Production and Chromatographic Analysis of Polyhydroxybutyrate from Waste Product Streams by Azohydromonas Lata

Hochschule Rhein-Waal

Rhine-Waal University of Applied Sciences

Faculty of Life Sciences

Production and Chromatographic Analysis of Polyhydroxybutyrate from waste product streams by Azohydromonas lata

A Thesis Submitted in

Partial Fulfilment of the

Requirements of the Degree of

Bachelor of Science in Bioengineering

By Dominic Kösters

Matriculation Number: 19056

Supervisors: Prof. Dr. Joachim Fensterle

Prof. Dr. Peter Simon

Submission Date: 04.11.2019

Executive Summary

As conventional plastic production receives much criticism for being harmful to both humans and the environment, this work focused on establishing a batch fermentation based on waste substrates from local industries to produce polyhydroxybutyrate (PHB) with Azohydromonas lata (A. lata). Additionally, the produced PHB was characterised by qualitative and quantitative gas chromatography coupled to a mass spectrometer (GC- MS) and gel permeation chromatography (GPC). Multiple substrates, including rhubarb waste, pea waste, and dairy industry wastewater (KAAS medium), were tested for successful fermentation. Rhubarb and pea waste could not be used as a fermentation substrate due to obstacles in upstream processing. KAAS medium was prepared by centrifugation and sterile filtration and tested in a 1 L and 4 L fermentation yielding non- competitive biomass and PHB concentrations with 0.09 g/L and 0.004 g/L, respectively. A 3 L fermentation with pure saccharose as the carbon source served as a model for comparison and analysis and resulted in a measured biomass concentration of 3.58 g/L with a PHB content of 1.43 g/L after Soxhlet extraction. The GPC analysis led to no conclusive elograms and results due to issues in non-destructively extracting and solving the PHB. The GC-MS analysis showed excellent linearity with an R2 value of 0.9906 for the standard curve and clear chromatograms, which allowed for both qualitative and quantitative analysis of PHB. Since PHB concentrations from KAAS medium were low, no quantitative analysis could be performed. Only samples from the saccharose-based fermentation could be assessed quantitatively. The found maximum PHB concentration was 2.95 g/L, which deviated from the found 1.43 g/L after Soxhlet extraction and was therefore discussed in this work. No locally available waste substrate could be found to be suitable for an industrial scale fermentation. The findings during this work led to the conclusion that semi-sterile fermentation approaches shall be investigated in future works handling waste products as they can potentially overcome upstream processing steps and, hence, make rhubarb or pea waste a potential candidate for PHB production.

Acknowledgement

I am grateful for the assistance given by my supervisors Prof. Dr. Joachim Fensterle and Prof. Dr. Peter Simon, who supported and motivated me during my thesis.

I am particularly thankful to Ralf Lucassen and Dr. Stefan Weber for their constant constructive support and the time they invested in this work.

Additionally, I would like to thank Chika Igwe and Felicia Zlati for providing their knowledge on the production of biopolymers from microorganisms.

iii

Table of Contents Contents 1 Introduction ...... 1

1.1 Current Situation ...... 1

1.2 Biopolymers ...... 2

1.2.1 Poly-3-hydroxybutyrate (PHB) Classification ...... 3

1.3 PHB Application ...... 4

1.4 PHB Production in Azohydromonas lata ...... 5

1.5 PHB Analysis ...... 7

1.6 Substrates ...... 8

1.7 Objective ...... 9

2 Materials and Methods ...... 10

2.1 Chemicals and Media ...... 10

2.2 Equipment ...... 12

2.2.1 Devices and Apparatus ...... 12

2.2.2 Software ...... 13

2.3 Data Assessment ...... 13

2.3.1 Optical Density (OD) ...... 13

2.3.2 Dry Cell Weight ...... 13

2.3.3 Biomass Concentration ...... 14

2.3.4 Total Cell Counts ...... 14

2.3.5 Growth Parameters ...... 14

2.3.6 pH ...... 15

2.3.7 Dissolved Oxygen (DO) ...... 15

2.3.8 Antifoam Control ...... 15

2.4 Bacterial Strain ...... 16

2.5 Substrates and Preparation ...... 16

2.5.1 Dairy Industry Wastewater ...... 16

2.5.2 Rhubarb Waste ...... 16

2.5.3 Pea Waste ...... 16

2.5.4 Saccharose Medium ...... 16

2.6 Substrate Analysis ...... 16

2.7 Fermentation ...... 17

2.7.1 Pre-Culture ...... 17

2.7.2 Main-Culture Flask Fermentation ...... 17

2.7.3 Main-Culture Bioreactor Fermentation ...... 17

2.8 Polymer Extraction and Purification ...... 18

2.8.1 Polymer Extraction 3 L and 7 L Scale-Up Fermentation ...... 18

2.9 Polymer Analytics ...... 18

2.9.1 Fluorescence Microscopy ...... 18

2.9.2 Spectrofluorometry...... 18

2.9.3 Gel Permeation Chromatography (GPC) ...... 18

2.9.4 Gas Chromatography-Mass Spectrometry (GC-MS) ...... 19

3 Results ...... 21

3.1 1 L KAAS Medium Fermentation ...... 21

3.1.1 Sugar Composition ...... 21

3.1.2 Supplementation for Optimal Growth Conditions ...... 21

3.1.3 Growth Assessment ...... 23

3.2 3 L Saccharose Fermentation ...... 25

3.2.1 Growth Assessment ...... 25

3.2.2 Spectrofluorometric Analysis ...... 27

3.2.3 PHB Extraction ...... 28

3.3 7 L KAAS Fermentation ...... 29

3.4 GC-MS Analysis of Fermentation Products ...... 30

3.4.1 Qualitative PHB Analysis ...... 30

3.4.2 Quantitative PHB Analysis ...... 32

3.4.3 Analysis of KAAS Medium Samples ...... 35

3.5 GPC Analysis of Fermentation Products ...... 36

3.6 Rhubarb Waste Fermentation ...... 36

4 Discussion ...... 39

4.1 Substrates ...... 39

4.1.1 Pea Liquid Waste ...... 39

4.1.2 Rhubarb Liquid Waste ...... 39

4.1.3 Substrate Preparation Cost ...... 40

4.1.4 Substrate Analysis in Flask Fermentation ...... 40

4.1.5 Renewable Substrate Availability ...... 41

4.2 Upstream and Downstream Processing ...... 41

4.2.1 Upstream Processing ...... 41

4.2.2 Downstream Processing ...... 42

4.3 Fermentations ...... 43

4.3.1 Fermentation Approach ...... 43

4.3.2 KAAS Fermentation ...... 43

4.3.3 Saccharose Fermentation ...... 44

4.4 GC Analysis ...... 45

4.4.1 GC Error Sources ...... 45

4.4.2 PHB Concentration Determination ...... 47

4.5 GPC Analysis ...... 48

5 Conclusion ...... 49

6 References ...... 50

7 Declaration ...... 55

8 Appendix...... 56

8.1 KAAS Medium 1 L Fermentation Additional Data ...... 56

8.2 Saccharose Medium 3 L Fermentation Additional Data ...... 56

8.3 KAAS Medium 7 L Fermentation Additional Data ...... 58

8.4 GC-MS Analysis ...... 59

8.4.1 Qualitative Analysis ...... 59

8.4.2 Quantitative Analysis ...... 61

1 Introduction

1.1 Current Situation In today’s world, plastic - besides glass and paper - is one of the primary packaging materials. Besides being one of the most convenient ways of packaging, plastic usage comes along with huge drawbacks, making it one of the most polluting manufactured materials on the planet (UN environment, 2018). Two of the most significant disadvantages are the usage of fossil oil and gas as a substrate for plastic production and the degradation time of decades to millennia depending on the type of plastic (Hopewell et al., 2009). Since they are inexpensive to produce, lightweight, as well as endurable, plastics have still become one of the major and most convenient packaging materials (Andrady & Neal, 2009). A study done by Geyer et al. projects that in 2050 25,000 million metric tonnes of plastic waste will have accumulated and only 9,000 million metric tonnes of it will be recycled, while the difference will either be discarded or incinerated (Geyer et al., 2017). One of the reasons for this gap being that around half of the plastics produced are single-use disposable products for packaging or other disposable items, while the other half is used for mid- and long-term applications such as electronics and infrastructure (Hopewell et al., 2009).

Therefore, the treatment of waste management is a crucial part of the plastic production cycle. Although most of the plastic waste is produced on land, especially countries with middle- to low-income are facing problems of waste management infrastructure, resulting in the release of plastic into the oceans, ultimately leading to massive plastic accumulation and strong environmental impact on both humans and nature (Ritchie & Roser, 2018). On the land sides, wastewater effluents of the plastic industry poison soil and groundwater, whereas plastic pieces in the ocean are often mistakenly ingested by aquatic life, which eventually serves as a food source for humans (Ilyas et al., 2018). Besides the mistakenly ingested plastic, microplastics are often responsible for the accumulation of plastic in aquatic organisms because with particle sizes of 5 mm and below, microplastics are easily ingested by accident (Smith et al., 2018).

In contrast to all the negative aspects of plastic production and usage, the benefits for society are certainly not negligible. The low weight of plastics combined with its relative strength helps reducing weight in car components, aeroplane components, and generally any packaging, leading to a decrease in fuel consumption compared to, e.g. glass or metal (Thompson et al., 2009). In the health care system, plastic delivers a convenient opportunity of packaging medical equipment in an airtight and sterile manner, making it

1 cheap and easy to have safe medical treatment even in developing countries (Andrady & Neal, 2009). One possible solution to eliminate many drawbacks of plastics while keeping its benefits, is the usage of renewable materials in a fermentation approach with substrates such as molasses, dairy industry wastewater, or agricultural waste biomass and is described further in this paper (Aslan et al., 2016; Baei et al., 2009).

1.2 Biopolymers As mentioned above, an alternative approach to synthetic polymers that are based on fossil fuels is the production of biopolymers from renewable feedstocks in a fermentation set-up (Vroman & Tighzert, 2009). Microorganisms can grow and accumulate biopolymers as energy storage when fed with substrates such as simple sugars or lipids (Aslan et al., 2016). However, the usage of pure substrates such as glucose, fructose, or xylose is not optimal due to high substrate costs and the need to purposely grow the precursor material they are coming from, like sugarcane or sugar beet (Gahlawat & Soni, 2017). Therefore, industrial waste material is a promising source of renewable carbon for biodegradable plastic production as it cuts down substrate price, which accounts for 20-45 % of total production cost (Nath et al., 2008).

Biopolymers are divided into many classes like proteins, DNA, or RNA, which all account for essential cell functions, however, the biopolymer classes of interest here are the ones that can be further processed into plastics (Nagy & Oostenbrink, 2013). Furthermore, it is essential to notice that biopolymers can either be classified as bio-based or biodegradable, whereas biodegradable does not necessarily include that the product is bio-based and vice versa (Song et al., 2009). Polylactic acid is an example of a bio- based biodegradable polymer, whereas, e.g. bio-polyethylene is bio-based but not biodegradable and polycaprolactone on the other side is biodegradable but not bio- based (Babu et al., 2013). Two promising and well-established classes of biodegradable biopolymers are polyhydroxyalkanoates (PHA) and its numerous members as well as polylactic acid (PLA) (Andreeßen & Steinbüchel, 2019).

As of now, out of the 335 million tonnes of annually produced plastic, bioplastics constitute for only one per cent of the total production with a total of 2.112 million tonnes in 2018 and an estimated increase to 2.616 million tonnes by 2023 (European bioplastics, 2018). Especially in years 2003 to 2007 the market for bio-based plastics experienced a steep increase from 0.1 million tonnes in 2003 to 0.36 million tonnes in 2007, accounting for an annual growth rate of 38 % (Shen et al., 2009). In 2018, about a quarter of total bioplastic production results in the non-biodegradable polyethylene terephthalate (PET), PLA and starch blends make up the major fraction for biodegradable materials produced (10.3 % and 18.2 % accordingly), while the group of

PHA only constitutes for 1.4 % of total production (European bioplastics, 2018). The most active participant of annual bioplastic production is Asia with 63.3 % of the total production volume in 2016, while North America, Europe, and South America make up for roughly equal amounts of 13.4 %, 12.9 %, and 9.9 % respectively and Australia/Oceania for the missing 0.4 % (IfBB Hannover, 2017).

1.2.1 Poly-3-hydroxybutyrate (PHB) Classification Poly-3-hydroxybutyrate is the most abundant representative of the PHA group and is stored in inclusion bodies in cells as an energy storage material when excess carbon is available, and a nutrient stress condition is given (Getachew & Woldesenbet, 2016). Since the first discovery of PHB in 1926 by M. Lemoigne in Bacillus megaterium, more than 300 gram-negative as well as gram-positive bacteria have been described that can accumulate PHAs in different conditions (Maestro & Sanz, 2017). PHAs consist of R-3- hydroxyalkanoic acid monomers and are typically classified according to their chain length: short-chain-length (SCL) PHAs consist of three to five carbons, medium-chain- length (MCL) of six to fourteen carbons, and the rarely found long-chain-length PHAs of more than fourteen carbons (Anderson et al., 2011; Maestro & Sanz, 2017). Below, a picture of the general molecular formula of PHAs and a depiction of the structure of PHB are given:

Figure 1 Adapted from Zerin (2017). Depiction of the general structure of PHAs and the specific molecular structure of PHB

Since PHA is such an interesting biopolymer with the potential to replace some petroleum-based plastics like polypropylene (PP), it has been studied to the point where more than 150 kinds of building blocks with different structures and properties are reported (Wang et al., 2016). The inclusion bodies that PHA accumulates in are covered in a protein layer (granule-associated proteins or GAPs) mainly consisting of PHA synthase (PhaC), PHA depolymerase (PhaZ), phasins (PhaP), and a potential phospholipid layer that may only occur due to PHA extraction steps, but may not be present in vivo according to some experimental data (Maestro & Sanz, 2017; Chek et al., 2017). Phasins are small amphiphilic proteins that impact the synthesis and granule formation of PHB by covering the major part of the granule even though they have no catalytic function, but nevertheless form a protective barrier between the cytoplasm and

3 the hydrophobic biopolymer (Wang et al., 2007; Maestro & Sanz, 2017). PhaC is an enzyme responsible for synthesising PHA from its substrates in any of the 14 described catalytic pathways resulting in PHA accumulation, whereas PhaZ is converting PHA to usable energy during carbon scarcity (Chek et al., 2017).

The physical properties of biopolymeric PHAs determine its usability in industrial applications. The tables below depict properties of PHB against conventional PP as well as properties of different PHA blends in order to overcome material deficiencies like the brittleness of PHB (Markl et al., 2018):

Table 1 Comparison of PHB to conventional PP (adapted from Markl et al., 2018).

Property PHB PP Crystalline melting point (°C) 175 176 Crystallinity (%) 80 70 Molecular weight (Da) 5x 105 2 x 105 Glass transition temperature (°C) 4 -10 Density (g/cm3) 1.250 0.905 Flexural modulus (GPa) 4.0 1.7 Tensile strength (MPa) 40 38 Extension to break (%) 6 400 Ultraviolet resistance Good Poor Solvent resistance Poor Good

Table 2 Comparison of PHB, conventional PP, and PHA blends (adapted from Balaji et al., 2013). PHBV: 3-hydroxybutyrate-co-3-hydroxyvalerate, PHB4B: 3- hydroxybutyrateeco-4-hydroxybutyrate, PHBHx: 3-hydroxyl-hexanoate

Property PHB PHBV PHB4B PHBHx PP Melting temperature (°C) 177 145 150 127 176 Glass transition temperature (°C) 2 -1 -7 -1 -10 Crystallinity (%) 60 56 45 34 50-70 Tensile strength (MPa) 43 20 26 21 38 Extension to break (%) 5 50 444 400 400

PHBV and PHB4B are classified as scl-copolymers, whereas PHBHx is a mcl-copolymer all of which can be produced when mixing different substrates (hence copolymer), e.g. a mixture of glucose and valerate that yields PHBV (Balaji et al., 2013). Furthermore, metabolic engineering and genetic modification are two approaches to alter the synthesis and composition of produced PHAs (Anderson et al., 2011).

