Biotechnology and Bioengineering最新文献

Cellulose-active Lytic Polysaccharide Monooxygenases (LPMO) facilitate plant cell wall deconstruction by attacking ordered regions of cellulose. In vitro, reductants (e.g., ascorbic acid) reduce LPMOs to Cu(I)-LPMO, and hydrogen peroxide (H₂O₂) serves as co-substrate for oxidative cleavage of cellulose glycosidic bonds. Super-resolution single-molecule imaging by total internal reflection fluorescence microscopy was used to visualize and enumerate binding events of fluorescently-labeled Thermothielavioides terrestris AA9E (TtAA9E) on highly ordered cellulose fibrils in oxygen-scavenging buffer systems. In the glucose oxidase/catalase (GODCAT) system, oxygen is converted to H₂O₂, then removed by catalase. Adding ascorbic acid to the GODCAT system promoted rapid binding to cellulose by TtAA9E. In contrast, absent both oxygen and H₂O₂ in the protocatechuic acid/protocatechuate 3,4-dioxygenase (PCA/PCD) oxygen-scavenging system, adding ascorbic acid nearly eliminated cellulose binding by TtAA9E. Our results suggest that in the GODCAT system, TtAA9Es are reduced by ascorbic acid and activated by H₂O₂, facilitating binding to cellulose. In the PCA/PCD system, reduced TtAA9Es are not activated due to the lack of H₂O₂, suggesting that reduced Cu(I)-TtAA9E cannot bind to cellulose without H₂O₂. Notably, in the PCA/PCD system with ascorbic acid, oxidized sugar release initially lagged but was observed at longer reaction times, suggesting that H₂O₂ could be a limiting reactant generated in situ as oxygen becomes absorbed into solution. Binding durations of LPMO to cellulose were independent of experimental conditions: ( 82% ± 6%) of cellulose-bound LPMOs resided briefly for 14 ± 2.5 s, while 16% ± 5% of the bound enzymes remained for 60 ± 9 s.

Chinese hamster ovary (CHO) cells are the leading mammalian system for recombinant therapeutic protein production. However, optimizing transgene expression remains challenging due to the limited understanding of the regulatory mechanisms controlling gene expression in CHO cells. Towards overcoming this barrier, here we provide a systematic characterization of cis-regulatory elements in CHO cells. Using genome-wide STARR-seq, a high-throughput method for quantifying enhancer strength, we identified regions with enhancer activity in the CHO cell genome. By integrating these data with ATAC-seq and histone modification profiles, we were able to characterize the chromatin state of these regions. Our analysis revealed thousands of newly identified enhancer sequences. The most active sequences could drive transgene expression at levels similar to or higher than strong viral enhancers. Notably, half of the regions found to have enhancer activity were within inaccessible chromatin in their native context. We observed that accessible enhancers were primarily near to transcriptional start sites and associated with ubiquitously-expressed genes, whereas inaccessible enhancers were predominantly intergenic and associated with tissue-specific genes. Additionally, through a deep-learning-based approach ETS and YY1 transcription factor (TF) binding motifs were identified as key determinants of enhancer identity and strength. Disrupting YY1 binding motifs led to reduced enhancer activity, thereby highlighting the importance of YY1 as a transcriptional activator in CHO cells. Our study demonstrates the first comprehensive map of functionally-validated enhancers in CHO cells and generates new insights into gene regulation and the role of TFs in determining enhancer strength. This study helps to lay the foundation for strategic engineering of CHO cell transcriptional networks to achieve enhanced biopharmaceutical production.

The development of novel antimicrobial agents that are effective against Gram-negative bacteria is hindered by the dual membrane cell envelope of these bacteria. To reach their intracellular targets, most small-molecule antibiotics must first pass through protein channels called porins; however, a common mechanism of acquired resistance is decreased expression of these outer membrane proteins. Additionally, parameters such as size, shape, and charge regulate passage of antibiotics through porins, further limiting the design space of novel antibiotic molecules. Inspired by the ability of bacterial outer membrane vesicles (OMVs) to deliver cargo to the bacterial cytosol, we hypothesized that encapsulation of small molecule antibiotics within OMVs would improve the activity of the drugs by facilitating uptake. To test this hypothesis, we investigated the ability of imipenem-encapsulated OMVs to inhibit the growth of several Gram-negative bacteria, including multidrug-resistant (MDR) clinical isolates. Our results demonstrated that encapsulation within OMVs significantly lowers the effective concentration of imipenem in several MDR isolates. Using a panel of porin knockout strains, we further demonstrated that this mechanism of antibiotic delivery does not require porin expression. Together, our results demonstrate the potential of OMVs as novel antibiotic delivery vehicles to treat antibiotic-resistant bacterial infections by improving drug uptake.

Dynamic metabolic control allows key metabolic fluxes to be modulated in real time, enhancing bioprocess flexibility and expanding available optimization degrees of freedom. This is achieved, for example, via targeted modulation of metabolic enzyme expression. However, identifying optimal dynamic control policies is challenging due to the generally high-dimensional solution space and the need to manage metabolic burden and cytotoxic effects arising from inducible enzyme expression. The task is further complicated by stochastic dynamics, which reduce bioprocess reproducibility. We propose a reinforcement learning framework to derive optimal policies by allowing an agent (the controller) to interact with a surrogate dynamic model. To promote robustness, we apply domain randomization, enabling the controller to generalize across uncertainties. When transferred to an experimental system, the agent can in principle continue fine-tuning the policy. Our framework provides an alternative to conventional model-based control such as model predictive control, which requires model differentiation with respect to decision variables; often impractical for complex stochastic, nonlinear, stiff, and piecewise-defined dynamics. In contrast, our approach relies on forward integration of the model, thereby simplifying the task. We demonstrate the framework in two Escherichia coli bioprocesses: dynamic control of acetyl-CoA carboxylase for fatty-acid synthesis and of adenosine triphosphatase for lactate synthesis.

The supramolecular assembly of short peptides into ordered structures offers promise for developing bio-nanomaterials with diverse applications in drug delivery, electronics, and optical engineering. Intrinsic fluorescence of polypeptide aggregates is typically associated with delocalization of electron densities in dense hydrogen bonding networks, dipolar coupling of aromatic amino acid residues, and possibly by the ‘cluster-derived luminescence’ associated with supramolecular structures. In a handful of examples, self-assembly of short peptides has provided ordered intrinsically fluorescent nanostructures. In this study, replacement of a single arginine residue with citrulline in a macrocyclic peptide has led to intrinsic fluorescence. The parent arginine-containing peptide macrocycle was previously shown to adopt a β-sheet conformation that aggregated into nonfluorescent spherical particles. The change from the electrostatic positive charge of the guanidine side chain to a hydrogen bonding neutral urea caused the β-sheet peptide to aggregate into larger-sized intrinsically fluorescent rods by a phenomenon that is ascribed to electron delocalization through π-π stacking interactions.