Unraveling the Evolution of Dynamic Active Sites of LaNixFe1-xO3 Catalysts During OER

Perovskites have attracted tremendous attention as potential catalysts for the oxygen evolution reaction (OER). It is well-known that the introduction of Fe into rare earth perovskites such as LaNiO3 enhances the intrinsic OER activity. Despite numerous studies on structure-property relationships, the origin of the activity and the nature of the active species are still elusive and unclear. In this work, we study a series of LaNixFe1-xO3 perovskites using in situ electrochemical surface-enhanced Raman spectroscopy and electron energy loss spectroscopy to decipher the surface evolution and formation of active species during OER. While the origin of the activity arises from NiOOH species formed from the active Ni centers in LaNiO3, our work shows that Fe serves as the active center in LaNi0.5Fe0.5O3 and forms Fe-O-Ni and FeOOH species during OER. The OER activity of LaFeO3 originates from FeOOH species, which interact with the soluble Ni species in the electrolyte forming an active electrode-electrolyte interface with high-valent stable surface iron species (Fe4+) and thereby improving the performance. Our work provides deeper insights into the synergistic effects of Ni and Fe on the catalytic activity, which in turn provides new design principles for perovskite catalysts for the OER.

A radiation of Psylliodes flea beetles on Brassicaceae is associated with the evolution of specific detoxification enzymes

Flea beetles of the genus Psylliodes have evolved specialized interactions with plant species belonging to several distantly related families, mainly Brassicaceae, Solanaceae, and Fagaceae. This diverse host use indicates that Psylliodes flea beetles are able to cope with different chemical defense metabolites, including glucosinolates, the characteristic defense metabolites of Brassicaceae. Here we investigated the evolution of host use and the emergence of a glucosinolate-specific detoxification mechanism in Psylliodes flea beetles. In phylogenetic analyses, Psylliodes species clustered into four major clades, three of which contained mainly species specialized on either Brassicaceae, Solanaceae, or Fagaceae. Most members of the fourth clade have broader host use, including Brassicaceae and Poaceae as major host plant families. Ancestral state reconstructions suggest that Psylliodes flea beetles were initially associated with Brassicaceae and then either shifted to Solanaceae or Fagaceae, or expanded their host repertoire to Poaceae. Despite a putative ancestral association with Brassicaceae, we found evidence that the evolution of glucosinolate-specific detoxification enzymes coincides with the radiation of Psylliodes on Brassicaceae, suggesting that these are not required for using Brassicaceae as hosts but could improve the efficiency of host use by specialized Psylliodes species.

Toward a modeling, optimization, and predictive control framework for fed-batch metabolic cybergenetics

Biotechnology offers many opportunities for the sustainable manufacturing of valuable products. The toolbox to optimize bioprocesses includes extracellular process elements such as the bioreactor design and mode of operation, medium formulation, culture conditions, feeding rates, and so on. However, these elements are frequently insufficient for achieving optimal process performance or precise product composition. One can use metabolic and genetic engineering methods for optimization at the intracellular level. Nevertheless, those are often of static nature, failing when applied to dynamic processes or if disturbances occur. Furthermore, many bioprocesses are optimized empirically and implemented with little-to-no feedback control to counteract disturbances. The concept of cybergenetics has opened new possibilities to optimize bioprocesses by enabling online modulation of the gene expression of metabolism-relevant proteins via external inputs (e.g., light intensity in optogenetics). Here, we fuse cybergenetics with model-based optimization and predictive control for optimizing dynamic bioprocesses. To do so, we propose to use dynamic constraint-based models that integrate the dynamics of metabolic reactions, resource allocation, and inducible gene expression. We formulate a model-based optimal control problem to find the optimal process inputs. Furthermore, we propose using model predictive control to address uncertainties via online feedback. We focus on fed-batch processes, where the substrate feeding rate is an additional optimization variable. As a simulation example, we show the optogenetic control of the ATPase enzyme complex for dynamic modulation of enforced ATP wasting to adjust product yield and productivity.

