203, 2012. [15] M. Sermakkani and V. Thangapandian, "GC-MS analysis of Cassia italica leaf methanol extract," Asian J Pharm Clin Res, vol. 5, pp. 90-94, 2012. [16] C. Botinestean, N. Hadaruga, D. Hadaruga, and I. Jianu, "Fatty acids composition by gas chromatography–mass spectrometry (GC-MS) and most important physical-chemicals parameters of tomato seed oil," Journal of Agroalimentary Processes and Technologies, vol. 18, pp. 89-94, 2012. [17] F. Douglas. (1999, 20th May). GC/MS Analysis. Available: http://www.scientific.org/tutorials/articles/gcms.html [18] L. Ettre, "Nomenclature for chromatography (IUPAC Recommendations 1993)," Pure and Applied Chemistry, vol. 65, pp. 819-872, 1993. [19] B. Brügger, G. Erben, R. Sandhoff, F. T. Wieland, and W. D. Lehmann, "Quantitative analysis of biological membrane lipids at the low picomole level by nano-electrospray ionization tandem mass spectrometry," Proceedings of the National Academy of Sciences, vol. 94, pp. 2339-2344, 1997. [20] J. V. Busik, G. E. Reid, and T. A. Lydic, "Global analysis of retina lipids by complementary precursor ion and neutral loss mode tandem mass spectrometry," Lipidomics, ed: Springer, pp. 33-70, 2009. [21] C. S. Ejsing, J. L. Sampaio, V. Surendranath, E. Duchoslav, K. Ekroos, R. W. Klemm, et al., "Global analysis of the yeast lipidome by quantitative shotgun mass spectrometry," Proceedings of the National Academy of Sciences, vol. 106, pp. 2136-2141, 2009. [22] F. Beppu, K. Yasuda, A. Okada, Y. Hirosaki, M. Okazaki, and N. Gotoh, "Comparison of the distribution of unsaturated fatty acids at the sn-2 position of phospholipids and triacylglycerols in marine fishes and mammals," Journal of oleo science, p. ess17132, 2017. [23] I. R. Sitepu, A. L. Garay, T. Cajka, O. Fiehn, and K. L. Boundy-Mills, "Laboratory Screening Protocol to Identify Novel Oleaginous Yeasts," in Microbial Lipid Production, ed: Springer, pp. 33-50, 2019. [24] R. K. Blashfield, "Mixture model tests of cluster analysis: Accuracy of four agglomerative hierarchical methods," Psychological Bulletin, vol. 83, p. 377, 1976. [25] H. Abdi and L. J. Williams, "Principal component analysis," Wiley interdisciplinary reviews: computational statistics, vol. 2, pp. 433-459, 2010.

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Chapter 5: Development and Evaluation of a Rapid Analytical Method for the Simultaneous Determination of Polar and Non-Polar Metabolites from Yeasts

In Chapter 4, the evaluation and use of a proposed analytical method for analyte identification was described. In this chapter, the optimisation and evaluation of the analytical method is presented. The results show that the amino acids, fatty acids, and representative complex lipids present in microbe biomass can be identified and quantified using only 5 mg of freeze-dried biomass sample. Results of this study show that this analytical method is reproducible, so it can be used routinely. The new method allows rapid compositional analysis for compounds at trace level, therefore allowing deeper and more detailed analysis of the chemical composition of microbial biomass.

5.1 Introduction

Microorganisms containing important nutritional compounds can be used as feed supplements to enhance livestock productivity. However, microbe-derived feed supplements contain both polar and non-polar compounds, which is a challenging matrix for determination of composition with the same analytical method [1, 2]. The use of an appropriate analytical method plays an important role in nutrient profiling to achieve quantitative and reliable microbe characterisation using experimental data.

In regard to nutritional analysis, there are several chromatographic methods reported in the literature that were developed for the determination of either amino acid profiles [3, 4] or fatty acid profiles [5-7]. Analytical methods for detailed

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quantification of both polar and non-polar nutrients are still required. Selection of the appropriate analytical method and extraction procedure is essential for accurate nutrient profiling and identification, as well as to ensure that analytes have higher recovery rate for more precise quantification [8-10]. The Universal Metabolite and Lipid Extraction Protocol described in Chapter 4 was developed for rapid and simultaneous identification of amino acids and lipids. The research presented in this chapter is therefore focused on quantifying the nutrient contents of the benchmark strain S. cerevisiae, including the profiles of amino acid, fatty acid and lipid species. The obtained nutrient profiles were compared with the literature to ensure the proposed method is reliable and reproducible, as well as to evaluate the nutritional value of S. cerevisiae.

Method optimisation was conducted for both metabolite extraction and detection procedures, as well as for quantitative determination of selected analytes. The method for nutritional analysis was performed according to the requirements of United States Food and Drug Administration (US FDA) as a preliminary guideline for evaluation and validation [11].

5.2 Materials and Methods

5.2.1 Strains and Culture Conditions The Saccharomyces cerevisiae Fali strain was used as a benchmark strain for method development and evaluation. It was obtained from AB Mauri, Sydney. Yeast was cultured with a growth medium which was prepared by mixing 15 g.L-1 glucose and 15 g.L-1 yeast extract. Freeze dried cell biomass (about 1.37% moisture content) was obtained from Mrs Remya Purushothaman Nair for nutrient profiling. The obtained biomass used in this study was stored at -80°C.

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5.2.2 Metabolite Extraction Protocol The proposed polar and non-polar metabolite extraction and detection method used in this study was described in Section 4.2.2. The method has been applied to several filamentous fungi and yeasts, and achieved qualitative nutrient profiling with coefficient variation (CV) of less than 20%. Further method development and evaluation is required in order to achieve quantitative analysis, as well as to ensure the method is reproducible for high throughput nutritional analysis of feed supplement microbes. The flowchart of the proposed analytical method is presented as shown in Figure 5.1. A brief description of each step in the method development is shown as follow:

1. Literature search and microbial sample characterisation of a basic nutrient profile of testing samples (e.g. total nitrogen and total carbon content). 2. Method requirements and protocol; the proposed method is designed for amino acid, fatty acid and lipid species analysis. 3. Instrumental setup and preliminary studies, and analyte identification with various strains (i.e. filamentous fungi and yeast). 4. Optimisation of parameters (extraction and MS setting parameters). 5. Documenting Standard Operating Procedure (SOP). 6. Evaluation of the development method with standards. 7. Quantitative analysis with benchmark strain S.cerevisiae using calibrated instrumentation with the corresponding SOP.

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Microbial sample characterisation

Protocol and instrumental setup

Preliminary studies with various strains

Development and optimisation of parameters

Method validation with benchmark strain

Define whether the recovery/accuracy has No Change the method reached the required level or not

Yes Use method in sample quantification

Figure 5.1. Methodology flowchart. The development and validation cycle of the proposed analytical method for nutrient profiling.

5.2.3 Chemicals and Sample Preparation Chemicals and sample preparation used in this study is described in Section 4.2.3. The composition and concentration of the internal standard mixture was presented in Appendix C in detail. Standards used in this study are described in Section 3.1.2, and the preparation of standard solution was performed as described in Appendix D.

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5.2.4 Instrumentation: Instrument Selection and Instrumental Setup Instrumental setup and operating conditions used in this study are described in Section 4.2.4.

5.2.5 Method Development and Optimisation Freeze dried biomass was used in this study in order to decrease inaccuracies in the measurement of cell biomass concentration, as the water soluble contents (e.g. inorganic material, non-structural sugar, etc.) are the major factor that increases the error tolerance of the final result [12]. Therefore, a freeze drying process and moisture content test are required for sample preparation in order to optimise the accuracy of analyte quantification.

To achieve better metabolite extraction efficiency, both sonication and liquid homogenisation techniques were applied to the sample biomass to enhance cell membrane disruption. Acid and alkaline peptide hydrolysis were examined for protein-bound amino acid extraction. Acid hydrolysis was performed by mixing the collected protein pellets with 5 mL 20% HCl, solution was transfer to a seal flask and heat in an oil bath at 110°C for 24 hours. In contrast, alkaline hydrolysis was performed using 5 mL 20% NaOH in the same operating conditions. The obtained hydrolysed samples were resuspended with 5mL 0.5% FA, and 0.1 mol/mL of NaOH or HCl were added to balance the pH value to 3. Filtration and dilution were required before instrument injection (Refer Appendix B for detail). Dithiothreitol (DTT, 20 mM) was also tested as additive to the hydrolysis process to avoid oxidisation of methionine and tryptophan.

To ensure the best performance of the detection conditions for individual analytes, development and optimisation were carried out in regard to the solvent system for sample infusion, and liquid chromatography (LC) or gas chromatograph (GC) operating parameters. The key parameters influencing the identification and quantification of target analytes included:

HPLC or GC: composition of mobile phases, stationary phase of the column, chromatographic resolution of analytes with close m/z.

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MS: scan mode, collision energy (CE), de-clustering potential (DP), dwell time, and detection time range. For analytes with more than one product ion, the one with higher signal to noise ratio was selected as the quantifier ion. The appropriate concentration of samples and internal standards used in the optimised analytical method have been decided based upon the basis of the detection sensitivity, and the acceptable chromatographic in terms of peak resolution, sharpness, symmetry, and tailing. The mobile phase solvent has been used as a “solvent blank” for all sample testing, in order to reduce chromatographic background noise, as well as to eliminate any unwanted solvent peaks.

Derivatisation is required for detecting ergosterol from sample extracts. A solution of ergosterol acetate (as prepared by QUT PhD student Venkat Narreddula) was first prepared to find out the optimised condition for ergosterol detection. Ergosterol (95% purity) was purchased from Sigma-Aldrich (NSW, Australia). Ergosterol was purified by dissolving ergosterol (0.25 M) with acetic anhydride (0.5 M) in pyridine (15 mL); dichloromethane (5 mL) was added to enhance purification. The solution was stirred at room temperature for 16 h to complete the reaction. Dichloromethane was removed with nitrogen and the remaining filtrates were combined with water (15 mL). Centrifugation was applied to remove precipitates, and the obtained supernatant was filtered through 0.22 µM membrane and evaporated to give a 75% yield of off-white solids. The obtained ergosterol methyl esters were dissolved with 2:1 (V/V) methanol/chloroform 5 mM ammonium acetate for resuspension. Once the MS parameters for ergosterol detection were optimised, a 1:1 mixture of sample lipid extracts in chloroform was prepared, and 40 µL aliquot transferred into a glass vial. The organic solution was then added with 110 µL of 1:12 (V/V) acetic anhydride/chloroform and vortex for 5 mins to enhance the reaction. The solution was evaporated under nitrogen, follow by reconstitution in 2:1 (V/V) methanol/chloroform 5 mM ammonium acetate for shotgun lipidomics analysis.

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5.2.6 Method Evaluation with S.cerevisiae Aspects of the developed rapid analytical method for the simultaneous determination of polar and non-polar metabolites from microorganisms were evaluated including: system suitability, repeatability, reproducibility, specificity, linearity, accuracy, method sensitivity, and robustness, as well as analyte recovery. Each analytical sequence consisted of testing of biological extract samples, pooled biological quality control (PBQC) samples, calibration standards, and positive and negative control samples, to ensure analytical consistency. Two analysis sequences were completed within one month with triplicate injection tests.

System suitability

System suitability was verified by reducing the tailing factor in the LC or GC system, to ensure that there was no carry over ions for the next sample injection. Repeatability and peak resolution were assessed by injecting three replicates of blank mobile phase solution followed by six replicates of standard mixture of each analytical test.

Specificity

The specificity of the proposed analytical method for the determination of target nutritional analytes was established by comparing the MS chromatogram peak of analytes to blank solvent, PBQC samples, and standards. The standard used for the evaluation consisted of the most possible target analytes. All target analytes were resolved without a matrix interference effect (no shift in analytical results).

Reportable range

The concentration of target analytes in sample extracts was determined by comparing the analyte peak area to that of standard samples with known concentration. The reportable range was then assessed by applying the best fit line to the linear portion of the data using linear regression statistics.

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The mixture of ten essential and ten non-essential amino acid standard solutions were serially diluted in the concentration range of 0.15 to 5.00 nM/mL. Triplicates of such a concentration range were prepared and plotted on a 6-point calibration curve using a linear regression model with intercept (i.e. not forced through zero) term.

The mixture of thirty-seven fatty acid standards was serially diluted in the concentration range of 5.00 to 80.00 ppm. Triplicates of such a concentration range were prepared and plotted on a 5-point calibration curve using a linear regression model with no intercept term.

Method sensitivity, LOD and LOQ

Method sensitivity was assessed by evaluating the signal response of the analytes in the serial dilution standard solutions until the signal to noise ratio of the individual analyte reached the limit of detection (LOD) and limit of quantification (LOQ) levels. LOD and LOQ for analytes were calculated based on standard deviation of the intercept and the slope from the linear regression obtained from calibration curve, using the formula as shown below:

푄 퐿푂퐷 = 3.3 푆 푄 퐿푂푄 = 10.0 푆 Where Q represents the standard deviation of the intercept and S represents the slope of the calibration curve.

Precision, repeatability and reproducibility

For quantitative analysis of biological samples with diverse nutrient profiles, method precision and repeatability were assessed by triplicate extraction of the same sample (e.g. extract the same biological sample 3 times (n=1)), and the sample extracts were tested on the same day under the same operating conditions with triplicate injection. Reproducibility was assessed by comparing the obtained nutrient profile of two sample sets on two different days. The precision of concentration of the target analytes was expressed as the coefficient of variation (CV %).

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Accuracy study and recovery

Accuracy of the proposed analytical method was evaluated by comparing the obtained nutrient profile of the sample to the literature. The determination of recovery rate was carried out by spiking surrogate standards to sample lysates with known concentration. Recovery of target analytes was calculated using the ratio of observed amount compared to the amount of surrogate initially added to the sample lysate. Three independent technical replicates were recorded to obtain the mean and standard deviation.

푃퐴퐸 (퐸푥푡푟푎푐푡푒푑 푠푎푚푝푙푒) % 푅푒푐표푣푒푟푦 = × 100 푃퐴퐼 (푈푛푒푥푡푟푎푐푡푒푑 푠푎푚푝푙푒)

Where, PAE represents the mean peak response of surrogate standards after sample extraction, whereas PAI represents the mean peak response of the initial amount of surrogate standard. The surrogate standards used for amino acid, fatty acid

13 and lipid species analysis were Cvaline, C19:0, and lipid standard mix (a matrix of phospholipid and sphingolipid standards), respectively.

Robustness

To ensure the analytical method is able to handle various chromatographic or environmental conditions such as flow rate, temperature and different time periods, these variations have been evaluated for resolution of peaks of analytes.

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5.3 Results and Discussion

5.3.1 Extraction Method Optimisation Wet/dry cell biomass comparison

The degree of repeatability and reproducibility for quantification of analytes was improved by adjusting the sample size and sample preparation for metabolite extraction. The concentration level of analytes within wet cell biomass were more dependent on a wide variety of factors, such as moisture contents, centrifugation methods, and supernatant removal. Drying biomass by centrifugation can only partially remove the moisture contents, thus the biomass obtained using this method is likely to contain different amounts of water, thereby affecting the repeatability or reproducibility of the experiment. On the other hand, oven dried or freeze dried cell biomass is completely dehydrated, resulting in a more stable biomass weight of replicates. Hence dry cell biomass was used in order to reduce the influence of sample moisture contents on the final concentration calculation of target analytes. The statistical analysis in Table 5.1 shows that dry cell biomass was taken to be more statistically significant, since the p-value of six replicates was less than 0.05. In addition, preliminary tests indicated that severe dilution is required to avoid saturation peaks in MS analysis; however, increasing the dilution ratio will lead to a higher error tolerance. Hence, the sample size was reduced by half (i.e. 5 mg) to ensure the final concentration was within the detection limit, as well as to lower dilution ratio.

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Table 5.1. Statistical analysis of biomass weight. Weight measurement of R.glutinis (RP2) and R.toruloides (RP15) biomass. Strains were cultivated under YPD medium with and without amino acids respectively. The minimum, maximum, mean, upper and lower endpoint of the 95% confidence interval, and p value of the measurements were indicated. (a) Wet cell biomass measurement. The statistical analysis was performed using 6 replicates. (b) Dry cell biomass measurement. The statistical analysis from 6 replicates.