1.3 PHB Application Besides its potential to replace conventional plastics like polyethylene or polypropylene, PHB is a great candidate for medical applications such as tissue engineering, drug delivery, and organ construction due to the possibility to alter its chemical composition as well as its inherent product purity and high mammalian cell biocompatibility (Peña et al., 2014). Furthermore, poly(3-hydroxypropionate) [poly(3HP)] serves as an excellent 4 candidate for slow-release drug capsule treatments since it is biodegradable and biocompatible at the same time (Andreeßen et al., 2014). In this sense, Silva-Valenzuela et al. (2010) showed that PHB in addition with hydrophilic and smectitic clay could be utilized as a polymeric microcapsule for drug delivery with enhanced properties. Besides PHB as a microcapsule for drug delivery, it can also be prepared to have the size of a nanoparticle - with the same purpose - by a dialysis method (Errico et al., 2009). Additionally, PHB granules may serve as biomarkers or biosensors due to the potential to immobilize proteins at the granule surface through GAPs and that way produce nano- /micro-beads that are interesting for biotechnological applications (Grage et al., 2009). An example of such a nano-/micro-bead is used in Fluorescence-Activated Cell Sorting (FACS), where the PHB bead in the cell is displaying an antigen and can then be detected in the presence of specific antibodies (Draper & Rehm, 2012). Additionally, implant materials like titanium might be coated with antibiotic-loaded PHB and poly(ethylene glycol) (PEG) that exhibit antibacterial properties, in order to decrease the risk of infections after medical operations (Rodríguez-Contreras et al., 2016). Maseali et al. (2012) reported that PHB scaffolds produced by either electrospinning or salt-leaching are usable in tissue engineering, but the method of production affects the physiochemical properties of such and therefore its biocompatibility as well. Furthermore, they described that electrospun nanofibers consisting of a blend of PHB/PHBV (3-hydroxybutyrate-co- 3-hydroxyvalerate) could be used as scaffolds for nerves in applications like myelin sheath regeneration (Maseali et al., 2013).

Looking at the possible applications for PHB and its derivatives, it becomes evident that it lacks prominence in essential industries such as food packaging. For food packaging, polysaccharides derived from plants, algae, fungi, and yeast appear to be the substrate of choice due to their inherent oxygen and carbon dioxide barrier properties as well as their decent mechanical properties while their only critical drawback is the low water vapour barrier due to hydrophilic properties (Ferreira et al., 2016).

1.4 PHB Production in Azohydromonas lata Azohydromonas lata (A. lata) is a nitrogen-fixing and hydrogen-oxidizing bacterial strain within the phylum of proteobacteria (Xie & Yokota, 2005). Furthermore, it is gram- negative and requires oxygen for growth and metabolism (Zafar et al., 2012) The cells are short to straight coccoid rods or rods with a pH growth range of 5.0 - 8.5, a NaCl growth range of 0 - 2 %, and a temperature growth range of 15 - 40 °C with an optimal range of 28 - 32 °C (Nguyen & Kim, 2017). A. lata has been reported to utilize simple sugars like glucose, fructose, and saccharose for PHB conversion, however, saccharose appears to be the most successful substrate for this purpose (Zafar et al., 2012). Apart from sugar sources, other bacterial strains are able to utilize substrates like lipids, fatty 5 acids, or lignocellulosic material (Aslan et al., 2016). The PHA production in cells starts with the conversion of sugar to pyruvate, which gets further converted to acetyl-CoA and from that point, two molecules of acetyl-CoA are condensed into acetoacetyl-CoA by 3- ketothiolase encoded by the gene phbA (Zhang et al., 2014). This molecule is then converted to 3-hydroxybutyryl-CoA by acetoacetyl-CoA reductase encoded by the gene phbB and lastly, a PHA synthases, encoded by phbC, polymerizes the monomers into chain-like structures (Peña et al., 2014). PHB is classified as a scl-polymer (Luef et al., 2015).

Figure 2 Metabolic pathway for PHB production (adapted from Zhang et al., 2014).

A. lata is a reasonable candidate for PHA production since it produces PHA during growth phase as well as during non-growth phases (Zafar et al., 2012).

A critical factor in the overall production efficiency, complexity, and cost of a fermentation is the operation mode. As the batch operation mode is rather straight-forward and less cost-intensive, it is often the approach of choice as e.g. described by Baei et. Al (2010) where A. lata was grown on dairy industry wastewater in a batch operation that generally requires no additional calculation and optimisation of feed rates but may results in lower yields compared to more complex operations. An alternative approach is the fed-batch operation mode, in which fresh medium is continuously pumped into the bioreactor. An example for this is a study performed by Lee et al. (2000) where recombinant Escherichia coli was grown on highly concentrated whey solution in fed-batch operation, yielding up to 119.5 g/L dry cell mass and 96.2 g/L PHB. Since renewable or waste substrates that potentially serve as a feedstock for biopolymer production may be highly diluted, Koller (2018) reviewed a fermentation approach that is based on fed-batch operation coupled 6 to cell recycling, which increases volumetric productivity. Additionally, he describes a multi-stage batch operation with Azohydromonas australica grown on saccharose that results in total productivity of 27.9 g/L dry cell mass and 20.6 g/L PHB (Koller, 2018). A further operation mode of choice is the single- or multi-stage chemostat method that is usually more complex but may result in higher yields. Atlié et al. (2011) show an example of a multi-stage chemostat experiment with the aim to increase productivity and PHB accumulation in Cupriavidus necator. Choosing the most effective fermentation approach does not only depend on the specific experimental set-up but also on the production as well as product cost and must be decided upon individually for each case (Koller, 2018).

1.5 PHB Analysis Due to its distinct chemical properties, PHB can be extracted as well as detected and analysed using various methods. A standard method to quantitatively determine PHAs is a simple weight measurement after the polymer has been extracted through a solvent from lyophilized cells, which is, however, limited to high PHA contents (Godbole, 2016). Furthermore, PHB granules accumulating in cells can simply be observed under a microscope when applying suitable magnifications as image 1 indicates (Tian et al., 2005).

Image 1 PHB granules in W. eutropha. Bar, 0.5 um (Tian et al., 2005)

A further microscopic method to visualize and determine PHB contents is fluorescence microscopy in which cells get stained with Nile Red dye that exclusively binds to PHB and then emits fluorescence, which has the potential to serve as a quantitative analysis as well (Alves et al., 2017). A more elaborate quantitative method is Ultraviolet (UV) spectrophotometry in which preparation of the sample involves multiple treatment steps as described by Hoseinabadi et al. (2015). Ion exchange high-performance liquid 7 chromatography (HPLC) is a rapid and straightforward method that can identify low amounts of crotonic acid in samples that were generated through PHB acid digestion (Godbole, 2016). Moreover, Karr et al. (1983) established a method that is based on an Aminex HPX-87-H ion exclusion resin for organic acid analysis and can detect PHB concentrations in samples as low as 0.01 - 14 µg (Godbole, 2016).

Mild acid methanolysis with a subsequent gas chromatographic step allows for analysis of purified PHB or untreated cell suspensions with a lower determination threshold of 10- 5 g/L and a work cycle of just 4 h (Braunegg et al., 1978). The work cycle involves the esterification of PHB and an internal standard through heat with a subsequent phase separation through the addition of water. The sample, now containing monomers, is then injected in liquid or gas phase and suitable for qualitative and quantitative measurement. A mass spectrometer (MS) can be added to the gas chromatograph (GC-MS) to serve as a detector, having the advantage that every peak from the gas chromatograph belongs to a specific mass spectrum that can be uploaded to a database, which then identifies the peak as a substance (Juengert et al., 2018). Braunegg et al. (1978) showed that this method is both suitable for extracted PHB samples and cell samples that only require centrifugation as a pre-treatment, making this method straightforward and more practical for on-line analyses. Since GC-MS analysis is based on the monomerization of PHB through esterification, it gives no information about the molecular weight distribution of the biopolymer under analysis. In order to determine molecular weight averages, gel permeation chromatography (GPC) – also named size exclusion chromatography (SEC) - with a proper solvent, column and standard can be used; being a suitable addition to the qualitative and quantitative analysis like gas chromatography (GC) (Godbole, 2016). The GPC method is rather quick and requires little preparation but does only work with extracted polymer, which is time-consuming and can be costly, making this method more sophisticated compared to GC-MS (Labuzek & Radecka, 2001).

1.6 Substrates The ideal cycle to produce biopolymers is based on renewable substrates that do not compete with any land usage and food crops. Hence, the utilization of waste products from already established industries is a reasonable starting point. Aslan et al. (2016) show that for this purpose, various microorganisms can be used to convert sugars, lipids, and even lignocellulosic materials to PHAs. More specifically, Wisuthiphaet & Napathorn (2015) showed that sugar molasses from the cane sugar industry could be used to produce PHB by A. lata, reaching a dry cell weight of 16.9 ± 0.2 g/L with a PHB content of 83.9 ± 2.5 % and a product yield of 0.40 g PHB/g saccharose. Further possible waste products are glycerol from the biodiesel industry as well as wastewater sludge that is locally available in most regions (Hermann-Krauss et al., 2013; Cha et al., 2015). Cha et 8 al. (2015) managed to establish a sequential batch fermentation based on wastewater with average product yields of 42 % and maximum product yield of 63 %. In their study, Hermann et al. (2013) reveal a fermentation approach based on Haloferax mediterranei utilising crude glycerol phase from the biodiesel industry, resulting in a biopolymer with a molar fraction of 0.90 mol/mol of 3-hydroxybutyrate (3HB) and 0.10 mol/mol 3- hydroxyvalerate (3HV), while not requiring sterilisation of the bioreactor. Additionally, Baei et al. (2010) proved that dairy industry wastewater contains simple sugars that – after a rather labour-intensive and lengthy pre-treatment - can be utilized by A. lata for biopolymer production with a biopolymer yield of 1.66 g/L of which 1.21 g/L were PHB and 0.45 g/L poly-3-hydroxyvalerate. A slightly different approach was described by Zahari et al. (2012), who discovered that A. lata could grow on oil palm frond juice, which is obtained by pressing oil palm leaves and contains high levels of glucose (53,95 ± 2,86 g/L). The sterilized oil palm frond juice was then added to MSM in varying concentrations ranging from 10 – 40 % to serve as the growth-medium. A total of 8.42 g/L dry cell weight with 32 % (w/w) of PHB could be harvested at 30 % (v/v) oil palm frond juice (Zahari et al., 2012). Other examples of substrates studied for PHB production in various bacteria include wheat straw hydrolysate, extruded rice bran and wheat bran, rice grain distillery wastewater, olive mill wastewater, waste rapeseed oil, pineapple juice, sugar beet juice and maple sap (Aslan et al., 2016).

1.7 Objective Due to its local availability and abundance, dairy industry wastewater serves as an interesting substrate for biopolymer production and will thus be focused on in this paper. Furthermore, the approach described by Zahari et al. (2012) leaves the question of whether more plant-based substrates contain enough simple sugars and could serve as a feedstock for A. lata. Thus, this paper will, to a lesser extent, describe possible plant- based substrates that could potentially serve as feedstocks for PHA production.

As the analysis of the fermentation outcome is a crucial part of determining its usability for e.g. industrial applications, this work will examine a gel permeation chromatography method and aims to establish a qualitative and quantitative gas chromatography protocol.

Since fermentation outcomes from unknown substrate compositions - like dairy industry wastewater - are hard to analyse with methods that are not yet fully established, all analytical methods shall be based on fermentations with known substrate composition, such as a pure saccharose based medium. Furthermore, these results shall serve as a comparison to unconventional fermentation approaches.

2 Materials and Methods

This chapter depicts all the materials and methods that were used in order to conduct the various experiments leading to the below-described results (Section 3).

2.1 Chemicals and Media The following table lists all chemicals used in the experiments:

Table 3 List of Chemicals

Chemical Formula Company Company Location

Disodium hydrogen Na2HPO4 x 2 H2O Applichem Darmstadt, phosphate GmbH Germany

Potassium di- KH2PO4 Applichem Darmstadt, hydrogen phosphate GmbH Germany

Urea CH4N2O Carl Roth Karlsruhe, Germany GmbH

Magnesium sulphate MgSO4 VWR Leuven, Belgium International BVBA Ammonium iron(III) Fe(III)-NH4-Citrate Carl Roth Karlsruhe, Germany citrate GmbH

Calcium Chloride CaCl2 Merck KGaA Darmstadt, Germany

Boric acid H3BO3 VWR Leuven, Belgium International BVBA Cobalt(II) chloride CoCl2 x 6 H2O Carl Roth Karlsruhe, Germany hexahydrate GmbH

Zinc sulphate ZnSO4 x 7 H2O VWR Leuven, Belgium heptahydrate International BVBA Sodium molybdate NaMoO4 x 2 H2O Carl Roth Karlsruhe, Germany dihydrate GmbH

Manganese(II) chloride MnCl2 x 4 H2O Carl Roth Karlsruhe, Germany tetrahydrate GmbH

Nickel(II) sulphite NiSO4 x 7 H20 AppliChem Darmstadt, heptahydrate GmbH Germany

Copper(II) sulphate CuSO4 x 5 H2O Applichem Darmstadt, pentahydrate GmbH Germany

D(+)-Saccharose C12H22O11 Carl Roth Karlsruhe, Germany GmbH

Nile red C20H18N2O2 Carl Roth Karlsruhe, Germany GmbH Meat Extract - Carl Roth Karlsruhe, Germany GmbH Tryptone/Peptone - Carl Roth Karlsruhe, Germany GmbH

Hydrochloric acid HCl VWR Fontenay-sous- International Bois, France S.A.S. Sodium hydroxide NaOH AppliChem Darmstadt, GmbH Germany Silicone anti-foaming - Carl Roth Karlsruhe, Germany emulsion GmbH Poly[(R)-3- C4H8O3 Sigma-Aldrich Steinheim, hydroxybutyric acid] Chemie GmbH Germany Chloroform CHCl3 AppliChem Darmstadt, GmbH Germany Sulfuric acid H2SO4 AppliChem Darmstadt, GmbH Germany Methanol CH3OH Merck Hohenbrunn, Schuchardt Germany OHG 2,6-Di-tert-butyl-4- C15H24O Merck Hohenbrunn, methylphenol Schuchardt Germany OHG Hexafluoroisopropanol C3H2F6O Carl Roth Karlsruhe, Germany GmbH

The experiments required the following media to be prepared:

Nutrient Broth Medium (NB-Medium) Trace Element Solution

Beef-extract 3.00 g Fe(III)-NH4-Citrate 6.00 g

Peptone 5.00 g CaCl2 10.00 g

H2Odest 1000 mL H3BO3 0.30 g

CoCl2 x 6 H2O 0.20 g

Mineral-Salt-Medium (MSM) ZnSO4 x 7 H2O 0.10 g

NaMoO4 x 2 H2O 0.03 g

Na2HPO4 x 2 H2O 2.31 g MnCl2 x 4 H2O 0.03 g

KH2PO4 1.50 g NiSO4 x 7 H2O 0.02 g

Urea 1.98 g CuSO4 x 5 H2O 0.01 g

MgSO4 0.10 g H2Odest 1000 mL Trace Element Solution 1.00 mL

H2Odest 1000 mL pH 6.9

The nitrogen source in Mineral-Salt-Medium was changed from ammonium chloride

(NH4Cl) to urea (CH4N2O) according to the findings of Zafar et al. (2012). The required 11 weight of Urea to reach equal nitrogen levels as before was calculated and adjusted in the recipe above.

