Humusmonitoring in NRW - Ergebnisse der ersten Jahre

Seit 2009 werden in NRW an 45 ackerbaulich genutzten Untersuchungsflächen jährlich Bodenproben entnommen. Jeweils 15 Flächen liegen in den drei unterschiedlichen Landschaftsräumen Westfälische Bucht, Niederrheinisches Tiefland, Rheinische Bucht. Beprobt wird der Ap-Horizont und der darunter liegende Boden bis 60cm Tiefe. Ziel ist die Erfassung und Charakterisierung des Kohlenstoffgehaltes und -vorrates sowie deren Veränderungen mit der Zeit. Die Ergebnisse zeigen, dass die Corg-Gehalte im Ap und Unterboden des  Niederrheinischen Tieflandes und der Rheinischen Bucht sehr ähnlich sind. Abweichend von den dortigen (Pseudogley)-(Para)Braunerden aus Löss zeigen die Gley-Podsole aus Flugsand in der Westfälischen Bucht meist deutlich höhere Corg-Gehalte. Als möglicher Grund für die höheren Corg-Gehalte sind sowohl Auswirkungen der Flächenbewirtschaftung, Podsol-typische Anreicherungen als auch der (ehemalige) Einfluss des Grundwassers wahrscheinlich. Nach einer Laufzeit von sieben Jahren (2009-2016) lassen sich aktuell keine statistisch abgesicherten flächenhaften Trends des Corg-Gehaltes, weder im Ap noch im Unterboden, erkennen

Random sampling of elementary flux modes in large-scale metabolic networks

Motivation: The description of a metabolic network in terms of elementary (flux) modes (EMs) provides an important framework for metabolic pathway analysis. However, their application to large networks has been hampered by the combinatorial explosion in the number of modes. In this work, we develop a method for generating random samples of EMs without computing the whole set. Results: Our algorithm is an adaptation of the canonical basis approach, where we add an additional filtering step which, at each iteration, selects a random subset of the new combinations of modes. In order to obtain an unbiased sample, all candidates are assigned the same probability of getting selected. This approach avoids the exponential growth of the number of modes during computation, thus generating a random sample of the complete set of EMs within reasonable time. We generated samples of different sizes for a metabolic network of Escherichia coli, and observed that they preserve several properties of the full EM set. It is also shown that EM sampling can be used for rational strain design. A well distributed sample, that is representative of the complete set of EMs, should be suitable to most EM-based methods for analysis and optimization of metabolic networks

Ergebnisse aus 10 Jahren Humusmonitoring auf Ackerflächen in Nordrhein-Westfalen

Seit 2009 wird vom Landesamt für Natur, Umwelt und Verbraucherschutz Nordrhein-Westfalen (LANUV NRW) in Zusammenarbeit mit dem Geologischen Dienst NRW, der Landwirtschaftskammer NRW sowie der Universität Bonn ein Humusmonitoringprogramm in Nordrhein-Westfalen durchgeführt. Ziele sind die Bestimmung der Gehalte und Vorräte an organischem Kohlenstoff (Corg) von Ackerböden in Nordrhein-Westfalen sowie die Abschätzung von Veränderungen der Corg-Gehalte und Vorräte. Zusätzlich sollen die Einflussfaktoren, wie z.B. Klimawandel und die landwirtschaftliche Nutzung, auf Gehalte und Vorräte ermittelt werden. Die Ergebnisse sollen auch eine wissen-schaftlich fundierte Basis schaffen, um die Landwirte bei allen Fragen der Humuspflege optimal zu beraten. Die Corg-Gehalte der Beprobung von 197 Flächen in 2009 liegen in einer Spanne von 0,7 bis 3,4 % mit deutlichen Unterschieden zwischen den untersuchten naturräumlichen Regionen. Rund 75 % der Flächen weisen Gehalte zwischen 1 und 2 % Corg auf. Die bisherigen Ergebnisse im Intensivmonitoring (2009 – 2018) von 45 ausgewählten Ackerflächen zeigen bisher keine NRW-weit gerichtete Entwicklung bei den Corg-Gehalten. Es sind jedoch regionale Trends zu beobachten. Multivariate Auswertungen haben gezeigt, dass insgesamt die organische Düngung, der Corg-Gehalt zu Beginn der Messreihe sowie die Temperaturzunahme den größten Einfluss auf die Veränderung der Corg-Gehalte aller 45 Flächen haben. Hervorzuheben sind dabei das Niederrheinische Tiefland und die Rheinische Bucht, in denen in den letzten zehn Jahren im Oberboden eine signifikante Zunahme der Corg-Gehalte zu beobachten ist und dies vermutlich vor allem auf die Zufuhr organischer Dünger zurückzuführen ist

Development of a framework for metabolic pathway analysis-driven strain optimization methods