Homogenisation method

In addition to sample size selection, the study optimised the homogenisation method to enhance cell membrane disruption, which can greatly affect the metabolite extraction efficiency. Preliminary experiments used Tissue Lyser to homogenise samples at 30 Hz for 90 s. No significant protein precipitates presented after polar and non-polar phase separation, and the observed chromatographic peaks of protein- bound amino acids (i.e. hydrolysed protein) were below the LOD level. Results indicated that the preliminary homogenisation method was unable to extract insoluble analytes efficiently, particularly the protein-bound amino acids. Hence, an additional sonication process was applied after liquid homogenisation. The sonication process provides a shear force to assist in breaking cell membranes. Hence, the homogenisation method developed in the present procedure facilitated even particle

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distribution and viscosity reduction in cell lysate. In addition, according to Koh et al. [13], better heat stability of proteins was achieved with the combination of homogenisation and heat treatment. Therefore, the obtained cell lysates were heated to 60 °C and agitated at 850 rpm for 15 min to enhance metabolite extraction. As illustrated in Figure 5.2, a significant amount of protein precipitate was observed with the updated homogenisation method, which indicates much more efficient cell lysis and thus better extraction efficiency for protein bound amino acids.

Figure 5.2. Precipitated cell proteins from extraction process. Fig (L) is precipitated protein with cell debris after homogenisation, and Fig (R) is precipitated protein pellet after polar and non-polar phase separation.

Protein bound amino acid hydrolysis

Protein content was estimated using the “Bio-Rad protein assay”. It is a dye- based assay, hence the amount of protein residues remaining in the hydrolysed sample solution was recorded as absorbance [14]. The protein test was used in order to ensure that no protein residues remained in the hydrolysis solution. The observed result presented in Table 5.2 shows that protein is only partially hydrolysed using the alkaline method, indicating a lower recovery rate than for the acid hydrolysis method. Alkaline hydrolysis was not effective due to the possibility that the concentration of NaOH used for the hydrolysis reaction was too low, or the incubation time needed to be extended. Acid hydrolysis provided a complete protein hydrolysis, and most amino acids were recovered by this method. Therefore, acid hydrolysis was selected for subsequent protein bound amino acid extraction.

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Table 5.2. Acid vs alkaline hydrolysis protein test using the Bio-Rad protein Assay. The injection of standard and negative control (reagent blank) were performed in the same conditions as for samples. Instrument run without injection was recorded as the instrument

blank.

Absorbance Sample Actual reading Subtract instrument blank

Negative Control H2O 0.33 0.29

Standard BSA std_0.5 mg/100 uL 1.55 1.51

Hydrolysed solution, replicate 1 0.32 0.28 Hydrolysed solution, replicate 2 0.30 0.26 Acid hydrolysis Hydrolysed solution with 100x dilution, replicate 1 0.32 0.28 Hydrolysed solution with 100x dilution, replicate 2 0.32 0.28

Hydrolysed solution, replicate 1 1.31 1.27 Hydrolysed solution, replicate 2 1.32 1.28 Alkaline hydrolysis Hydrolysed solution with 100x dilution, replicate 1 0.32 0.28 Hydrolysed solution with 100x dilution, replicate 2 0.32 0.28

*Spectrophotometer Parameter: 25.5 °C; 595 nm

There is no any easily obtainable internal standard for the hydrolysis process, because the hydrolysis efficiency of different proteins is not the same. The recovery rate of the internal standard does not correlate with the recovery rate of all amino acids within the sample. It was found that the acidic hydrolysis resulted in protein extracts hydrolysed to a varying degree. However, amino acid profile (proportion of different amino acids to each other) was reproducible. Therefore, to compare the absolute quantity of amino acids between strains, the following approach was implemented as presented in Figure 5.3. A brief description of each step in the method development is as follows:

For amino acid profiling:

1. Run total nitrogen test before extraction to find out the starting amount of protein of 5 mg freeze dried biomass. 2. Calculate concentration of both free amino acids and protein-bound amino acids in regard to six-point linear calibration curve.

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3. Convert weight (mg/g) to weight % for both free and bound amino acids for consistency. Free amino acid results were normalised using internal

13 standard Cvaline. 4. Refer results back to the starting amount of protein.

For fatty acid and lipid species profiling:

1. Prepare four bio-replicates for non-polar metabolite extraction: three replicates extract with internal standards, and one replicate extract without internal standards. 2. Perform a second lipid extraction for protein purification. 3. Calculate concentration of analytes in regard to internal standards.

Figure 5.3. Metabolite extraction flowchart for: (a) AA profiling and (b) lipid profiling.

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5.3.2 Analytical method optimisation In addition to enhanced extraction efficiency, this study also optimised LC-MS operating parameters to obtain the best performance for detecting and quantifying target analytes. In this investigation, operating parameters including dwell time, detection time range, scan type, collision energy (CE), declustering potential (DP), scan speed, and scan cycle were evaluated. For amino acid detection, the initial parameters were taken from Shimadzu metabolite package [15] and by doubling the dwell time (i.e. duration in which each ion signal is collected) for methionine and tryptophan from 6 to 12 s, a 2-fold improvement in signal response was observed. To improve specificity and selectivity, a scheduled detection time range was applied. Detection time for individual amino acids was reduced from 15 to 4 mins time range of the target retention time detection. (Further details appear in Appendix B). Lipid detection was based on collision-induced dissociation which is instrument dependent. Compound-dependent parameters such as scan type, DP, and CE are the major factors in performance, and these parameters were adjusted to optimise the fragmentation efficiency of target analytes. As illustrated in Figure 5.4, 1 µM PA 34:0 standard solution was injected and detected by neutral loss (NL) scanning of m/z 98 + + and 115 for [PA + H] and [PA + NH4] ions. Similarly, 1 µM PG 34:0 standard solution was detected by NL scanning of m/z 172 and 189 for [PG + H]+ and + [PG + NH4] ions. The most intense signals observed at m/Z 694.5 and 768.5 are + [M + NH4] adducts of PA 34:0 and PG 34:0, which provided at least 10-fold improvement in signal response compared to that of [M + H]+ adducts observed at m/z 677.5 and 751.5. In addition, from the spectra result of NL scanning of m/z 98 and 172, the signal observed at m/z 699.5 and 773.5 represent the contamination peaks. In other words, the specific detection of PA and PG lipids were optimised using NL scanning of m/z 115 and 189 to obtain better ionisation and MS response at high signal to noise (S/N) ratios.

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Figure 5.4. Specific detection of phosphatidic acid (PA) standard PA 17:0/17:0 and phosphatidylglycerol (PG) standard PG 17:0/17:0. (a) Detection of [PA+H]+ by NL scan using

+ m/z 98.0 CE 30 eV; (b) Detection of [PA + NH4] by NL scan using m/z 115.0 CE 30 eV; (c)

+ + Detection of [PG+H] by NL scan using m/z 172.0 CE 25 eV; (d) Detection of [PG + NH4] by NL scan using m/z 189.0 CE 25.

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Both positive and negative polarity scans were then tested in order to find out the most efficient ionisation mode of the target analytes. Figure 4.5 demonstrates the specific detection of phosphatidylinositol (PI) in both scan modes using the same amount of lipid extract of S.cerevisiae. Figure 5.5(a) shows the spectra results of the NL scanning of m/z 277 under positive ion mode. The most intense signals observed at m/z 826.5, 854.5 and 882.5 were assigned to lipid species PI 32:1, PI 34:1 and PI 36:1, respectively. Figure 5.5(b) shows the spectra results of the precursor ion scanning of m/z 241 under negative ion mode. The most abundant lipid species obtained by this mode were consistent with the results obtained in positive mode. The obtained results also indicated that ionisation of PI lipids in positive ion mode achieved a 1.5-fold improvement in signal response and provided better S/N ratio compared to the negative ion mode.

Figure 5.5. Specific detection of PI using lipid extract of S.cerevisiae. (a) Detection of PI by NL scan using m/z 277.0, CE 30 eV in positive ion scan. (b) Detection of PI by precursor ion scan using m/z 241.0, -55 eV in negative ion scan.

As outlined in Figure 5.6, ergosterol or cholesterol from lipid extracts of the sample were converted to their respective acetate esters by derivatisation. Samples were diluted with resuspension solvent and analysed in positive ion mode using the

+ transitions [cholesterol acetate +NH4] m/z 446.4 → 369.4, and [ergosterol acetate

+ +NH4] m/z 456.4 → 379.4. Both transitions indicate the loss of a neutral fragments

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[CH3CO2H+NH3] of 77 Da, which can be used as their common fragment ion for specific detection. DP and CE were adjusted stepwise by a difference of value 5 in order to optimise the product-precursor ion transitions. Hence, the optimised condition for ergosterol detection was achieved using neutral loss scanning of m/z 77 at CE 14 eV, DP 10.

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Figure 5.6. Schematic representation of derivatisation and fragment transition of ergosterol and cholesterol. The chemical structures at each step are also presented.

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The fragmentation efficiencies of 1 µM cholesterol and ergosterol acetates are demonstrated in Table 5.3. The obtained product ion scanning results show that the peak area ratio of product ion (m/z 379) to precursor ion (m/z 456) for + [Ergosterol + NH4] is 0.1. Whereas the peak area ratio of product ion (m/z 369) to + precursor ion (m/z 446) for [Cholesterol + NH4] is 0.3. This indicates that + [M+NH4] cholesteryl acetate decomposes more readily than equivalent ergosterol acetate ion under the same conditions and thus calibration is required to use the cholesterol as an internal standard for quantification and a calibration curve of cholesterol and ergosterol is required for concentration correction.

Table 5.3. Fragmentation efficiency of ergosterol and cholesterol. The concentration of ergosterol and cholesterol mixture solution is 1:1. Data were expressed as mean of three replicates. Ergosterol m/z Peak Area Prod/Prec

Product ion scan Precursor ion 456 7.36E+04 0.1 DP10, CE 14 Product ion 379 8.34E+03 Neutral loss scan m/z 77 Product ion 456 9.23E+03 DP 10, CE14

Cholesterol m/z Peak Area Prod/Prec Product ion scan Precursor ion 446 5.74E+04 0.3 DP10, CE 14 Product ion 369 1.63E+04 Neutral loss scan m/z 77 Product ion 446 1.44E+04 DP 10, CE14

The calibration curve of cholesterol and ergosterol was developed in order to correct the concentration of ergosterol from samples over the entire dynamic range. A six-point ergosterol to cholesterol calibration curve was generated by the derivatised ergosterol/cholesterol mixture. The mixture of ergosterol and cholesterol was prepared using constant cholesterol concentration 0.4 µM and varied ergosterol concentration from the ratio of 1:10 to 5:2 (i.e. concentration from 0.04 µM to 1.0 µM). The mixture was derivatised using the method mentioned in Section 5.2.5 in order to eliminate bias in derivatisation. Figure 5.7(a) below demonstrates the relationship of the peak area ratio of ergosterol to cholesterol against that of the

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concentration ratio at each point. Linear regression was obtained with R-square greater than 0.97. According to the results, non-linear response was shown between the lowest four points. The inconsistent response indicate the injection concentration is approaching the quantification limit, however, it doesn’t affect the accuracy of ergosterol analysing, since only the concentration that meet the linear regression (i.e. ≥ 0.32 µM) was used for ergosterol quantification. Figure 5.7(b) displays the ion abundances of ergosterol and cholesterol acetate derived from the sample extract. Results showed the peak area ratio of ergosterol to cholesterol was approximately 1:1, which is a good statistic for ergosterol quantification. Taking these factors into consideration, the calibration curve is now valid for ergosterol quantification.

Figure 5.7. Specific detection of cholesterol and ergosterol. (a) Six-point calibration curve of cholesterol and ergosterol. (b) Detection of cholesterol and ergosterol by NL scan using 77 Da loss, DP10, CE 14 eV. The signal observed at m/z 446.3 and 456.3 are cholesterol and ergosterol respectively.

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In summary, the polarity of ionisation can affect the MS performance for analyte detection [16]. The positive ion mode was preferred for lipid detection as it facilitated ionisation of the majority of target analytes. Lipids such as sphingomyelin, ergosterol (as the acetate derivative), ceramide, and all glycerophospholipids showed higher signal responses in positive ion mode. The use of 5 mM ammonium acetate in the infusion solvent may facilitate ionisation in positive mode better than in the negative mode. In addition, compound-dependent parameters can also affect MS performance. The generic improvement in signal response for most analytes can be attributed to the increased scan speed (1000 Da/s) with more cycles of scan (> 5). Low DP and CE are important for ionisation of some lipid classes such as ergosterol and cholesterol, because ammonia may drive loss in the source when DP is too high.

5.3.3 Analytical Method Evaluation with S. cerevisiae 5.3.3.1.1 System suitability System suitability of the optimised analytical method was evaluated by calculating the CV% of the peak area and/or retention time of three replicate injections of standard solution. The obtained results (Figure 5.8) showed that the parameters tested were within the acceptable range. Regarding the amino acid detection, 20 amino acids were identified correctly, expressing clear resolution between peaks with CV% of the recorded peak area and retention time less than 10% and 11% respectively. Regarding the fatty acid detection, 37 fatty acids in a standard mixture were identified correctly, expressing clear resolution between peaks with CV% of the recorded peak area and retention time less than 12% and 20%, respectively.

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Figure 5.8. Instrument repeatability results based on triplicate injection of system suitability sample. (a) Repeatability (CV %) of peak area of amino acid standards. (b) Repeatability (CV %) of retention time of amino acid standards. (c) Repeatability (CV %) of peak area of fatty acid standards. (b) Repeatability (CV %) of retention time of fatty acid standards. The x-axis represents the targeted analytes for detection. Blue, purple and brown colors correspond to different ions monitored for each compound (note: alanine (Aln) had only one ion monitored)

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5.3.3.1.2 Specificity and Selectivity Specificity of the optimised analytical method was demonstrated by a suitable separation of target analytes (i.e. all tested standards) with sufficient resolution. Selectivity of the optimised analytical method was evaluated by comparing the chromatograms of individual analytes and the internal standard. Figure 5.9 demonstrates that the specificity of the analytical method was confirmed, as individual analytes from a standard mixture solution can be detected respectively. The chromatogram results also show that there was no interference or overlapping peaks observed. None of the carry-over ions or saturated peaks were present in amino acid, fatty acid and lipid class detection. Therefore, complete peak separation was achieved for all target analytes, indicating promising specificity and selectivity of this analytical method.

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Figure 5.9. Instrument specificity and selectivity results based on triplicate measurement. (a) Chromatogram of 20 amino acid standards; no carry-over ions and good peak separation were presented between different analytes and in particular baseline separation of isobaric amino acids i.e. leucine and isoleucine. (b) Chromatogram of 37 fatty acid standards; good peak separation was presented between different analytes. (c) Overlaid mass spectra of six complex lipid standards in positive ion mode. Please note in the case of amino acids, perfect separation of all compounds from one another was not required because most of them were sufficiently separated based on their different m/z.

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5.3.3.1.3 Calibration, Linearity and Sensitivity Calibration parameters, coefficient of determination (R-squared), and figures of merit (LOD and LOQ) of all target analytes were obtained using standard solutions under the optimised operating conditions. In this experiment, a calibration curve was applied to verify the validity of the experiment. Calibration of metabolite analysis involves analysing the QC standards of corresponding types of metabolite at a number of concentrations, in order to determine the detection range and response factor of analysis for each targeting analytes [9, 16].

A figures of merit test was applied to verify the sensitivity of the experiment. LOD represents as the lowest concentration of standards that resulted in a response signal three times higher than background noise. LOQ represents as the lowest quantifiable concentration of standards that resulted in a response signal ten times higher than background noise [9, 11, 16]. In other words, the detection range can be achieved by comparing measured signals from standards with known concentrations of analytes with those of blank samples, then establishing the minimum concentration at which the analytes can be reliably detected.

The QC standard for amino acid analysis consists of a mixture of 10 essential and 10 non-essential amino acids. A six-point calibration curve was prepared by series dilution to ensure the concentration of analytes was the interval between the upper and lower concentration. The upper concentration of amino acid standard was 5 nM/mL. The standard calibration quantification of all analytes is presented in Table 5.4. The ratio of peak area of each analyte to that of standards against the analyte concentration was linear in the range of ng/mL for individual analytes. Hence, calibration linearity was achieved by the mentioned concentration ranges for individual amino acid compounds with R-squared greater than 0.97 for all instances. Table 5.4 also shows that the measured LODs and LOQs for amino acids were in the ranges of 0.002-2.794 ng/mL and 0.005-8.468 ng/mL respectively, indicating great sensitivity achieved using this analytical method. The same calibration curve is used for both free and protein-bound amino acid quantification.

