Microbial gas fermentation technology for sustainable food protein production

The development of novel, sustainable, and robust food production technologies represents one of the major pillars to address the most significant challenges humanity is going to face on earth in the upcoming decades - climate change, population growth, and resource depletion. The implementation of microfoods, i.e., foods formulated with ingredients from microbial cultivation, into the food supply chain has a huge potential to contribute towards energy-efficient and nutritious food manufacturing and represents a means to sustainably feed a growing world population. This review recapitulates and assesses the current state in the establishment and usage of gas fermenting bacteria as an innovative feedstock for protein production. In particular, we focus on the most promising representatives of this taxon: the hydrogen-oxidizing bacteria (hydrogenotrophs) and the methane-oxidizing bacteria (methanotrophs). These unicellular microorganisms can aerobically metabolize gaseous hydrogen and methane, respectively, to provide the required energy for building up cell material. A protein yield over 70% in the dry matter cell mass can be reached with no need for arable land and organic substrates making it a promising alternative to plant- and animal-based protein sources. We illuminate the holistic approach to incorporate protein extracts obtained from the cultivation of gas fermenting bacteria into microfoods. Herein, the fundamental properties of the bacteria, cultivation methods, downstream processing, and potential food applications are discussed. Moreover, this review covers existing and future challenges as well as sustainability aspects associated with the production of microbial protein through gas fermentation.

Recent Developments in Bioprocessing of Recombinant Proteins: Expression Hosts and Process Development

Infectious diseases, along with cancers, are among the main causes of death among humans worldwide. The production of therapeutic proteins for treating diseases at large scale for millions of individuals is one of the essential needs of mankind. Recent progress in the area of recombinant DNA technologies has paved the way to producing recombinant proteins that can be used as therapeutics, vaccines, and diagnostic reagents. Recombinant proteins for these applications are mainly produced using prokaryotic and eukaryotic expression host systems such as mammalian cells, bacteria, yeast, insect cells, and transgenic plants at laboratory scale as well as in large-scale settings. The development of efficient bioprocessing strategies is crucial for industrial production of recombinant proteins of therapeutic and prophylactic importance. Recently, advances have been made in the various areas of bioprocessing and are being utilized to develop effective processes for producing recombinant proteins. These include the use of high-throughput devices for effective bioprocess optimization and of disposable systems, continuous upstream processing, continuous chromatography, integrated continuous bioprocessing, Quality by Design, and process analytical technologies to achieve quality product with higher yield. This review summarizes recent developments in the bioprocessing of recombinant proteins, including in various expression systems, bioprocess development, and the upstream and downstream processing of recombinant proteins.

Sampling of Tissues with Laser Ablation for Proteomics: Comparison of Picosecond Infrared Laser and Microsecond Infrared Laser

It was recently shown that sampling of tissues with a picosecond infrared laser (PIRL) for analysis with bottom-up proteomics is advantageous compared to mechanical homogenization. Because the cold ablation of tissues with PIRL irradiation is soft, proteins remain intact and even enzymatic activities are detectable in PIRL homogenates. In contrast, it was observed that irradiation of tissues with a microsecond infrared laser (MIRL) heats the tissue, thereby causing significant damage. In this study, we investigated the question if sampling of tissues with a MIRL for analysis of their proteomes via bottom-up proteomics is possible and how the results are different from sampling of tissues with a PIRL. Comparison of the proteomes of the MIRL and PIRL tissue homogenates showed that the yield of proteins identified by bottom-up proteomics was larger in PIRL homogenates of liver tissue, whereas the yield was higher in MIRL homogenates of muscle tissue, which has a significantly higher content of connective tissue than liver tissue. In the PIRL homogenate of renal tissue, enzymatic activities were detectable, whereas in the corresponding MIRL homogenate, enzymatic activities were absent. In conclusion, MIRL and PIRL pulses are suited for sampling tissues for bottom-up proteomics. If it is important for bottom-up proteomic investigations to inactivate enzymatic activities already in the tissue before its ablation, MIRL tissue sampling is an option, because the proteins in the tissues are denatured and inactivated by the heating of the tissue during irradiation with MIRL irradiation prior to the ablation of the tissue. This heating effect is absent during irradiation of tissue with a PIRL; therefore, sampling of tissues with a PIRL is a choice for purifying enzymes, because their activities are maintained.