Influence of Ion Source Geometry on the Repeatability of Topographically Guided LAESI-MSI

Spatially resolving the relative distribution of analyte molecules in biological matter holds great promise in the life sciences. Mass spectrometry imaging (MSI) is a technique that can provide such spatial resolution but remains underused in fields such as chemical ecology, as traditional MSI sample preparation is often chemically or morphologically invasive. Laser ablation electrospray ionization (LAESI)-MSI is a variation of MSI particularly well-suited for situations where chemical sample preparation is too invasive but provides new challenges related to the repeatability of measurement outcomes. We assess the repeatability of LAESI-MSI by sampling a droplet of [ring-¹³C₆]l-phenylalanine with known concentration and expressing the resulting variability as a coefficient of variation, c_v. In doing so, we entirely eliminate variability caused by surface morphology or underlying true sample gradients. We determine the limit of detection (LOD) for¹³C₆-Phe by sampling from droplets with successively decreasing but known concentration. We assess the influence of source geometry on the LOD and repeatability by performing these experiments using four distinct variations of sources: one commercial and three custom-built ones. Finally, we extend our study to leaf and stem samples Arabidopsis thaliana and Gossypium hirsutum. We overcome the challenges of LAESI associated with three-dimensional surface morphology by relying on work previously published. Our measurements on both controlled standard and realistic samples give strong evidence that LAESI-MSI's repeatability in current implementations is insufficient for MSI in chemical ecology.

Model-based approach for predicting the impact of genetic modifications on product yield in biopharmaceutical manufacturing-Application to influenza vaccine production

A large group of biopharmaceuticals is produced in cell lines. The yield of such products can be increased by genetic engineering of the corresponding cell lines. The prediction of promising genetic modifications by mathematical modeling is a valuable tool to facilitate experimental screening. Besides information on the intracellular kinetics and genetic modifications the mathematical model has to account for ubiquitous cell-to-cell variability. In this contribution, we establish a novel model-based methodology for influenza vaccine production in cell lines with overexpressed genes. The manipulation of the expression level of genes coding for host cell factors relevant for virus replication is achieved by lentiviral transduction. Since lentiviral transduction causes increased cell-to-cell variability due to different copy numbers and integration sites of the gene constructs we use a population balance modeling approach to account for this heterogeneity in terms of intracellular viral components and distributed kinetic parameters. The latter are estimated from experimental data of intracellular viral RNA levels and virus titers of infection experiments using cells overexpressing a single host cell gene. For experiments with cells overexpressing multiple host cell genes, only final virus titers were measured and thus, no direct estimation of the parameter distributions was possible. Instead, we evaluate four different computational strategies to infer these from single gene parameter sets. Finally, the best computational strategy is used to predict the most promising candidates for future modifications that show the highest potential for an increased virus yield in a combinatorial study. As expected, there is a trend to higher yields the more modifications are included.

In the present work, we use a sophisticated simulation-based methodology to account for the impact of genetic modifications in producer cell lines on the yield of biomanufacturing processes. Furthermore, our approach opens the possibility to predict the most promising genetic modifications instead of identifying them in costly and time-consuming screening experiments. As an example, we apply our methodology to cell culture-based influenza vaccine production, a process that is of tremendous importance for the maintenance of public health. Here, we consider cell lines in which genes coding for one or more cellular factors are up-regulated by genetic engineering to increase the virus yield. However, the gene editing procedure increases the heterogeneity in the producer cell population because genetic modifications do not occur equally in each cell. This cell-to-cell variability is taken into account in a population balance modeling framework, thus providing a more accurate prediction of the virus yield in the heterogeneous population. Finally, we use our approach and a concise experimental data set from cell lines with one gene modification to predict the virus yield of cell lines with multiple genetic modifications. Thereby, we facilitate the experimental screening of potential candidates. We suggest that this methodology is transferable to a wide range of biomanufacturing processes and constitutes a valuable contribution to experimental design.