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Table 5.4. Calibration curve parameters and sensitivity of the optimised analytical method for amino acid detection. Data was expressed as means of triplicate measurements of amino acid standards. Abbreviation LOD represents limit of detection, while LOQ represents limit of quantification. The analysis was performed by LC-MS/MS using MRM scanning mode.

The QC standard for fatty acid analysis consists of a mixture of 37 fatty acids. A five-point calibration curve was prepared by series dilution. The upper concentration of fatty acid standard was 80 ppm. The standard calibration quantification of all analytes is presented in Table 5.5. The ratio of peak area of each analyte to that of standards against the analyte concentration was linear in the range of microgram/mL for individual analytes. The calibration linearity was achieved by the mentioned concentration ranges for individual fatty acid compounds, with R-squared greater than 0.99 for all instances. Table 5.5 also shows that the measured LODs and LOQs for fatty acids were in the ranges of 1.70-17.45 µg/mL and 5.16-52.88 µg/mL respectively, indicating great sensitivity achieved using this analytical method. The observed calibration curve can be used for fatty acid quantification.

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Table 5.5. Calibration parameters and sensitivity of the optimised analytical method for fatty acid detection. Data were expressed as means of triplicate measurements using fatty acid standards. Abbreviations: LOD represents limit of detection, LOQ represents limit of

quantification. The analysis was performed by GCMS using targeted MRM scan.

Calibration Curve Figures of Merit Fatty Acid Standards Slope Intercept R Squared LOD (ug/mL) LOQ (ug/mL)

C 4:0 2.98E+04 -3.20E+04 0.9982 5.07 15.37 C 6:0 9.16E+04 -7.27E+04 0.9977 5.33 16.16 C 8:0 1.26E+05 -6.16E+04 0.9967 6.60 20.00 C 10:0 1.47E+05 -8.10E+04 0.9968 6.67 20.23 C 11:0 7.60E+04 -4.91E+04 0.9967 3.47 10.50 C 12:0 1.58E+05 -6.99E+04 0.9954 7.79 23.61 C 13:0 8.14E+04 -8.26E+04 0.9970 3.18 9.64 C 14:0 1.64E+05 -4.97E+04 0.9948 8.56 25.93 C 14:1 cis (n-9) 3.79E+04 -2.38E+04 0.9964 3.82 11.59 C 15:0 8.50E+04 -8.97E+04 0.9968 3.48 10.54 C15:1 cis (n-10) 3.87E+04 -2.97E+04 0.9965 3.37 10.21 C 16:0 2.39E+05 4.44E+04 0.9919 15.84 47.99 C 16:1 cis (n-9) 3.59E+04 -2.33E+04 0.9940 4.35 13.18 C 17:0 8.44E+04 -1.01E+05 0.9963 3.52 10.65 C 17:1 cis (n-10) 3.72E+04 -3.62E+04 0.9963 3.66 11.10 C 18:0 1.63E+05 -7.86E+04 0.9932 10.23 30.99 C 18:1 trans (n-9) [Elaidic Acid] 3.61E+04 -3.22E+04 0.9956 4.15 12.56 C 18:1 cis (n-9) [Oleic Acid] 7.04E+04 -7.96E+03 0.9922 9.79 29.67 C 18:2 trans (n-9,12) 4.74E+04 -6.80E+04 0.9962 4.21 12.76 C 18:2 cis (n-9,12) [Linoleic Acid] 4.69E+04 -6.06E+04 0.9948 4.55 13.79 C 20:0 1.36E+05 1.93E+05 0.9782 17.45 52.88 C 18:3 cis (6,9,12) [γ-Linolenic Acid] 3.65E+04 -2.97E+04 0.9945 4.36 13.22 C 18:3 cis (n-9,12,15) [α-Linolenic Acid] 2.47E+04 -2.80E+04 0.9947 4.41 13.38 C 20:1 cis (n-11) [Gondoic Acid] 3.70E+04 1.73E+04 0.9847 7.41 22.46 C 21:0 6.90E+04 -3.52E+04 0.9977 2.94 8.90 C 20:2 cis (n-11,14) [Eicosadienoic Acid] 4.60E+04 -2.76E+04 0.9888 6.63 20.10 C 22:0 1.35E+05 -1.79E+05 0.9984 5.05 15.29 C 20:3 cis (n-11,14,17) [Dihomo-γ-Linolenic Acid] 3.25E+04 -1.80E+04 0.9905 6.16 18.67 C 20:3 cis (n-8,11,14) [Eicosatrienoic Acid_ETE] 4.30E+04 -2.03E+04 0.9890 7.05 21.37 C 22:1 cis (n-13) [Erucic Acid] 5.09E+04 -3.14E+04 0.9972 3.55 10.75 C 20:4 cis (n-5,8,11,14) [Arachidonic Acid] 3.11E+04 -3.39E+04 0.9925 5.56 16.85 C 23:0 7.26E+04 -2.10E+05 0.9966 3.63 11.01 C 22:2 cis (n-13,16) 4.44E+04 -6.70E+04 0.9996 1.70 5.16 C 20:5 cis (n-5,8,11,14,17) [Eicosapentaenoic Acid_EPA] 4.36E+04 -3.21E+04 0.9927 4.98 15.08 C 24:0 1.35E+05 -3.13E+05 0.9938 9.90 30.01 C 24:1 cis (n-15) 3.84E+04 -1.05E+05 0.9954 3.64 11.03 C 22:6 cis (n-4,7,10,13,16,19) [Docosahexaenoic Acid_DHA] 3.27E+04 1.54E+04 0.9924 5.27 15.97

The QC standard for lipid species analysis consisted of a mixture of 1:1:1:1:1:1 of PC 34:0, PE 34:0, PS 34:0, PG 34:0, PA 34:0 and SM (d18:1/17:0) in methanol. Lipid standard solution was diluted to three concentrations, 0.1 µM, 1.0 µM, and 10 µM, for MS analysis. Experiments showed that the optimised signal response was achieved using 1.0 µM injection concentration; the corresponding signal intensity for each lipid standard is presented in Table 5.6. The detection limit of lipidomic quantification was

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in the low nanomole range, indicating great sensitivity of this analytical method. The mass tolerance for analyte identification was set to be ± 0.2 m/z, and the minimum S/N ratio was set to be greater than 10 times for analyte quantification.

Table 5.6. The signal intensity and m/Z value of 1.0 µM phospholipid standard mixture in positive and negative ion mode. Data was expressed as means of triplicate measurements using lipid standard mixture.

5.3.3.1.4 Accuracy, Precision and Repeatability The determination of accuracy was evaluated by comparing the obtained nutrient profiles of S.cerevisiae to the literature. The corrected concentration of targeted analytes was calculated by normalising the baseline corrected peak area to that of standards at known concentration. Precision and repeatability of the analytical method for target analytes were evaluated by analysing the CV% of the signal

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responses (i.e. baseline corrected peak area) using three replicates with three injection measurements.

According to the results obtained in Figure 5.10 (a), the total amino acid composition for S.cerevisiae was quantified. Results indicated that it is a protein-- enhanced strain that produced a relatively higher abundance of essential amino acids such as lysine, arginine and leucine, and non-essential amino acids such as glutamic acid, aspartic acid and serine. The obtained amino acid profile is consistent with the result analysed by Huyben et al [17]. However, the total protein content from the tested biomass was approximately 732 milligrams, which is 1.57 times higher than that of the amount stated in Huyben’s investigation [17]. One possible explanation for the high protein content is that the tested benchmark yeast was cultivated with sufficient nitrogen source, resulting in an enhanced production of protein. Figure 5.10 (b) shows significant precision and repeatability, with CV in the range of 4.5-17.2% for targeted amino acids at trace level detection. And Figure 5.10 (c) demonstrates the inter-day reproducibility of the analyte concentration measurement. Results presented good reliability for assessing the consistency of amino acid detection. The obtained results fulfilled the criteria set in US FDA guidelines, which indicate that the amino acid analysis is reliable, and the measurement of amino acid contents should be consistent for all instances using this analytical method.

According to the fatty acid composition presented in Table 5.7, the quantified lipid result demonstrated that the tested biomass was composed of nine different types of fatty acids. In particular, it produced abundance of mono-unsaturated fatty acids such as C16:1 and C18:1, and saturated fatty acid C 16:0. The fatty acid profile reported by Guo et al. [18] and Stukey et al. [19] also show that C16:1, C18:1 and C16:0 are the dominant fatty acids from S.cerevisiae. The composition of other fatty acids was slightly different to each other. In addition, by comparing the obtained fatty acid profile to that of commercial S.cerevisiae, result shows that the obtained fatty acid contents were four times lower than that of commercial S.cerevisiae, this was likely due to the variations in growth conditions (Ref Appendix E for detail). The results also showed significant precision and repeatability, with average CV of 8.15% for targeted fatty acids at trace level detection.

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Figure 5.10. The total amino acid profile and intermediate test of benchmark strain S.cerevisiae. (a) Detailed composition and weight-based dosing of essential and non-essential amino acid content from sample biomass. (b) Repeatability (CV %) of baseline corrected peak area for amino acid detection. (c) The inter-day variability and reproducibility of target analytes from the same extract solution; the mean and standard deviation of the measurements is indicated.

Table 5.7. The fatty acid profile of benchmark strain S.cerevisiae. Fatty acid contents obtained from non-polar extracts of S.cerevisiae biomass. The analysis was performed by GCMS. The weight-based dosing of individual fatty acid was calculated by normalising their baseline corrected peak area to that of internal standard C 19:0 with known concentration. Data was

expressed as means of three independent bio-replicates.

Ave. RT Normalised to IS C19 [mg/g biomass] Fatty Acid 37mix Standard Sample Mean Rep 1 Mean Rep 2 Mean Rep 3 Mean SD CV%

C 12:0 10.05 10.07 0.15 0.12 0.13 0.13 0.02 11.23 C 14:0 12.15 12.17 0.33 0.32 0.30 0.32 0.02 4.77 C 14:1 12.92 12.93 0.14 0.12 0.14 0.13 0.01 5.67 C 15:0 13.16 13.17 0.04 0.04 0.03 0.04 0.00 4.84 C 16:0 14.15 14.17 4.88 5.83 5.54 5.41 0.49 8.99 C 16:1 cis (n-9) 14.81 14.82 10.74 9.80 11.30 10.61 0.76 7.12 C 18:0 16.18 16.19 1.37 1.24 1.45 1.35 0.10 7.65 C 18:1 cis (n-9) 16.78 16.78 5.62 5.12 6.25 5.66 0.57 10.04 C 18:1 cis (n-11) N/a 16.94 0.52 0.54 0.66 0.57 0.08 13.12

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The lipid profile of the sample biomass is presented in Figure 5.11(a); the total fatty acid composition and the dominant lipid species for S.cerevisiae was quantified. The lipid profile displayed in Figure 5.11(a) is similar to those reported by Ejsing et al. [10]. The quantified lipid profile indicates that the ratio between dominant lipid classes PC, PE, and ergosterol is 1.00:1.04:0.98, which is equivalent to the value obtained in this study; the ratio between PC, PE and ergosterol is 1.00:1.29:0.81. The ratio of other phospholipids including PA, PG, PS, and PI to PC was 0.58, 0.00, 0.13, and 1.42 respectively, which was comparable to the ratio obtained in this study except PI and PG. The obtained ratio of PA, PG, PS, and PI to PC in this study was 0.32, 0.11, 0.73 and 0.34, respectively. The low PI content may be caused by different cultivation conditions, or the concentration of internal standard PI 18:0/18:0 not being enough for precise quantification. Similarly, the different cultivation condition may be the factor that induce the present of PG content in testing sample. The quantified lipid results also show that lipids such as PC 32:2 (81.4 nmol/5mg biomass), PE 32:2 (91.8 nmol/5mg biomass), PE 32:2 (71.8 nmol/5mg biomass), PS 34:1 (67.7 nmol/5mg biomass) and ergosterol (150.6 nmol/5mg biomass) were the dominant lipid species within the testing sample biomass (Refer Appendix E for details). Figure 5.11(b) shows the inter-day reproducibility of the concentration measurement for the fatty acid profile. And Figure 5.11(c) demonstrates the inter-day reproducibility of the concentration measurement for targeted lipid classes. Both results present good reliability for assessing the consistency of target analyte detection.

In addition, a comparison of the nutrient profiles of S.cerevisiae using different analytical methods was presented in Figure 5.12. Figure 5.12 (a) and (b) demonstrates the nutrient composition obtained using the proposed analytical method, which shows the contents and composition of essential and non-essential amino acids, fatty acids, phospholipids, and ergosterol from sample biomass. In comparison, the nutrient composition obtained by the commonly used method (Figure 5.12 (c)) only shows the contents of protein, fats and other metabolites (e.g. carbohydrates). Both profiles show that the tested S.cerevisiae is a protein enhanced strain with approximately 73% of protein and 3% of lipids. However, the profiles that obtained using the proposed

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analytical method provided a more detailed analysis, which helps to provide more accurate profile of each nutrient for nutritional evaluation, which can be used to improve the effectiveness of new microbe-derived supplements for animal production.

Overall, the proposed analytical method can be used to identify and quantify targeted fatty acids and complex lipids, and it shows good repeatability, indicating it is reliable to use this method for fatty acid analysis and complex lipid analysis. The quantification of fatty acid contents and lipid species contents should be consistent for all instances using this analytical method.

Figure 5.11. The fatty acid profile, target lipid class composition, and intermediate test of benchmark strain S.cerevisiae. (a) Detailed composition and weight-based dosing of fatty acid content from sample biomass, and the composition of dominant lipid classes from sample biomass. (b) The inter-day variability and reproducibility of target fatty acids using the same extract solution. (c) The inter-day variability and reproducibility of target lipid classes using the same extract solution.

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Figure 5.12. Composition overview of target nutrients of S.cerevisiae. (a) The composition and weight-based dosing of important nutrient content from sample biomass, including essential and non-essential amino acids, fatty acids, phospholipids, and ergosterol. (b) The nutrient profile obtained by the proposed analytical methods. (c) The nutrient profile obtained by the commonly used method.

5.3.3.1.5 Recovery The recovery of the optimised analytical method was evaluated by calculating

13 the extraction efficiency using surrogate standard Cvaline. Consistent recovery rate was achieved using this method, and the average recovery value was 62%. The obtained recovery rate only represents the extraction efficiency of the sample preparation; the ionisation efficiency was analysed during the chromatographic analysis, and analytes were quantified using internal standards. The matrix effects were also included; the signal response obtained from negative control with spiked internal standards was compared to that of standard solutions. Results showed no significant signal augmentation or suppression for target analytes, which is similar to the investigation provided by Zhao et al. [16].

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5.3.3.1.6 Robustness No significant changes were detected upon applying small variations to the chromatographic conditions, ensuring that the method is robust to small changes applied in terms of age of standards, operating temperature, and flow rate. Results for robustness are presented in Section 5.3.2 and section 5.3.3.4.

5.4 Conclusion

According to the present study, the proposed analytical method was developed based on the methods available at Central Analytical Research Facility (QUT) or from literature. This method has been evaluated and applied to the absolute quantification of nutrients of interest in yeast at trace level detection, such as amino acids, fatty acids and lipid species. Therefore, this analytical method is capable of measuring the absolute amount of nutrients per mg of tissue, and it has the ability to provide detailed, sensitive and high-throughput nutritional analysis for different microbial supplements.

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Chapter 6: Conclusions

6.1 Main Conclusions

The aim of this research work was to develop an effective analytical method for nutrient profiling of microbe-derived feed supplements. In this work, the essential ingredients and nutritional requirements in feed formulations for monogastric and ruminant livestock were defined to target specific nutrient species for microbial biomass compositional analysis. Amino acids such as lysine, methionine and threonine, fatty acids such as linolenic, linoleic and eicosapentaenoic acid, and lipid groups such as phospholipids and sphingolipids, were identified as target nutrient species.