2.2 Equipment This section describes all Hardware and Software that was needed in order to perform the experiments.

2.2.1 Devices and Apparatus Table 4 List of Devices

Device Name Company Company Location Photometer Eppendorf Eppendorf AG Hamburg, Germany BioPhotometer plus Microscope Primo Star Zeiss Göttingen, Germany Centrifuge Heraeus Fresco 17 Thermo Scientific Osterode am Hartz, Germany Centrifuge Sorvall Lynx 6000 Thermo Scientific Osterode am Hartz, Germany Centrifuge Haraeus Fresco 21 Thermo Scientific Osterode am Hartz, Germany Vacuum Centrifuge Eppendorf Eppendorf AG Hamburg, Germany Concentrator Plus Incubator KS 4000 i control IKA® Staufen, Germany shaker Pump Incubator Reglo ICC Pump ISMATEC® Barrington, Ireland AL01-06-100 53L Avantage-Lab Autoclave Systec VX-150 Systec Wittenberg, Germany Off-gas Sensor BlueInOne Sensor BlueSens GmbH Herten, Germany

pH-meter Seven Compact Systec Gießen, Germany pH/Ion DO Sensor

Laboratory My-Control applikon® Delft, Netherlands Fermenter Biotechnology Water Cooling Water Console applikon® Delft, Netherlands Device Biotechnology Fermenter BIOSTAT®Cplus- Sartorius AG Goettingen, C10-3 CC Germany Centrifuge Sorvall Lynx 6000 Thermo Scientific Osterode am Hartz, Germany Fluorescence Imager.M2 Axio Zeiss Göttingen, Microscope Germany Spectrofluorometer Varioskan Flash Thermo Scientific Osterode am Hartz, Germany

Gas GC 8000Top CE Instruments Wigan, UK Chromatograph Ltd Mass Spectrometer MD 800 Fisons Ipswich, UK Instruments Autosampler Unit HS 800 Fisons Ipswich, UK Instruments Screw-capped Culture tubes with DWK Life Mainz, Germany glass tube DIN thread and Sciences GmbH screw cap from PBT Gel Permeation SECurity GPC PSS GmbH Mainz, Germany Chromatograph System

2.2.2 Software Table 5 List of Software

Software Version Company Company Location BlueVis 4.0.0 BlueSens GmbH Herten, Germany Zen 2.6 Zeiss Göttingen, Germany SkanIt 2.4.5 Thermo Scientific Osterode am Hartz, Germany Xcalibur - Thermo Scientific Osterode am Hartz, Germany Autotune.tun - Thermo Scientific Osterode am Hartz, Germany PSS WinGPC - PSS GmbH Mainz, Germany

2.3 Data Assessment

2.3.1 Optical Density (OD) The optical density was assessed at a wavelength of 600 nm using a Spectrophotometer. Blank was always taken as the pure medium the culture was in at the sampling time point. Any signal outputs above 0.3 were diluted to stay in the linear measurement range of the device (0.1 - 0.3) (Steinbüchel et al., 2012).

2.3.2 Dry Cell Weight Dry cell weight (dcw) biomass measurement was performed as described by Steinbüchel et al. (2012) and slightly adapted. To get the dry cell weight, 1 mL of the sample was centrifuged at 5,000xg and 23°C for 10 min. The supernatant was discarded, the sample washed with distilled water, and then centrifuged again with the same parameters. The supernatant was discarded, and the sample was vacuum centrifuged at 60°C for two hours. Afterwards, the dry cell weight was assessed and transformed to g L-1. If centrifugation was not sufficient to yield a solid pallet, the procedure was repeated. Especially at the early stages of fermentation, 1 mL of the sample might not yield a detectable amount of dry cell weight. Thus, the sample size was increased to 2 mL if required and finally calculated to g L-1 again. 13

2.3.3 Biomass Concentration To assess the biomass concentration, there are two options. First, the dry cell weight can be measured at every time point with the method described above. Second, the OD is assessed at every sampling point, while the dry cell weight is only measured at a few sampling points, so that one has a sufficient amount of data points to reliably plot the dry cell weight against OD and get a plot function, which is then used to correlate the OD to the dry cell weight at every given sampling point. In either way, one should get values for the biomass concentration in g L-1 at every sampling point, which is then used to yield a growth curve by plotting the biomass concentration against time (the plot may also include the cell number in cells/mL) (Steinbüchel et al., 2012).

2.3.4 Total Cell Counts Cell counting was performed as described by Steinbüchel et al. (2012). The Thoma counting chamber used, has a depth of 0.100 mm and a surface area of 0.0025 mm2. 8 µL of the sample were applied to the chamber and under a microscope with a 40x magnification the cell number of 3 b-fields, each b-field consisting of 16 c-fields, was counted. This number was then used to calculate the average cell number of one c-field. If counted cell numbers per b-field exceeded 200, the sample was diluted accordingly. The final cells per millilitre were calculated with the equation below, where c is the average cell number of one c-field, Vc is the chamber volume in millilitre, and f is the dilution factor.

푐 푐푒푙푙 푛푢푚푏푒푟 = 푥 푓 Equation 1 푉푐

2.3.5 Growth Parameters Growth analysis methods were performed as described by Igwe (2018). Growth visualization was assessed by plotting OD, total cell counts, and/or dry cell weight against time. In order to identify the exponential growth phase, a semi-log plot of total cell counts or dry cell weight against time is plotted, and a linear regression line is added through the time interval that shows exponential growth. The regression line then reveals a function of which the slope is considered the maximum specific growth rate (µmax),

-1 which originates from Equation 2, where x is the cell concentration in gL , x0 the initial cell concentration in gL-1, t the time in h, and µ the specific growth rate in h-1, which was considered as µ = µma x= constant during growth phase.

µ_ 푚푎푥 푡 푥 = 푥0 ∗ 푒 Equation 2 Furthermore, growth was considered exponential if the coefficient of determination (R2) was equal to or higher than 0.95. If the coefficient of determination was below 0.95, growth could not be considered exponential, and the growth rate had to be calculated 14 individually for every time point according to the following formula, where rx is the volumetric rate of biomass production in gL-1 h-1:

푑푥 푟 = = µ푥 Equation 3 푥 푑푡 All individually calculated growth rates can be averaged to yield the average specific growth rate (µavg) and the highest individual specific growth rate is considered the maximum specific growth rate (µmax).

A further growth parameter during exponential growth is the doubling time, which is calculated according to the following formula, where td is the doubling time in h:

푙푛 2 푡 = Equation 4 푑 µ

-1 -1 The overall biomass and PHA productivity of the processes in g L h (pbio,avg) are calculated using equation 5, where t is the time in h, and x the biomass in g/L.

푥−푥 푝 = 0 Equation 5 푡−푡0 The maximum productivity is calculated by using the values during exponential growth, while the average productivity is calculated based on the whole fermentation run.

2.3.6 pH For batch cultures, the media were adjusted to their required pH if applicable. A steady pH control was only performed in bioreactors, where a pH sensor was attached to a control loop to ensure a constant pH of 7.0. Hydrochloric acid (0.1 M solution) and sodium hydroxide (1 M solution) were added to the control loop. Calibration of the pH sensor was done with two solutions, one with a pH of 4.01 and one with 7.00 (Zlati, 2019).

In order to adjust the pH of the different substrates tested for fermentation suitability, a pH meter was used. 5 % or 37 % HCl was used to increase toxicity if the substrate was too alkaline, whereas 0.1 M or 5.0 M NaOH was used if the pH of the substrate was too low.

2.3.7 Dissolved Oxygen (DO) The dissolved oxygen concentration was only measured in bioreactor fermentations. To assess the DO, the sensor is connected to a control loop that monitors the DO concentration and automatically adjusts either stirrer speed or inlet airflow in order to sustain the pre-set dissolved oxygen concentration of 30 % in this case (Zlati, 2019).

2.3.8 Antifoam Control For antifoam control, a sensor was set up in a control loop. 10 % (w/v) silicone emulsion was pumped in if the foam reached a critical height (Zlati, 2019).

2.4 Bacterial Strain The bacterial strain used throughout the experiments was Azohydromonas lata (A. lata), formerly known as Alcaligenes latus. The strain was obtained from DSMZ (Braunschweig, Germany) and kept in one-millilitre aliquots at -80°C. Alcaligenes latus was first described by Palleroni and Palleroni (1978) and later reclassified as Azohydromonas lata by Xie and Yokota (2005).

2.5 Substrates and Preparation Several substrates were tested for their fermentation suitability.

2.5.1 Dairy Industry Wastewater The dairy industry wastewater, from now on referred to as KAAS medium, was obtained from Friesland Campina Rijkevoort, Netherlands. To keep the medium fresh, it was frozen at -20°C until needed. To prepare the medium for fermentation, it was centrifuged at 9000 rpm for 1 h and 23°C. The supernatant was then put through a 5 - 8 µm filter in order to remove any clogging agents and finally put through a sterile filter apparatus with a pore size of 0.22 µm. The pH was adjusted to 7 ± 0.1 before sterile filtration.

2.5.2 Rhubarb Waste Rhubarb waste was provided in liquid form by Grasser, Netherlands. The liquid was obtained by pressing all waste products from the rhubarb harvest, yielding a dark green and slightly viscous liquid waste. The medium was frozen at -20°C until needed. The preparation steps for fermentation equal the ones for the KAAS medium. First, it was centrifuged at 14,000 rpm for 1 h and 23°C. The supernatant was then put through a 5 - 8 µm filter in order to remove any clogging agents and finally put through a sterile filter apparatus with a pore size of 0.22 µm. The pH was adjusted to 7 ± 0.1 before sterile filtration.

2.5.3 Pea Waste Pea waste was provided in liquid form by Grasser, Netherlands. The properties and preparation procedure are the same as described in the section above. However, the pea waste could not be sterile filtered due to clogging of the filter, which will be discussed in further detail in section 4.1.1.

2.5.4 Saccharose Medium For analytics establishment, one 3 L fermentation was run with a 2 % (w/v) saccharose solution in MSM medium.

2.6 Substrate Analysis HPLC data was provided to identify the sugar contents of substrates. Due to the clogging problem with pea waste as described above, it was unsuitable for fermentation and 16 therefore not analysed further. Since sugar content was considered the most important factor for PHA production and the waste products are complex media, a complete analysis of substrate composition was not performed.

2.7 Fermentation Fermentation experiments were carried out in either a 500 mL Erlenmeyer flask, a 1 L, 3 L, or a 7 L bioreactor.

2.7.1 Pre-Culture Pre-cultures for all flask fermentations were grown over one to two days. On the first day, 400 µL of A. lata was applied to 25 mL sterile NB-Medium in a baffled (if available) 250 mL Erlenmeyer flask, which was then incubated for 18 - 24 hours at 30°C and 200 rpm (pre-culture I). On the second day, 5 mL of the first pre-culture was added to 50 mL sterile NB-Medium in a 500 mL Erlenmeyer flask and again grown for 18 - 24 hours at 30°C and 200 rpm (pre-culture II). If for any experiment, more than 50 mL of pre-culture was needed for the main culture, the second pre-culture was simply be prepared in doublet, triplet, or higher.

2.7.2 Main-Culture Flask Fermentation Experiments in Erlenmeyer flasks were carried out in either 250 mL Erlenmeyer flasks with 25 mL of medium plus 2.5 mL pre-culture, 500 mL Erlenmeyer flasks with 50 mL medium plus 5 ml pre-culture, or 1 L Erlenmeyer flasks with 250 mL medium plus 25 mL pre-culture. In some experiments, pre-culture I was used to inoculate the main fermentation whereas in some experiments pre-culture II was used, depending on the experimental set-up. Experiments were run between 24 - 48 h depending on the experimental set-up. Flask fermentations were incubated under the same conditions as the pre-culture (see section 2.7.1).

2.7.3 Main-Culture Bioreactor Fermentation For fermentations in the 1 L bioreactor the medium volume was always set to 800 mL in order to have enough spare space for anti-foam control, agitation, and oxygen feeding. In addition to the 800 mL, 50 mL of pre-culture was used as inoculum, yielding a total volume of 850 mL in the bioreactor. Experiments in the bioreactor were run for about 48 h to monitor growth kinetics. The temperature was set to 30 °C, the pH was adjusted to 7.0, the agitation speed was 300 rpm, and the lower limit for DO was set to 30 %. Fermentations run in the 3 L bioreactor had a medium volume of 2 L plus 200 mL of inoculum, yielding a total volume of 2.2 L. If DO concentrations throughout the fermentation were below the critical point, stirring speed was manually adjusted to higher values. The fermentation in the 7 L bioreactor was run under the same conditions. The total volume of the medium was 4 L instead of 7 L due to costly and time-intensive

17 preparation for larger amounts of KAAS medium. In addition to the medium, the bioreactor was fed with 200 mL of pre-culture prepared as described in section 2.7.1.

2.8 Polymer Extraction and Purification The produced polymer was extracted for further analytical analysis.

2.8.1 Polymer Extraction 3 L and 7 L Scale-Up Fermentation The outcomes of the 3 L and 7 L scale-up fermentation were extracted via a Soxhlet- apparatus after they were freeze-dried and weighed for dry biomass determination. For this method, the medium was centrifuged at 23°C, 9,000 rpm and 45 min, the supernatant was discarded, and the cells were frozen at -20°C. In the next step, the frozen cells were freeze-dried for 24 - 30 h. Extraction could then be performed using a Soxhlet-apparatus connected to a reflux-condenser with chloroform as a solvent. A rotary evaporator was used to remove the chloroform phase. The extracted PHA was then stored and could be used for various analytical methods (Igwe, 2018).

2.9 Polymer Analytics In order to analyse the fermentation outcomes qualitatively and quantitatively, several analysis methods were used.

2.9.1 Fluorescence Microscopy Fluorescence microscopy was conducted to verify PHA accumulation in cells before and during fermentation. Since A. lata already produces PHA in the growth phase, the method was used to check for possible contaminations in pre-cultures. Nile red served as a PHA specific dye, and x1000 magnification was used to identify fluorescing cells. Probes were prepared by mixing 100 µL of the sample with 1 µL Nile red dye, followed by incubation for 10 - 15 min (Zlati, 2019).

2.9.2 Spectrofluorometry Samples from the 3 L bioreactor fermentation were examined for their OD and fluorescence intensity (FI) through a spectral scanning multimode reader. 100 µL of each sample was transferred to one well of a 96-well plate and then mixed with 1 µL of Nile red dye through repeated pipetting. It was then incubated for 10 min and mixed through pipetting again before the actual measurements were taken. As standard, the OD was measured at a wavelength of 600 nm. Fluorescence intensity was measured at 552 nm excitation wavelength and 636 nm emission wavelength (Zlati, 2019).

2.9.3 Gel Permeation Chromatography (GPC) Gel permeation chromatography was used to assess the molar mass distribution. The columns used were two PSS SDV 5 µ linear columns with a length of 30 cm and a diameter of 0.80 cm at 25 °C. Detection was done with the refractive index (RI) and UV 18 at λ = 300 nm. The injection volume was 100 µL, the flow rate was set to 1 mL/min, and 2,6-Di-tert-butyl-4-methylphenol (BHT) served as an internal standard. The sample concentration was set to 2 mg/mL. The sample was solved in chloroform or chloroform and hexafluoroisopropanol (HFIP) for better solvability.

