Production and characterization of Chitin-Glucan Complex by Komagataella pastoris

This thesis was focused on the production, extraction and characterization of chitin:β-glucan complex (CGC). In this process, glycerol byproduct from the biodiesel industry was used as carbon source. The selected CGC producing yeast was Komagataella pastoris (formerly known as Pichia pastoris), due the fact that to achieved high cell densities using as carbon source glycerol from the biodiesel industry. Firstly, a screening of K. pastoris strains was performed in shake flask assays, in order to select the strain of K. pastoris with better performance, in terms of growth, using glycerol as a carbon source. K. pastoris strain DSM 70877 achieved higher final cell densities (92-97 g/l), using pure glycerol (99%, w/v) and in glycerol from the biodiesel industry (86%, w/v), respectively, compared to DSM 70382 strain (74-82 g/l). Based on these shake flask assays results, the wild type DSM 70877 strain was selected to proceed for cultivation in a 2 l bioreactor, using glycerol byproduct (40 g/l), as sole carbon source. Biomass production by K. pastoris was performed under controlled temperature and pH (30.0 ºC and 5.0, respectively). More than 100 g/l biomass was obtained in less than 48 h. The yield of biomass on a glycerol basis was 0.55 g/g during the batch phase and 0.63 g/g during the fed-batch phase. In order to optimize the downstream process, by increasing extraction and purification efficiency of CGC from K. pastoris biomass, several assays were performed. It was found that extraction with 5 M NaOH at 65 ºC, during 2 hours, associated to neutralization with HCl, followed by successive washing steps with deionised water until conductivity of ≤20μS/cm, increased CGC purity. The obtained copolymer, CGCpure, had a chitin:glucan molar ratio of 25:75 mol% close to commercial CGC samples extracted from A. niger mycelium, kiOsmetine from Kitozyme (30:70 mol%). CGCpure was characterized by solid-state Nuclear Magnetic Resonance (NMR) spectroscopy and Differential Scanning Calorimetry (DCS), revealing a CGC with higher purity than a CGC commercial (kiOsmetine). In order to optimize CGC production, a set of batch cultivation experiments was performed to evaluate the effect of pH (3.5–6.5) and temperature (20–40 ºC) on the specific cell growth rate, CGC production and polymer composition. Statistical tools (response surface methodology and central composite design) were used. The CGC content in the biomass and the volumetric productivity (rp) were not significantly affected within the tested pH and temperature ranges. In contrast, the effect of pH and temperature on the CGC molar ratio was more pronounced. The highest chitin: β-glucan molar ratio (> 14:86) was obtained for the mid-range pH (4.5-5.8) and temperatures (26–33 ºC). The ability of K. pastoris to synthesize CGC with different molar ratios as a function of pH and temperature is a feature that can be exploited to obtain tailored polymer compositions.(...)

Optimization of bioplastics production from cheese whey

Dissertation presented in partial fulfillment of the requirements for the degree of Master in Biotechnology

Production of chitin-glucan complex by Pichia pastoris

Dissertation for the Degree of Master in Biotechnology

Production of bacterial biopolymers from industrial fat-containing wastes

Dissertation presented in partial fulfilment of the Requirements for the Degree of Master in Biotechnology