A nutrient profile screening strategy for growing various microbes on standard and stickwater-based microbial growth media was developed and applied. This method was used to analyse microbial strains for their nutritional potential as feed supplements when grown on meat processing stickwater wastes. Nine different filamentous fungi and yeast strains were cultivated on the two different growth media, and their nutrient profiles were analysed. Findings according to their nutrient composition reflect a correlation between the yield of target nutrient production and the growth conditions. Microbes that were cultivated in the same growth medium showed similar nutrient profiles, which indicates that the growth medium may account for the differentiation in nutritional profiles. Results also indicated that the stickwater-based medium may induce metabolic changes in microorganisms, including enhanced production of sphingolipids or sterols.

In conclusion, the proposed analytical method was developed and validated for simultaneous identification and quantification of amino acids, fatty acids, and lipid species from biomass. The extraction efficiency was improved by appropriate sample preparation, an additional homogenisation process, and a hydrolysis process. Optimisation of the analytical method was conducted by adjusting parameters in regard to operating conditions, analyte detection, and MS performance. Low LOD and

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LOQ for all target analytes enable the detection and quantification of some essential nutrients (e.g. methionine) at trace levels. Detailed quantification of target nutrient contents was achieved by using the proposed method.

In terms of the practical aspects of the compositional analytical method, this method can now be used for detailed, sensitive and high-throughput compositional analysis of products that contain organic compounds. For example, it is suitable for rapid nutrient screening of organic wastes or new microorganisms, as well as detailed nutritional analysis of microbe-derived feed supplements. Nutrient profiling can be done routinely in testing laboratories equipped with instruments such as LCMS and GCMS (refer Section 3.3 for details). A sensitive and high-throughput compositional analysis can help to analyse increased yields of organic compounds and identify valuable trace compounds. These analyses will help to demonstrate the practical and economic value of new microorganisms or waste conversion processes. As the method and technology matures in the future, the fixed cost for this method will be reduced. This method can also potentially be applied in other fields of biochemistry, industrial biotechnology and microbiology.

6.2 Limitations of the Study

Several limitations were noticed whilst conducting this study, which include:

1) After acidic hydrolysis, a portion of asparagine and glutamine were hydrolysed to aspartic acid and glutamic acid, respectively. It is necessary to increase the concentration of phenol for acid hydrolysis to avoid decarboxylation. Tryptophan and methionine were partially oxidised during hydrolysis, therefore, the concentration of reducing agent dithiothreitol may need readjustment for hydrolysis process. Cysteine was completely oxidised so it cannot be directly determined.

2) Degradation is likely the major factor that contributes to the insignificant intermediate precision of glutamine.

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3) The maximum detection m/Z value for SCIEX Q-trap 6500 is 1200, hence it is hard to detect analyte with high molecular weight, especially for sphingolipids such as mannosyl-inositolphosphoceramid (MIPC) and

mannosyl-diinositolphosphoceramide (M(IP)2C).

4) Lack of internal standards for sphingolipids quantification.

5) The proposed analytical method is unable to characterise and quantify triglycerides (TAG)

6.3 Future Research Work

The concentration of internal standards is one of the key factors affecting the quantification of target analytes. It is necessary to analyse the nutrient profiles of more strains to find out the appropriate concentration range of internal standards. Further knowledge regarding the structural information of target lipid species is required. Benchmarking of the proposed analytical method with other established analytical methods is also required, in order to define the gaps of the current method. And it is important to determine a possible solution to increasing the yield of selected valuable substances by growth medium modification. A cost analysis should be conducted in future research tasks.

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Appendices

Appendix A: Summary of Essential Nutrients for Animal Feeding

Basic Information Std hydrolysis tubes Normal glass tubes with Teflon-lined screw caps [ref. L.Z.Guang et al.] Completely evacuate the tube Completely evacuate the tube, purge with nitrogen Category Classes [ref. Purge with nitrogen for more than Name Formula Abbreviations M.W R Group Ideal Molar ratio before seal it. 6M HCl for 24h before sealing it. It may be necessary to freeze the sigma] 30s before sealing it. 6M HCl for at 110C in Vacuum condition sample to prevent boiling during evacuation. 6M 24h at 110C in Vacuum condition [ref. Fountoulakis] HCl for 24h at 110C in Vacuum condition

Arginine Arg [R] 174.188 HN=C(NH2)-NH-(CH2)3- Hydrophilic 1 Losses of 4% Losses of 4% Losses of 7%

Histidine His [H] 155.141 N=CH-NH-CH=C-CH2- Hydrophilic 1 Losses of 10% Losses of 10% Losses of 4%

Very Isoleucine Ile [I] 131.160 CH3-CH2-CH(CH3)- 1 Losses of 4% Losses of 4% Losses of 8% Hydrophobic

Very Leucine Leu [L] 131.160 (CH3)2-CH-CH2- 6 Losses of 7.33% Losses of 7.33% Losses of 6.5% Hydrophobic

Lysine [limiting] Lys [K] 146.170 H2N-(CH2)4- Hydrophilic 2 Losses of 9% Losses of 9% Losses of 10.5% Essential AA Methionine Very Met [M] 149.199 CH3-S-(CH2)2- No record No record No record No record [limiting] Hydrophobic

Very Phenylalanine Phe [F] 165.177 Phenyl-CH2- 3 Losses of 6.33% Losses of 6.33% Losses of 10% Hydrophobic

Partially hydrolysed, Partially hydrolysed, losses of 5%[1], Threonine Thr [T] 119.105 CH3-CH(OH)- Neutral 1 Losses of 16% losses of 5%[1], 16%[2] 16%[2]

Very Tryptophan Trp [W] 204.213 Phenyl-NH-CH=C-CH2- No record Completely destroyed Completely destroyed Completely destroyed Hydrophobic

Very Valine Val [V] 117.133 CH3-CH(CH2)- 5 Losses of 2.2% Losses of 2.2% Losses of 4.2% Hydrophobic

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Alanine Ala [A] 89.079 CH3- Hydrophobic 3 Losses of 3.67% Losses of 3.67% Losses of 7%

Asparagine [+ Completely hydrolysed to Completely hydrolysed to Asn [N] 132.104 H2N-CO-CH2- Hydrophilic N/a Completely hydrolysed to aspartic acid Aspartate] aspartic acid aspartic acid Can not be directly Can not be directly determined [1]; Losses Cysteine Cys [C] 121.145 HS-CH2- Hydrophobic 6 determined [1]; Losses of Losses of 26.83% of 26.17% 26.17% Glutamine [+ Completely hydrolysed to Completely hydrolysed to Gln [Q] 146.131 H2N-CO-(CH2)2- Neutral N/a Completely hydrolysed to glutamic acid Glutamate] glutamic acid glutamic acid

Glycine Gly [G] 75.052 H- Neutral 4 Losses of 5% Losses of 5% Losses of 1.75% Non-essential AA Proline Pro [P] 115.117 N-(CH2)3-CH- Hydrophilic 1 Losses of 7% Losses of 7% Losses of 2%

Partially hydrolysed, Partially hydrolysed, losses of 10%[1], Serine Ser [S] 105.078 HO-CH2- Neutral 3 Losses of 17.67% losses of 10%[1], 15%[2] 15%[2]

Partially destroyed, Tyrosine Tyr [Y] 181.176 4-OH-Phenyl-CH2- Hydrophobic 4 Partially destroyed, losses of 12% Losses of 14% losses of 12%

Aspartic Acid Asp [D] 133.089 HOOC-CH2- Neutral 3 Losses of 0.33% Losses of 0.33% Losses of 6.33%

Glutamic Acid Glu [E] 147.116 HOOC-(CH2)2- Neutral 7 Losses of 3.43% Losses of 3.43% Losses of 4.29%

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Basic Information Category Name Formula Common Name Alternatived Names M.W [g/mol] Target species Key natural sources Benefits Absorption Notes

C20H30O Oil, fats, fruit and vegetables Absorption rate of Vitamin A and Vitamin A [orange] is required by all animals and is a Retinol (alcohol) Dairy/Heifer/Beef Helps maintain vision [carrots, grass, peas, milk, fish oil, Carotene is 80-90% and 50-60% product of animal metabolism, it is essential for livestock Retinyl palmitate liver and etc] Maintains surface skin layers and respectivily. It will release from protein reproduction, vision and involved in disease resistance. Sheep (ester) mucous membranes by pepsin in stomach and enzymes in Yellow pigment beta-carotene can be converted to Retinal/Retinene Grower/Finisher/Breeding [Pig] Essential for growth and reproduction small intestine. Absorption affected by vitamin A in animal body. About 50 carotenoids yield (aldehyde) Vitamin A carotenoids 286.46 bile, dietary lipid, protein levels and vitamin A activity with beta-carotene having highest Feeds of animal origin only found Retinoic acid (acid) Broiler/Breeder/Layer Essential for optimum immune system antioxidants. [1mg retinol=3333IU vit A; activity. as vitamin A (retinol) conversion ratio of carotene into vit A Helos improve ovulation and Vitamin A2 Catfish, trout, salmon for ruminant, pig and poultry are 12.5%, implantation of the ovum, embryonic 15% and 42.5%] Milk fed [Calf, lamb] & Other and foetal development and hormone Axerophtol species activation for pregnancy C27H44O Cod and hilibut oils, egg yolk, milk UV light irradiation of foods such as mil, Vit D [white] is a fat soluble vitamin that essential in Dairy/Heifer/Beef fat, sun dried green forage butter can fortify them with vit D. calcium and phosphorous metabolism. It can be Ergocalciferol (D2) - Growth of sound bones and teeth Beet pulp, cocoa shells, corn Absorbed, with lipids, from the small converted from its chemical forms into vit D by the action predominantly plant Sheep leaves intestine, about 50% absorption rate. of ultra-violet light. The provitamins are precursors of a origin Plant: in yeasts and fungi Grower/Finisher/Breeding [Pig] Allowance is generally 1/10 of amount steroid hormone known as calcitriol. Vit D has an ergosterol converts to vit D2 Vitamin D Vit D 384.62 Kidney function of vit A. And only protected forms of vit interrelationship with calcium, phosphorous and to some Broiler/Breeder/Layer Animal: 7-dehydrocholesterol D should be used in feeds. [Standard extent sodium, potassium and magnesium. converts to vit D3 [it is derived concentration of vit D3 = 500,000 IU/g] Cholecalciferol (D3) - Catfish, trout from cholesterol or squalene Fat/Water animal origin In pregnancy, reduces congenital bone synthesised in the body and soluable Milk fed [Calf, lamb] & Other malformations present in large amounts in skin] vitamins species C29H50O2 Efficiency of beta-tocopherol and alpha- Vit E [Light yellow] is originally isolated from wheat germ Dairy/Heifer/Beef Prevents tissue damage Rich in wheat germ oil, grass, tocotrienol is 15-40% and 15-30% oil and it is critical as both an inter and intra-cellular clover, alfalfa, green meal, cereal Improved immune response and disease respectivily. PUFA inhibit absorption antioxidants [protects against heavy metal toxicity and Tocopherol Sheep germ, uncrushed oilseeds, resistance [Hence increasing dietary lipid content other toxic substances]. Alpha-tocopherol (100% vegetable oils, liver, eggs Stabilisation of lipid in animal products Grower/Finisher/Breeding [Pig] increases vit E supplementation need] efficiency) has the greatest activity. The utilisation of vit E [meat, milk, eggs] Vitamin E α-tocopherol 430.69 whereas medium chain (8-16) is dependant on adequate selenium. Preparation for pregnancy and Broiler/Breeder/Layer triglycerides enhance absorption. protection against abortion Poor in maize silage, moist feeds, Maintains integrity and optimum Tocotrienols Catfish, carp, trout, salmon, tilapia extracted oilseed meals, milk. function of the reproductive, muscular, Milk fed [Calf, lamb] & Other circulatory, nervous and immune species systems C63H88CoN14O14P Fishmeal, meat byproducts, milk, Dietary B12 is bound to food proteins, it Vit B12 [red] is the only vitamin to contain a mineral Grower/Finisher/Breeding [Pig] eggs, liver, kidney and plants. Normal blood formation can be released by peptic digestion. element, 4.5% cobalt, it is essential to basic protein, fat Only 1-3% of microbial vitamin B12 can and carbohydrate metabolic functions. It is the key Vitamin B12 Cobalamin [yeast be absorbed by ruminants. Calcium, vitamin in preventing anaemia. B12 can be synthesised by Broiler/Breeder/Layer Aids nervous system [Not Vit B12 fermentation]; 1355.37 copper and ferrous ions improve B12 bacteria but not yeast or fungi[1]. Approximately 3% of compulsory] Cyanocobalamin absorption and funciton. B12 improves ingested cobalt can be converted to B12 in rumen by Milk fed [Calf, lamb] Important in growth and development uptake of carotenes from intestine. bacteria synthesis. The requirement affected by methionine and folacin levels in diet

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Basic Information Category Systematic Name ["Z" represent Main Class Sub-Class Headgroup Common Name Synonyms Formula Abbreviations M.W Notes the location of the double bond] cis-9, cis-12-octadecadienoic acid; cis,cis- Linoleic acid; cis,cis-9,12-octadecadienoic Linoleic acid 9Z,12Z-octadecadienoic acid C18H32O2 C18:2n-6 280.24 acid; Linoleate; 9Z,12Z-Linoleic acid;Telfairic acid; C18:2n-6,9; Linolic acid; Leinolic acid Here is the list of α-Linolenic acid [require more vit cis-9, cis-12, cis-15-octadecatrienoic acid; common FA of animal 9Z,12Z,15Z-octadecatrienoic acid C18H30O2 C18:3n-3 278.22 E) C18:3n-3,6,9 origin. Dietary lipids generally rich in 5Z,8Z,11Z,14Z-icosatetraenoic acid; AA; unsaturated fatty acids. 5Z,8Z,11Z,14Z-eicosatetraenoic Arachidonate; cis-5,8,11,14-Eicosatetraenoic Arachidonic acid C20H32O2 C20:4n-6 304.240 [Ref. WattAgNet; acid acid; (all-Z)-5,8,11,14-icosatetraenoic acid; all- Analysis of fats in the Unsaturated cis-5,8,11,14-Eicosatetraenoic acid ruminant diet]EPA, DHA fatty acids 5Z,8Z,11Z,14Z,17Z- (5Z,8Z,11Z,14Z,17Z)-Icosapentaenoic acid; and linolenic acid are Fatty acids and EPA (Eicosapentaenoic acid) C20H30O2 C20:5n-3 302.220 eicosapentaenoic acid Timnodonic acid; C20:5n-3,6,9,12,15 essential for normal conjugates Fatty Acyls [FA] HOOC- DHA (Docosahexaenoic acid) 4Z,7Z,10Z,13Z,16Z,19Z- Cervonic acid; Docosahexaenoic acid; C22:6n- growth and health, and C22H32O2 C22:6n-3 328.240 [more prone to peroxidation] docosahexaenoic acid 3,6,9,12,15,18 have been associated 9-octadecylenic acid; cis-9-octadecenoic with cardiovascular Oleic aicd [deficiency in pigs] 9Z-octadecenoic acid acid; cis-Oleic acid; cis-Oleate; Oleate; Elaic C18H34O2 C18:1n-9 282.26 health, reduced acid; Elaidoic acid; C18:1n-9; Rapinic acid inflammation, and Cis-Erucic acid [deficiency in pigs] 13Z-docosenoic acid cis-13-docosenoic acid; C22:1n-9 C22H42O2 C22:1n-9 338.32 normal development of cis-11-octadecenoic acid; C18:1n-7; Asclepic the brain, eyes, and Cis-vaccenic acid 11Z-octadecenoic acid C18H34O2 C18:1n-7 282.26 acid nerves. [Ref. Kerr et al] Myristic acid tetradecanoic acid N/a C14H28O2 C14:0 228.21 The digestibility of free Straight chain Cetylic acid; Palmitate; n-Hexadecanoic acid; fatty acid is lower than Palmitic acid hexadecanoic acid C16H32O2 C16:0 256.24 fatty acids C16:0; Aethalic acid that of triglycerides. Neostearic acid [TBD]; Stearic 15,15-dimethyl-hexadecanoic acid N/a C18H36O2 C18:0 284.27 Other 7Z,10Z,13Z,16Z,19Z- Clupanodonic acid; C22:5n-3,6,9,12,15; Docosanoids DPA C22H34O2 C22:5n-3 330.260 Docosanoids docosapentaenoic acid Osbond’s acid Cholesterol and Cholesterol; Cholest-5-en-3-ol; (3β)-Cholest- HO- Cholesterol cholest-5-en-3β-ol C27H46O Chol 386.35 derivatives 5-en-3-ol; Cholesteryl alcohol High energy source for Sterol Lipids [ST] Sterols Ergosterol and Ergosterol; Provitamin D2; Ergosterin; Steroid animal feeding HO- Ergosterol Stigmasta-5,7,22-trienol C28H44O Ergo 396.35 derivatives alcohol Glycerophospholipids PC/PE/PA/PG/ Phospholipid is a good emulsifier, it increases digestion and absorption performance as compare to Triglyceride. Dietary phospholipids have been found to have a beneficial effect on the growth and survival of several types of [GP] PS/PI animal. SM/Cer/IPC/ Sphingolipids may facilitate mycotoxin elimination, there’s a hypothesis shows that SP in food may compete for cellular binding sites and therefore facilitate the elimination of pathologic organisms from the intestine. Sphingolipids [SP] MIPC

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Appendix B: Standard Operating Procedure for Polar and Non-polar Metabolite Extraction and Detection

SCP Standard Test Procedures

SCP [Fast Growing]: Strain R.glutinis; R.dairenensis; R.toruloides; R.mucilaginosa and S. cerevisiae.

