Dorothee Barth

Xylose-induced dynamic effects on metabolism and gene expression in engineered Saccharomyces cerevisiae in anaerobic glucose-xylose cultures

Xylose is present with glucose in lignocellulosic streams available for valorisation to biochemicals. Saccharomyces cerevisiae has excellent characteristics as a host for the bioconversion, except that it strongly prefers glucose to xylose, and the co-consumption remains a challenge. Further, since xylose is not a natural substrate of S. cerevisiae, the regulatory response it induces in an engineered strain cannot be expected to have evolved for its utilisation. Xylose-induced effects on metabolism and gene expression during anaerobic growth of an engineered strain of S. cerevisiae on medium containing both glucose and xylose medium were quantified. The gene expression of S. cerevisiae with an XR-XDH pathway for xylose utilisation was analysed throughout the cultivation: at early cultivation times when mainly glucose was metabolised, at times when xylose was co-consumed in the presence of low glucose concentrations, and when glucose had been depleted and only xylose was being consumed. Cultivations on glucose as a sole carbon source were used as a control. Genome-scale dynamic flux balance analysis models were simulated to analyse the metabolic dynamics of S. cerevisiae. The simulations quantitatively estimated xylose-dependent flux dynamics and challenged the utilisation of the metabolic network. A relative increase in xylose utilisation was predicted to induce the bi-directionality of glycolytic flux and a redox challenge even at low glucose concentrations. Remarkably, xylose was observed to specifically delay the glucose-dependent repression of particular genes in mixed glucose-xylose cultures compared to glucose cultures. The delay occurred at a cultivation time when the metabolic flux activities were similar in the both cultures.

A design–build–test cycle using modeling and experiments reveals interdependencies between upper glycolysis and xylose uptake in recombinant S. cerevisiae and improves predictive capabilities of large-scale kinetic models

BackgroundRecent advancements in omics measurement technologies have led to an ever-increasing amount of available experimental data that necessitate systems-oriented methodologies for efficient and systematic integration of data into consistent large-scale kinetic models. These models can help us to uncover new insights into cellular physiology and also to assist in the rational design of bioreactor or fermentation processes. Optimization and Risk Analysis of Complex Living Entities (ORACLE) framework for the construction of large-scale kinetic models can be used as guidance for formulating alternative metabolic engineering strategies.ResultsWe used ORACLE in a metabolic engineering problem: improvement of the xylose uptake rate during mixed glucose–xylose consumption in a recombinant Saccharomyces cerevisiae strain. Using the data from bioreactor fermentations, we characterized network flux and concentration profiles representing possible physiological states of the analyzed strain. We then identified enzymes that could lead to improved flux through xylose transporters (XTR). For some of the identified enzymes, including hexokinase (HXK), we could not deduce if their control over XTR was positive or negative. We thus performed a follow-up experiment, and we found out that HXK2 deletion improves xylose uptake rate. The data from the performed experiments were then used to prune the kinetic models, and the predictions of the pruned population of kinetic models were in agreement with the experimental data collected on the HXK2-deficient S. cerevisiae strain.ConclusionsWe present a design–build–test cycle composed of modeling efforts and experiments with a glucose–xylose co-utilizing recombinant S. cerevisiae and its HXK2-deficient mutant that allowed us to uncover interdependencies between upper glycolysis and xylose uptake pathway. Through this cycle, we also obtained kinetic models with improved prediction capabilities. The present study demonstrates the potential of integrated “modeling and experiments” systems biology approaches that can be applied for diverse applications ranging from biotechnology to drug discovery.

DesinFix TM 135 in fermentation process for bioethanol production

Brazil has the world’s largest ethanol production from sugarcane, but bacterial contamination decreases the ethanol yields. It was shown that the biocide DesinFix™ 135 can reduce the contamination without decreasing the yeasts’ viability or negatively affecting the ethanol production.

Growth of marine fungi on polymeric substrates.

