Recombinant AAV genome size effect on viral vector production, purification, and thermostability.

Adeno-associated virus (AAV) has shown great promise as a viral vector for gene therapy in clinical applications. The present work studied the effect of genome size on AAV production, purification, and thermostability by producing AAV2-GFP using suspension-adapted HEK293 cells via triple transfection using AAV plasmids containing the same GFP transgene with DNA stuffers for variable-size AAV genomes consisting of 1.9, 3.4, and 4.9 kb (ITR to ITR). Production was performed at the small and large shake flask scales and the results showed that the 4.9 kb GFP genome had significantly reduced encapsidation compared to other genomes. The large shake flask productions were purified by AEX chromatography, and the results suggest that the triple transfection condition significantly affects the AEX retention time and resolution between the full and empty capsid peaks. Charge detection-mass spectrometry was performed on all AEX full-capsid peak samples showing a wide distribution of empty, partial, full length, and copackaged DNA in the capsids. The AEX-purified samples were then analyzed by differential scanning fluorimetry, and the results suggest that sample formulation may improve the thermostability of AAV genome ejection melting temperature regardless of the packaged genome content.

Enzyme selection, optimization, and production toward biodegradation of post-consumer poly(ethylene terephthalate) at scale.

Poly(ethylene terephthalate) (PET) is one of the world's most widely used polyester plastics. Due to its chemical stability, PET is extremely difficult to hydrolyze in a natural environment. Recent discoveries in new polyester hydrolases and breakthroughs in enzyme engineering strategies have inspired enormous research on biorecycling of PET. This study summarizes our research efforts toward large-scale, efficient, and economical biodegradation of post-consumer waste PET, including PET hydrolase selection and optimization, high-yield enzyme production, and high-capacity enzymatic degradation of post-consumer waste PET. First, genes encoding PETase and MHETase from Ideonella sakaiensis and the ICCG variant of leaf-branch compost cutinase (LCCICCG ) were codon-optimized and expressed in Escherichia coli BL21(DE3) for high-yield production. To further lower the enzyme production cost, a pelB leader sequence was fused to LCCICCG so that the enzyme can be secreted into the medium to facilitate recovery. To help bind the enzyme on the hydrophobic surface of PET, a substrate-binding module in a polyhydroxyalkanoate depolymerase from Alcaligenes faecalis (PBM) was fused to the C-terminus of LCCICCG . The resulting four different LCCICCG variants (LCC, PelB-LCC, LCC-PBM, and PelB-LCC-PBM), together with PETase and MHETase, were compared for PET degradation efficiency. A fed-batch fermentation process was developed to produce the target enzymes up to 1.2 g L-1 . Finally, the best enzyme, PelB-LCC, was selected and used for the efficient degradation of 200 g L-1 recycled PET in a well-controlled, stirred-tank reactor. The results will help develop an economical and scalable biorecycling process toward a circular PET economy.

Using oils and fats to replace sugars as feedstocks for biomanufacturing: Challenges and opportunities for the yeast Yarrowia lipolytica.

More than 200 million tons of plant oils and animal fats are produced annually worldwide from oil, crops, and the rendered animal fat industry. Triacylglycerol, an abundant energy-dense compound, is the major form of lipid in oils and fats. While oils or fats are very important raw materials and functional ingredients for food or related products, a significant portion is currently diverted to or recovered as waste. To significantly increase the value of waste oils or fats and expand their applications with a minimal environmental footprint, microbial biomanufacturing is presented as an effective strategy for adding value. Though both bacteria and yeast can be engineered to use oils or fats as the biomanufacturing feedstocks, the yeast Yarrowia lipolytica is presented as one of the most attractive platforms. Y. lipolytica is oleaginous, generally regarded as safe, demonstrated as a promising industrial producer, and has unique capabilities for efficient catabolism and bioconversion of lipid substrates. This review summarizes the major challenges and opportunities for Y. lipolytica as a new biomanufacturing platform for the production of value-added products from oils and fats. This review also discusses relevant cellular and metabolic engineering strategies such as fatty acid transport, fatty acid catabolism and bioconversion, redox balances and energy yield, cell morphology and stress response, and bioreaction engineering. Finally, this review highlights specific product classes including long-chain diacids, wax esters, terpenes, and carotenoids with unique synthesis opportunities from oils and fats in Y. lipolytica.

