Engineering yield and rate of reductive biotransformation in Escherichia coli by partial cyclization of the pentose phosphate pathway and PTS-independent glucose transport

Optimization of yields and productivities in reductive whole-cell biotransformations is an important issue for the industrial application of such processes. In a recent study with Escherichia coli, we analyzed the reduction of the prochiral β-ketoester methyl acetoacetate by an R-specific alcohol dehydrogenase (ADH) to the chiral hydroxy ester (R)-methyl 3-hydroxybutyrate (MHB) using glucose as substrate for the generation of NADPH. Deletion of the phosphofructokinase gene pfkA almost doubled the yield to 4.8 mol MHB per mole of glucose, and it was assumed that this effect was due to a partial cyclization of the pentose phosphate pathway (PPP). Here, this partial cyclization was confirmed by 13C metabolic flux analysis, which revealed a negative net flux from glucose 6-phosphate to fructose 6-phosphate catalyzed by phosphoglucose isomerase. For further process optimization, the genes encoding the glucose facilitator (glf) and glucokinase (glk) of Zymomonas mobilis were overexpressed in recombinant E. coli strains carrying ADH and deletions of either pgi (phosphoglucose isomerase), or pfkA, or pfkA plus pfkB. In all cases, the glucose uptake rate was increased (30–47%), and for strains Δpgi and ΔpfkA also, the specific MHB production rate was increased by 15% and 20%, respectively. The yield of the latter two strains slightly dropped by 11% and 6%, but was still 73% and 132% higher compared to the reference strain with intact pgi and pfkA genes and expressing glf and glk. Thus, metabolic engineering strategies are presented for improving yield and rate of reductive redox biocatalysis by partial cyclization of the PPP and by increasing glucose uptake, respectively

Big-Data-Driven Materials Science and its FAIR Data Infrastructure

This chapter addresses the forth paradigm of materials research -- big-data driven materials science. Its concepts and state-of-the-art are described, and its challenges and chances are discussed. For furthering the field, Open Data and an all-embracing sharing, an efficient data infrastructure, and the rich ecosystem of computer codes used in the community are of critical importance. For shaping this forth paradigm and contributing to the development or discovery of improved and novel materials, data must be what is now called FAIR -- Findable, Accessible, Interoperable and Re-purposable/Re-usable. This sets the stage for advances of methods from artificial intelligence that operate on large data sets to find trends and patterns that cannot be obtained from individual calculations and not even directly from high-throughput studies. Recent progress is reviewed and demonstrated, and the chapter is concluded by a forward-looking perspective, addressing important not yet solved challenges.Comment: submitted to the Handbook of Materials Modeling (eds. S. Yip and W. Andreoni), Springer 2018/201

MultiMetEval: comparative and multi-objective analysis of genome-scale metabolic models

Comparative metabolic modelling is emerging as a novel field, supported by the development of reliable and standardized approaches for constructing genome-scale metabolic models in high throughput. New software solutions are needed to allow efficient comparative analysis of multiple models in the context of multiple cellular objectives. Here, we present the user-friendly software framework Multi-Metabolic Evaluator (MultiMetEval), built upon SurreyFBA, which allows the user to compose collections of metabolic models that together can be subjected to flux balance analysis. Additionally, MultiMetEval implements functionalities for multi-objective analysis by calculating the Pareto front between two cellular objectives. Using a previously generated dataset of 38 actinobacterial genome-scale metabolic models, we show how these approaches can lead to exciting novel insights. Firstly, after incorporating several pathways for the biosynthesis of natural products into each of these models, comparative flux balance analysis predicted that species like Streptomyces that harbour the highest diversity of secondary metabolite biosynthetic gene clusters in their genomes do not necessarily have the metabolic network topology most suitable for compound overproduction. Secondly, multi-objective analysis of biomass production and natural product biosynthesis in these actinobacteria shows that the well-studied occurrence of discrete metabolic switches during the change of cellular objectives is inherent to their metabolic network architecture. Comparative and multi-objective modelling can lead to insights that could not be obtained by normal flux balance analyses. MultiMetEval provides a powerful platform that makes these analyses straightforward for biologists. Sources and binaries of MultiMetEval are freely available from https://github.com/PiotrZakrzewski/MetEv​al/downloads