BackgroundMarine fungi are a diverse group of opportunistic and obligate organisms isolated from marine environments. These fungi are now often included in screens for novel metabolites, while less attention has been given to their production of hydrolytic enzymes. Most enzymes derived from marine microorganisms have been obtained from marine bacteria. The enzymes produced by marine fungi may have different properties than those derived from bacteria or from terrestrial fungi. Here we assess the growth of six filamentous marine fungi on a wide range of polymeric substrates as an indication of their general capacity to produce hydrolytic enzymes.ResultsCalcarisporium sp. KF525, Tritirachium sp. LF562, Bartalinia robillardoides LF550, Penicillium pinophilum LF458, Scopulariopsis brevicaulis LF580 and Pestalotiopsis sp. KF079 all grew on both casein and gelatin as N-source, indicating secretion of proteases. All species also grew on starch, laminarin, xylan, pectin and oil, indicating production of amylases, glucanases, xylanases, pectinases and lipases. Growth on cellulose occurred but was weaker than on xylan. All strains also grew to some extent on sulphated arabinogalactan, although only LF562 could utilise arabinose. Four strains grew on the sulphated ulvans, whereas only KF525 grew on agar or carrageenan. KF525 and LF562 showed limited growth on alginate. Although fucose was used as carbon source by several species, fucoidan did not support biomass production.ConclusionsMarine fungi could be excellent sources of a wide range of hydrolytic enzymes, including those able to hydrolyse various seaweed polymers. Although the native hosts may secrete only small amounts of these enzymes, the genes may provide a rich source of novel enzymes.

The effects of pH oscillation on Lactobacillus rhamnosus batch cultivation

Inhomogeneous mixing in industrial-sized fermentation processes causes oscillations in process parameters such as temperature or pH value in the cultivation medium, which causes stress to the bacteria being cultivated. In this work, the impact of extracellular pH oscillations on the production of Lactobacillus rhamnosus, a well-studied probiotic bacteria, were investigated by means of a scale-down batch process, simulating inhomogeneous pH values by controlling the pH value of the medium on sinusoidal trajectories. Effects of pH stimulation on the bacteria were assessed by testing storage and freeze-drying stability of harvested cells, two factors relevant for the industrial process. Furthermore, gene expressions of six selected genes, i.e. atpA, fat, cfa, groEL, hrcA, and pstS, known to be related to stress response were monitored. Although storage stability is only slightly negatively affected by pH stimulation of the bacteria, gene expression of four of the studied genes, i.e. fat, hrcA, groEL, and pstS show to correlate with amplitude and frequency of the oscillation.

Whole-genome metabolic model of Trichoderma reesei built by comparative reconstruction.

BackgroundTrichoderma reesei is one of the main sources of biomass-hydrolyzing enzymes for the biotechnology industry. There is a need for improving its enzyme production efficiency. The use of metabolic modeling for the simulation and prediction of this organism’s metabolism is potentially a valuable tool for improving its capabilities. An accurate metabolic model is needed to perform metabolic modeling analysis.ResultsA whole-genome metabolic model of T. reesei has been reconstructed together with metabolic models of 55 related species using the metabolic model reconstruction algorithm CoReCo. The previously published CoReCo method has been improved to obtain better quality models. The main improvements are the creation of a unified database of reactions and compounds and the use of reaction directions as constraints in the gap-filling step of the algorithm. In addition, the biomass composition of T. reesei has been measured experimentally to build and include a specific biomass equation in the model.ConclusionsThe improvements presented in this work on the CoReCo pipeline for metabolic model reconstruction resulted in higher-quality metabolic models compared with previous versions. A metabolic model of T. reesei has been created and is publicly available in the BIOMODELS database. The model contains a biomass equation, reaction boundaries and uptake/export reactions which make it ready for simulation. To validate the model, we dem1onstrate that the model is able to predict biomass production accurately and no stoichiometrically infeasible yields are detected. The new T. reesei model is ready to be used for simulations of protein production processes.