Point-of-care technologies in heart, lung, blood and sleep disorders from the Center for Advancing Point-of-Care Technologies.

Recent advancements in point-of-care technologies have transformed care for patients with heart, lung, blood, and sleep disorders by providing rapid, cost-effective, and accessible solutions to challenges in the detection and management of many health conditions. However, major barriers exist throughout the technology development process that inhibit the actualization of many promising and potentially successful ideas. The Center for Advancing Point of Care Technologies has established a system for supporting further innovation in this field and bridging the gap between initial idea conception and implementation. We highlight current and emerging point-of-care technologies throughout the development spectrum and emphasize the need for a needs-driven model of health technology development that involve appropriate stakeholders in the process.

Cellular and metabolic engineering of oleaginous yeast Yarrowia lipolytica for bioconversion of hydrophobic substrates into high-value products.

The non-conventional oleaginous yeast Yarrowia lipolytica is able to utilize both hydrophilic and hydrophobic carbon sources as substrates and convert them into value-added bioproducts such as organic acids, extracellular proteins, wax esters, long-chain diacids, fatty acid ethyl esters, carotenoids and omega-3 fatty acids. Metabolic pathway analysis and previous research results show that hydrophobic substrates are potentially more preferred by Y. lipolytica than hydrophilic substrates to make high-value products at higher productivity, titer, rate, and yield. Hence, Y. lipolytica is becoming an efficient and promising biomanufacturing platform due to its capabilities in biosynthesis of extracellular lipases and directly converting the extracellular triacylglycerol oils and fats into high-value products. It is believed that the cell size and morphology of the Y. lipolytica is related to the cell growth, nutrient uptake, and product formation. Dimorphic Y. lipolytica demonstrates the yeast-to-hypha transition in response to the extracellular environments and genetic background. Yeast-to-hyphal transition regulating genes, such as YlBEM1, YlMHY1 and YlZNC1 and so forth, have been identified to involve as major transcriptional factors that control morphology transition in Y. lipolytica. The connection of the cell polarization including cell cycle and the dimorphic transition with the cell size and morphology in Y. lipolytica adapting to new growth are reviewed and discussed. This review also summarizes the general and advanced genetic tools that are used to build a Y. lipolytica biomanufacturing platform.

Robustness of chemometrics-based feature selection methods in early cancer detection and biomarker discovery.

In omics studies aimed at the early detection and diagnosis of cancer, bioinformatics tools play a significant role when analyzing high dimensional, complex datasets, as well as when identifying a small set of biomarkers. However, in many cases, there are ambiguities in the robustness and the consistency of the discovered biomarker sets, since the feature selection methods often lead to irreproducible results. To address this, both the stability and the classification power of several chemometrics-based feature selection algorithms were evaluated using the Monte Carlo sampling technique, aiming at finding the most suitable feature selection methods for early cancer detection and biomarker discovery. To this end, two data sets were analyzed, which comprised of MALDI-TOF-MS and LC/TOF-MS spectra measured on serum samples in order to diagnose ovarian cancer. Using these datasets, the stability and the classification power of multiple feature subsets found by different feature selection methods were quantified by varying either the number of selected features, or the number of samples in the training set, with special emphasis placed on the property of stability. The results show that high consistency does not necessarily guarantee high predictive power. In addition, differences in the stability, as well as agreement in feature lists between several feature selection methods, depend on several factors, such as the number of available samples, feature sizes, quality of the information in the dataset, etc. Among the tested methods, only the variable importance in projection (VIP)-based method shows complementary properties, providing both highly consistent and accurate subsets of features. In addition, successive projection analysis (SPA) was excellent with regards to maintaining high stability over a wide range of experimental conditions. The stability of several feature selection methods is highly variable, stressing the importance of making the proper choice among feature selection methods. Therefore, rather than evaluating the selected features using only classification accuracy, stability measurements should be examined as well to improve the reliability of biomarker discovery.