SCP [Slow Growing]: Strain M.isabillina; A.oryzae; T.terricola; G.candidum; G.lucidum and P.ostreatus.

No. of testing samples: 88 samples (four bio-replicates, 3 replicates extract with internal standard, and 1 replicate extract without internal standard). [5 mg freeze dried sample with two different culture mediums: rendering waste based medium and Glucose & yeast extract based medium; 3 tests for each extraction: Lipid, FAMEs and amino acids.

Polar & Non-polar metabolites Extraction

Extraction procedures:

The quantitative chemical analysis of selected microorganisms are conducted by using Mass Spectrometry technique [Both LCMS and GCMS techniques]. The metabolite and lipid extraction procedures are shown as follow:

1. Run total nitrogen and total carbon test before extraction.

2. Prepare ice-cold MS-grade methanol solution. Solution containing internal standards.

3. AA and Polar metabolites analysis: final conc. is 0.25 µM (16.24 nmol) 13C_valine.

4. FAMEs analysis: final conc. of C19 is 260.42 µM (156.25 nmol) for MS detection [Min detection value of C19:0 is 200 µM].

5. Lipid analysis: final conc. of IS mix (with 0.01% BHT) is approx. 2.0 µM (80 nmol) for MS detection [Min detection value of ISPC is 1.0 µM].

I.e. 8.49 mL MeOH with 100 µL 13C_valine (1000ppm); 312.5 µL C19 [25mM] and total of 1.1 mL Lipid IS mix. Add up to 10.0 mL.

6. 2 Replicates: Transfer 5 mg PBS-washed [Proteins would be solubilised after PBS wash] cell culture (freeze dried) pellet into a clean 2 mL tube.

NOTE: Dry cell pellet procedure, weight empty tube (2mL) Centrifuge 1 mL cell culture/broth first (13000rcf, 4C, 5 min), remove supernatant and weight the wet pellet, add addition broth

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and repeat the procedure if the weight of pellet is less than 20 mg. Wash with PBS for two times in order to get rid of amino acid residues from culture broth: add 500 µL of PBS then centrifuge with the same condition, discard the supernatant and repeat this process again. Freeze dry wet cell pellet and weight. Separate pellet to 2 sets, oven dry one set for moisture measurement.

7. For cell culture pellet (hot extraction): Add 200 µL of ice-cold methanol with ALL internal standards and quickly homogenise 90 sec (1 cycle) using TissueLyser, follow by 10 min sonication using Ultrasonic Bath Sonicator.

NOTE: TissueLyser Procedure: Place three 3mm beads in each samples, vortex for 5-10sec, then place samples in tray and lock into balance position, set frequency for 30/s; time for 90s. For sample tray on the right-hand side: Clockwise to tight and anticlockwise to loose

8. Agitate for 15 min at 60 ºC, 850rpm to enhance extraction.

9. Add 350 µL of MS-grade water and thoroughly agitate [Vortex].

10. Centrifugation (15,000 rcf, 10 min, 4 ºC) of samples are required [so as to get rid of cell debris, the pellet of cell debris with protein will save for acid hydrolysis].

11. Transfer 500 µL of cell lysates (supernatant) into a clean 2 mL tube, keep “protein + cell debris pellet…1” for protein-bound amino acid analysis.

12. Add 400 µL of MS-grade MTBE (Thermo cat no ACR177040010, with 0.01% BHT, 5 mg BHT in 50 mL MTBE) and thoroughly agitate.

NOTE: By using MTBE, the MTBE layer will form at the top, water layer at the bottom, and starch and proteins will form a solid pellet at the bottom of the tube.

13. Centrifuge (20,000 rcf, 10 min, 21 ºC).

14. Top layer for non-polar metabolites analysis (lipids): Combine 125uL of NH4OAC solution with 300 µL of MTBE layer in a clean 1.5 mL tube [2nd extraction, lipid extract purification; 150 mM Ammonium acetate (Sigma cat no A1542) in MS-grade water, i.e. 578 mg NH4OAC in 50 mL MS-grade water]. Vortex for 20s. Centrifuge, 15,000 rcf for 5 min, 4 ºC, there will be separated to two phase, MTBE layer on the top and water with free amino acid at the bottom, collect 120 - 240 µL supernatant [MTBE layer, collect the supernatant not too close to the water layer] and store it at - 20 ºC until further analysis.

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FAMEs Analysis

Realistic stoichiometric ratio for TMSH to fatty acids solution is 1:2 when derivatising multiple compounds, hence add 20 µL methylation reagent TMSH (trimethylsulfonium hydroxide, which is water-stable and easily methylates virtually any compound with labile protons), with 40 µL of MTBE layer in a clean 1.5 mL vial (with insert) for silylation. Vortex 60 s at 30C with 500 rpm. Store it at - 20 ºC until further analysis [Can also use Auto-sampler to do this step].

Lipid Analysis

A loading test require 100 times dilution using re-suspending solution of CHCl3/MeOH (1:2 with 0.01%BHT) and 5mM NH4OAC for MS analysis. Hence transfer 10µL samples and negative control into glass vial with 990µL re-suspending solution.

Method for Ergosterol purification: Dissolving ergosterol (0.25 M) with acetic anhydride (0.5 M) in pyridine (15 mL), dichloromethane (5 mL) was added to enhance purification. Stirred at room temperature for 16hr. Dry with nitrogen gas and the remaining filtrates were combined with water (15 mL). Centrifugation was applied to remove precipitates, and the obtained supernatant was filtered through 0.22 µM membrane and evaporated to give a 75% yield of off-white solid. The obtained ergosterol methyl esters were dissolved with 2:1 (V/V) methanol/chloroform 5 mM ammonium acetate for resuspension, analyse with DP10_CE14_NL77 positive ion scan (MCA).

Derivatisation is required for Ergosterol detection. Use Cholesterol [MW 386.4] as an internal standard and spike it to samples for derivatisation and detection.

Method for derivatisation: Prepare 1:1 mixture of sample extract (20 µL) with chloroform (20 µL) in glass vial, then add 110 uL aliquot in 1/12 V/V acetic anhydride/chloroform. Dry down under N2 follow by reconstitute in methanol/chloroform 2/1 with 5 mM ammonium acetate. Analyse on QTRAP 6500 with DP10_CE14_NL77 positive ion scan (MCA).

NOTE: To make CHCl3/MeOH (1:2 with 0.01%BHT) and 5mM NH4OAC

1. 5 mg BHT in 50 mL CHCl3 [solution 1]

2. 20 mg BHT in 200 mL MeOH [solution 2]

3. 578 mg NH4OAC in 50 mL MeOH – 150 mM [solution 3]

4. 25.34 mL [solution 2] + 13.33 mL [solution 1] + 1.33 [solution 3]

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15. Bottom layer for polar metabolites analysis (free amino acids, carbohydrates etc.): Filter 400 µL of the methanol/water layer through 0.22 µM filter membrane [with small diameter] in a clean 1.5mL tube.

AA analysis

Transfer 50uL filtrate into a glass insert (Fisher, cat no: THC06090865) inserted into a clean 1.5 mL tube and vacuum dry at 40 degree for 20 min (no heating). Re-suspending samples [and positive & negative controls] with 60 µL 0.1% Formic acid in MS-grade water. . A loading test require 100 times dilution using 0.1% Formic acid.

Polar metabolites analysis

Prepare MeOX solution for derivatisation (Oximation): Fresh 30mg Methoxy (l) amine hydrochloride and 1000 µL Pyridine, vortex and incubate at 50℃ for 5min. Transfer 20 µL filtrate into glass insert and vacuum dry at 40 degree for 10 min, and aliquot 20μl of MeOX solution. Vortex and incubate at 37℃ for 2hr in thermomixer at 500rpm.

Notes: Oximation is performed in order to stabilize sugars in the open-ring conformation. Oximes exist as two stereoisomers (syn and anti), and therefore are often present as two peaks per compound in the chromatogram (denoted MX1 and MX2).

Silylation: Add 40μl BSTFA/1% TMCS, vortex and incubate for 40min at 37℃, 500rpm. For steroids, at 70℃ -OH groups are derivatised faster and more efficiently than –COOH and –NH2 groups. [Use within 2 days]

NOTE: Vacuum dry procedure - Vacuum concentrate all Free AA samples, positive and negative control samples to dryness

1) Open lid, turn on pump [Start], turn valve to vertical position, and ensure the pressure drops to 6 bar.

2) Place samples

3) Go to ‘Manual’, set Temperature and Time at Evaporation manual [40℃ for 20min]

4) Use Evaporation manual

5) Close lid and check conditions (Temp: -54℃ and Pressure: 6bar)

6) Start

7) Open lid get all samples, close the lid

8) Stop pump, press "vent" button for 5sec and let pressure go back to 10 bar, slowly turn valve to horizontal position.

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16. Pellet for protein-bound amino acids analysis: Collect the protein pellet…2 from the bottom of the tube. Additionally MS-grade MeOH wash to remove free amino acids and keep proteins in a form of pellet. Conduct BCA protein assay to estimate protein concentration. Conduct acid hydrolysis to hydrolyse proteins to individual amino acid residues. Adjust pH to a value compatible with chromatographic column used. [pH will be adjusted by using F5 HPLC column]

NOTE: To perform an acid hydrolysis for partial AA analysis

The ratio between protein and acid during hydrolysis should be 1mg protein to 400 µL acid. For each sample, prepare hydrolysis solution in glass vial using 5 mL HCl with 15.5 mg DTT [20mM] with 1 mL of 2 mg/mL starting protein to avoid oxidisation of methionine & tryptophan.

1) Combine protein pellets, prepare negative control and BSA sample.

2) Add collected protein pellet to 5 ml hydrochloric acid (with DTT) (20%) into a 100 mL spherical Ace® Glass pressure flask and mix gently by swirling the flask.

3) Seal the flask with PTFE screw cap and heat in an oil bath at 110 C, for 24 h.

4) After cooling, use a transfer pipette to move the sample mixture into a 100 mL quick- fit round bottom flask.

5) (Must be done in fume hood). Remove most of the HCl using a vacuum pump with an in-line liquid nitrogen cold trap. The sample flask can be warmed to 45-50 C to speed up the process. The vacuum should be reduced slowly until slight bubbling of the sample mixture occurs. This process may take 30 – 60 minutes.

6) Remove the sample flask from the vacuum apparatus and place aside.

7) (Must be done in fume hood). Neutralise condensed HCl vapour by decanting ~500 mL of a 1 % sodium bicarbonate solution into the cold trap, and then remove the trap from the liquid nitrogen.

8) Move the sample flask to a rotary evaporator to remove traces of HCl and the bulk water. Use a water bath temperature of 45-50 C. The evaporation is performed until the mixture appears dry. 30 mbar final vacuum at a 45 C water bath temperature will be sufficient.

9) Resuspend hydrolysed sample into 5 mL 0.5% formic acid and 2 mL 0.1 mol/mL NaOH solution to balance pH value.

10) Filter using 0.22 µm disposable syringe filter to remove solid residue.

11) Perform a 1:100 dilution using 0.1% formic acid solution before LC/MS injection.

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NOTE: BSA standards for protein hydrolysis - The method for back-calculating the total amount of protein in sample based on the efficiency of the hydrolysis methods by comparing the hydrolysis of sample to the hydrolysis of a known, purified protein (e.g. BSA lyophilized powder). I.e. would be more appropriate if using the same hydrolysis method to hydrolyse a purified protein powder and find out the factor to estimate the amount of protein-bound AA, then it will be able to check if there is any correlation between free AA and protein-bound AA of each strain. [Ref. the correction factor for 0.1mg protein is 51.2]

Samples will be ready for analysis together with:

Negative control

Process solution through extraction. [I.e. follow all the extraction steps without using samples to eliminate possible contaminants from extraction. Chemicals include MeOH, water, MTBE. Additional AmOAc for lipid extraction]

QC/Positive control

For AA analysis

– QC [6pt calibration curves]. E.g. SH01 (6th): Dilute amino acid mix 100 times with 0.1% FA in MS-water [10 µL AA mix with 990 µL solution into glass vial], as well as new sample of 1 ppm cysteine, asparagine and glutamine (R block) in MS-water. Transfer 60 µL into glass insert and insert into glass vial.

– Positive standard: Prepare 5 mg of BSA sample and run extraction with samples.

For polar metabolites analysis

– Positive standard: Prepare separate stocks of 1 mg/mL (final conc. 1000 ppm) in MS-grade water of glycerol/glutamic acid/glucose/mannose and sucrose. Dilute each stock 100x with MS-grade methanol (final conc. 10 ppm). Aliquot 6 µL of each into the same kind of glass insert and vacuum dry. And run with alkane standard mix.

For FAMEs analysis

- QC: 37 mix Restek standards [5pt calibration cures]. E.g. 20 times dilution standard mix is the 5th point, dilute it for 5 times.

For lipid analysis

- QC: PL mix & SM, CE, Ergo

- PBQC: Make up this sample by combining 5 µL aliquot of each study sample after re- suspending them prior MS analysis [After vacuum drying and resuspending them in a final volume].

- Calibration curve: Prepare if necessary. Process through extraction.

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NOTE: Under above conditions:

1) Carboxylated amino acids [Glutamic acid residues] lose carboxylation. [Non-essential]

2) Asparagine and glutamine are completely hydrolysed to aspartic acid and glutamic acid, respectively. [Non-essential]

3) Tryptophan and methionine may partially detect by using DTT during hydrolysation [Essential feed nutrient].

4) Cysteine cannot be directly determined. [essential feed nutrient] – Some authors suggest alkylating cysteines to preserve them. Iodoacetamide and chloroacetamide reagents are available in R block.

5) Other amino acids can also be affected.

6) Alkaline hydrolysis is also possible (pH would need to be adjusted). [Alkaline hydrolysis used for determination of tryptophan, it also applied if the protein sample contains a large percentage of carbohydrates.]

NOTE: Before conducting a large scale study, a loadings test exercise must be conducted to optimise the amount of extract injected into the instrument and to verify the preparation (e.g. to ensure the signal of interest is free from interference and below the detector saturation level (no carry over ions/peaks) and within the linear range of the instrument. [TEST PBQC sample for 100x dilution]

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Polar/Non-polar metabolites Profiling and Quantification

Amino Acid Data Collection and Analysis:

Instrument: LCMS 8050 Shimadzu – tri-column. This instrument is not well suited for identification of unknown compounds due to its low resolution, low mass accuracy and the lack of appropriate spectral libraries.

Purpose: For the targeted quantitation of analytes of AA and vitamins.

Available scan modes: Q3 (full scan), PRECURSOR ION (PIS), NEUTRAL LOSS (NLS), PRODUCT ION (PROD) and MRM/SRM (multiple/selected reaction monitoring). Available ionisation techniques: heated electrospray (HESI) using dual functionality DUIS source and atmospheric pressure chemical ionisation (APCI) using dedicated APCI source. Injections volumes up to 50uL.