Piezoelectric Microchip for Cell Lysis through Cell-Microparticle Collision within a Microdroplet Driven by Surface Acoustic Wave Oscillation

Cell lysis is an important and crucial step for the detection of intracellular secrets. Usually, cell lysis is based on strong ultrasonic waves or toxic chemical regents, which require a large amount of cell suspension. To obtain high efficiency cell lysis for a small amount of sample, a mechanical cell lysis method based on a surface acoustic wave (SAW) microchip is proposed. The microchip simply consists of a piece of LiNbO₃ crystal substrate, interdigitated transducers (IDTs) with 80 pairs of parallel electrodes and 3M Magic Tapes. The modulated input electrical signal is coupled into the substrate through IDTs, which produces an acoustic stream in the droplet on the surface of a substrate. When a biofluid droplet containing cells and microparticles is dropped on the surface of the microchip, the cells and microparticles are accelerated and collide with each other. The fluorescence staining results illustrate that the cell membrane is efficiently destroyed and that proteins as well as nucleic acids inside the cell are released. The experimental results show that this method has a high efficiency and low sample consumption. The potential application is the pretreatment of a small amount of tested sample in a hospital or biolab.

Response surface optimization, Ex vivo and In vivo investigation of nasal spanlastics for bioavailability enhancement and brain targeting of risperidone

Transnasal brain drug targeting could ensure better drug delivery to the brain through the olfactory pathway. Risperidone bioavailability is 66% in extensive metabolizers and 82% in slow metabolizers. The aim of this study is to investigate the ability of the nanovesicular spanlastics to effectively deliver risperidone through the nasal route to the brain and increase its bioavailability. Spanlastics formulae, composed of span and polyvinyl alcohol, were designed based on central composite statistical design. The planned formulae were prepared using ethanol injection method. The prepared formulae were characterized by testing their particle size, polydispersity index, zeta potential and encapsulation efficiency. The optimized formula having the lowest particle size, polydispersity index, the highest zeta potential and encapsulation efficiency was subjected to further investigations including characterization of its rheological properties, elasticity, transmission electron microscopy, in vitro diffusion, ex vivo permeation, histopathology and in vivo biodistribution. The optimized formula was composed of 5mg/mL span and 30mg/mL polyvinyl alcohol. It showed significantly higher transnasal permeation and better distribution to the brain, when compared to the used control regarding the brain targeting efficiency and the drug transport percentage (2.16 and 1.43 folds increase, respectively). The study introduced a successful and promising formula to directly and effectively carry the drug from nose to brain.

Artificial Intelligence vs. Statistical Modeling and Optimization of Continuous Bead Milling Process for Bacterial Cell Lysis

For a commercially viable recombinant intracellular protein production process, efficient cell lysis and protein release is a major bottleneck. The recovery of recombinant protein, cholesterol oxidase (COD) was studied in a continuous bead milling process. A full factorial response surface methodology (RSM) design was employed and compared to artificial neural networks coupled with genetic algorithm (ANN-GA). Significant process variables, cell slurry feed rate (A), bead load (B), cell load (C), and run time (D), were investigated and optimized for maximizing COD recovery. RSM predicted an optimum of feed rate of 310.73 mL/h, bead loading of 79.9% (v/v), cell loading OD600nm of 74, and run time of 29.9 min with a recovery of ~3.2 g/L. ANN-GA predicted a maximum COD recovery of ~3.5 g/L at an optimum feed rate (mL/h): 258.08, bead loading (%, v/v): 80%, cell loading (OD600nm): 73.99, and run time of 32 min. An overall 3.7-fold increase in productivity is obtained when compared to a batch process. Optimization and comparison of statistical vs. artificial intelligence techniques in continuous bead milling process has been attempted for the very first time in our study. We were able to successfully represent the complex non-linear multivariable dependence of enzyme recovery on bead milling parameters. The quadratic second order response functions are not flexible enough to represent such complex non-linear dependence. ANN being a summation function of multiple layers are capable to represent complex non-linear dependence of variables in this case; enzyme recovery as a function of bead milling parameters. Since GA can even optimize discontinuous functions present study cites a perfect example of using machine learning (ANN) in combination with evolutionary optimization (GA) for representing undefined biological functions which is the case for common industrial processes involving biological moieties.