Genome-scale metabolic models (GSMMs) have become important assets for rational design of compound overproduction using microbial cell factories. Most computational strain optimization methods (CSOM) using GSMMs, while useful in metabolic engineering, rely on the definition of questionable cell objectives, leading to some bias. Metabolic pathway analysis approaches do not require an objective function. Though their use brings immediate advantages, it has mostly been restricted to small scale models due to computational demands. Additionally, their complex parameterization and lack of intuitive tools pose an important challenge towards making these widely available to the community. Recently, MCSEnumerator has extended the scale of these methods, namely regarding enumeration of minimal cut sets, now able to handle GSMMs. This work proposes a tool implementing this method as a Java library and a plugin within the OptFlux metabolic engineering platform providing a friendly user interface. A standard enumeration problem and pipeline applicable to GSMMs is proposed, making use by the community simpler. To highlight the potential of these approaches, we devised a case study for overproduction of succinate, providing a phenotype analysis of a selected strategy and comparing robustness with a selected solution from a bi-level CSOM.The authors thank the project “DeYeastLibrary—Designer yeast strain library optimized for metabolic engineering applications”, Ref. ERA-IB-2/0003/2013, funded by national funds through “Fundação para a Ciência e Tecnologia / Ministério da Ciência, Tecnologia e Ensino Superior”.info:eu-repo/semantics/publishedVersio

Identification of metabolic engineering targets through analysis of optimal and sub-optimal routes

Identification of optimal genetic manipulation strategies for redirecting substrate uptake towards a desired product is a challenging task owing to the complexity of metabolic networks, esp. in terms of large number of routes leading to the desired product. Algorithms that can exploit the whole range of optimal and suboptimal routes for product formation while respecting the biological objective of the cell are therefore much needed. Towards addressing this need, we here introduce the notion of structural flux, which is derived from the enumeration of all pathways in the metabolic network in question and accounts for the contribution towards a given biological objective function. We show that the theoretically estimated structural fluxes are good predictors of experimentally measured intra-cellular fluxes in two model organisms, namely, Escherichia coli and Saccharomyces cerevisiae. For a small number of fluxes for which the predictions were poor, the corresponding enzyme-coding transcripts were also found to be distinctly regulated, showing the ability of structural fluxes in capturing the underlying regulatory principles. Exploiting the observed correspondence between in vivo fluxes and structural fluxes, we propose an in silico metabolic engineering approach, iStruF, which enables the identification of gene deletion strategies that couple the cellular biological objective with the product flux while considering optimal as well as sub-optimal routes and their efficiency.This work was supported by the Portuguese Science Foundation [grant numbers MIT-Pt/BS-BB/0082/2008, SFRH/BPD/44180/2008 to ZS] (http://www.fct.pt/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Dynamical chiral symmetry breaking and confinement with an infrared-vanishing gluon propagator?

We study a model Dyson-Schwinger equation for the quark propagator closed using an {\it Ansatz} for the gluon propagator of the form \mbox{$D(q) \sim q^2/[(q^2)^2 + b^4]$} and two {\it Ans\"{a}tze} for the quark-gluon vertex: the minimal Ball-Chiu and the modified form suggested by Curtis and Pennington. Using the quark condensate as an order parameter, we find that there is a critical value of $b=b_c$ such that the model does not support dynamical chiral symmetry breaking for $b>b_c$. We discuss and apply a confinement test which suggests that, for all values of $b$, the quark propagator in the model {\bf is not} confining. Together these results suggest that this Ansatz for the gluon propagator is inadequate as a model since it does not yield the expected behaviour of QCD.Comment: 21 Pages including 4 PostScript figures uuencoded at the end of the file. Replacement: slight changes of wording and emphasis. ADP-93-215/T133, ANL-PHY-7599-TH-93, FSU-SCRI-93-108, REVTEX 3.

Central nervous system and muscular bundles preserved in a 240 million year old giant bristletail (Archaeognatha: Machilidae)

Among the incomparably diverse group of insects no cases of central nervous system (CNS) preservation have been so far described in compression fossils. A third of the fossil insects collected from a 240-239 million year old (Ma) level at Monte San Giorgio UNESCO World Heritage (SwitzerlandItaly) underwent phosphatization, resulting in the extraordinary preservation of soft tissues. Here we describe Gigamachilis triassicus gen. et sp. nov. (Archaeognatha: Machiloidea: Machilidae) that, with an estimated total length of similar to 80 millimeters, represents the largest apterygote insect ever recorded. The holotype preserves: (i) components of the CNS represented by four abdominal ganglia, optic lobes with neuropils and compound retina;(ii) muscular bundles. Moreover, G. triassicus, possessing morphological features that prompt its assignment to the extant archaeognathan ingroup Machilidae, places the origin of modern lineages to Middle Triassic. Interestingly, at Monte San Giorgio, in the same stratigraphic unit the modern morphology of G. triassicus co-occurs with the ancient one represented by Dasyleptus triassicus (Archaeognatha: dagger Monura). Comparing these two types of body organization we provide a new reconstruction of the possible character evolution leading towards modern archaeognathan forms, suggesting the acquisition of novel features in a lineage of apterygote insects during the Permian or the Lower Triassic

Derivation of a biomass proxy for dynamic analysis of whole genome metabolic models