This SOP refers to a scheduled MRM analysis of 1 µL of peptide sample consisting of 25fmol commercially pre-digested β-galactosidase (BGal) protein. Conditions:

- Electrospray ionisation (ESI)

- F5 column

- Mobile phase A: water/0.1% FA, mobile phase B: ACN/0.1% FA

FA = formic acid

ACN = acetonitrile

1. Ensure to book instrument through CARF and fill in login information [Excel data] for each use.

2. Transfer samples, blanks and standards into plastic auto-sampler vials with light blue caps. Label the caps of the vials with name followed by a number. [glass vial may also applicable]

SAMPLES SHOULD:

 NOT contain any solid material. Filter all your samples through 0.22 µm filter until samples are clear and no particulates remain.

 NOT contain any non-volatile salts (E.g. phosphate buffer). Desalt all your samples using tips packed with C18 material (zip tips or stage tips). Alternatively use method with valve switching so that salts are washed away at the beginning of the run.

 NOT be too concentrated. Approximately 500 ppm of each compound is desired amount to be loaded onto a column otherwise you may end up having a problem with carry over. Concentrations can be increased if necessary, but always start low and work up, never start from high going to low concentration.

 Be dissolved in a suitable solvent (E.g. methanol, ACN, water, isopropanol, up to 10% of either acetone, DCM, chloroform, DMF or DMSO.

 Be at pH that is compatible with the analytical column

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 Peptide samples should be processed through zip tips, stage tips or similar.

 Be accompanied by blanks and negative controls

 Use only LCMS grade solvents/reagents for sample preparation

3. Place samples into auto-sampler tray/rack. (Note: Tray 1-7 for samples; Tray 0 for QC (Blank – 1 and SH01 - 2) [1% Amino acid mix] and blank [0.1% FA])

4. Start the LCMS Lab Solutions software by double-clicking the desktop icon.

5. On the ‘Lab Solutions Main’ page, double-click ‘2’ icon. Should hear a beep sound and the instrument should start in standby mode, and importantly, LC and MS indicators be green and display ‘Ready’ message (contact CARF staff if any of the above indicators is not green, especially if you see ‘Not connected’ message).

Note: Indicators must display ‘Ready’

6. Check if the right column is installed. Open oven compartment and if no column is present or a different one is installed, using fingers unscrew beige plastic cap first and then using spanners unscrew the metal cap (be careful not to disassemble the guard

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column from the analytical column and remember about capping the removed column so that it doesn’t dry out). Attach the correct column, this time start with using spanners to screw the metal cap [Not too tight or too loose, low operating temperature present when column is not attached properly] and then using fingers screw the beige plastic cap. Close oven compartment.

7. Tubings R0 – R3 for Needle wash bottle. Check if the right mobile phases are connected and if their level is sufficient for the analysis (for BGal standard you need water/0.1%FA to be connected to tubing A and ACN/0.1%FA to be connected to tubing B). If different mobile phases are connected replace with the required ones. Ensure tubings inside mobile phases are properly immersed. See properly immersed tubing below

8. Ensure the Seal wash bottles in pumps A and B are filled up with 10% isopropanol.

9. Ensure the Needle wash bottle is filled up with 70% MeOH.

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10. Purge pumps A and B by opening purge valves ([Note: need to press ‘Sleep’ on each of the machine compartment to wake up machine if orange lights visible on the menu] then turn both knobs 180o anti-clockwise) and press ‘purge’ on each compartment. If purging stops in less than 10 sec, repeat procedure until purging lasts for about 20 sec. After purging appears complete [for 2-5min], visually inspect all the tubings for any air bubbles and close both purge valves. Turn both knobs 180 clockwise to the original position to close the valves [Not too tight].

11. Purge lines R0, R1 and R2 by pressing 'sleep' button to activate the keypad, then pressing ‘Function’ 4 times, then ‘CE’, so that ‘Control’ is on the screen, press 'enter' then ‘Function’ 4 times again, so that ‘Manual prime’ is on the screen. Now press ‘0’ and ‘enter’ and wait about 2 minutes. Repeat, pressing ‘1’ and ‘enter’, waiting 2 minutes and then ‘2’, ‘enter’ and waiting further 2 minutes. When finished, press ‘3’ and ‘enter’. To purge line R3, press ‘Function’ 3 times, so that ‘Purge next pump’ is on screen, and press ‘Enter’. After purging appears complete, visually inspect all the

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tubing for any air bubbles and if none are apparent, press ‘CE’ several times to revert to the main menu.

12. Configure valve switching: Salts need to be purge to waste stream before entering to MS, whereas ions will react with reagents from column and goes to MS [For acidic protein hydrolysis]. Node #1 connects to 'Oven'; Node #2 connects to 'MS' (Short wire with smaller node cap); Node #6 connects to 'Waste Tube' (Longer wire with bigger node cap)

13. From the ‘data’ folder on the desktop, open the ‘standards’ folder and double click to select the method: “Scheduled amino acid detection with valve switching”.

14. Turn the oven on and wait 10min for it to reach the desired temp (40oC, which is set in the method). Once the temp is reached, turn the pumps on and wait for the pressure to equilibrate (5min). NOTE: 2600 - 3500 psi

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15. Click the ‘Batch Table’ tab to open the ‘Batch Processing’ window. Enter in your sample information. NOTE: QC*3; blank; PBQC & Sample Loading Test (100 times dilution); blank & PBQC; Samples*10 (3 injection of each sample); blank & PBQC;

Samples*10; …; blank & PBQC.

16. In the ‘settings’ tab, click ‘shutdown’, then in that tab, click ‘shutdown’ and check everything is selected except ‘shutdown method file’. NOTE: Cancel shutdown to modify method while doing a loading test.

17. Ensure that each sample line has the vial number that matches the sample’s position in the tray.

18. Click “instrument on” button. Click the ‘play’ (start) button on the left-hand side or top toolbars. If you have multiple samples, highlight them all in the batch processing window, press play, and then make sure the correct rows are shown in the ‘select batch execution range’ tab and then click ‘start’.

During a run, the method or data file name cannot be changed or the LC trace manipulated. To analyse data during a run, click ‘snapshot’ on the left hand acquisition menu to open the ‘Post Run’ analysis software. To analyse data after a run, click the corresponding file in your data folder.

19. To suspend or cancel a task, do as follows.

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1) Suspending a task. If you select the batch being run and click the “Pause” icon on the toolbar, the acquisition and queue is paused. To resume the suspended task, highlight the samples that you want to run and click the “Play/Run” icon.

2) To delete samples that have not yet started running, pause the batch and edit it as needed. Resume as above.

3) To stop a run that has already been sent to the machine, click the, ‘Stop Acquisition,’ button. Samples should not be stopped in this way if they have already been injected onto the column.

20. Be sure to save all results into the folder you created for yourself and to take them, and all samples, with you.

- Emergency Procedures

In an emergency, the machine may be stopped, and then switched off by the power levers on the back. The machine can be shut down manually (time permitting) by clicking on the ‘main’ tab, then ‘system control’, then ‘auto shutdown’.

 Make staff aware of any untidy or unsafe equipment prior to use.

 Leave the machine in a safe, clean and tidy state.

 All sample and reagent waste should be disposed of in accordance with the laboratory’s waste disposal procedures.

Failure to follow and perform proper shutdown procedures and leaving the instrument in an untidy status will result in refusal for future access to the instrument.

Polar Metabolites (GCMS) Analysis

1) Analyse 1μl of each sample on the instrument using “Generic metabolomics method (Ute)”. If tetra saccharides are of interest the final hold step must be increased from 3.5min to at least an hour.

2) Set up a batch. Remember about sample randomization and running PBQC sample at the beginning and end. Also include at least 1 injection of 5ppm alkane standard mix for retention time locking

- 3 injections of hexane blank sample (slot position 1)

- 1 injection of 5ppm alkane mix sample (45μl hexane and 5μl alkane)

- 3 injections of negative control sample

- 1 injection of positive control sample

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- 1 injection of PBQC sample

- Group of up to 3 injections of randomized samples each separated by sweep run (sweep sample consists of ethyl acetate)

- 1 injection of negative control and PBQC at the end

- Shut down

NOTE: To add sample analysis row to running batch, right click batch → pause button→ edit batch table by adding new rows → highlight rows for MS analysis → pause button→ start.

NOTE: Both AA and metabolite data should import to “Skyline” analytical software for analysation (25pt Savilzky-Golay smoothing peaks).

- For free AA and protein-bound AA analysis:

a) Cysteine is not valid in results

b) The retention time of isoleucine is earlier than that of leucine.

c) The retention time of Threonine appears at 6.5 in samples.

- For polar metabolite analysis:

a) Follow steps from “GC solution to Excel”

b) Follow steps from “Excel to Skyline”

- Ensure QC and PBQC results have CV% less than 20%

- For sample results, choose fragment with only one peak that has strong signals. For fragment with more than two peaks, which indicate the fragment gets more interference, yet it can be used to check/define the chemical compound as compared to the fragment that has stronger signal. [Check if all fragments align with the same RT]

- Use RT to differentiate AA that has the same fragment value (e.g. Lysine and Glutamine).

Upper limit: TIC is about 2.0*10^6

PCA plot: vector is the loading sector of each variable (AA/Lipid species), score is variance

Volcano plot: False discovery rate (FDR) adjusted p-value (or q-value) of 0.05 implies that 5% of significant tests will result in false positives, t-test.

Fold change: ratio of intensity.

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Lipid Data Collection and Analysis

Instrument: QTRAP 6500 LC-MS/MS SCIEX.

Purpose: For the targeted quantitation of analytes of Lipids. Identification and quantification at low abundances in complex sample environments. Dual switchable mass range up to 2000 Da.

Available scan modes: Q3 (full scan), PRECURSOR ION (PIS), NEUTRAL LOSS (NLS), PRODUCT ION (PROD) and MRM/SRM (multiple/selected reaction monitoring). Injections volumes set as 100uL.

1. Ensure to book instrument through CARF and fill in login information for each use.

2. Transfer samples, blanks and standards into glass vials with name followed by a number. [No glass inserts]

3. Electrospray ionisation (ESI)

4. Mobile phase: MeOH with 5mM NH4OAC

SAMPLES SHOULD:

 NOT contain any solid material nor biohazard material. [e.g. Lipid extractions only]

 NOT be too concentrated. Approximately 100 times dilution of each sample is desired amount to be loaded onto a column otherwise you may end up having a problem with carry over. Concentrations can be increased if necessary, but always start low and work up, never start from high going to low concentration.

 Be dissolved in a suitable solvent (E.g. methanol, chloroform, MTBE and etc.).

 Peptide samples should be processed through zip tips, stage tips or similar.

 Be accompanied by blanks and negative controls

 Use only LCMS grade solvents/reagents for sample preparation

5. Place samples into auto-sampler tray/rack

6. Connects lines from sample rack (Node position 5) to IonDrive.

7. Ensure the Seal wash bottles in pumps A and B are filled up with MeOH 5 mM NH4OAc.

8. Ensure the Needle wash bottle is filled up with MeOH.

9. Start the LCMS Analyst software.

10. On the main page, click “Hardware configuration”, then select “Low Mass Mode (LCMS)” and activate it →Acquire Mode → Select own folder and open.

11. Open file and select method. [For SCP analysis, select method 15 for positive ion mode; Method 16 for negative scan. Modify method for other samples, table below shows the m/z, detection range and collision energy of targeted lipid species in positive ion mode. Use MCA 5 for 30 cycles.]

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Positive Ion Scan Mode Lipid Species Scan Mass [Precursor Ion] Range CE (eV)

Phosphatidylcholine PC Prec 184.1 640-850 40 Lyso-Phosphatidylcholine LPC Prec 184.1 490 - 590 35 Lyso-Phosphatidylethanolamine LPE NL 141.0 420 - 540 30 Phosphatidylethanolamine PE NL 141.0 680-830 30 Phosphatidylserine PS NL 185.0 720 - 860 25 Phosphatidylinositol PI NL 277.0 500-1000 30 Phosphatidic Acid PA [NH3+]* NL 115.0 600 - 800 30 Phosphatidylglycerol PG [NH3+]* NL 189.0 700 - 860 25

Sphingomyelin SM Prec 184.1 600-1000 40 Ceramide Cer Prec 264.3 530 - 730 20

Sterol Lipids Erg NL 77.0 350 - 500 15

Negative Ion Scan Mode

Lipid Species Scan Mass [Precursor Ion] Range CE (eV)

Inositolphosphoceramide IPC Prec 241 650 - 1000 55/65 Mannosyl-inositolphosphoceramide MIPC Prec 403.1 690 - 1200 80 Mannosyl-diinositolphosphoceramide M(IP)2C Prec 241.0 400 - 730 50

253.2 [16:1] 600 - 900 255.2 [16:0] 640 - 930 55 269.3 [17:0] 277.2 [18:3] 660 - 950 279.2 [18:2] 40 281.3 [18:1] 670 - 960 283.2 [18:0] Phosphalipid fatty acids Plipid FA Prec 297.3 [19:0] 301.2 [20:5] 55 303.2 [20:4] 305.2 [20:3] 690 - 980 40 307.2 [20:2] 309.2 [20:1] 311.2 [20:0] 17155 327.3 [22:6] 710 - 1010 40

Note: Indicators must display ‘Ready’ for any further procedures.

12. View Batch → view the queue → Equilibrate → start purge the pumps

13. Purge pumps A and B by opening purge valves. [Note: need to press ‘Sleep’ on each of the machine compartment to wake up machine if orange lights visible on the menu] Turn the knobs 180o anti-clockwise and press ‘purge’ on each compartment. If purging stops in less than 10 sec, repeat procedure until purging lasts for about 20 sec. After purging appears complete [for 2-5min], visually inspect all the tubings for any air bubbles and close purge valves by turning knobs 180 clockwise to the original position [Not too tight]. Ensure pump pressure and flow rate should be 170 psi and 20 µL/min respectively [Use pump A only if pressure is fluctuating].

NOTE: if pump doesn’t work, restart the pump and purge.

NOTE: Restart everything [in order of software, computer, pumps and MS instrument (RED button, permission is required)] for MS connection error.

NOTE: Use Syringe pump for method modification, select “QTRAP 6500” for hardware configuration. The flow rate of syringe should be 7 µL/min. save method and acquire data after any changes.

14. Open a batch file. Enter in [OR edit] the sample information. NOTE: blank*3; QC*3, PBQC Sample Loading Test (50-100 times dilution); blank/PBQC/negative control; Samples*10 (3 injection of each sample); blank & PBQC; Samples*10; …; blank & PBQC.

NOTE: QC standard [1 µM conc.] 5 µL of 6 lipid IS mix and 995 µL CHCl3/MeOH 5mM NH4OAc buffer solution.