Production of polyhydroxyalkanoates from oil-containing substrates

Different oil-containing substrates, namely, used cooking oil (UCO), fatty acids-byproduct from biodiesel production (FAB) and olive oil deodorizer distillate (OODD) were tested as inexpensive carbon sources for the production of polyhydroxyalkanoates (PHA) using twelve bacterial strains, in batch experiments. The OODD and FAB were exploited for the first time as alternative substrates for PHA production. Among the tested bacterial strains, Cupriavidus necator and Pseudomonas resinovorans exhibited the most promising results, producing poly-3-hydroxybutyrate, P(3HB), form UCO and OODD and mcl-PHA mainly composed of 3-hydroxyoctanoate (3HO) and 3-hydroxydecanoate (3HD) monomers from OODD, respectively. Afterwards, these bacterial strains were cultivated in bioreactor. C. necator were cultivated in bioreactor using UCO as carbon source. Different feeding strategies were tested for the bioreactor cultivation of C. necator, namely, batch, exponential feeding and DO-stat mode. The highest overall PHA productivity (12.6±0.78 g L-1 day-1) was obtained using DO-stat mode. Apparently, the different feeding regimes had no impact on polymer thermal properties. However, differences in polymer‟s molecular mass distribution were observed. C. necator was also tested in batch and fed-batch modes using a different type of oil-containing substrate, extracted from spent coffee grounds (SCG) by super critical carbon dioxide (sc-CO2). Under fed-batch mode (DO-stat), the overall PHA productivity were 4.7 g L-1 day-1 with a storage yield of 0.77 g g-1. Results showed that SCG can be a bioresource for production of PHA with interesting properties. Furthermore, P. resinovorans was cultivated using OODD as substrate in bioreactor under fed-batch mode (pulse feeding regime). The polymer was highly amorphous, as shown by its low crystallinity of 6±0.2%, with low melting and glass transition temperatures of 36±1.2 and -16±0.8 ºC, respectively. Due to its sticky behavior at room temperature, adhesiveness and mechanical properties were also studied. Its shear bond strength for wood (67±9.4 kPa) and glass (65±7.3 kPa) suggests it may be used for the development of biobased glues. Bioreactor operation and monitoring with oil-containing substrates is very challenging, since this substrate is water immiscible. Thus, near-infrared spectroscopy (NIR) was implemented for online monitoring of the C. necator cultivation with UCO, using a transflectance probe. Partial least squares (PLS) regression was applied to relate NIR spectra with biomass, UCO and PHA concentrations in the broth. The NIR predictions were compared with values obtained by offline reference methods. Prediction errors to these parameters were 1.18 g L-1, 2.37 g L-1 and 1.58 g L-1 for biomass, UCO and PHA, respectively, which indicates the suitability of the NIR spectroscopy method for online monitoring and as a method to assist bioreactor control. UCO and OODD are low cost substrates with potential to be used in PHA batch and fed-batch production. The use of NIR in this bioprocess also opened an opportunity for optimization and control of PHA production process.

Modeling and optimization of extracellular polysaccharides production by Enterobacter A47

Polysaccharides are gaining increasing attention as potential environmental friendly and sustainable building blocks in many fields of the (bio)chemical industry. The microbial production of polysaccharides is envisioned as a promising path, since higher biomass growth rates are possible and therefore higher productivities may be achieved compared to vegetable or animal polysaccharides sources. This Ph.D. thesis focuses on the modeling and optimization of a particular microbial polysaccharide, namely the production of extracellular polysaccharides (EPS) by the bacterial strain Enterobacter A47. Enterobacter A47 was found to be a metabolically versatile organism in terms of its adaptability to complex media, notably capable of achieving high growth rates in media containing glycerol byproduct from the biodiesel industry. However, the industrial implementation of this production process is still hampered due to a largely unoptimized process. Kinetic rates from the bioreactor operation are heavily dependent on operational parameters such as temperature, pH, stirring and aeration rate. The increase of culture broth viscosity is a common feature of this culture and has a major impact on the overall performance. This fact complicates the mathematical modeling of the process, limiting the possibility to understand, control and optimize productivity. In order to tackle this difficulty, data-driven mathematical methodologies such as Artificial Neural Networks can be employed to incorporate additional process data to complement the known mathematical description of the fermentation kinetics. In this Ph.D. thesis, we have adopted such an hybrid modeling framework that enabled the incorporation of temperature, pH and viscosity effects on the fermentation kinetics in order to improve the dynamical modeling and optimization of the process. A model-based optimization method was implemented that enabled to design bioreactor optimal control strategies in the sense of EPS productivity maximization. It is also critical to understand EPS synthesis at the level of the bacterial metabolism, since the production of EPS is a tightly regulated process. Methods of pathway analysis provide a means to unravel the fundamental pathways and their controls in bioprocesses. In the present Ph.D. thesis, a novel methodology called Principal Elementary Mode Analysis (PEMA) was developed and implemented that enabled to identify which cellular fluxes are activated under different conditions of temperature and pH. It is shown that differences in these two parameters affect the chemical composition of EPS, hence they are critical for the regulation of the product synthesis. In future studies, the knowledge provided by PEMA could foster the development of metabolically meaningful control strategies that target the EPS sugar content and oder product quality parameters.