2.9.4 Gas Chromatography-Mass Spectrometry (GC-MS) Gas chromatography, coupled to mass spectrometry served as a qualitative and quantitative analysis tool. The procedure and parameters were adjusted from Juengert et al. (2018) and Braunegg et al. (1978). For sample preparation, either Soxhlet extracted PHB or centrifuged cells (from now on referred to as ‘cell extracted PHB’) was used, whereas store-bought PHB served as a substrate for validation chromatograms. A defined amount of substrate was added to 2 mL chloroform in a chloroform resistant screw-capped glass tube. If the PHB happened to be insoluble, the sample was heated to 100°C for 3 h. Once the PHB was solved, 2 mL of acidified methanol with 10 % (v/v) sulfuric acid and 8 mg/mL benzoic acid, which serves as an internal standard (I.S.), were added. The sample was then heated to 100°C for 3 h and afterwards cooled down to room temperature. 1 mL of distilled water was added to the glass tube, shaken or vortexed for 30 seconds to secure optimal mixing and then placed into a tube holder for about 1-2 min to allow for phase separation. The organic phase, containing the solved PHB methyl ester and the benzoic acid methyl ester, can then be found on the bottom third of the glass tube and shall be identified as 3-hydroxybutyryl methyl ester (3-HBME) and methyl benzoate by the GC-MS, respectively. The organic phase was then transferred to a fresh gas-tight tube and stored until used.

2 µL of the sample was injected into the GC with the help of a microlitre syringe. Before and after every use, the microlitre syringe shall be flushed with acetone by simple pipetting for cleaning. The injection temperature was 220°C. The Oven temperature was 80°C, which was held for 2 min and then ramped up to 180°C at 10°C/min and held for 1 min (Total cycle: 13 min). Note that for unknown samples, the final temperature may has to be increased to 245°C to allow for the detection of substances with longer retention times. The flow rate was 0.8 ml/min, helium was used as a carrier gas, and the split flow was set to 1:100. The ionization mode of the MS was set to electron impact (EI+), the source temperature to 200°C and the GC interface temperature to 250°C. The scan range was 30 to 150 amu with 6.0 scans per second. The MS scan range might also be adjusted for unknown samples.

For quantification analysis, the parameters were slightly adjusted to stay within the linear detection range of the device. The split flow was set to 1:200, and all samples measured for the standard curve have been diluted at a 1:10 ratio with chloroform after preparation as described in the first paragraph of this chapter. For the standard curve, samples with 19 concentrations of 0.5, 1.0, 1.5, 2.0, 2.5, 3.0 mg/mL PHB have been prepared and measured. The internal standard concentration was held at 8 mg/mL for all samples. The PHB area was divided by the internal standard area and then plotted against the standard samples with the concentrations mentioned above to obtain the standard curve. The equation of the standard curve was used to calculate the PHB concentration in samples with unknown concentrations. In case the chromatogram revealed imperfect peaks that were not integrated properly, the integration was performed manually.

3 Results

In the first set of experiments, KAAS medium was tested as a potential candidate for a productive fermentation. In addition, flask fermentation experiments were carried out to assess the feasibility of different renewable substrates as possible fermentation media that possibly serve as a basis for future works.

3.1 1 L KAAS Medium Fermentation

3.1.1 Sugar Composition HPLC was used to reveal the sugar contents of the KAAS medium. The HPLC data was provided by Britta Brands (HSRW Kleve).

Table 6 HPLC results for KAAS medium (Provided by Britta Brands, HSRW Kleve)

Sample RT [min] Type Width Area Height Area % Component Conc. [min] [mg/mL] KAAS 7.243 BH 0.3006 19095 9277.71 82.981 Unidentified 3.93 6.13 00 0 9.524 HH 0.5081 39164. 1129.86 17.019 Lactose 0.22 14 07 0

The HPLC method was able to generate two peaks, of which one can be correlated to lactose, and the other one remains unidentified. Quantitatively, it was found that lactose has a concentration of 0.22 mg/mL, whereas the unidentified substance is present in a significantly higher concentration with 3.93 mg/mL. Despite the unknown sugar composition, growth was assessed in a 1 L bioreactor fermentation.

3.1.2 Supplementation for Optimal Growth Conditions Since the optimal standard medium for A. lata consists of MSM with added saccharose, in which MSM serves as a trace nutrient supply as well as the nitrogen source, KAAS medium was supplemented with different percentages of MSM to monitor changes in growth yields. Because the dairy industry wastewater is a complex medium that changes composition daily, MSM medium was thought to add a stable trace nutrient supply that will enhance cell growth. To find the optimal growth conditions, A. lata was grown in different KAAS/MSM (v/v) compositions in triplets, and the cell counting method as well as a tend dry cell weight measurement were used as growth indicators.

Figure 3 Total cell count results of A. lata in different KAAS/MSM compositions. Values displayed are averages of triplets, and standard deviations were calculated from triplet results

Figure 3 shows that A. lata cultured in B (10 % v/v MSM) and C (25 % v/v MSM) compositions are the only ones that show growth. After 48 h, it can be considered that growth stopped since there is almost no difference in cell counts from 48 to 72 h. Furthermore, the total cell counts of B and C are almost identical and show no significant difference. Set-ups A (0 % v/v MSM) and D (50 % v/v MSM) even show a decrease in cell numbers after 24 h and then slightly increase again until 72 h. After 72 h A has a slightly higher cell count than at 0 h but was determined not to have experienced cell growth since this increase is negligible. Hence, this statement is also true for D, because after 24, 48, 72 h the cell number was found to be lower than at 0 h. Error bars indicate the standard deviation, which was calculated based on the triplet set-up. Error bars of A interfere with error bars of B and partially of C, making it difficult to judge to what extent a 10 % or 25 % addition of MSM to the KAAS medium affects cell growth.

The dry cell weight in g/L of all probes at tend has been calculated based on triplet 10 mL samples to have a second parameter that reveals suitable growth conditions.

Table 7 Average dry cell weight in g/L of A. lata grown in various KAAS/MSM compositions.

Sample dcw (g/L) stdev. dcw A 0,37 0,05 B 0,32 0,10 C 0,26 0,02 D 0,30 0,14

The table shows that A has the highest biomass concentration with 0,37 g/L, while C has the lowest concentration with 0,26 g/L. Since the differences in biomass concentrations are minor, these results shall be compared to the total cell counts above, and a reasonable estimation shall be established.

Looking at the cell counts, set-ups A and D did not experience growth but have the highest and third-highest dry cell weight, respectively. B and C have the highest cell numbers, but only the second highest and lowest dry cell weight, respectively. The dry cell weight results do not reflect the results of the total cell counts, however, both methods have a high source of error, which is numerically reflected by the standard deviations that go as high as 46.67 % of the measurement value for the dry cell weight and 61 % of the measurement value for the total cell counts (see section 8.1). This circumstance makes judging the optimal growth conditions rather tricky and can only be seen as an estimation. Set-up B shows high cell numbers as well a high dry weight. Additionally, KAAS medium already has low amounts of sugars available, meaning that a higher percentage of MSM dilutes the medium further. Therefore, set-up B with 10 % (v/v) MSM was chosen to be the best suitable set-up.

3.1.3 Growth Assessment Growth assessment was conducted in a 1 L bioreactor with the chosen parameters described in section 2.7.3. The fermentation was stopped after 30 h because no more growth could be monitored. OD, cell counts, and dcw were used as assessment methods. However, dcw and OD could not be correlated, since the measured dry cell weights were negative and therefore had to be neglected.

Figure 4 Growth assessment of A. lata, via OD and cell counts, grown in KAAS medium with 10 % (v/v) MSM

The Figure above does not represent a classical growth curve in which the strain undertakes exponential growth. Virtually all phases of a standard growth curve cannot be observed when A. lata is grown on KAAS medium. The OD reaches its maximum value of 0.324 after 2h of fermentation, while the maximal measured cell concentration is 6.07E+07 cells/mL after 4 h. After these time points, the measured values steadily decrease to a point where the cell concentration after 29.5 h is lower than at 0 h. The final OD values are a bit higher than at 0 h, but not to a point where one can argue that cells experienced long-term growth. More specifically, the initial cell concentration was 3.63E+07. Comparing this to the highest cell concentration of 6.07E+07, yields a factor of 1.67, meaning that the cell concentration at its maximum did not even double.

As a further growth indicator, the DO concentration inside the medium was monitored.

Figure 5 DO concentration in % of A. lata grown in KAAS medium with 10% (v/v) MSM

Figure 5 correlates with the results from the figure above. The DO concentration rapidly drops to the set threshold of 30.0 % with a stirring speed of 300 rpm within the first 2 h and then steadily increases again to 45.2 % after 30 h. This trend is in line with the results from figure 4, in which values only increased for the first 2 h and then decreased again. Figure 5, therefore, serves as a further indicator that cell growth activity is only given for about 2 h.

This behaviour is even further reflected by the gas composition measured throughout the fermentation run.

Figure 6 Gas composition of A. lata grown in KAAS medium

The measured alterations in the gas composition are barely mentionable since their change is in the range of 0.1 % for both CO2 and O2. However, two peaks within the first few hours are recognizable, which are the only two areas for CO2 above zero with about 0.01 - 0.02 %. Besides, at 5 h, one more peak can be identified, which may be neglected since it is below zero. All three plots reflect that cell growth only occurred for the first 2 h.

3.2 3 L Saccharose Fermentation A 3 L fermentation based on a simple saccharose medium served as both a model fermentation and an analytical reference. Since the KAAS medium is complex and may result in more complicated analysis, PHB extracted from the saccharose fermentation is crucial for comparison of analytical results.

3.2.1 Growth Assessment The fermentation conditions were as described in section 2.7.3. The volumes were set to 2 L of MSM medium containing 20 g/L saccharose and an inoculation volume of 200 mL. The fermentation was run for 50.5 h as the growth plateaued and a sufficient amount of PHB should be produced at that point. The following growth kinetics could be observed.

Figure 7 Growth assessment of A. lata, via calculated biomass and cell counts, grown in MSM with 20 g/L saccharose

Figure 7 shows that growth almost increases linearly until 50 h and then plateaus. However, since no samples could be taken between 7 – 22 h and 30 – 47 h, this statement is only an estimation. The maximum calculated biomass is 3.82 g/L, and the maximum cell number is 6.68E+08 cells/mL. After finishing the fermentation, two litres of the fermentation broth were centrifuged and freeze-dried in order to perform a Soxhlet extraction. The freeze-dried biomass was found to be 3.58 g/L and is in the same range as the final calculated biomass with 3.66 g/L.

The obtained biomass values were used to calculate the maximum specific growth rate, the doubling time, and the maximum biomass productivity according to formulas in section 2.3. Exponential growth was found to be between 0 and 7 h (see figure 23). However, this might only be an estimation since no samples could be taken and analysed overnight where main exponential growth may have occurred, which is reflected by the higher average productivity compared to the productivity during exponential growth (see table 8).

Table 8 Calculated growth parameters of A. lata during the 3 L fermentation

-1 µmax (h ) 0.40 td (h) 1.73 pb,max (g/L*h) 0.054 Pb,avg (g/L*h) 0.072

Figure 8 depicts the CO2 and O2 concentrations in % in the off-gas of the fermentation run. It is clearly visible that the positive and negative peaks of CO2 and O2 counteract each other, meaning that when, e.g. the CO2 concentration increases, the O2 concentration decreases. Within the first few hours, the CO2 curve experiences two peaks with a maximum percentage of 0.2. After the second peak, the concentration 26 steadily goes down until it quadruples at around 24 hours and then steadily increases - with only a few significant negative peaks - until the end of the fermentation, reaching a maximum CO2 concentration of around 0.7 %. Contrary, the O2 curve behaves almost strictly opposite to the CO2 curve. However, the data from around 7 – 22 h that shows no significant slope or peaks until it quadruples at around 24 h may be judged carefully. The period refers to night time in which no samples could be taken. As the growth parameters in figure 7 show a steady increase, one would expect the same behaviour in the gas composition. The reasons for the unexpected behaviour in this period are unknown.

Figure 8 Gas composition of A. lata grown in MSM medium with 20 g/L saccharose

3.2.2 Spectrofluorometric Analysis Samples from every time point of the 3 L fermentation with saccharose were taken to be analysed for their OD and fluorescence intensity (FI) through staining with Nile Red dye.

Figure 9 OD and fluorescence intensity of A. lata grown in MSM medium with 20 g/L saccharose

Figure 9 shows that, compared to the OD, the FI has a steeper slope for the first 24 h of fermentation and then almost plateaus until the last measuring point. The OD starts with a lower slope but increases throughout the measurement points, which causes the OD to be relatively higher from 30 h until 50.5 h of the fermentation. Overall, the OD and FI show a decent correlation and may be used to reveal information about the relative PHB content in the cells by plotting the ratio of FI and OD over time.

Figure 10 FI/OD against time of A. lata grown in MSM medium with 20 g/L saccharose

In figure 10, the FI/OD ratio increases for the first 4 h of fermentation with a maximum of 349,95 and then steadily decreases until the end of the fermentation, reaching a final ratio of 149,84, which is less than half of the maximum peak ratio and close to the ratio at t0. From the graph, it can be concluded that the highest PHB content per cell is probably between 4 – 7 h. However, this may not be the highest total PHB content in the fermentation broth since the growth rates throughout the fermentation have to be considered.

3.2.3 PHB Extraction The biomass from the 3 L fermentation was treated according to the method described in section 2.8.1 and then weighed to evaluate the PHB content in per cent. The dry biomass was found to be 3.58 g/L with a PHB content of 1.43 g/L, making up for 39.9 % of the total cell weight. The image below shows a close-up view of the extracted PHB.

Figure 11 Close-up image of extracted PHB from A. lata grown in MSM medium with 20 g/L saccharose

3.3 7 L KAAS Fermentation A 7 L scale-up fermentation was performed to generate enough amounts of product that could then be analysed. Due to costly and time-intensive medium preparation, the total medium volume was set to 4 L, containing 10 % (v/v) MSM. The fermentation was stopped as growth parameters were observed to decline, which happened after approximately 5 h. The following figure reveals that – similarly to the findings in section 3.1.3 – growth is barely occurring in KAAS medium.

Figure 12 Growth assessment of A. lata via OD, cell counts and dry cell weight, grown in KAAS medium with 10 % (v/v) MSM

Figure 12 shows that all assessed growth parameters did not experience noticeable increase. The maximum dry cell weight measured was 0.25 g/L, which is less than

29 double the dry cell weight at t0. The same behaviour can be observed from the cell numbers, which are also less than double compared to t0. Only the OD values at 2 h, 4 h, and 5.25 h are roughly twice as high as compared to t0.

The biomass from the 4 L medium was treated according to the method described in section 2.8.1 and then weighed to evaluate the PHB content in per cent. The dry biomass was found to be 0.09 g/L - which is lower than the found biomass above and may be explained through losses in the measurement procedure - and has a PHB content of 0.004 g/L, making up for 4.37 % of the total dry cell weight.

3.4 GC-MS Analysis of Fermentation Products Gas chromatography, coupled to a mass spectrometer, was used to establish a qualitative and quantitative analysis method for the detection of PHB. The method was first established with pure store-bought PHB and then tested with fermentation products from the 3 L saccharose based fermentation. This data could then be used for comparison with the outcomes of the 7 L fermentation with KAAS medium.