Check before running the samples:

Interface voltage = 5500 V (Pos), 4500 V (Neg)

Precursor ion scan

-Pos (Q1 = unit, Q3 = unit)

-Neg (Q1 = unit, Q3 = unit)

Neutral loss scans

-Pos (Q1 = unit, Q3 = unit)

-Neg (Q1 = unit, Q3 = unit)

Rinsing speed 35 uL/sec

Sampling speed 15 uL/sec

Scan speed 1250-1300 u/sec

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 Internal Standards [General]

Internal Std Positive [+H unless noted] Negative [+H unless noted]

Ergosterol [m/z 396.35] 456.3554 [M+NH4] - Cholesterol [m/z 386.35] 446.3554 [M+NH4] - DAG 17:0/17:0 614.5718 [M+NH4] 655.5518 [M+Acetate] TAG D5 48:0 829.8016 [M+NH4] - TAG 17:1/17:1/17:1 860.7702 [M+NH4] 901.7502 [M+Acetate] PC 15:0/15:0 706.5381 764.5436 [M+Acetate] PC 17:0/14:1 718.5381 776.5447 [M+Acetate] PC 17:0/ 17:0 762.6007 820.6073 [M+Acetate] PC 19:0/ 19:0 818.6633 876.6688 [M+Acetate] PC 20:0/ 20:0 846.6949 904.7001 [M+Acetate] LPC 17:0 510.3554 568.3620 [M+Acetate] LPC 17:1 508.3398 566.3463 [M+Acetate] PE 17:0/14:1 676.4912 674.4766 PE 17:0/17:0 720.5538 718.5381 LPE 14:0 426.2615 424.2459 LPE 17:1 466.2928 464.2782 PG 17:0/14:1 707.4858 705.4712 PG 17:0/17:0 751.5483 749.5338 PA 17:0/14:1 633.4489 631.4344 PA 17:0/17:0 677.5116 675.4970 LPA 17:1 423.2506 421.2361 PS 17:0/14:1 720.481 718.4665 PS 17:0/17:0 764.5436 762.5291 LPS 17:1 510.2826 508.2681 PI 17:0/17:0 839.5644 837.5498 PI 18:0/18:0 835.5438 883.17; 865 [- NH4] LPI 17:0 587.3191 583.3045 SM 18:0;3/ 18:0 749.6167 747.6022 LCB 17:0;2 288.2897 286.2752 CL 15:0/15:0/15:0/16:1 1309.9169 1307.9024 LCBP 17:0;2 368.256 366.2415 Cer 17:0/ 17:0 552.535 - Cer 18:0;3/ 18:0;0 584.5612 582.5467 IPC 18:0;2/ 26:0;0 922.7107 920.6961 MIPC 18:0;2/ 26:0;0 1084.7635 1082.7490 M(IP)2C 18:0;2/ 26:0;0 1326.7827 1324.7681

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 Internal Standards [Tested: QC standards]

Lipid Catalogue no. (Avanti) +ve (+H unless noted) -ve (-H unless noted) Peak Int. 3.80E+08 [Prec-positive] PC 17:0/17:0 850360P 762.5 820.6073 (M+Acetate) 6.00E+08 [Q3-positive] 1.30E+07 [Q3-negative] 2.20E+08 [Prec-positive] SM (d18:1/17:0) 860585P 717.5 775.7 (M+Acetate) 3.90E+08 [Q3-positive] 1.70E+07 [Q3-negative] 5.60E+07 [NL-positive] PE 17:0/17:0 830756P 720.5538 718.5381 2.00E+07 [Q3-negative] 2.10E+07 [NL-positive] 2.60E+07 [Q3-negative] PS 17:0/17:0 840028P 764.5436 762.5291 8.50E+05 [Prec-negative] 4.60E+06 [NL-negative] 1.50E+05 [NL-positive] 677.7 3.80E+07 [NL-positive (M+NH4)] PA 17:0/17:0 830856P 675.497 2.90E+07 [Q3-negative] 694.5 (M+NH4) 5.80E+05 [Prec-negative] 2.30E+06 [NL-positive] 751.5483 2.50E+07 [NL-positive (M+NH4)] PG 17:0/17:0 830456P 749.5338 5.25E+07 [Q3-negative] 768.6 (M+NH4) 3.30E+05 [Prec-negative] 4.50E+07 [Q3-negative] PI 18:0/18:0 850143P N/a 865.6 1.50E+06 [Prec-negative] NOTE: Mix of lipid IS (PC, PE, PS, PA, PG, SM and PI) will be diluted for MS analysis. [Dilute IS mix to 1 µM concentration (100 times dilution)]

NOTE: Structure of common yeast sphingolipids

1. Inositolphosphoceramide [IPC]:

2. Mannosyl-inositolphosphoceramide [MIPC]:

3. Mannosyl-diinositolphosphoceramide [M(IP)2C]:

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[E.g. for M(IP)2C 20:0;3/26:0;1: 20 and 26 are the number of carbon atoms, 0 is the number of C=C double bond, 3 and 1 are the number of hydroxyl groups]

NOTE:

1. Quantification of Ergosterol will be achieved by chemical acetylation followed by MRM analysis.

2. Stigmasta-5, 7, 22-trienol is isolated by preparative chromatography after alkaline hydrolysis of stigmasta-5, 7, 22-trienol acetate.

3. Standards for IPC, MIPC and M (IP) 2C are isolated from crude sphingolipid extracts of sur2Δscs7Δ.

Lipid Classes Iron Mode Specific Prec Ions Structure

282.3; LCB-2H2O Positive 310.3 282+2[-CH2] IPC 259; [IP]- Negative 241; [IP-H2O]-

282.3; LCB-2H2O Positive 310.3 282+2[-CH2] MIPC 421.1; [MIP]- Negative 403.1; [MIP-H2O]-

241; [IP-H2O]-

M(IP)2C Negative 331; [M(IP)2]2-

583.1; [M(IP)2-P]-

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Option: for 2 steps polar and non-polar lipid extraction,

- 1st step: Apolar Lipid Extraction. Cell lysates with internal standards will be extracted with CHCl3/MeOH (17:1, V/V) for apolar lipids.

- 2nd step: Polar Lipid Extraction. Subsequent extraction of remaining water fractions will be extracted with CHCl3/MeOH (2:1, V/V) for polar lipids

For negative ion scan, lipid extract will be infused using 0.2 mM methylamine in CHCl3/MeOH (1:5) for shotgun lipidomic analysis. For positive ion scan, lipid extract will be infused using 7.5mM ammonium acetate in CHCl3/MeOH/2-propanol (1:2:4)

15. Ensure that each sample line has the vial number that matches the sample’s position in the tray.

16. Click “Equilibrate” button and wait for the pressure to equilibrate (5min).

17. Review batch information, then click “submit”. For multiple sample analysation/Loading tests, highlight sample set and submit that sample set only, then click ‘start’.

NOTE: PBQC loading test prior sample testing is required. Dilute PBQC sample 100 times using 5 µL PBQC sample with 495 µL CHCl3/MeOH (1:2) 5 mM NH4OAc solution.

18. Use Analyst software for general result checking before Lipid View software for quantitation analysis of targeted lipid species. Click “Explore Mode” to check overall results. [i.e. Analyst to check if samples are tested under consistent condition, smooth curve indicate normalized pump pressure and flow rate during testing]

All data should be imported to “Lipid View” analytical software for analysation.

Lipid View is to characterise and quantify lipid species from electrospray mass spectrometry data. This software enable lipid profiling by searching fragment-ion masses against a lipid fragment database that containing over 25,000 lipid molecular species [e.g. lipid classes, FA, and long chain bases]. It is for the identification and quantification of lipids in complex biological extracts.

1. Create a new section on “Lipid View”, and start with PBQC analysis to make a target list.

2. Processing setup: Edit method

NOTE: For complex lipid analysis, identify species are [Total chain of double bonds from 0 to 6, include lyso- species and ether species, H+ in positive scan]:

Positive Scan

1) Glycerophospholipids: PC; PE; PA; PS; PG

2) Sphingolipids: SM; Cer

3) Sterol Lipids: CE = Cholesteryl Ester

Analyse Triglycerolipid separately [i.e. separate headgroup to different scan], identify species are:

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1) Glycerolipids: TAG

Negative Scan

1) Glycerophospholipids: PI; CL [PS/PA/PG]

2) Sphingolipids: IPC; MIPC; M(IP)2C

For validation analysis, set mass tolerance to ± 0.3 m/z; set minimum S/N to 3 times higher than the background noise level [OR use negative control as a reference background level]; set intensity percentage is greater than 0.2 [i.e. identify the peak above 0.2% of intensity counts]; set the average spectrum from top 95% TIC reading; consider “Deisotope” in sample results [i.e. consider natural isotopic masses in results, consider the 1% possibility of heavy ion 13C in results]; assume ether group/ odd chain FA species in results. [Apply method “Neg_PS_NL_QTRAP_BP_24_SCP” for negative spectra result analysis; and apply “Pos_PS_NL_QTRAP_BP_19_SCP” for positive spectra result analysis.]

3. Assign method to all samples.

4. Click “Find Lipids”, save in new file name and click “Start”

5. Click “Advanced profile”, then select “Corrected Area” for data type [literature show 5% difference from area counts to intensity counts]. Select “Lipid class profile” for main plot type.

NOTE: Data can be normalised (to 100%) when there’s no internal standards, but better not use it when results show inconsistent peak reading, separate headgroup to different scan, i.e. separate PL scan to PC/SM; PE; PS and etc.

6. A target list can be generated by running the profile test of PBQC analysis. Then open the txt file using “Sublime” software. Delete isomers with -FA (NH4), delete isomers with incorrect m/z reading to the verified lipid class. Keep FA with cholesterol and ether species by now, delete if result is out of detection range [i.e. the modified target list can be used in sample analysis].

7. Start a new section, select all samples. Use the modified target list for process method editing. Then assign method to all samples. Followed the procedures as mentioned above. [export group lipid data to excel for modification]

8. Export data files and delete lipid species that have inconsistent intensity.

9. Normalised to IS and subtract negative result.

NOTE: LIPID MAP tools

1) Mass calculation for lipid class [e.g. lipid species “PC-O-36:2” from PC profile]

- Class of lipid

- [M+H]+, headgroup PC, sn1: 16:0, sn2: 18:2

- Result shows ion fragment of PC-O-36:2

2) General MS precursor ion search

- Input the ion fragment

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- Class of lipid

- Mass tolerance [M+H]+ of 0.1 m/z

- Look for lipids results

- Result shows that possibility of PC-O-36:2 and PC-35:2

FAMEs analysis

1. The injection volume of each sample to the instrument is 1 μL, using method of “FAME_37mix_comprehensive”. This method analyse analytes after 4.5 min, max temp of 250 degree and able to detect from C6 to C24.

2. Set up a batch. Remember about sample randomization and running PBQC sample at the beginning and end. Also include at least 1 injection of 37 lipid standard mix for retention time locking

- 3 injections of MTBE blank sample (slot position 1)

- 1 injection of 37 IS mix sample

- 3 injections of negative control sample

- 1 injection of PBQC sample

- Group of up to 10 injections of randomized samples each separated by negative and PBQC runs, and run PBQC at the end.

- Shut down

3. Auto-sampler setup

- WASH: vial 1 with 50% ethyl acetate + 50% acetone; vial 2 with n-hexane - RINSE: clean volume 70%; fill speed 25 μL/sec; viscosity delay 0 sec; ejection speed 450 μL/sec - SAMPLE ADDING: fill volume 50 μL; fill stroke 5; fill speed 1 μL/sec; add volume 20 μL/sec; add speed 30 μL/sec; viscosity delay 5 sec; ejection speed 50 μL/sec. - Load “FAME injection method” - Load and edit Prep Sequence Method

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- Run Prep Sequence (Note: the vial# on GCMS batch should consistent with the vial# on Prep Sequence)

4. Edit fragment table by right clicking result graphs. “TIC” for total ions counts of compounds; “Fragments with different m/z value” for interested ions analysation, and change the factor to 5 times higher for easier comparison. For resulting graphs with the highest amount of following fragment:

Fragment 74 m/z – SFA

Fragment 55 m/z – one C=C double bond

Fragment 67 m/z – tow C=C double bonds

Fragment 79 m/z – more than three C=C double bonds

Fragment 91 m/z – more than three C=C double bonds

5. For similarity search results, remove one C reading (the methyl group) to identify the actual compound within samples. For H counting, e.g. in total of 23 C including C from methyl group, should have 23*2 = 46 H, result show detection of 34 H, then 46-34=12 H missing and 12/2 = 6 of C=C double bonds, i.e. C22:6 (all-cis-4,7,10,13,16,19) Methyl docosahexaenoate.

NOTE: 37 lipid IS mix, Chain, Compound (CAS#), % by Weight C4:0 Methyl butyrate (623-42-7), 4% C6:0 Methyl caproate (106-70-7), 4% C8:0 Methyl caprylate (111-11-5), 4% C10:0 Methyl decanoate (110-42-9), 4% C11:0 Methyl undecanoate (1731-86-8), 2% C12:0 Methyl dodecanoate (111-82-0), 4% C13:0 Methyl tridecanoate (1731-88-0), 2% C14:0 Methyl myristate (124-10-7), 4% C14:1 (cis-9) Methyl myristoleate (56219-06-8), 2% C15:0 Methyl pentadecanoate (7132-64-1), 2% C15:1 (cis-10) Methyl pentadecenoate (90176-52-6), 2% C16:0 Methyl palmitate (112-39-0), 6% C16:1 (cis-9) Methyl palmitoleate (1120-25-8), 2% C17:0 Methyl heptadecanoate (1731-92-6), 2% C17:1 (cis-10) Methyl heptadecenoate (75190-82-8), 2% C18:0 Methyl stearate (112-61-8), 4% C18:1 (trans-9) Methyl octadecenoate (1937-62-8), 2% C18:1 (cis-9) Methyl oleate (112-62-9), 4% C18:2 (all-trans-9,12) Methyl linoleaidate (2566-97-4), 2%

179

C18:2 (all-cis-9,12) Methyl linoleate (112-63-0), 2% C18:3 (all-cis-6,9,12) Methyl linolenate (16326-32-2), 2% C18:3 (all-cis-9,12,15) Methyl linolenate (301-00-8), 2% C20:0 Methyl arachidate (1120-28-1), 4% C20:1 (cis-11) Methyl eicosenoate (2390-09-2), 2% C20:2 (all-cis-11,14,) Methyl eicosadienoate (2463-02-7), 2% C20:3 (all-cis-8,11,14) Methyl eicosatrienoate (21061-10-9), 2% C20:3 (all-cis-11,14,17) Methyl eicosatrienoate (55682-88-7), 2% C20:4 (all-cis-5,8,11,14) Methyl arachidonate (2566-89-4), 2% C20:5 (all-cis-5,8,11,14,17) Methyl eicosapentaenoate (2734-47-6), 2% C21:0 Methyl heneicosanoate (6064-90-0), 2% C22:0 Methyl behenate (929-77-1), 4% C22:1 (cis-13) Methyl erucate (1120-34-9), 2% C22:2 (all-cis-13,16) Methyl docosadienoate (61012-47-3), 2% C22:6 (all-cis-4,7,10,13,16,19) Methyl docosahexaenoate (2566-90-7), 2% C23:0 Methyl tricosanoate (2433-97-8), 2% C24:0 Methyl lignocerate (2442-49-1), 4% C24:1 (cis-15) Methyl nervonate (2733-88-2), 2%

6. Create an analytical method using RT of 37 IS mix and PBQC sample result to identify the peaks [i.e. to check whether the peaks from samples are aligned with the peaks from 37 IS mix or not]. Then create an analytical method include all peaks from PBQC and run all sample results to quantify the peaks.

7. Normalised to IS C19:0 and subtract negative results.

180

Appendix C: Internal Standards Preparation

Testing protocol Ref. Paper: Ejsing et al OD unit 0.6 OD unit 0.2 Num. of cells 5mg freeze dried biomass Num. of cells 2.00E+06 Vol. of cell lysates [uL] 500 Vol. of cell lysates [uL] 200 Ini mole. [nmol] 40.000 Ini. Conc. [pmol] 4394.5 Dilution 200.000 Dilution 93.500 Final mole. [pmol] 200.000 Final conc. [pmol] 47.000 Lipid IS mix PC 17:0/17:0 40 nmol PC 17:0-14:1 47 pmol PE 17:0/17:0 40 nmol PE 17:0-14:1 47 pmol PG 17:0/17:0 20 nmol PG 17:0-14:1 12 pmol PA 17:0/17:0 20 nmol PA 17:0-14:1 23 pmol PS 17:0/17:0 40 nmol PS 17:0-14:1 39 pmol PI 18:0/18:0 40 nmol PI 17:0-17:0 50 pmol SM d18:1/17:0 20 nmol Stigmasta-5,7,22-trienol 140 pmol CE std 40 nmol