Cost-effectiveness of oral alitretinoin in patients with severe chronic hand eczema - a long-term analysis from a Swiss perspective

BACKGROUND: The impact on patients suffering from chronic hand eczema (CHE) is enormous, as no licensed systemic treatment option with proven efficacy for CHE is available. Alitretinoin is a novel agent which showed high clinical efficacy in patients with severe, refractory CHE. We assessed the cost-effectiveness of alitretinoin for CHE patient treatment from a Swiss third party payer perspective. A further objective of this study was to determine the burden of disease in Switzerland. METHODS: A long-term Markov cohort simulation model was used to estimate direct medical costs (euro) and clinical effectiveness (quality adjusted life years, QALYs) of treating severe CHE patients with alitretinoin. Comparison was against the standard treatment of supportive care (optimised emollient therapy). Information on response rates were derived from a randomized controlled clinical trial. Costs were considered from the perspective of the Swiss health system. Swiss epidemiological data was derived from official Swiss Statistic institutions. RESULTS: Annual costs of alitretinoin treatment accounted for 2'212 euro. After a time horizon of 22.4 years, average remaining long-term costs accounted for 42'208 euro or 38'795 euro in the alitretinoin and the standard treatment arm, respectively. Compared with the standard therapy, the addition of alitretinoin yielded an average gain of 0.230 QALYs at the end of the simulation. Accordingly, the incremental cost-effectiveness ratio resulted in 14'816 euro/QALY gained. These results were robust to changes in key model assumptions. CONCLUSION: The therapy for CHE patients is currently insufficient. In our long-term model we identified the treatment with alitretinoin as a cost-effective alternative for the therapy of CHE patients in Switzerland

From bit to it: How a complex metabolic network transforms information into living matter

Organisms live and die by the amount of information they acquire about their environment. The systems analysis of complex metabolic networks allows us to ask how such information translates into fitness. A metabolic network transforms nutrients into biomass. The better it uses information on available nutrient availability, the faster it will allow a cell to divide. I here use metabolic flux balance analysis to show that the accuracy I (in bits) with which a yeast cell can sense a limiting nutrient's availability relates logarithmically to fitness as indicated by biomass yield and cell division rate. For microbes like yeast, natural selection can resolve fitness differences of genetic variants smaller than 10-6, meaning that cells would need to estimate nutrient concentrations to very high accuracy (greater than 22 bits) to ensure optimal growth. I argue that such accuracies are not achievable in practice. Natural selection may thus face fundamental limitations in maximizing the information processing capacity of cells. The analysis of metabolic networks opens a door to understanding cellular biology from a quantitative, information-theoretic perspective

Role of Duplicate Genes in Robustness against Deleterious Human Mutations

It is now widely recognized that robustness is an inherent property of biological systems [1],[2],[3]. The contribution of close sequence homologs to genetic robustness against null mutations has been previously demonstrated in simple organisms [4],[5]. In this paper we investigate in detail the contribution of gene duplicates to back-up against deleterious human mutations. Our analysis demonstrates that the functional compensation by close homologs may play an important role in human genetic disease. Genes with a 90% sequence identity homolog are about 3 times less likely to harbor known disease mutations compared to genes with remote homologs. Moreover, close duplicates affect the phenotypic consequences of deleterious mutations by making a decrease in life expectancy significantly less likely. We also demonstrate that similarity of expression profiles across tissues significantly increases the likelihood of functional compensation by homologs

Fault Tolerance in Protein Interaction Networks: Stable Bipartite Subgraphs and Redundant Pathways