3.4.1 Qualitative PHB Analysis For the purpose of qualitative analysis, a spectrum of pure PHB serves as a standard. Spectra of Soxhlet extracted PHB and cell extracted PHB can then be analysed and compared to the standard. Samples for pure PHB chromatograms contained 2.5 mg/mL PHB, whereas samples for extracted chromatograms contained 10.0 mg/mL PHB.

RT: 0.00 - 13.15 8.49 NL: 80000000 8.06E7 TIC MS Liquid_PH 75000000 B_Pure_Te st_1 2.10

70000000

65000000 2.01

60000000

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8.97 11.65 5000000 12.32 3.84 7.58 13.03 2.29 2.85 7.23 10.64 10.80 11.50 0.11 0.44 0.77 2.91 4.47 4.80 5.10 5.67 6.32 6.66 7.72 9.67 10.12 0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 Time (min)

Figure 13 GC-MS Chromatogram of pure store-bought PHB. Peak 6.78: 3- hydroxybutyryl methyl ester (3-HBME); Peak 8.49: methyl benzoate 30

The chromatogram of pure PHB shows multiple, partially undistinguishable peaks from 1.5 to 2.5 min. Within these peaks, chloroform and acetone could be identified, which are used for sample preparation and will be visible in every following chromatogram and therefore not mentioned again. The two major peaks of interest could be identified at 6.78 min and 8.49, with 3-HBME and methyl benzoate, respectively. Both peaks are clearly distinguishable from both the baseline and one another. Peaks emerging after the internal standard are based on column material. This chromatogram now serves as a reference for qualitative analysis of Soxhlet and cell extracted PHB. The following figure shows the obtained chromatogram for Soxhlet extracted PHB.

RT: 0.00 - 13.17 6.83 NL: 8.51 8.90E7 TIC MS 85000000 liquid_phb _extracted_ test_1 80000000

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Figure 14 GC-MS Chromatogram of Soxhlet extracted PHB from A. lata grown on saccharose. Peak 6.83: 3-HBME; Peak 8.51: methyl benzoate; (Peak 3.84: butyric acid 4-isopropoxy methyl ester; Peak 7.23: butanoic acid 3-hydroxy ethyl ester; peak 7.58: sulfuric acid dimethyl ester; Peak 8.97: column material)

The retention time of both the PHB and the internal standard peak are slightly shifted to the right compared to figure 13. This deviance is due to the manual injection procedure but is so little that it may be neglected since it does not affect the peak analysis. In this chromatogram, four additional recognizable peaks appear, which are named below the above figure. Two of the peaks are derivatives of the PHB methyl ester, one peak is based on sulphur, and the last peak could be identified as column material. Since the peaks are comparably small and do not appear in other samples, they may be neglected in this case. The concentration of PHB in the sample was 10 mg/mL, which has a visible effect on the height of the peak of PHB compared to the chromatogram of figure 13 with a concentration of only 2.5 mg/mL. The peak does not go down to the baseline in a

31 straight line, but rather approaches it in a curve, making proper integration more difficult. This behaviour is negligible in the qualitative analysis, but an important factor in quantitative analysis and will, therefore, be discussed in section 4.4.1. Lastly, a chromatogram of cell extracted PHB was measured.

RT: 0.00 - 13.17 2.10 NL: 2.01 6.77E7 66000000 TIC MS 8.41 liquid_phb 64000000 _cell_test1 62000000

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24000000 6.73 22000000 20000000 13.15 18000000 12.86 12.41 16000000 11.97 14000000 11.63 11.32 12000000 1.52 10.93 10000000 10.58 10.08 8000000 8.92 9.78 6000000 2.83 7.54 8.26 4000000 2.91 7.46 3.19 3.58 4.11 4.53 5.27 5.91 6.27 6.63 2000000 0.70 1.02 1.38 0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 Time (min)

Figure 15 GC-MS Chromatogram of cell extracted PHB. Peak 6.73: 3-HBME; Peak 8.41: methyl benzoate

The cell extracted PHB chromatogram clearly shows the two sought after peaks of PHB and internal standard, making this method viable for direct cell probe measurements without prior treatment or extraction except for centrifugation. The baseline in this chromatogram shows a steady increase, especially after 8.5 min. This problem was due to the GC-MS apparatus and was solved by running a cleaning method. Nevertheless, the baseline behaviour does not affect the qualitative properties of the chromatogram. Figures 13 - 15 show excellent correlation for both the PHB and internal standard peaks, allowing for quick and easy identification of PHB in unknown cell samples.

3.4.2 Quantitative PHB Analysis In order to establish a standard curve that allows for quantitative analysis, linear range tests first had to be performed. A sample with a pure PHB concentration of 2.5 mg/mL was prepared in various dilutions, which were then measured and plotted against their corresponding dilution range. The R2 value was then used as a measure of linearity. The following table shows the dilution rates with the corresponding integrated areas of both the PHB and the internal standard.

Table 9 Linear range assessment. Dilution rates of a PHB sample with 2.5 mg/mL PHB

Sample dilution PHB Area Internal Std. Area 0,25000 (1/4) 13.346.532,00 63.734.726,00 0,12500 (1/8) 6.798.966,00 38.365.004,00 0,06250 (1/16) 3.303.950,00 17.354.718,00 0,03125 (1/32) 1.808.214,00 8.424.364,00 0,01563 (1/64) 849.444,00 3.815.671,00

The data from table 9 results in the following plots.

Figure 16 Linear range assessment for PHB; R2 = 0.9998

Figure 17 Linear range assessment for the internal standard; R2 = 0.9985

The plotted dilution series for PHB shows excellent linearity with an R2 value of 0.9998. For the I.S., the area for the ¼ dilution was not low enough to be in the linear range and has therefore been neglected, which then results in excellent linearity with an R2 value of 0.9985. These results reveal the measurement boundaries for the standard curve.

Based on these results, a standard curve was established with sample concentrations ranging from 0.5 to 3.0 mg/mL PHB in increasing steps of 0.5.

Table 10 Measurement results for the PHB standard curve

PHB Std. g/L PHB Area I.S. Area PHB Area/I.S. Area 0,5 1.010.740,00 30.106.456,00 0,0336 1,0 1.875.247,00 28.094.130,00 0,0667 1,5 3.314.147,00 37.098.673,00 0,0893 2,0 5.093.503,00 40.256.864,00 0,1265 2,5 7.334.873,00 42.760.833,00 0,1715 3,0 8.428.052,00 40.392.758,00 0,2087

In order to get the standard curve, the PHB area is divided by that of the I.S. and plotted against the concentration. The resulting function has an R2 value of 0.9906 showing excellent linearity, leading to the assumption that the linear range limit for the I.S. is not strictly at 38.3 million, but can be seen as an approximation.

Figure 18 GC-MS standard curve for analysis of 3 L saccharose fermentation; R2 = 0.9906

The obtained function was solved for x, cell samples for different time points of the fermentation were measured and could then be quantified with the help of the standard curve function.

Table 11 GC-MS quantification results for 3 L saccharose fermentation

Sample PHB Area Internal Std. Area PHB/I.S. PHB g/L T4 (7 h) 626.747,00 36.774.671,00 0,0170 0,34 T6 (24 h) 4.119.020,00 40.111.123,00 0,1027 1,56 T7 (26 h) 3.482.436,00 34.655.783,00 0,1005 1,53 T11 (49 h) 7.744.456,00 38.727.891,00 0,2000 2,95 T12 (50.5 h) 7.845.436,00 40.858.676,00 0,1920 2,83

Sample T4 has a PHB concentration of 0.34 g/L, which is below the boundaries of the standard curve that was measured with 0.5 g/L being the lowest concentration. Therefore, it cannot be argued that this is an exact value and should only be seen as an estimation. All other values lie within the range of the standard curve, with 2.83 g/L being the highest concentration measured. The PHB concentration measured at 50.5 h is 2.83 g/L, which by far exceeds the result from section 3.2.3 with only 1.43 g/L of PHB measured after Soxhlet extraction. Reasons behind this are to be discussed in section 4.4.2.

3.4.3 Analysis of KAAS Medium Samples Samples from the 4 L KAAS medium fermentation were taken to be analysed. Soxhlet extracted material was used to get a qualitative chromatogram. Cell samples were prepared and tried to analyse quantitatively. However, quantitative analysis could not be performed due to deficient levels of PHB in already low amounts of cell biomass (section 3.3). Even adjusting the GC-MS method did not result in identifiable PHB peaks. Therefore, the only analysable result is a qualitative chromatogram of Soxhlet extracted PHB.

RT: 0.00 - 13.16 2.07 NL: 6.93E7 68000000 2.03 TIC MS 66000000 KAAS_4L_ Extracted_ 64000000 Liquid 62000000 60000000

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6000000 2.39 7.04 2.57 2.93 8.77 9.38 11.58 12.00 12.42 3.71 4.28 5.20 5.92 7.38 9.51 10.55 10.67 11.35 12.78 4000000 4.32 5.70 7.51 0.46 1.10 1.37 2000000 0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 Time (min)

Figure 19 GC-MS Chromatogram of Soxhlet extracted PHB from A. lata grown on KAAS medium. Peak 6.56: 3-HBME; Peak 8.25: methyl benzoate

The chromatogram reveals the two sought-after peaks of PHB at 6.56 min and methyl benzoate at 8.25 min. Furthermore, a bump between roughly 6.5 and 7.25 min can be observed, which is directly connected to the PHB peak. This bump could not be assigned to any substance. Its proximity to the PHB peak as well as its shape suggests that it may be a derivative of PHB or a substance closely related to it. The complex sugar composition in the KAAS medium may be a reason for this.

3.5 GPC Analysis of Fermentation Products GPC analysis led to no conclusive results. The obtained elogram showed peaks close to the internal standard, which indicate very low molar masses. The elograms were not conclusive, and no meaningful molar mass distribution could be obtained. Possible reasons for this behaviour will be discussed in section 4.5.

3.6 Rhubarb Waste Fermentation Rhubarb waste in liquid form, obtained by pressing any leftovers from the rhubarb harvest, was tested for its potential to serve as a substrate in sustainable and renewable fermentation approaches, because of its high sugar levels. Its pH was found to be 3.5 and adjusted to 7.0 ± 0.1 with NaOH. While adjusting the pH, the medium turned from light yellow-brown to a deep blue and almost black. Possible reasoning behind this colour

36 change will be discussed in section 4.1.2. Despite the unexpected colour change, the medium was still tested for its suitability. Furthermore, growth in rhubarb waste with its natural pH of 3.5 could not be tested, since there was no more available substrate to test with.

The liquid rhubarb waste was assessed for its sugar contents via HPLC. The HPLC data was provided by Britta Brands (HSRW Kleve).

Table 12 HPLC results for Rhubarb liquid waste provided by Britta Brands (HSRW Kleve)

Sample RT Type Width Area Height Area % Component Conce [min] [min] ntratio n [mg/m L] Rhubarb 7.464 HH 0.7932 85182 17218.3 19.47 Unidentified 0.19 9 9.339 HH 1.3872 12569 12613.7 28.74 Unidentified 76.25 5

10.94 HH 0.8345 11002 17990.0 25.15 Glucose 7.15 7 16.50 6 12.27 HH 1.2220 62521 8083.90 14.29 Unidentified 0 3.31 14.04 HB 0.5038 54001 16092.8 12.35 Arabinose 3.93 8 3.69 4

Out of the five peaks that were generated, only two could be identified. Glucose was found to have a concentration of 7.15 mg/mL and arabinose of 3.93 mg/mL. Due to the three remaining peaks that could not be identified, there is no way to draw conclusions on the total usable sugar contents of the rhubarb medium. However, 7.15 g/L of glucose alone should already serve as a sufficient nutrient source to support cell growth.

A triplet flask fermentation approach was conducted to assess possible cell numbers in rhubarb medium.

Figure 20 A. lata cell numbers grown on rhubarb medium with different MSM concentrations

The figure shows that slightly higher cell numbers could be reached with a 10 % (v/v) addition of MSM. It can also be seen that cell numbers after at 24 h and 48 h are about equal, meaning that the cells did not experience growth. Moreover, the total cell numbers are only in the range of 2.28 - 2.63E+07, which is rather low compared to cell numbers of 6.68E+08 reached in the saccharose fermentation. Possible reasons for this behaviour that may be linked to the colour change of the medium will be discussed in section 4.1.2.

4 Discussion

4.1 Substrates

4.1.1 Pea Liquid Waste As already described, the pea waste was provided in liquid form, the colour of the medium is turbid dark green, and small particles throughout the liquid are visible. Since the pea waste is a plant-based waste product, it contains sugars as well as proteins and is therefore unsuitable for autoclavation sterilisation due to Maillard reactions that would cause the simple sugars to be partially unavailable. Furthermore, a dark and turbid liquid makes reliable OD analysis especially tricky. Hence, a different approach needs to be established that does not involve heat for sterilisation. The simplest method is centrifugation, followed by a possible pre-filtration step and a final sterile filtration. Without a pre-filtration step, the sterile filter almost immediately cloted and was no longer usable. Performing a pre-filtration with a 5 - 8 µm filter could potentially solve this problem but also cloted after a few millilitres have passed through, which makes the process lengthy and requires to use new filters multiple times until a sufficient amount for sterile filtration has been recovered.

Since a fermentation approach should be designed to be rather cheap and straightforward, it is questionable if pea waste serves as a practicable substrate due to the high workload for preparation and the subsequent increase in cost. One of the main advantages to use waste products is that they cut cost, which is not given any more if the waste product requires extensive preparation steps before it can even be used. Therefore, pea waste was excluded as a potential candidate for PHB production.

4.1.2 Rhubarb Liquid Waste A different problem emerged when treating rhubarb waste. According to the HPLC findings, rhubarb waste in liquid form could potentially serve as a renewable substrate due to its high glucose contents of 7.15 g/L and various other sugars that may be utilized by A. lata. The idea to use plant waste products from harvests was already proven successful through the work of Zahari et al. (2012) in which they used oil palm frond juice that contained 53.95 ± 2.86 g/L glucose as the primary medium and reached dry cell weights of 8.42 g/L with 32 % PHB in Cupriavidus necator. Therefore, liquid rhubarb waste was tested as the primary medium after preparation steps involving centrifugation, pH adjustment, and sterile filtration. According to literature, rhubarb leaves contain high levels of malic acid and oxalic acid, which explains the low measured pH of about 3.5 (Pucher et al., 1938). Since the optimal pH for most microorganisms is around 7.0 and growth may be immensely inhibited with substantial deviations from the optimal pH, the medium was adjusted to pH 7.0 ± 0.1 with NaOH. While adjusting the pH, the medium 39 turned in colour: at pH 3.5 it was a clear yellow-brown liquid, whereas it turned to a deep blue and almost black colour while getting closer to pH 7.0. Nevertheless, growth tests were still performed. As the results already indicate, the bacteria were not able to grow in the pH adjusted medium despite its high sugar levels, with low cell numbers of around 2.6E+07 after 24 h and 2.3E+07 after 48 h. No possible explanation for the colour change and its effects on the medium composition could be found in the literature.

4.1.3 Substrate Preparation Cost A general problem of all bio-based waste products is that they are complex and most likely contain molecules such as sugars and proteins. As already mentioned above, this makes them unsuitable for heat sterilisation approaches. Baei et al. (2010) described a preparation method for dairy industry wastewater that involves autoclavation. However, their method involves a multistep process with acidification, autoclavation, centrifugation, refrigeration, and pH adjustment. This, again, makes the preparation process lengthy as well as costly and therefore counters the idea to establish a cheaper fermentation approach utilizing waste products.