To Prepare 10 mL IS stock Internal standard mix Conc._IS [mM] Vol_Stock [uL] Conc._Stock [uM] Vol_add to sample [uL] Conc._Sample [M] Mole_Sample [nmol] Dilution Conc_Injection [uM] Mole_Injection [nmol] MeOH N/a 8387.5 N/a N/a Polar IS Preparation 13C_valine 8.12 100.0 81.20 8.12E-05 16.24 330 0.25 0.05 FA C19:0 25.00 312.5 781.25 7.81E-04 156.25 3.00 260.42 52.08 CE Std 7.20 100.0 7.20E+01 7.20E-05 14.40 180.00 0.40 0.08 SM d18:1/17:0 10.00 100.0 100 1.00E-04 20.000 200.00 0.50 0.10 PG 17:0/17:0 10.00 100.0 100 200 1.00E-04 20.000 200.00 0.50 0.10 Lipid IS Preparation PS 17:0/17:0 20.00 100.0 200 2.00E-04 40.000 200.00 1.00 0.20 PI 18:0/18:0 1.00 500.0 50 5.00E-05 10.000 200.00 0.25 0.05 PE 17:0/17:0 20.00 100.0 200 2.00E-04 40.000 200.00 1.00 0.20 PA 17:0/17:0 5.00 100.0 50 5.00E-05 10.000 200.00 0.25 0.05 PC 17:0/17:0 20.00 100.0 200 2.00E-04 40.000 200.00 1.00 0.20 Total Vol_Stock [uL] 10000 Add to sample directly_All Internal standard mix Conc._IS [mM] Vol_Stock [uL] Total vol_to sample [uL] Conc._Sample [M] Mole_Sample [nmol] Dilution Conc_Injection [uM] Mole_Injection [nmol] Polar metabolites IS 13C_valine 8.12 2.00 8.12E-05 16.24 330.00 0.25 0.05 FA C19:0 25.00 6.25 7.81E-04 156.25 3.00 260.42 52.08 CE Std 7.20 2.00 7.20E-05 14.40 180.00 0.40 0.08 SM d18:1/17:0 10.00 2.00 1.00E-04 20.00 200.00 0.50 0.10 PG 17:0/17:0 10.00 2.00 1.00E-04 20.00 200.00 0.50 0.10 200 Lipid IS PS 17:0/17:0 20.00 2.00 2.00E-04 40.00 200.00 1.00 0.20 PI 18:0/18:0 1.00 10.00 5.00E-05 10.00 200.00 0.25 0.05 PE 17:0/17:0 20.00 2.00 2.00E-04 40.00 200.00 1.00 0.20 PA 17:0/17:0 5.00 2.00 5.00E-05 10.00 200.00 0.25 0.05 PC 17:0/17:0 20.00 2.00 2.00E-04 40.00 200.00 1.00 0.20 Vol_MeOH [uL] 167.75

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Appendix D: Standard Mixture Preparation

[CAG mix] cysteine, asparagine and glutamine (R block) in MS-water (1ppm = 1 ug/mL) Option 1 - Amino Acid Standard Mix - without 13C 0.000001 g/mL Cys 121.16 g/mol Asn 132.12 g/mol AA std mix stock 0.5 uM/mL Gln 146.14 g/mol 100x Stock 5 nM/mL 10uL AA/CAG mix each + 960 uL 0.1%FA Total 1000 uL

i.e. 1ppm stcok conc Cys 8.253549026 nM/mL No. Conc [nM/mL] AA std mix [uL] 0.1%FA vol [uL] Total vol [uL] 60uL each Asn 7.568876779 nM/mL 1 SH01 5 100x stock [100uL] Gln 6.842753524 nM/mL 2 2.5 100 uL #1 100 200 3 1.25 100 uL #2 100 200 4 0.625 100 uL #3 100 200 Target Cys Conc 5 uM/mL 5 0.3125 100 uL #4 100 200 Required Conc 5.00E-06 Mol/mL 6 blank 0.15625 100 uL #5 100 200 Cys std 121.16 g/mol 6.06E-04 g 13C val 0.6058 mg/mL 13C val Option 2 - Amino Acid Standard Mix - with 13C Cys preparation Solvent: MS_H2O 2mL stock 1.2116 mg/2mL 10x_0.5 uM/mL 100 uL stock + 900uL solvent 13C stock Conc 8.12 uM/mL NOTE: 8.12 uM/mL = 8.12 mM Required Conc 8.12E-06 Mol/mL Target Asn Conc 5 uM/mL 13C_val 123.1 g/mol 0.001 g 13C val Required Conc 5.00E-06 Mol/mL 1.00 mg/mL 13C val Asn std 132.12 g/mol 6.61E-04 g 13C val 13C_val Target conc: 2mL stock 2.00 mg/2mL 0.6606 mg/mL 13C val preparation(Solv 0.0812mM 50x_0.16 uM/mL 20 uL (8.12uM/mL) + 980 uL 0.1%FA 2mL stock 1.3212 mg/2mL Asn preparation Solvent: MS_H2O 10x_0.5 uM/mL 100 uL stock + 900uL solvent AA std mix stock 0.5 uM/mL 50x 10 nM/mL 20uL AA/CAG mix each + 960 uL 0.1%FA Total 1000 uL Target Gln Conc 5 uM/mL 100x Stock 5 nM/mL 10uL AA/CAG mix each + 960 uL 0.1%FA from Option 1 Required Conc 5.00E-06 Mol/mL Gln std 146.14 g/mol 7.31E-04 g 13C val No. Conc [nM/mL] AA std mix [uL]13C_val 0.6nM/mL [uL] 0.1%FA vol [uL] Total vol [uL] 0.7307 mg/mL 13C val 1 SH01 5 100 (10nM/mL) 100 0 200 2mL stock 1.4614 mg/2mL 2 2.5 50 (10nM/mL) 100 50 200 Gln preparation Solvent: MS_H2O 10x_0.5 uM/mL 100 uL stock + 900uL solvent 3 1.25 25 (10nM/mL) 100 75 200 4 0.625 100 (5nM/mL) 100 0 200 AA std mix stock soln 0.5 uM/mL 5 0.3125 50 (5nM/mL) 100 50 200 6 blank 0.15625 25 (5nM/mL) 100 75 200

0.15uM = 0.00015 uM/mL = 0.15 nM/mL

37mix - 20x (50uL stock +950uL methylene chloride)

No. Conc [ppm] Solution 1 80.0 100 uL 20x stock 2 40.0 100uL #1 + 100 uL solvent 3 20.0 100uL #2 + 100 uL solvent 4 10.0 100uL #3 + 100 uL solvent 5 5.0 100uL #4 + 100 uL solvent NOTE : Add IS C 19:0 as an option

182

Appendix E: Preliminary analytes profiling (Supporting Materials)

The composition of the low temperature rendering stickwater collected from Australian Country Choice, Brisbane, Australia.

Fatty acyl scans for m/z 283.3 and m/z 281.3 using lipid extract of R.glutinis and R.toruloides biomass

183

OzID/MS of total lipid extract of R.glutinis and R.toruloides biomass in negative ion full scan

184

185

Amino acid and lipid profiling of filamentous fungi and yeast strains for HCA and PCA

mg/g A.oryzae_ws_1 A.oryzae_ws_2 T.terricola_ws_1 T.terricola_ws_2 M.isabellina_ws_1 M.isabellina_ws_2 R.glutinis_ws_1 R.glutinis_ws_2 R.dairenensis_ws_1 R.dairenensis_ws_2 R.toruloides_ws_1 R.toruloides_ws_2 R.mucilaginosa_ws_1 R.mucilaginosa_ws_2 G.candidum_ws_1 G.candidum_ws_2 S.cerevisiae _ws_1 S.cerevisiae _ws_2 Arginine 40.64 42.69 31.87 32.91 27.25 27.93 42.91 44.73 49.57 50.7 48.86 50.05 52.98 55.49 40.64 49.54 76.29 80.96 Histidine 11.15 10.15 57.65 56.02 2.72 2.57 6.3 5.71 15.64 14.95 5.85 5.47 31.54 29.4 16.3 15.45 52.47 51.45 Threonine 3.99 4.32 5.8 6.48 2.24 2.2 6.2 6.55 5.51 5.75 7.01 7.07 5.73 5.83 13.06 14.06 24.8 25.97 Lysine 41.91 41.14 33 31.62 60.19 58.79 64.25 62.53 80.63 81.66 40.89 39.07 76.23 79.05 79.2 80.72 103.85 110.19 Valine 45.81 46.38 18.43 17.32 3.15 3.05 22.23 20.72 12.89 12.17 22.94 21.87 11.1 10.41 45.69 46.34 61.35 63.93 Methionine 28.44 27.71 7.87 7.29 1.89 1.73 5.83 5.31 2.66 2.5 4.6 4.19 2.99 2.71 42.16 40.3 9.58 9.23 Isoleucine 31.11 31.62 14.38 13.92 3.24 2.93 10.59 8.95 6.46 5.25 12.93 11.3 7.15 6.13 27.49 27.98 31.94 31.52 Leucine 51.01 51.5 20.03 19.44 6.29 5.6 21.12 19.36 13.9 12.12 22.17 20.79 14.37 12.68 42.15 42.57 39.87 39.76 Phenylalanine 25.99 26.48 12.06 11.72 3.27 2.93 6.5 5.51 3.6 3.11 6.8 6.08 4.72 4.15 23.89 24 19.03 19.35 Tryptophan 28.03 24.83 7.46 6.14 1.61 1.36 4.76 3.92 6.49 5.68 4.87 4.26 2.97 2.6 19.48 16.98 24.03 22.52 Tyrosine 35.18 29.03 13.55 11.64 4.35 3.67 4.06 3.46 2.47 2.09 4.4 3.68 3.21 2.79 31.28 27.6 20.6 17.96 Proline 40.86 41.36 18.99 19.19 2.5 2.47 8.68 8.42 6.07 6.12 9.99 10.01 5.87 5.85 65.73 66.33 103.79 112.18 Serine 29.33 34.96 20.63 21.14 6.09 5.96 12.32 12.43 18.63 19.04 15.95 15.93 15.6 16.37 44.71 46.41 25.12 24.99 Aspartic acid 15.31 19.75 9.27 9.33 2.53 2.42 10.42 10.2 20.56 20.89 12.12 11.92 29.28 30.79 5.87 5.92 19.96 19.99 Glutamine 22.11 24.15 43.35 48.6 27.05 26.27 70.8 77.36 62.6 66.1 57.87 61.19 74.42 80.71 67.19 73.08 46.39 49.17 Glutamic acid 2.65 2.59 6.75 6.78 10.59 10.07 23.87 24.91 26.46 27.15 25.2 25.78 30.27 31.69 24.06 24.25 31.24 32.93 Cysteine 0.07 0.08 0.05 0.06 0.06 0.06 0.06 0.06 0.05 0.06 0.06 0.06 0.13 0.15 0.13 0.16 0.14 0.16 Glycine 15.45 17.81 26.79 28 3.47 3.44 8.07 9.03 7.84 8.42 30.09 32.4 5.22 6.1 40.29 43.36 24.3 25.65 Asparagine 5.65 7.06 10.57 11.13 2.05 2.1 5.04 4.92 6.94 7.28 4.63 4.73 10.02 10.67 39.82 40.99 19.1 19.55 Alanine 11.04 11.87 13.72 15.07 6.2 6 16.38 17.47 16.63 17.41 15.44 15.92 17.82 18.79 24.14 25.39 29.47 31.23 mg/g A.oryzae_ref_1 A.oryzae_ref_2 T.terricola_ref_1 T.terricola_ref_2 M.isabellina_ref_1 M.isabellina_ref_2 R.glutinis_ref_1 R.glutinis_ref_2 R.dairenensis_ref_1 R.dairenensis_ref_2 R.toruloides_ref_1 R.toruloides_ref_2 R.mucilaginosa_ref_1 R.mucilaginosa_ref_2 G.candidum_ref_1 G.candidum_ref_2 S.cerevisiae _ref_1 S.cerevisiae _ref_2 Arginine 29.74 29.01 46.48 47.41 8.59 8.26 62.2 62.54 83.93 85.72 68.77 70.66 66.49 67.52 0.12 0.13 102.19 105.86 Histidine 9.37 9.03 13.27 12.3 0.17 0.16 8.04 7.66 13.65 13.06 7.21 6.92 10.3 9.89 0 0 52.8 51.35 Threonine 19.08 19.82 9.43 9.85 1.31 1.26 9.03 9 5.75 6.47 9.61 9.54 6.27 6.14 0.08 0.08 29.54 30.38 Lysine 26.06 28.2 71.4 69.57 12.37 11.7 77.26 77.95 101.84 105.74 39.29 37.93 96.79 99.76 0.12 0.12 139.91 148.95 Valine 26.82 26.06 29.52 28.6 1.93 1.84 41.73 41.34 30.61 29.28 43.28 43.76 14.41 13.47 0.05 0.05 69.03 71.84 Methionine 17.63 16.68 10.4 9.35 0.68 0.63 4.91 4.55 2.93 2.63 4.15 3.91 3.03 2.82 0 0.01 8.17 7.91 Isoleucine 23.23 23.78 20.7 20.29 2.18 2.21 12.81 12.67 14.1 12.89 11.86 11.61 8.42 7.56 0.02 0.01 39.54 41.16 Leucine 32.5 34.47 25.42 26.05 3.64 3.6 20.78 20.5 22.35 22.28 17.59 17.43 16.14 14.67 0.02 0.02 43.3 46.19 Phenylalanine 18.03 18.12 14.83 14.73 1.97 1.89 9.14 8.59 8.72 8.05 8.24 7.95 8.37 7.82 0.01 0.01 23.79 24.75 Tryptophan 12.41 11.54 10.84 9.58 0.54 0.53 8.27 7.69 14.61 13.08 4.52 4.29 3.61 3.48 0.03 0.03 23.89 22.78 Tyrosine 13.89 13.08 11.45 10.59 0.61 0.56 5.67 5.27 5.07 4.67 4.29 4.22 3.73 3.52 0 0 19.14 18.5 Proline 21.04 21.73 25.28 24.64 0.82 0.81 14.63 14.18 13.42 13.57 13.31 12.98 7.65 7.28 0.04 0.03 118.83 125.81 Serine 42.96 43.03 25.82 26.65 2.94 2.84 20.59 20.59 21.57 22.79 21.83 21.78 19.42 19.22 0.22 0.21 27.26 28.85 Aspartic acid 30.02 29.66 12.26 12.33 2.6 2.48 9.55 9.56 17.69 18.64 8.41 8.5 18.82 18.76 0.13 0.13 25.95 27.28 Glutamine 58.69 61.85 27.71 29.91 2.84 2.69 77.46 79.12 65.55 70.37 37.61 37.84 80.9 83.51 0.09 0.09 71.86 75.62 Glutamic acid 8.6 9.23 4.19 4.01 2.51 2.46 32.36 32.38 31.08 32.66 26.77 26.91 27.67 28.31 0.09 0.09 51.78 54.32 Cysteine 0.09 0.1 0.05 0.06 0.05 0.05 0.06 0.06 0.06 0.06 0.06 0.06 0.13 0.14 0.06 0.06 0.17 0.19 Glycine 14.72 15.61 9.75 10.64 2 1.92 10.56 10.83 8.24 10.21 22.17 22.71 7.43 7.83 0.27 0.3 14.09 15.54 Asparagine 6.25 6.28 12.14 12.41 0.98 0.95 4.64 4.6 6.22 6.47 2.76 2.72 7.67 7.66 0 0 23.67 24.29 Alanine 9.06 9.79 9.9 10.07 1.14 1.11 29.45 29.78 20.8 22.02 23.82 24.3 18.52 18.89 0.07 0.07 48.86 54.26

186

187

Detailed nutrient profile of Benchmark strain S.cerevisiae

Fatty Acid Composition (mg/g)

0.00 2.00 4.00 6.00 8.00 10.00 12.00

C 12:0

C 14:0

C 14:1

C 15:0

C 16:0

C 16:1 cis (n-9)

C 18:0

C 18:1 cis (n-9)

C 18:1 cis (n-11)

C 18:2

R09_Ref. Medium R09_Commercial

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A m i n o A c i d C o m p o s i t i o n ( m g / g )

8 0

F r e e A A ( m g / g )

s H y d r o l y s i s A A ( m g / g )

s 6 0

r d

4 0

f o

2 0

g m

e e e e e e e e e e e e e e e e e e n i d d n n n n n n n n n a n n n n i n n n n i i i i i i i i i i n i i i c i c i i i i s l l h l r i n i d n a n c c n s o a a e c g n t y a u u p o r e m t y a a g o V i o a c a c s l l r s e L V e e l o r P S i t i r i r _ h l a t y t y G a A A t L l p r u H h C o T l m C p e s y y a a s T 3 I n r p G t 1 M T A e s l u S h A I P G

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Appendix F: Required Statements

Coursework

The IFN001 assessment has already been submitted to AIRS.

Research Ethics

Intellectual Property Statements

Intellectual Property Statement has been completed.

Data Storage

Data storage was complied with QUT’s data storage policies (QUT MOPP D/2.8 Management of research data) and protocols (https://www.library.qut.edu.au/research/data/).

My data is located at Research Data Storage Service folder, research projects, sef faculty, the folder name is comanalmicro.

U:\Research\projects\sef\comanalmicro\

190

Health and Safety Statements

Chemical and biological risk assessment has been completed for health and safety purpose, to ensure that all chemical or biological hazards and risks have been considered in this project. The project MAPs number is 10920.

iThenticate Report

Include quotes, bibliography, small sources and small matches, the similarity index is 18%.

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