As increasing amounts of high-throughput data for the yeast interactome become available, more system-wide properties are uncovered. One interesting question concerns the fault tolerance of protein interaction networks: whether there exist alternative pathways that can perform some required function if a gene essential to the main mechanism is defective, absent or suppressed. A signature pattern for redundant pathways is the BPM (between-pathway model) motif, introduced by Kelley and Ideker. Past methods proposed to search the yeast interactome for BPM motifs have had several important limitations. First, they have been driven heuristically by local greedy searches, which can lead to the inclusion of extra genes that may not belong in the motif; second, they have been validated solely by functional coherence of the putative pathways using GO enrichment, making it difficult to evaluate putative BPMs in the absence of already known biological annotation. We introduce stable bipartite subgraphs, and show they form a clean and efficient way of generating meaningful BPMs which naturally discard extra genes included by local greedy methods. We show by GO enrichment measures that our BPM set outperforms previous work, covering more known complexes and functional pathways. Perhaps most importantly, since our BPMs are initially generated by examining the genetic-interaction network only, the location of edges in the protein-protein physical interaction network can then be used to statistically validate each candidate BPM, even with sparse GO annotation (or none at all). We uncover some interesting biological examples of previously unknown putative redundant pathways in such areas as vesicle-mediated transport and DNA repair

Biological response of an in vitro human 3D lung cell model exposed to brake wear debris varies based on brake pad formulation

Wear particles from automotive friction brake pads of various sizes, morphology, and chemical composition are significant contributors towards particulate matter. Knowledge concerning the potential adverse effects following inhalation exposure to brake wear debris is limited. Our aim was, therefore, to generate brake wear particles released from commercial low-metallic and non-asbestos organic automotive brake pads used in mid-size passenger cars by a full-scale brake dynamometer with an environmental chamber simulating urban driving and to deduce their potential hazard in vitro. The collected fractions were analysed using scanning electron microscopy via energy-dispersive X-ray spectroscopy (SEM-EDS) and Raman microspectroscopy. The biological impact of the samples was investigated using a human 3D multicellular model consisting of human epithelial cells (A549) and human primary immune cells (macrophages and dendritic cells) mimicking the human epithelial tissue barrier. The viability, morphology, oxidative stress, and (pro-)inflammatory response of the cells were assessed following 24 h exposure to similar to 12, similar to 24, and similar to 48 A mu g/cm(2) of non-airborne samples and to similar to 3.7 A mu g/cm(2) of different brake wear size fractions (2-4, 1-2, and 0.25-1 A mu m) applying a pseudo-air-liquid interface approach. Brake wear debris with low-metallic formula does not induce any adverse biological effects to the in vitro lung multicellular model. Brake wear particles from non-asbestos organic formulated pads, however, induced increased (pro-)inflammatory mediator release from the same in vitro system. The latter finding can be attributed to the different particle compositions, specifically the presence of anatase.Web of Science9272351233

Impact of stoichiometry representation on simulation of genotype-phenotype relationships in metabolic networks.

<div><p>Genome-scale metabolic networks provide a comprehensive structural framework for modeling genotype-phenotype relationships through flux simulations. The solution space for the metabolic flux state of the cell is typically very large and optimization-based approaches are often necessary for predicting the active metabolic state under specific environmental conditions. The objective function to be used in such optimization algorithms is directly linked with the biological hypothesis underlying the model and therefore it is one of the most relevant parameters for successful modeling. Although linear combination of selected fluxes is widely used for formulating metabolic objective functions, we show that the resulting optimization problem is sensitive towards stoichiometry representation of the metabolic network. This undesirable sensitivity leads to different simulation results when using numerically different but biochemically equivalent stoichiometry representations and thereby makes biological interpretation intrinsically subjective and ambiguous. We hereby propose a new method, Minimization of Metabolites Balance (MiMBl), which decouples the artifacts of stoichiometry representation from the formulation of the desired objective functions, by casting objective functions using metabolite turnovers rather than fluxes. By simulating perturbed metabolic networks, we demonstrate that the use of stoichiometry representation independent algorithms is fundamental for unambiguously linking modeling results with biological interpretation. For example, MiMBl allowed us to expand the scope of metabolic modeling in elucidating the mechanistic basis of several genetic interactions in <em>Saccharomyces cerevisiae</em>.</p> </div