The method of choice here was, therefore, centrifugation- and filtration-based. Centrifugation and pre-filtration for rhubarb waste and dairy industry wastewater are fast and straightforward. The only downside to this approach is the cost of sterile filtration. It is a single-use device that is not endlessly usable because it will eventually clot even with a pre-filtration step. Since the dairy industry wastewater has low levels of sugar, the only reasonable approach is to use high volumes of this substrate, which, at least in laboratory scale, requires multiple sterile filtration devices that significantly increase the cost of operation.

4.1.4 Substrate Analysis in Flask Fermentation Some experiments were carried out in Erlenmeyer flasks in a shaking incubator, which allows specifying temperature as well as rpm. However, since only Erlenmeyer flasks of needed size for the main fermentation without baffles were available, optimal oxygen supply was not given. This, plus the need to adapt to a new medium, causes a stressful environment for the cells, which results in the formation of small flaky biofilms that float throughout the medium. The standard analysis of bacterial growth includes OD measurements, cell counting, and dry cell weight measurements, all of which require a homogenous medium to ensure reliable data assessment. Given the flaky particles in flask fermentation, it is difficult to reliably take samples and measure them, because the measurement outcome relies on how much of the flakes one has in their sample, which is random at every sampling point and may cause fluctuations in measurement outcomes. Once one has taken the sample, flakes can be homogenized by sonication, which makes OD measurements more reliable but does not fully resolve the issue for 40 cell counting since some significant cell clusters can still be observed that may prevent accurate cell counting. Biofilm accumulation in the flasks could potentially be prevented by using baffled Erlenmeyer flasks, due to better oxygen supply and physical mixing of the medium.

4.1.5 Renewable Substrate Availability A major issue for fermentation approaches based on renewable substrates is their availability. Since renewable substrates are generally plant-based, they are subject to growing and harvesting cycles, meaning that a constant and reliable supply is unlikely, as it was the case with rhubarb waste for this work. Many crops or crop wastes are only available in their harvesting periods, which account for a few months of the year, making steady fermentation difficult. In addition, the substrates should be regionally available, since transportation of high volumes is costly and would contrast the idea of cutting cost through using waste materials. Thus, dairy industry wastewater is a good candidate in theory, since it is locally available in many regions and delivers a constant wastewater supply because dairy products are produced throughout the year. Concluding, two major practical factors for substrate choice need to be considered: 1) Constant substrate availability, 2) Regional availability/Substrate transportation.

4.2 Upstream and Downstream Processing Fermentations are generally coupled to up- and downstream operations. Upstream steps may involve substrate treatment like filtration and/or sterilisation, whereas downstream steps usually consist of extraction and purification operations like filtration, centrifugation, or Soxhlet extraction. As every operation adds time and cost to the production cycle, the up- and downstream approach may be discussed to meet both economic and practical demands. Thus, the approach in this work will be discussed below.

4.2.1 Upstream Processing As already mentioned above, plant-based substrates usually contain both sugars and proteins, making them potentially unsuitable for heat sterilisation due to Maillard reactions. Especially when treating highly diluted media such as the tested KAAS medium, a further loss of available sugars should be avoided. Since the medium should be sterile, at least in laboratory-scale tests, a different approach has to be designed. In this case, the pre-treatment of the media involved centrifugation, followed by filtration and subsequent sterile filtration instead of heat sterilisation. This results in a lengthier and potentially more expensive three-step upstream treatment instead of single-step heat sterilisation due to the high cost of sterile filters that need to be renewed frequently. Baei et al. (2010) even described a multi-step upstream process for dairy industry wastewater that involves acidification to remove proteins, autoclaving, sterile

41 centrifugation, refrigerating, and sterile pH adjustment. This approach may be suitable for small-scale fermentations but may be too complex for large industrial volumes. In their study, Zahari et al. (2012) used centrifuged and filtered oil palm frond juice with 53.95 ± 2.86 g/L sugar, which was then heat sterilised and added to MSM in different concentrations with a maximum PHB production of 8.42 g/L, showing that plant-based substrates can actually be suitable for heat sterilisation, but the losses due to the sterilisation step remain unanswered. Generally, a fermentation that is based on a renewable substrate that can be highly diluted should be straightforward, robust, and cost-effective since the media composition changes annually or even frequently and may yield low product levels; thus, the design needs to be tailored individually to every case.

However, bioreactors do not necessarily need to be sterile in some cases. Koller (2018) reviews a study in which the extremely halophile Hfx. Mediterranei was continuously grown on glucose for three months in a non-sterile long-term bioreactor with a PHA yield of 1.5 g/L and no microbial contamination. This study reveals that non-sterile batch operations are possible and should also be considered when designing fermentations for waste streams and products.

4.2.2 Downstream Processing In order to extract and purify the cell internally stored PHB, one has to perform a series of downstream processes. The first step is the concentration of the biomass by simple centrifugation. In laboratory scale, the cells get then freeze-dried to assess biomass production and later compare it to the coupled PHB production. This step is followed by a Soxhlet extraction of PHB in chloroform, which is finally dried in a rotary evaporator. The critical step in the described process is Soxhlet extraction. Soxhlet extraction requires Chloroform – that shall be recycled to save cost – as well as steady heat for at least 7 - 9 h for one batch. This process is energy-intensive and elevates the downstream processing cost in the described process, as the remaining steps are reasonably straightforward. In an industrial scale, the downstream process shall be as straightforward and cost-effective as possible. Thus, methods that require high amounts of heat, like Soxhlet extraction or distillation, should be avoided. Heinrich et al. (2012) describe a simplified method for large scale PHA recovery that achieved a purity of 93.32 % ± 4.62 % and a recovery rate of 87.03 % with an extraction volume of 50 L for PHB. Their approach is based on five steps in which the cells are concentrated through continuous centrifugation, followed by freeze-drying and subsequent digestion of non- PHB material by sodium hypochlorite with separation of PHB and non-PHB material through sedimentation. The polymer is then washed with isopropanol in a small-scale centrifuge and finally dried through evaporation of the isopropanol (Heinrich et al., 2012).

4.3 Fermentations The introduction section covered some possible fermentation approaches, like batch, fed-batch, or multi-step batch bioreactors. Every approach comes along with advantages and disadvantages. Thus, the choice of the fermentation mode depends on the specific case as well as the overall aim of the work.

4.3.1 Fermentation Approach This work aimed to try renewable substrates or waste streams from established industries and, therefore, chose a classical batch operation as the method of choice, since it is straightforward and allowed to test for multiple substrates without the need for additional feed calculations and experiments. Moreover, when experimenting with a new substrate, the starting procedure shall be simple and robust for initial testing. Once this is established, the knowledge may be used to proceed to more complicated operations, such as fed-batch coupled to cell recycling, which would be particularly useful in the case of KAAS medium since it is highly diluted. Another advantage of starting with a batch fermentation is the option to perform multiple sets of flask fermentations with varying conditions that gather insight for optimal growth conditions.

4.3.2 KAAS Fermentation A 1 L and a 4 L KAAS fermentation have been performed. The 1 L fermentation was aimed at assessing the growth kinetics of A. lata in KAAS medium, whereas the 4 L fermentation was aimed at generating enough PHB for analysis. Nevertheless, both fermentations shall be discussed regarding their results.

The first experiment for the 1 L KAAS fermentation was designed to test if medium supplementation with MSM would increase the growth of the microorganism. Figure 3 shows that almost no growth occurred, regardless of supplementing with MSM. However, the best result was obtained with an MSM concentration of 10 % and 25 % (v/v). 10 % MSM concentration was taken as the optimum not further to dilute the medium. This knowledge was used to perform the 1 L fermentation, which yielded similar results. Figure 4 reveals that the only growth occurs for around 4- 5 h and then cell numbers steadily decrease slowly. The DO concentration over time from figure 5 supports this statement, as it drops to the critical level of 30 % within the first few hours and then steadily and slowly increases for the remaining time. The third measurement taken was the gas composition, which again reflects a similar behaviour (figure 6). Three CO2 peaks can be seen: one at 0 h, one at around 2 h, and the third one at around 5 h. However, only the first two peaks reach concentrations above 0, while the remaining data points are below 0. As the concentrations are that low and even below 0, this data shall be handled carefully but still reflects the trend of figures 4 and 5. The data show that if

43 growth occurs, it does so within the first 5 – 6 h of the fermentation until all available substrate is consumed. This behaviour would make the medium more suitable for a continuous fermentation coupled to cell recycling, similarly to the approaches described by Koller (2018).

As already mentioned, the 4 L KAAS fermentation was aimed at generating enough PHB for analysis. Based on the findings of the first fermentation, this one was stopped after about 5 h. Figure 12 shows the known behaviour again, in which the growth parameters increase for a few hours and then stagnate or even decrease again, reaching a maximum dcw of 0.25 g/L after 4 h and a maximum cell concentration of 4.51E+07 after 2 h. Both the 1 L and 4 L fermentation show similar behaviours and are in this state not suitable for a simple batch fermentation.

4.3.3 Saccharose Fermentation The saccharose based fermentation was mainly performed to generate PHB from a single carbon source substrate, that could then be used to help establish the analytical methods and serve as a comparison. The growth trend in figure 7 seems to be linear. The identified exponential growth phase was found to be between 0 and 7 h. This behaviour deviates from the usual expectations of a growth curve, in which the first hours are usually coupled to the lag phase. However, in this case, it is difficult to precisely identify the exponential growth phase since no data could be measured between 7 – 22h and 30 – 47 h. The missing data points might influence the interpretation of the overall growth kinetics as well as the exponential growth phase. Figure 7 does not represent a classical growth curve with a clear lag phase and exponential phase but shows a rather linear increase throughout; neglecting the missing data points. Figure 8 depicts an unexpected behaviour. For the first 7 – 8 h the concentration of CO2 increases to a maximum of 0.2 % but then slowly decreases back to 0.1 until around 23 h. This stagnation behaviour is not in line with the change in biomass over that period, which is almost five times higher at 22 h compared to 7 h. The unexpected slight decrease in CO2 lies within the same time frame in which no samples have been taken, but no possible explanation for this behaviour could be found. Contrarily, the OD and FI plot (figure 9) depict a closer relation to the data obtained in figure 7. In figure 9 the OD and FI increase until around 30 h and then stay in the same range for the last 4 sampling points, suggesting that no increase in biomass and PHB occurred, which is different to the findings in figure 7, where biomass increased until the second last sampling point. Measurement uncertainties may account for that difference. Taking the ratio of OD and FI helps in identifying the highest PHB contents per cell. In figure 10, the highest OD/FI ratio is at 4 h with 345.95. The ratio stays at that range until 7 h and then steadily decreases to a value of 149.84 at 50 h. Looking at this data alone, the optimal

44 fermentation time may be between 4 – 7 h since these data points have the highest ratio, but this figure must be compared to the growth kinetics of the fermentation. The ratio might be two times higher at the beginning of the fermentation compared to the end, but the final biomass concentration is about 9 times higher at around 50 h compared to 7 h, meaning that the total produced PHB will still be significantly higher after 50 h of fermentation.

4.4 GC Analysis Results from the GC experiments show its suitability for qualitative and quantitative analysis. However, error assessment is much more critical for quantitative analysis than qualitative analysis. When testing for qualitative traits of the sample, small deviations through man-made or machine-made errors are negligible if the chromatogram shows peaks that are clearly distinguishable from the baseline and other peaks. However, in quantitative analysis, the integrated area of the sample and internal standard peak is the basis for the calculation that eventually leads to the PHB concentration in the sample. This is influenced by sample preparation as well as sample handling and machine error and is discussed below.

4.4.1 GC Error Sources A few difficulties occurred when establishing the GC method. According to Braunegg et al. (1978), PHB is soluble in chloroform. When preparing the samples according to the protocol described in section 2.9.4, it was observed that the PHB did not solve in chloroform but rather stayed flaky, despite thorough vortexing. When adding the acidified methanol and the internal standard to the flaky PHB-chloroform mixture, followed by heating at 100°C for 3 h, the PHB did still not fully solve in some cases. The only proposed solution is a two-step heating process in which the PHB-chloroform mixture is heated to 100°C for 3 h, and afterwards, the acidified methanol with the internal standard is added with subsequent heating to 100°C for 3 h. This procedure allowed the PHB to solve in the chloroform. As this two-step procedure takes twice the amount of time for sample preparation, future works may focus on improving the solubility and/or the procedure itself.

Besides, the injection procedure of choice was manual liquid injection as this turned out to be the more accessible approach for method establishment. The positives of this method are a quick sample injection and a homogenous liquid. In contrast, this approach is more error-prone since the injection is manual and dependent on the accuracy of the operator. The injection device is a microlitre pipette with an injection volume of 2 µL. Measuring tiny volumes likes this repeatedly is tricky. Thus, this is the most prominent

45 error source for manual liquid injection. A second injection approach is the automatic headspace gas-phase injection with which the injection procedure is fully automized. For gas injection, the sample is first heated to reach an equilibrium state and then automatically injected. In this works experiments, two problems emerged with automatic gas-phase injection. First, reproducibility tests of the same sample resulted in different results though the method was not changed. In some chromatograms, the target peaks were barely high enough to distinguish them from the baseline. Second, heating time and temperature impact the equilibrium state, and as 3-HBME and methyl benzoate may have different condensation behaviours, perfect conditions for the gas phase must be identified to ensure reliable reproducibility. As this work failed to establish said conditions, the manual liquid injection was the method of choice. Future works on this topic may work on the automatic gas-phase injection as this is less error-prone and allows to measure multiple consecutive samples without the need for an operator.

A further parameter that needs to be optimized is the concentration of the internal standard. According to the work of Monteil-Rivera et al. (2007), an internal standard concentration of 8 mg/mL was chosen. However, the maximal PHB concentration measured was 2.95 mg/mL. Thus, in future experiments, it is advisable to adjust the internal standard concentration to lower values that match the concentration range of the samples measured and the linear range of the device. Another problem coupled to the internal standard emerged when closely looking at the target peaks. Peaks identified as PHB are clearly distinguishable from the baseline as the peak has a sharp incline followed by a sharp decline back to the baseline level. Peaks identified as methyl benzoate also show a sharp incline but are tailored as they approach the baseline, which effects automatic integration (figure 21).

RT: 0.04 - 13.14 1.18 1.66 2.06 2.48 2.73 3.10 3.69 3.92 6.53 8.21 NL: 1300000 3.30 6.69E7 1280000 TIC MS std_curve_ 1260000 1to10_3

1240000

1220000

1200000

1180000 3.98 1160000 4.10

1140000 4.17 1120000 4.28 4.32 1100000 4.43 1080000 4.48 1060000 4.53 5.13 1040000 4.61 1020000 8.53 4.68 8.74 1000000 Intensity 1.06 980000 5.30 8.79 960000 13.06 5.35 5.52 8.86 12.81 940000 5.76 8.98 12.38 6.71 9.07 12.29 6.98 920000 11.86 7.38 9.24 9.41 11.38 900000 7.56 9.68 10.20 11.12

880000

860000

840000 0.90 820000 0.59

800000

780000

760000

740000

720000

1 2 3 4 5 6 7 8 9 10 11 12 13 Time (min)

Figure 21 Internal standard peak tailoring problem (methyl benzoate)

This problem persisted when tested with different dilutions. Integration and reliable data generation are still possible as the results show, nonetheless it is desirable to obtain sharp peaks like the one of PHB.