A whole genome metabolic model (GEM) is essentially a reconstruction of a network of enzyme-enabled chemical reactions representing the metabolism of an organism, based on information present in its genome. Such models have been designed so that flux balance analysis (FBA) can be applied in order to analyse metabolism under steady state. For this purpose, a biomassfunctionisaddedtothesemodelsasanoverallindicatorofthemodel’s viability. Our objective is to develop dynamic models based on these FBA models in order to observe new and complex behaviours, including transient behaviour. There is however a major challenge in that the biomass function does not operate under dynamic simulation. An appropriate biomass function would enable the estimation under dynamic simulation of the growth of both wildtype and genetically modified bacteria under different, possibly dynamically changing growth conditions. Using data analytics techniques, we have developed a dynamic biomass function which acts as a faithful proxy for the FBA equivalent for a reduced GEM for E. coli. This involved consolidating data for reaction rates and metabolite concentrations generated under dynamic simulation with gold standard target data for biomass obtained by steady state analysis using FBA. It also led to a number of interesting insights regarding biomass fluxes for pairs of conditions. These findings were reproduced in our dynamic proxy function

Stoichiometric representation of geneproteinreaction associations leverages constraint-based analysis from reaction to gene-level phenotype prediction

Genome-scale metabolic reconstructions are currently available for hundreds of organisms. Constraint-based modeling enables the analysis of the phenotypic landscape of these organisms, predicting the response to genetic and environmental perturbations. However, since constraint-based models can only describe the metabolic phenotype at the reaction level, understanding the mechanistic link between genotype and phenotype is still hampered by the complexity of gene-protein-reaction associations. We implement a model transformation that enables constraint-based methods to be applied at the gene level by explicitly accounting for the individual fluxes of enzymes (and subunits) encoded by each gene. We show how this can be applied to different kinds of constraint-based analysis: flux distribution prediction, gene essentiality analysis, random flux sampling, elementary mode analysis, transcriptomics data integration, and rational strain design. In each case we demonstrate how this approach can lead to improved phenotype predictions and a deeper understanding of the genotype-to-phenotype link. In particular, we show that a large fraction of reaction-based designs obtained by current strain design methods are not actually feasible, and show how our approach allows using the same methods to obtain feasible gene-based designs. We also show, by extensive comparison with experimental 13C-flux data, how simple reformulations of different simulation methods with gene-wise objective functions result in improved prediction accuracy. The model transformation proposed in this work enables existing constraint-based methods to be used at the gene level without modification. This automatically leverages phenotype analysis from reaction to gene level, improving the biological insight that can be obtained from genome-scale models.DM was supported by the Portuguese Foundationfor Science and Technologythrough a post-doc fellowship (ref: SFRH/BPD/111519/ 2015). This study was supported by the PortugueseFoundationfor Science and Technology (FCT) under the scope of the strategic fundingof UID/BIO/04469/2013 unitand COMPETE2020 (POCI-01-0145-FEDER-006684) and BioTecNorte operation (NORTE-01-0145FEDER-000004) fundedby EuropeanRegional Development Fund under the scope of Norte2020Programa Operacional Regional do Norte. This project has received fundingfrom the European Union’s Horizon 2020 research and innovation programme under grant agreementNo 686070. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Inclusion of maintenance energy improves the intracellular flux predictions of CHO

Chinese hamster ovary (CHO) cells are the leading platform for the production of biopharmaceuticals with human-like glycosylation. The standard practice for cell line generation relies on trial and error approaches such as adaptive evolution and high-throughput screening, which typically take several months. Metabolic modeling could aid in designing better producer cell lines and thus shorten development times. The genome-scale metabolic model (GSMM) of CHO can accurately predict growth rates. However, in order to predict rational engineering strategies it also needs to accurately predict intracellular fluxes. In this work we evaluated the agreement between the fluxes predicted by parsimonious flux balance analysis (pFBA) using the CHO GSMM and a wide range of 13C metabolic flux data from literature. While glycolytic fluxes were predicted relatively well, the fluxes of tricarboxylic acid (TCA) cycle were vastly underestimated due to too low energy demand. Inclusion of computationally estimated maintenance energy significantly improved the overall accuracy of intracellular flux predictions. Maintenance energy was therefore determined experimentally by running continuous cultures at different growth rates and evaluating their respective energy consumption. The experimentally and computationally determined maintenance energy were in good agreement. Additionally, we compared alternative objective functions (minimization of uptake rates of seven nonessential metabolites) to the biomass objective. While the predictions of the uptake rates were quite inaccurate for most objectives, the predictions of the intracellular fluxes were comparable to the biomass objective function.COMET center acib: Next Generation Bioproduction, which is funded by BMK, BMDW, SFG, Standortagentur Tirol, Government of Lower Austria and Vienna Business Agency in the framework of COMET - Competence Centers for Excellent Technologies. The COMET-Funding Program is managed by the Austrian Research Promotion Agency FFG; D.S., J.S., M.W., M.H., D. E.R. This work has also been supported by the PhD program BioToP of the Austrian Science Fund (FWF Project W1224)info:eu-repo/semantics/publishedVersio