4.4.2 PHB Concentration Determination In this work, the PHB concentration in the cells at different time points was established in two ways. Either the determination through the weight difference of the dry cell mass and extracted PHB after Soxhlet extraction or the quantitative GC approach. In theory, the two values should be closely related. However, the two values were found to be significantly different. The PHB concentration assessment through simple measuring was done at tend, which was at 50.5 h and turned out to be 1.43 g/L making up 39.9 % of the total dry cell weight (3.58 g/L). When assessing the same sampling time point with the quantitative GC method, the result is a PHB concentration of 2.83 g/L, making up 79.1 % of the total dry cell weight. Literature shows that the yields for A. lata are as high as 88.0 %, which makes the obtained result plausible but still leaves the question of the difference between the two methods unanswered (Blunt et al., 2018). The most plausible explanation for the vast difference is an incomplete Soxhlet extraction. This extraction method is dependent on the time that it is running. It has potentially been stopped when the extraction was not fully completed, or some of the material was lost within the apparatus. To check for completion of the extraction, one can perform the method in two steps. Once the Soxhlet is supposedly done, the round bottom flask is removed, and a new one with fresh chloroform is attached. This is then run for a few hours and dried in the rotary evaporator. If there is no PHB left, one can be sure that the extraction was complete with the first flask. This way, the optimal extraction time can be determined.

4.5 GPC Analysis As already stated in the results section, no conclusive results could be drawn from the GPC analysis. The obtained elogram showed peaks that were close to the internal standard for both store-bought PHB and the Soxhlet extracted PHB from the saccharose fermentation, which indicates very low molar masses in the samples but leaves no reliable estimation of the actual molar mass distribution. This may be due to several issues that occurred when treating the samples.

The first possible step affecting the polymer is the Soxhlet extraction. Soxhlet extraction involves the usage of heat and chloroform to lyse the dried cells and extract the polymer. The process runs at approximately 60 – 70 °C for 7 – 9 h. A potential effect of the heat and chloroform in combination is that not only the cell membrane and vesicles get lysed but also parts of the polymer. This would lead to a mixture of high molar weight polymers and the digested low molar parts of PHB that could be measured in the GPC. A possible option to minimize the risk of digesting the PHB is using HFIP as a solvent instead of chloroform. HFIP is more gentle to the polymer itself, but its ability to cleave the cell membrane and release the polymer into solution remains unknown. Further research on this aspect shall be done to reveal the suitability of HFIP for this purpose.

The GC-MS analysis already revealed that both the store-bought and Soxhlet extracted PHB do not fully solve in chloroform. In GC-MS analysis, this problem was overcome by heating the sample for 3 h at 100 °C. The same procedure was tested for GPC but did not lead to any conclusive results. When treating the polymer with heat, it is solved in solution, but the chloroform likely induces cleavage of the polymer, leading to low molar masses again. This is not an issue for GC-MS analysis as the polymer gets purposely monomerized, but it affects the outcome of GPC analysis. Experiments showed that PHB is entirely solvable in HFIP at room temperature. Thus, in future experiments, the mobile phase may be changed to HFIP or a mixture of HFIP and chloroform instead of only chloroform.

A third possible explanation involves the pre-filtration step before the sample is loaded onto the column. The filter may retain the high molar mass strands, and only lower molar mass strands enter the column and get measured, which would also explain the low molar masses that were measured.

In order to develop a functioning GPC method, future works may focus on adjusting the extraction of the polymer from the cell, bringing the polymer into solution without disrupting it, and potentially changing the columns and mobile phase used as different columns may be more suitable for the analysis of PHB.

5 Conclusion

The aim of this work was to produce PHB from a fermentation that is based on a waste product from established industries and furthermore analyse the produced PHB chromatographically. As the results and discussion reveal, no substrate was found to yield a competitive amount of PHB. Rhubarb waste and pea waste could not be appropriately tested due to problems in substrate preparation. KAAS medium was therefore chosen the substrate of most interest but did not meet the expectations of a successful fermentation. With biomass yields of just 0.09 g/L with a PHB concentration of 0.004 g/L this substrate is far from being suitable for an industrial scale fermentation. However, plenty of waste substrates remain untested in this work since A. lata can only grow on simple sugars, and the substrates were limited to their local availability, which narrows the potential candidates.

Generally, the upstream processing of waste substrates is one of the biggest bottlenecks besides the downstream processing, since it involves multiple processing steps like centrifugation, filtration, and sterilisation. As the waste products contain both sugars and proteins, they cannot simply be sterilised without losing a fraction of the available carbon source. Thus, future research may focus on semi-sterile fermentation processes, which are suitable for waste products as they erase the need for sterilisation of the medium and therefore the bottleneck in upstream processing.

Even though the KAAS fermentations were not successful, a qualitative and quantitative GC-MS method could be established with the help of store-bought PHB, and PHB produced from a fermentation based on pure saccharose as the carbon source. The peaks in the obtained chromatograms are clearly visible and distinguishable from the baseline, and the standard curve shows excellent linearity. Hence, GC-MS is a reasonably quick method for both qualitative and quantitative analysis with little sample preparation. In contrast, no GPC method with conclusive results could be developed.

In conclusion, the usage of waste products that do not compete with food sources or land usage is a promising field for future research. Even though this work was not able to find a suitable substrate, several substrates like rhubarb waste remain as potential candidates. Future works may find a way to utilize waste substrates more effectively in a straightforward fermentation approach to make the process economically competitive.

6 References

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7 Declaration

I, Dominic Kösters, declare that the research work presented here is from the best of my knowledge and belief, original and the result of my own investigations. The cooperation I got for this research work is clearly acknowledged. To the best of my knowledge, it does not contain any materials that are written by others or published already except mentioned with due reference in the text as well as with quotation marks. This work has not been published, submitted, either in part or whole intended for reward, degree at this or any other University.

Kleve, 04.11.2019

8 Appendix

8.1 KAAS Medium 1 L Fermentation Additional Data Figure 3 was based on the following raw data, which are averages of the triplicate set- up.

Table 13 Raw data for A. lata grown in different KAAS/MSM compositions

A B C D Time Cells/mL Stdev. Cells/mL Stdev. Cells/mL Stdev. Cells/mL Stdev. 0.00 2.18E+07 1.71E+06 2.39E+07 2.13E+06 2.19E+07 6.29E+05 2.84E+07 3.73E+06 24.00 1.56E+07 4.84E+06 3.24E+07 1.98E+07 3.08E+07 2.36E+06 1.19E+07 1.90E+06 48.00 2.35E+07 1.21E+07 3.96E+07 1.60E+07 4.19E+07 8.49E+06 1.97E+07 3.86E+06 72.75 6.24E+07 6.24E+06 4.23E+07 1.20E+07 4.20E+07 1.98E+07 1.45E+07 2.26E+06

The optimal growth conditions were chosen from these data. A 1 L fermentation was performed to assess growth kinetics, resulting in figure 4. This figure was based on the following raw data.

Table 14 Raw data for A. lata grown in 90/10 KAAS/MSM medium in 1 L bioreactor

Time (h) OD Cells/mL g dcw/L 0.00 0,168 3.63E+07 0.05 2.00 0,324 5,97E+07 4.00 0,316 6.07E+07 -0.05 6.50 0,298 4.70E+07 22.50 0,250 3.45E+07 -0.05 24.50 0,244 3.28E+07 27.00 0,238 3.23E+07 -0.05 29.50 0,262 3.25E+07

8.2 Saccharose Medium 3 L Fermentation Additional Data The following data were used to display the results in section 3.2.

Table 15 Raw data for A. lata grown on saccharose in a 3 L bioreactor

Time (h) OD ln OD Cells/mL g dcw/L calc. g dcw/L 0,00 0,169 -1,778 2,68E+07 0 0,03 2,00 0,289 -1,241 4,74E+07 0,05 4,00 1,064 0,062 1,11E+08 0,3 0,17 6,00 1,720 0,542 1,58E+08 0,27 7,00 2,600 0,956 1,33E+08 0,4 0,41 22,00 12,000 2,485 3,22E+08 1,91 24,00 13,500 2,603 3,95E+08 2,25 2,15 26,00 11,300 2,425 3,87E+08 1,80 28,00 14,500 2,674 3,75E+08 2,35 2,31 56

30,00 15,800 2,760 4,30E+08 2,52 47,00 22,600 3,118 6,68E+08 3,6 3,60 49,00 24,000 3,178 5,12E+08 3,82 50,50 23,000 3,135 5,15E+08 3,66

The calculated biomasses were based on the measured dry cell weights, which were plotted against OD for correlation.

4 y = 0,1592x + 0,0394 3,5

2,5

2 g dcw/L g 1,5

0,5

0 0,000 5,000 10,000 15,000 20,000 25,000 OD

Figure 22 Correlation of dry cell weight and OD for A. lata in a 3 L fermentation

These data were then used to identify the exponential growth time frame by plotting the calculated dry cell weight against time in a semi-log plot. Exponential growth was found to occur between 0 and 7 h. This was then used to calculate growth parameters in table 8.

2,00

1,00 y = 0,4041x - 3,6649 R² = 0,976

0,00 0,00 10,00 20,00 30,00 40,00 50,00 60,00

-1,00 lndcw/L) (g -2,00

-3,00

-4,00 Time (h)

Figure 23 Semi-log plot of the calculated dry cell weight against time. The displayed equation is only based on the data points that were found to exhibit exponential growth 57

The FI/OD plots were based on the following raw data.

Table 16 Fluorescence and OD values measured with a spectrofluorometer for A. lata grown on saccharose in a 3 L bioreactor

Time (h) Fluorescence (F) OD F/OD 0,00 6,761 0,048 140,85 2,00 20,240 0,085 238,12 4,00 52,930 0,153 345,95 6,00 78,940 0,235 335,91 7,00 101,000 0,292 345,89 22,00 186,000 0,79 235,44 24,00 168,900 0,912 185,20 26,00 188,800 1,034 182,59 28,00 200,600 0,937 214,09 30,00 174,900 1,081 161,79 47,00 168,200 1,324 127,04 49,00 187,800 1,217 154,31 50,50 185,500 1,238 149,84

8.3 KAAS Medium 7 L Fermentation Additional Data Figure 12 was based on the following raw data.

Table 17 Raw data for A. lata grown in KAAS medium with 10 % (v/v) MSM in a 7 L bioreactor

Time (h) OD Cells/mL g dw/L 0,00 0,127 2,77E+07 0,15 2,00 0,254 4,51E+07 0,2 4,00 0,26 3,94E+07 0,25 5,25 0,248 3,39E+07 0,2

Furthermore, the gas composition was measured.

Figure 24 Gas composition of A. lata grown in KAAS medium with 10 % (v/v) MSM in a 7 L bioreactor 8.4 GC-MS Analysis

8.4.1 Qualitative Analysis To test for reproducibility, every sample has been measured five times. The results could then be put together in a table for comparison. Below, the chromatograms and corresponding tables for all three samples can be seen.

RT: 0.00 - 13.18 RT: 8.49 NL: RT: 2.10 AA: 333682698 8.06E7 80000000 AA: 264669617 TIC MS ICIS Liquid_PHB_ 60000000 RT: 6.78 Pure_Test_1 AA: 113550642 40000000 RT: 1.51 AA: 56640443 20000000 RT: 8.97 RT: 11.65 RT: 2.85 RT: 3.84 RT: 5.10 RT: 7.58 RT: 10.20 RT: 10.80 RT: 12.32 AA: 8544529 AA: 14833518 AA: 2252835 AA: 2582471 AA: 151085 AA: 2895278 AA: 535757 AA: 569833 AA: 5792308 0 RT: 8.48 NL: RT: 2.10 AA: 287278485 7.57E7 AA: 247665783 TIC MS RT: 1.65 ICIS 60000000 AA: 143499229 liquid_phb_p RT: 6.78 ure_test_2 AA: 93100599 40000000 RT: 1.50 AA: 68313180 20000000 RT: 8.97 RT: 2.29 RT: 3.84 RT: 5.11 RT: 7.59 RT: 10.46 RT: 11.60 RT: 12.29 AA: 6618169 AA: 1847206 AA: 2305117 AA: 92361 AA: 4465056 AA: 985432 AA: 808787 AA: 734140 0 RT: 8.49 NL: RT: 2.11 AA: 292429425 7.63E7 AA: 241437117 TIC MS ICIS 60000000 liquid_phb_p RT: 6.79 ure_test_3 AA: 96220973 40000000 RT: 1.51

AA: 66637930 Intensity 20000000 RT: 8.97 RT: 2.85 RT: 3.85 RT: 5.11 RT: 7.59 RT: 10.20 RT: 10.61 RT: 12.33 AA: 6774189 AA: 1580525 AA: 1770358 AA: 106264 AA: 4175784 AA: 198668 AA: 54409 AA: 493594 0 RT: 8.48 NL: RT: 2.11 AA: 282840927 7.52E7 AA: 280956057 TIC MS RT: 1.66 ICIS 60000000 liquid_phb_p AA: 158782094 RT: 6.78 ure_test_4 AA: 91399440 40000000 RT: 1.51 AA: 65499473 20000000 RT: 0.08 RT: 2.85 RT: 3.84 RT: 5.10 RT: 7.59 RT: 8.97 RT: 10.20 RT: 11.63 RT: 12.32 AA: 126567 AA: 1510471 AA: 1705840 AA: 97368 AA: 4415570 AA: 6254271 AA: 193917 AA: 746858 AA: 728792 0 RT: 8.48 NL: RT: 2.08 AA: 304709569 7.78E7 AA: 291665354 TIC MS ICIS RT: 1.64 60000000 liquid_phb_p AA: 144813646 RT: 6.77 ure_test_5 AA: 97292650 40000000 RT: 1.50 AA: 54873277 20000000 RT: 8.96 RT: 0.68 RT: 2.82 RT: 3.82 RT: 5.09 RT: 7.58 RT: 10.20 RT: 11.63 RT: 12.32 AA: 147414 AA: 1587556 AA: 1907458 AA: 102201 AA: 4900445 AA: 6902964 AA: 199802 AA: 524667 AA: 435769 0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 Time (min)

Figure 25 Pure PHB chromatograms for qualitative analysis

Table 18 Pure PHB chromatogram data for qualitative analysis

Liquid Injection Pure PHB Sample 2.5 mg/mL PHB

Area Intensity Intensity Area Intensity Sample PHB PHB Area I.S. I.S. Ratio Ratio 1 1,14E+08 4,29E+07 3,34E+08 7,77E+07 0,340 0,552 2 9,31E+07 3,63E+07 2,87E+08 7,34E+07 0,324 0,495 3 9,62E+07 3,71E+07 2,92E+08 7,38E+07 0,329 0,503 4 9,14E+07 3,59E+07 2,83E+08 7,30E+07 0,323 0,492 5 9,73E+07 3,81E+07 3,05E+08 7,55E+07 0,319 0,505 Std. Dev. 8,84E+06 2,85E+06 2,04E+07 1,96E+06 0,0081 0,0247

RT: 0.00 - 13.18 RT: 6.83 RT: 8.51 NL: 8.90E7 AA: 357438829 AA: 471765004 RT: 2.10 TIC MS 80000000 ICIS AA: 230162573 liquid_phb_e RT: 1.65 60000000 xtracted_test AA: 123793072 _1 40000000 RT: 1.51 AA: 55996213 RT: 2.29 RT: 3.84 RT: 7.58 RT: 8.97 20000000 RT: 0.08 RT: 5.51 RT: 6.59 RT: 9.72 RT: 11.40 RT: 12.71 AA: 10819009 AA: 20467761 AA: 10970160 AA: 13721470 AA: 144499 AA: 293024 AA: 47251 AA: 974049 AA: 730855 AA: 1613731 0 NL: RT: 6.83 RT: 8.51 AA: 436643873 8.65E7 RT: 2.10 AA: 334106103 TIC MS 80000000 AA: 224212323 ICIS liquid_phb_e 60000000 xtracted_test _2 40000000 RT: 1.50 RT: 2.24 AA: 61702617 AA: 10817849 RT: 3.84 RT: 7.59 RT: 8.97 RT: 11.62 RT: 12.21 20000000 RT: 5.10 RT: 5.51 RT: 10.20 AA: 14325559 AA: 11376101 AA: 11461122 AA: 19363954 AA: 13558095 AA: 178216 AA: 231877 AA: 1686295 0 RT: 6.83 RT: 8.51 NL: AA: 348787262 AA: 452054978 8.82E7 TIC MS 80000000 RT: 2.03 ICIS AA: 65594927 liquid_phb_e 60000000 xtracted_test _3 40000000 RT: 1.51 RT: 6.59 Intensity AA: 57197476 AA: 33420 RT: 2.28 RT: 3.84 RT: 7.59 RT: 8.97 RT: 11.65 RT: 12.23 20000000 RT: 5.10 RT: 5.67 RT: 10.20 AA: 24881771 AA: 19397439 AA: 16779137 AA: 12754239 AA: 19811249 AA: 13428494 AA: 205598 AA: 110452 AA: 1755760 0 NL: RT: 6.84 RT: 8.53 AA: 377944855 9.42E7 AA: 514813511 TIC MS RT: 2.02 ICIS 80000000 AA: 120909981 liquid_phb_e RT: 1.67 xtracted_test 60000000 _4 AA: 129766391 40000000 RT: 6.60 AA: 35581 RT: 7.59 RT: 2.29 RT: 3.85 RT: 8.97 RT: 11.62 RT: 12.21 20000000 RT: 5.10 RT: 5.52 AA: 20151561 RT: 10.20 AA: 17878110 AA: 23925110 AA: 14740870 AA: 19631418 AA: 15107100 AA: 248847 AA: 322384 AA: 2045609 0 RT: 6.84 RT: 8.52 NL: 8.86E7 AA: 345656815 AA: 459174043 RT: 2.03 TIC MS 80000000 ICIS AA: 139752087 liquid_phb_e 60000000 RT: 1.65 xtracted_test AA: 163381483 _5 40000000 RT: 1.51 AA: 53386345 RT: 2.24 RT: 3.85 RT: 7.60 RT: 8.98 RT: 9.73 RT: 11.61 RT: 12.19 20000000 AA: 20032646 RT: 5.11 RT: 5.52 AA: 18726521 AA: 17164502 AA: 12948626 AA: 1214418 AA: 25178521 AA: 17212024 AA: 211323 AA: 273131 0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 Time (min)

Figure 26 Soxhlet extracted PHB chromatograms for qualitative analysis

Table 19 Soxhlet extracted chromatogram data for qualitative analysis

Liquid Injection Extracted PHB Sample 10 mg/mL PHB Area Intensity Intensity Area Intensity Sample PHB PHB Area I.S. I.S. Ratio Ratio 1 3,57E+08 8,27E+07 4,72E+08 8,30E+07 0,758 0,996 2 3,34E+08 7,76E+07 4,37E+08 8,23E+07 0,765 0,943 3 3,49E+08 8,19E+07 4,52E+08 8,32E+07 0,772 0,984 4 3,78E+08 8,59E+07 5,15E+08 8,57E+07 0,734 1,002 5 3,46E+08 8,06E+07 4,59E+08 8,34E+07 0,753 0,966 Std. Dev. 1,64E+07 3,03E+06 2,96E+07 1,29E+06 0,0143 0,0242

RT: 0.00 - 13.18 RT: 2.01 NL: RT: 8.41 AA: 280134813 6.77E7 AA: 199744693 TIC MS 60000000 RT: 1.67 ICIS AA: 100949144 liquid_phb_c ell_test1 40000000 RT: 6.73 AA: 51641549 RT: 12.18 RT: 12.49 RT: 1.52 RT: 10.68 AA: 633340 20000000 RT: 8.92 AA: 2723189 AA: 20982103 RT: 2.83 RT: 7.54 RT: 5.27 AA: 1574365 AA: 1834796 AA: 2026390 AA: 277768 AA: 1213311 0 RT: 8.41 NL: RT: 1.98 AA: 245651472 7.00E7 AA: 313572227 TIC MS 60000000 RT: 1.65 ICIS liquid_phb_c AA: 107236161 ell_test2 40000000 RT: 6.72 AA: 70834049 RT: 12.45 RT: 1.50 RT: 10.75 20000000 RT: 8.92 AA: 1802389 AA: 27602003 RT: 2.82 RT: 5.27 RT: 7.53 AA: 2441864 AA: 1314995 AA: 2782690 AA: 212213 AA: 3425296 0 RT: 2.00 RT: 8.42 NL: 6.68E7 AA: 295831503 AA: 214960357 TIC MS 60000000 RT: 1.65 ICIS AA: 114284686 liquid_phb_c ell_test3 40000000 RT: 6.73 AA: 58029065 RT: 1.51 RT: 12.42 Intensity RT: 11.73 20000000 AA: 23396382 RT: 8.92 RT: 10.75 AA: 1373154 RT: 2.83 RT: 5.28 RT: 7.55 AA: 89097 AA: 1707619 AA: 1934515 AA: 2222689 AA: 333641 AA: 2618920 0 RT: 2.01 RT: 8.41 NL: 6.71E7 AA: 279252435 AA: 210233163 TIC MS 60000000 ICIS RT: 1.66 liquid_phb_c AA: 142886638 ell_test4 40000000 RT: 6.72 AA: 54856922 RT: 12.43 RT: 1.51 RT: 11.72 20000000 RT: 8.91 RT: 10.74 AA: 1111045 AA: 23376312 RT: 2.83 RT: 5.27 RT: 7.53 AA: 350177 AA: 1532504 AA: 732528 AA: 1883635 AA: 183371 AA: 2852687 0 RT: 2.00 RT: 8.41 NL: 6.76E7 AA: 265022250 AA: 225434879 TIC MS 60000000 RT: 1.66 ICIS AA: 123095995 liquid_phb_c ell_test5 40000000 RT: 6.72 AA: 60894046 RT: 1.51 RT: 12.13 RT: 12.44 20000000 AA: 21989218 RT: 8.91 RT: 10.74 AA: 884308 RT: 2.83 RT: 5.26 RT: 7.53 AA: 481599 AA: 1967181 AA: 906302 AA: 2259631 AA: 389096 AA: 1836852 0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 Time (min)

Figure 27 Cell extracted PHB chromatograms for qualitative analysis

Table 20 Cell extracted PHB chromatogram data for qualitative analysis

Liquid Injection Cell PHB Sample Unknown Area Intensity Intensity Area Intensity Sample PHB PHB Area I.S. I.S. Ratio Ratio 1 5,16E+07 2,01E+07 2,00E+08 5,78E+07 0,259 0,348 2 7,08E+07 2,68E+07 2,46E+08 6,34E+07 0,288 0,422 3 5,80E+07 2,23E+07 2,15E+08 6,02E+07 0,270 0,370 4 5,49E+07 2,11E+07 2,10E+08 6,01E+07 0,261 0,350 5 6,09E+07 2,34E+07 2,25E+08 6,20E+07 0,270 0,377 Std. Dev. 7,34E+06 2,58E+06 1,74E+07 2,10E+06 0,0117 0,0301

8.4.2 Quantitative Analysis Below, the chromatograms for the linear range test, the standard curve and the cell samples that have been measured are listed.

RT: 0.00 - 13.20 RT: 2.00 NL: AA: 213652424 6.74E7 TIC MS 60000000 RT: 1.65 ICIS AA: 98426309 Std_Curve_1 40000000 to10_1 RT: 8.22 RT: 1.52 RT: 2.16 20000000 RT: 3.70 RT: 5.12 RT: 6.54 AA: 25413551 RT: 8.76 AA: 16934580 AA: 462249 AA: 497803 AA: 886065 AA: 916602 AA: 67820 0 RT: 2.00 NL: AA: 211730072 6.80E7 TIC MS 60000000 RT: 1.66 ICIS AA: 115616823 std_curve_1t 40000000 RT: 1.53 o10_2 AA: 17955977 RT: 8.22 RT: 2.17 20000000 RT: 3.71 RT: 5.11 RT: 6.54 AA: 23696266 RT: 8.75 AA: 358244 AA: 718740 AA: 687060 AA: 1790613 AA: 82379 0 RT: 1.99 NL: AA: 212730924 6.69E7 TIC MS 60000000 RT: 1.66 ICIS AA: 85832626 std_curve_1t 40000000 o10_3 RT: 8.21 20000000Intensity RT: 2.16 RT: 1.18 RT: 3.69 RT: 5.13 RT: 6.53 AA: 31627120 RT: 8.74 AA: 447762 AA: 1871694 AA: 743581 AA: 172811 AA: 3142746 AA: 74722 0 RT: 2.00 NL: AA: 228237662 6.83E7 TIC MS 60000000 RT: 1.67 ICIS AA: 90336225 std_curve_1t 40000000 o10_4 RT: 1.53 RT: 8.21 RT: 2.17 AA: 34869135 20000000 AA: 21727515 RT: 3.70 RT: 5.14 RT: 6.54 RT: 8.74 AA: 329022 AA: 978950 AA: 359097 AA: 4817967 AA: 104033 0 RT: 2.00 NL: AA: 220234947 6.80E7 TIC MS 60000000 RT: 1.66 ICIS AA: 110310388 std_curve_1t 40000000 o10_5 RT: 8.20 RT: 1.53 RT: 2.17 AA: 37954789 20000000 AA: 19654512 RT: 3.69 RT: 5.13 RT: 6.53 RT: 8.74 AA: 445105 AA: 1127989 AA: 256343 AA: 7076615 AA: 142541 0 RT: 2.00 NL: AA: 219563921 6.89E7 TIC MS 60000000 RT: 1.66 ICIS AA: 110566330 std_curve_1t 40000000 o10_6 RT: 1.53 RT: 8.21 RT: 2.16 RT: 6.53 AA: 34758972 20000000 AA: 21473978 RT: 3.69 RT: 5.14 RT: 7.35 RT: 8.74 AA: 360638 AA: 1184846 AA: 255055 AA: 8130631 AA: 52531 AA: 131800 0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 Time (min)

Figure 28 Standard curve chromatograms for quantitative analysis

RT: 0.00 - 13.22 RT: 2.00 NL: AA: 219791793 6.77E7 TIC MS 60000000 RT: 1.66 ICIS AA: 116802591 t4_cell_samp le_1to10 40000000

RT: 1.52 RT: 8.20 20000000 RT: 2.16 AA: 19973229 AA: 31860092 AA: 316667 RT: 3.69 RT: 5.10 RT: 6.52 RT: 7.59 RT: 8.73 AA: 1209881 AA: 314196 AA: 591975 AA: 75783 AA: 141180 0 RT: 2.00 NL: AA: 213302115 6.82E7 TIC MS 60000000 ICIS RT: 1.66 t6_cell_samp AA: 101514628 le_1to10 40000000 RT: 1.53 AA: 20074296 RT: 8.20 20000000 RT: 2.16 AA: 35223439 RT: 6.52 RT: 0.14 AA: 411971 RT: 3.69 RT: 5.13 RT: 7.59 RT: 8.73 AA: 84041 AA: 1258179 AA: 490210 AA: 3922806 AA: 69516 AA: 150159 0 RT: 2.00 NL: AA: 308952375 6.80E7 TIC MS 60000000 RT: 1.66 ICIS t7_cell_samp AA: 108615709 le_1to10 40000000

Intensity RT: 8.20 RT: 1.52 20000000 RT: 2.16 AA: 29987091 AA: 16714701 AA: 380598 RT: 3.69 RT: 5.13 RT: 6.53 RT: 7.59 RT: 8.73 AA: 1449411 AA: 340907 AA: 3349980 AA: 82118 AA: 139178 0 RT: 2.00 NL: AA: 206962910 6.86E7 TIC MS 60000000 ICIS t11_cell_sam RT: 1.67 ple_1to10 AA: 75982599 40000000

RT: 1.53 RT: 8.21 20000000 RT: 2.16 AA: 16329415 RT: 6.53 AA: 34047168 AA: 360324 RT: 3.69 RT: 5.13 RT: 7.59 RT: 8.74 AA: 7470129 AA: 1250946 AA: 418041 AA: 40527 AA: 128839 0 RT: 2.00 NL: AA: 221373636 7.10E7 TIC MS ICIS 60000000 RT: 1.66 t12_cell_sam AA: 108962913 ple_1to10 40000000 RT: 1.53 AA: 19325116 RT: 8.21 20000000 RT: 2.17 RT: 6.53 AA: 33977295 RT: 0.10 RT: 3.70 RT: 5.10 RT: 7.60 RT: 8.74 AA: 421065 AA: 7376383 AA: 59875 AA: 1019719 AA: 295543 AA: 59289 AA: 140332 0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 Time (min)

Figure 29 Tested cell samples for quantitative analysis

RT: 0.00 - 13.22 RT: 1.99 NL: AA: 304388102 6.88E7 TIC MS ICIS 60000000 RT: 1.65 RT: 8.21 AA: 129815996 T12_Linearit AA: 104184196 y_Test_1_SF 40000000 160 RT: 6.53 AA: 50817319 20000000 RT: 2.73 RT: 3.68 RT: 5.09 RT: 7.59 RT: 8.73 RT: 10.41 AA: 1292855 AA: 1633628 AA: 764837 AA: 667925 AA: 1886757 AA: 75895 0 RT: 1.99 NL: AA: 201511166 6.80E7 TIC MS 60000000 ICIS RT: 1.65 t12_linearity AA: 104768700 RT: 8.19 _test_2_sf16 40000000 AA: 82903155 0 RT: 6.50 20000000 AA: 26955482 RT: 0.15 RT: 2.72 RT: 3.67 RT: 5.08 RT: 7.57 RT: 8.71 AA: 45375 AA: 630338 AA: 1177388 AA: 633687 AA: 270923 AA: 817128 0 RT: 1.99 NL: AA: 308748247 6.61E7 TIC MS 60000000 RT: 1.65 ICIS AA: 110389196 t12_linearity _test_3_sf16 40000000 0 RT: 1.59 RT: 8.19

AA: 414306 AA: 47784153 Intensity 20000000 RT: 2.15 RT: 6.52 RT: 0.25 AA: 341354 RT: 3.68 RT: 5.09 AA: 12896241 RT: 7.58 RT: 8.73 AA: 243681 AA: 760034 AA: 662264 AA: 107856 AA: 302911 0 RT: 1.99 NL: AA: 303631879 6.79E7 TIC MS 60000000 ICIS RT: 1.65 t12_linearity AA: 101293860 _test_4_sf16 40000000 0

RT: 8.20 20000000 RT: 1.51 RT: 2.15 RT: 6.52 AA: 28366942 AA: 13257207 RT: 3.68 RT: 5.09 RT: 7.59 RT: 8.73 AA: 389869 AA: 7095531 AA: 454133 AA: 580739 AA: 58733 AA: 694859 0 RT: 2.05 NL: AA: 117192452 6.88E7 TIC MS 60000000 ICIS RT: 1.65 t12_linearity AA: 97403745 _test_5_sf16 40000000 0

20000000 RT: 1.52 RT: 2.16 RT: 8.20 RT: 3.69 RT: 5.10 RT: 6.53 RT: 8.75 AA: 8602238 AA: 353269 AA: 10869197 AA: 347250 AA: 593467 AA: 3670605 AA: 187462 0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 Time (min)

Figure 30 Linear range test chromatograms for quantitative analysis